feat(xml-jats): parse XML JATS documents (#967)
* chore(xml-jats): separate authors and affiliations In XML PubMed (JATS) backend, convert authors and affiliations as they are typically rendered on PDFs. Signed-off-by: Cesar Berrospi Ramis <75900930+ceberam@users.noreply.github.com> * fix(xml-jats): replace new line character by a space Instead of removing new line character from text, replace it by a space character. Signed-off-by: Cesar Berrospi Ramis <75900930+ceberam@users.noreply.github.com> * feat(xml-jats): improve existing parser and extend features Partially support lists, respect reading order, parse more sections, support equations, better text formatting. Signed-off-by: Cesar Berrospi Ramis <75900930+ceberam@users.noreply.github.com> * chore(xml-jats): rename PubMed objects to JATS Signed-off-by: Cesar Berrospi Ramis <75900930+ceberam@users.noreply.github.com> --------- Signed-off-by: Cesar Berrospi Ramis <75900930+ceberam@users.noreply.github.com>
This commit is contained in:
Cesar Berrospi Ramis
GitHub
parent
e1436a8b05
commit
428b656793
772
docling/backend/xml/jats_backend.py
Executable file
772
docling/backend/xml/jats_backend.py
Executable file
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import logging
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import traceback
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from io import BytesIO
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from pathlib import Path
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from typing import Final, Optional, Union
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from bs4 import BeautifulSoup
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from docling_core.types.doc import (
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DocItemLabel,
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DoclingDocument,
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DocumentOrigin,
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GroupItem,
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GroupLabel,
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NodeItem,
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TableCell,
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TableData,
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TextItem,
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)
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from lxml import etree
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from typing_extensions import TypedDict, override
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from docling.backend.abstract_backend import DeclarativeDocumentBackend
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from docling.datamodel.base_models import InputFormat
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from docling.datamodel.document import InputDocument
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_log = logging.getLogger(__name__)
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JATS_DTD_URL: Final = ["JATS-journalpublishing", "JATS-archive"]
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DEFAULT_HEADER_ACKNOWLEDGMENTS: Final = "Acknowledgments"
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DEFAULT_HEADER_ABSTRACT: Final = "Abstract"
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DEFAULT_HEADER_REFERENCES: Final = "References"
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DEFAULT_TEXT_ETAL: Final = "et al."
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class Abstract(TypedDict):
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label: str
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content: str
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class Author(TypedDict):
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name: str
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affiliation_names: list[str]
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class Citation(TypedDict):
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author_names: str
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title: str
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source: str
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year: str
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volume: str
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page: str
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pub_id: str
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publisher_name: str
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publisher_loc: str
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class Table(TypedDict):
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label: str
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caption: str
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content: str
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class XMLComponents(TypedDict):
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title: str
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authors: list[Author]
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abstract: list[Abstract]
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class JatsDocumentBackend(DeclarativeDocumentBackend):
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"""Backend to parse articles in XML format tagged according to JATS definition.
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The Journal Article Tag Suite (JATS) is an definition standard for the
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representation of journal articles in XML format. Several publishers and journal
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archives provide content in JATS format, including PubMed Central® (PMC), bioRxiv,
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medRxiv, or Springer Nature.
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Refer to https://jats.nlm.nih.gov for more details on JATS.
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The code from this document backend has been developed by modifying parts of the
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PubMed Parser library (version 0.5.0, released on 12.08.2024):
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Achakulvisut et al., (2020).
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Pubmed Parser: A Python Parser for PubMed Open-Access XML Subset and MEDLINE XML
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Dataset XML Dataset.
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Journal of Open Source Software, 5(46), 1979,
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https://doi.org/10.21105/joss.01979
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"""
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@override
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def __init__(
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self, in_doc: "InputDocument", path_or_stream: Union[BytesIO, Path]
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) -> None:
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super().__init__(in_doc, path_or_stream)
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self.path_or_stream = path_or_stream
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# Initialize the root of the document hiearchy
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self.root: Optional[NodeItem] = None
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self.valid = False
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try:
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if isinstance(self.path_or_stream, BytesIO):
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self.path_or_stream.seek(0)
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self.tree: etree._ElementTree = etree.parse(self.path_or_stream)
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doc_info: etree.DocInfo = self.tree.docinfo
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if doc_info.system_url and any(
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[kwd in doc_info.system_url for kwd in JATS_DTD_URL]
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):
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self.valid = True
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return
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for ent in doc_info.internalDTD.iterentities():
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if ent.system_url and any(
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[kwd in ent.system_url for kwd in JATS_DTD_URL]
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):
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self.valid = True
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return
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except Exception as exc:
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raise RuntimeError(
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f"Could not initialize JATS backend for file with hash {self.document_hash}."
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) from exc
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@override
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def is_valid(self) -> bool:
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return self.valid
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@classmethod
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@override
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def supports_pagination(cls) -> bool:
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return False
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@override
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def unload(self):
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if isinstance(self.path_or_stream, BytesIO):
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self.path_or_stream.close()
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self.path_or_stream = None
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@classmethod
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@override
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def supported_formats(cls) -> set[InputFormat]:
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return {InputFormat.XML_JATS}
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@override
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def convert(self) -> DoclingDocument:
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try:
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# Create empty document
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origin = DocumentOrigin(
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filename=self.file.name or "file",
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mimetype="application/xml",
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binary_hash=self.document_hash,
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)
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doc = DoclingDocument(name=self.file.stem or "file", origin=origin)
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# Get metadata XML components
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xml_components: XMLComponents = self._parse_metadata()
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# Add metadata to the document
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self._add_metadata(doc, xml_components)
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# walk over the XML body
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body = self.tree.xpath("//body")
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if self.root and len(body) > 0:
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self._walk_linear(doc, self.root, body[0])
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# walk over the XML back matter
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back = self.tree.xpath("//back")
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if self.root and len(back) > 0:
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self._walk_linear(doc, self.root, back[0])
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except Exception:
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_log.error(traceback.format_exc())
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return doc
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@staticmethod
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def _get_text(node: etree._Element, sep: Optional[str] = None) -> str:
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skip_tags = ["term", "disp-formula", "inline-formula"]
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text: str = (
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node.text.replace("\n", " ")
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if (node.tag not in skip_tags and node.text)
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else ""
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)
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for child in list(node):
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if child.tag not in skip_tags:
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# TODO: apply styling according to child.tag when supported by docling-core
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text += JatsDocumentBackend._get_text(child, sep)
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if sep:
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text = text.rstrip(sep) + sep
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text += child.tail.replace("\n", " ") if child.tail else ""
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return text
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def _find_metadata(self) -> Optional[etree._Element]:
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meta_names: list[str] = ["article-meta", "book-part-meta"]
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meta: Optional[etree._Element] = None
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for name in meta_names:
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node = self.tree.xpath(f".//{name}")
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if len(node) > 0:
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meta = node[0]
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break
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return meta
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def _parse_abstract(self) -> list[Abstract]:
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# TODO: address cases with multiple sections
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abs_list: list[Abstract] = []
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for abs_node in self.tree.xpath(".//abstract"):
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abstract: Abstract = dict(label="", content="")
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texts = []
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for abs_par in abs_node.xpath("p"):
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texts.append(JatsDocumentBackend._get_text(abs_par).strip())
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abstract["content"] = " ".join(texts)
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label_node = abs_node.xpath("title|label")
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if len(label_node) > 0:
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abstract["label"] = label_node[0].text.strip()
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abs_list.append(abstract)
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return abs_list
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def _parse_authors(self) -> list[Author]:
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# Get mapping between affiliation ids and names
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authors: list[Author] = []
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meta: Optional[etree._Element] = self._find_metadata()
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if meta is None:
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return authors
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affiliation_names = []
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for affiliation_node in meta.xpath(".//aff[@id]"):
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aff = ", ".join([t for t in affiliation_node.itertext() if t.strip()])
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aff = aff.replace("\n", " ")
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label = affiliation_node.xpath("label")
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if label:
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# TODO: once superscript is supported, add label with formatting
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aff = aff.removeprefix(f"{label[0].text}, ")
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affiliation_names.append(aff)
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affiliation_ids_names = {
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id: name
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for id, name in zip(meta.xpath(".//aff[@id]/@id"), affiliation_names)
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}
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# Get author names and affiliation names
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for author_node in meta.xpath(
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'.//contrib-group/contrib[@contrib-type="author"]'
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):
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author: Author = {
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"name": "",
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"affiliation_names": [],
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}
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# Affiliation names
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affiliation_ids = [
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a.attrib["rid"] for a in author_node.xpath('xref[@ref-type="aff"]')
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]
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for id in affiliation_ids:
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if id in affiliation_ids_names:
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author["affiliation_names"].append(affiliation_ids_names[id])
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# Name
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author["name"] = (
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author_node.xpath("name/given-names")[0].text
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+ " "
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+ author_node.xpath("name/surname")[0].text
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)
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authors.append(author)
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return authors
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def _parse_title(self) -> str:
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meta_names: list[str] = [
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"article-meta",
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"collection-meta",
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"book-meta",
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"book-part-meta",
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]
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title_names: list[str] = ["article-title", "subtitle", "title", "label"]
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titles: list[str] = [
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" ".join(
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elem.text.replace("\n", " ").strip()
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for elem in list(title_node)
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if elem.tag in title_names
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).strip()
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for title_node in self.tree.xpath(
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"|".join([f".//{item}/title-group" for item in meta_names])
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)
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]
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text = " - ".join(titles)
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return text
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def _parse_metadata(self) -> XMLComponents:
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"""Parsing JATS document metadata."""
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xml_components: XMLComponents = {
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"title": self._parse_title(),
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"authors": self._parse_authors(),
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"abstract": self._parse_abstract(),
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}
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return xml_components
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def _add_abstract(
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self, doc: DoclingDocument, xml_components: XMLComponents
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) -> None:
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for abstract in xml_components["abstract"]:
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text: str = abstract["content"]
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title: str = abstract["label"] or DEFAULT_HEADER_ABSTRACT
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if not text:
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continue
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parent = doc.add_heading(parent=self.root, text=title)
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doc.add_text(
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parent=parent,
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text=text,
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label=DocItemLabel.TEXT,
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)
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return
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def _add_authors(self, doc: DoclingDocument, xml_components: XMLComponents) -> None:
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# TODO: once docling supports text formatting, add affiliation reference to
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# author names through superscripts
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authors: list = [item["name"] for item in xml_components["authors"]]
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authors_str = ", ".join(authors)
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affiliations: list = [
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item
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for author in xml_components["authors"]
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for item in author["affiliation_names"]
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]
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affiliations_str = "; ".join(list(dict.fromkeys(affiliations)))
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if authors_str:
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doc.add_text(
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parent=self.root,
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text=authors_str,
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label=DocItemLabel.PARAGRAPH,
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)
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if affiliations_str:
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doc.add_text(
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parent=self.root,
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text=affiliations_str,
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label=DocItemLabel.PARAGRAPH,
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)
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return
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def _add_citation(self, doc: DoclingDocument, parent: NodeItem, text: str) -> None:
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if isinstance(parent, GroupItem) and parent.label == GroupLabel.LIST:
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doc.add_list_item(text=text, enumerated=False, parent=parent)
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else:
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doc.add_text(text=text, label=DocItemLabel.TEXT, parent=parent)
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return
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def _parse_element_citation(self, node: etree._Element) -> str:
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citation: Citation = {
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"author_names": "",
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"title": "",
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"source": "",
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"year": "",
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"volume": "",
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"page": "",
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"pub_id": "",
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"publisher_name": "",
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"publisher_loc": "",
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}
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_log.debug("Citation parsing started")
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# Author names
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names = []
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for name_node in node.xpath(".//name"):
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name_str = (
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name_node.xpath("surname")[0].text.replace("\n", " ").strip()
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+ " "
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+ name_node.xpath("given-names")[0].text.replace("\n", " ").strip()
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)
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names.append(name_str)
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etal_node = node.xpath(".//etal")
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if len(etal_node) > 0:
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etal_text = etal_node[0].text or DEFAULT_TEXT_ETAL
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names.append(etal_text)
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citation["author_names"] = ", ".join(names)
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titles: list[str] = [
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"article-title",
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"chapter-title",
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"data-title",
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"issue-title",
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"part-title",
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"trans-title",
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]
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title_node: Optional[etree._Element] = None
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for name in titles:
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name_node = node.xpath(name)
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if len(name_node) > 0:
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title_node = name_node[0]
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break
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citation["title"] = (
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JatsDocumentBackend._get_text(title_node)
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if title_node is not None
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else node.text.replace("\n", " ").strip()
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)
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# Journal, year, publisher name, publisher location, volume, elocation
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fields: list[str] = [
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"source",
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"year",
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"publisher-name",
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"publisher-loc",
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"volume",
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]
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for item in fields:
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item_node = node.xpath(item)
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if len(item_node) > 0:
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citation[item.replace("-", "_")] = ( # type: ignore[literal-required]
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item_node[0].text.replace("\n", " ").strip()
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)
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# Publication identifier
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if len(node.xpath("pub-id")) > 0:
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pub_id: list[str] = []
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for id_node in node.xpath("pub-id"):
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id_type = id_node.get("assigning-authority") or id_node.get(
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"pub-id-type"
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)
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id_text = id_node.text
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if id_type and id_text:
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pub_id.append(
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id_type.replace("\n", " ").strip().upper()
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+ ": "
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+ id_text.replace("\n", " ").strip()
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)
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if pub_id:
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citation["pub_id"] = ", ".join(pub_id)
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# Pages
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if len(node.xpath("elocation-id")) > 0:
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citation["page"] = (
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node.xpath("elocation-id")[0].text.replace("\n", " ").strip()
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)
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elif len(node.xpath("fpage")) > 0:
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citation["page"] = node.xpath("fpage")[0].text.replace("\n", " ").strip()
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if len(node.xpath("lpage")) > 0:
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citation["page"] += (
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"–" + node.xpath("lpage")[0].text.replace("\n", " ").strip()
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)
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# Flatten the citation to string
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text = ""
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if citation["author_names"]:
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text += citation["author_names"].rstrip(".") + ". "
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if citation["title"]:
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text += citation["title"] + ". "
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if citation["source"]:
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text += citation["source"] + ". "
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if citation["publisher_name"]:
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if citation["publisher_loc"]:
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text += f"{citation['publisher_loc']}: "
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text += citation["publisher_name"] + ". "
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if citation["volume"]:
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text = text.rstrip(". ")
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text += f" {citation['volume']}. "
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if citation["page"]:
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text = text.rstrip(". ")
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if citation["volume"]:
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text += ":"
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text += citation["page"] + ". "
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if citation["year"]:
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text = text.rstrip(". ")
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text += f" ({citation['year']})."
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if citation["pub_id"]:
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text = text.rstrip(".") + ". "
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text += citation["pub_id"]
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_log.debug("Citation flattened")
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return text
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def _add_equation(
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self, doc: DoclingDocument, parent: NodeItem, node: etree._Element
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) -> None:
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math_text = node.text
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math_parts = math_text.split("$$")
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||||
if len(math_parts) == 3:
|
||||
math_formula = math_parts[1]
|
||||
doc.add_text(label=DocItemLabel.FORMULA, text=math_formula, parent=parent)
|
||||
|
||||
return
|
||||
|
||||
def _add_figure_captions(
|
||||
self, doc: DoclingDocument, parent: NodeItem, node: etree._Element
|
||||
) -> None:
|
||||
label_node = node.xpath("label")
|
||||
label: Optional[str] = (
|
||||
JatsDocumentBackend._get_text(label_node[0]).strip() if label_node else ""
|
||||
)
|
||||
|
||||
caption_node = node.xpath("caption")
|
||||
caption: Optional[str]
|
||||
if len(caption_node) > 0:
|
||||
caption = ""
|
||||
for caption_par in list(caption_node[0]):
|
||||
if caption_par.xpath(".//supplementary-material"):
|
||||
continue
|
||||
caption += JatsDocumentBackend._get_text(caption_par).strip() + " "
|
||||
caption = caption.strip()
|
||||
else:
|
||||
caption = None
|
||||
|
||||
# TODO: format label vs caption once styling is supported
|
||||
fig_text: str = f"{label}{' ' if label and caption else ''}{caption}"
|
||||
fig_caption: Optional[TextItem] = (
|
||||
doc.add_text(label=DocItemLabel.CAPTION, text=fig_text)
|
||||
if fig_text
|
||||
else None
|
||||
)
|
||||
|
||||
doc.add_picture(parent=parent, caption=fig_caption)
|
||||
|
||||
return
|
||||
|
||||
# TODO: add footnotes when DocItemLabel.FOOTNOTE and styling are supported
|
||||
# def _add_footnote_group(self, doc: DoclingDocument, parent: NodeItem, node: etree._Element) -> None:
|
||||
# new_parent = doc.add_group(label=GroupLabel.LIST, name="footnotes", parent=parent)
|
||||
# for child in node.iterchildren(tag="fn"):
|
||||
# text = JatsDocumentBackend._get_text(child)
|
||||
# doc.add_list_item(text=text, parent=new_parent)
|
||||
|
||||
def _add_metadata(
|
||||
self, doc: DoclingDocument, xml_components: XMLComponents
|
||||
) -> None:
|
||||
self._add_title(doc, xml_components)
|
||||
self._add_authors(doc, xml_components)
|
||||
self._add_abstract(doc, xml_components)
|
||||
|
||||
return
|
||||
|
||||
def _add_table(
|
||||
self, doc: DoclingDocument, parent: NodeItem, table_xml_component: Table
|
||||
) -> None:
|
||||
soup = BeautifulSoup(table_xml_component["content"], "html.parser")
|
||||
table_tag = soup.find("table")
|
||||
|
||||
nested_tables = table_tag.find("table")
|
||||
if nested_tables:
|
||||
_log.warning(f"Skipping nested table in {str(self.file)}")
|
||||
return
|
||||
|
||||
# Count the number of rows (number of <tr> elements)
|
||||
num_rows = len(table_tag.find_all("tr"))
|
||||
|
||||
# Find the number of columns (taking into account colspan)
|
||||
num_cols = 0
|
||||
for row in table_tag.find_all("tr"):
|
||||
col_count = 0
|
||||
for cell in row.find_all(["td", "th"]):
|
||||
colspan = int(cell.get("colspan", 1))
|
||||
col_count += colspan
|
||||
num_cols = max(num_cols, col_count)
|
||||
|
||||
grid = [[None for _ in range(num_cols)] for _ in range(num_rows)]
|
||||
|
||||
data = TableData(num_rows=num_rows, num_cols=num_cols, table_cells=[])
|
||||
|
||||
# Iterate over the rows in the table
|
||||
for row_idx, row in enumerate(table_tag.find_all("tr")):
|
||||
# For each row, find all the column cells (both <td> and <th>)
|
||||
cells = row.find_all(["td", "th"])
|
||||
|
||||
# Check if each cell in the row is a header -> means it is a column header
|
||||
col_header = True
|
||||
for j, html_cell in enumerate(cells):
|
||||
if html_cell.name == "td":
|
||||
col_header = False
|
||||
|
||||
# Extract and print the text content of each cell
|
||||
col_idx = 0
|
||||
for _, html_cell in enumerate(cells):
|
||||
# extract inline formulas
|
||||
for formula in html_cell.find_all("inline-formula"):
|
||||
math_parts = formula.text.split("$$")
|
||||
if len(math_parts) == 3:
|
||||
math_formula = f"$${math_parts[1]}$$"
|
||||
formula.replaceWith(math_formula)
|
||||
text = html_cell.text
|
||||
|
||||
col_span = int(html_cell.get("colspan", 1))
|
||||
row_span = int(html_cell.get("rowspan", 1))
|
||||
|
||||
while grid[row_idx][col_idx] is not None:
|
||||
col_idx += 1
|
||||
for r in range(row_span):
|
||||
for c in range(col_span):
|
||||
grid[row_idx + r][col_idx + c] = text
|
||||
|
||||
cell = TableCell(
|
||||
text=text,
|
||||
row_span=row_span,
|
||||
col_span=col_span,
|
||||
start_row_offset_idx=row_idx,
|
||||
end_row_offset_idx=row_idx + row_span,
|
||||
start_col_offset_idx=col_idx,
|
||||
end_col_offset_idx=col_idx + col_span,
|
||||
col_header=col_header,
|
||||
row_header=((not col_header) and html_cell.name == "th"),
|
||||
)
|
||||
data.table_cells.append(cell)
|
||||
|
||||
# TODO: format label vs caption once styling is supported
|
||||
label = table_xml_component["label"]
|
||||
caption = table_xml_component["caption"]
|
||||
table_text: str = f"{label}{' ' if label and caption else ''}{caption}"
|
||||
table_caption: Optional[TextItem] = (
|
||||
doc.add_text(label=DocItemLabel.CAPTION, text=table_text)
|
||||
if table_text
|
||||
else None
|
||||
)
|
||||
|
||||
doc.add_table(data=data, parent=parent, caption=table_caption)
|
||||
|
||||
return
|
||||
|
||||
def _add_tables(
|
||||
self, doc: DoclingDocument, parent: NodeItem, node: etree._Element
|
||||
) -> None:
|
||||
table: Table = {"label": "", "caption": "", "content": ""}
|
||||
|
||||
# Content
|
||||
if len(node.xpath("table")) > 0:
|
||||
table_content_node = node.xpath("table")[0]
|
||||
elif len(node.xpath("alternatives/table")) > 0:
|
||||
table_content_node = node.xpath("alternatives/table")[0]
|
||||
else:
|
||||
table_content_node = None
|
||||
if table_content_node is not None:
|
||||
table["content"] = etree.tostring(table_content_node).decode("utf-8")
|
||||
|
||||
# Caption
|
||||
caption_node = node.xpath("caption")
|
||||
caption: Optional[str]
|
||||
if caption_node:
|
||||
caption = ""
|
||||
for caption_par in list(caption_node[0]):
|
||||
if caption_par.xpath(".//supplementary-material"):
|
||||
continue
|
||||
caption += JatsDocumentBackend._get_text(caption_par).strip() + " "
|
||||
caption = caption.strip()
|
||||
else:
|
||||
caption = None
|
||||
if caption is not None:
|
||||
table["caption"] = caption
|
||||
|
||||
# Label
|
||||
if len(node.xpath("label")) > 0:
|
||||
table["label"] = node.xpath("label")[0].text
|
||||
|
||||
try:
|
||||
self._add_table(doc, parent, table)
|
||||
except Exception as e:
|
||||
_log.warning(f"Skipping unsupported table in {str(self.file)}")
|
||||
pass
|
||||
|
||||
return
|
||||
|
||||
def _add_title(self, doc: DoclingDocument, xml_components: XMLComponents) -> None:
|
||||
self.root = doc.add_text(
|
||||
parent=None,
|
||||
text=xml_components["title"],
|
||||
label=DocItemLabel.TITLE,
|
||||
)
|
||||
return
|
||||
|
||||
def _walk_linear(
|
||||
self, doc: DoclingDocument, parent: NodeItem, node: etree._Element
|
||||
) -> str:
|
||||
# _log.debug(f"Walking on {node.tag} with {len(list(node))} children")
|
||||
skip_tags = ["term"]
|
||||
flush_tags = ["ack", "sec", "list", "boxed-text", "disp-formula", "fig"]
|
||||
new_parent: NodeItem = parent
|
||||
node_text: str = (
|
||||
node.text.replace("\n", " ")
|
||||
if (node.tag not in skip_tags and node.text)
|
||||
else ""
|
||||
)
|
||||
|
||||
for child in list(node):
|
||||
stop_walk: bool = False
|
||||
|
||||
# flush text into TextItem for some tags in paragraph nodes
|
||||
if node.tag == "p" and node_text.strip() and child.tag in flush_tags:
|
||||
doc.add_text(
|
||||
label=DocItemLabel.TEXT, text=node_text.strip(), parent=parent
|
||||
)
|
||||
node_text = ""
|
||||
|
||||
# add elements and decide whether to stop walking
|
||||
if child.tag in ("sec", "ack"):
|
||||
header = child.xpath("title|label")
|
||||
text: Optional[str] = None
|
||||
if len(header) > 0:
|
||||
text = JatsDocumentBackend._get_text(header[0])
|
||||
elif child.tag == "ack":
|
||||
text = DEFAULT_HEADER_ACKNOWLEDGMENTS
|
||||
if text:
|
||||
new_parent = doc.add_heading(text=text, parent=parent)
|
||||
elif child.tag == "list":
|
||||
new_parent = doc.add_group(
|
||||
label=GroupLabel.LIST, name="list", parent=parent
|
||||
)
|
||||
elif child.tag == "list-item":
|
||||
# TODO: address any type of content (another list, formula,...)
|
||||
# TODO: address list type and item label
|
||||
text = JatsDocumentBackend._get_text(child).strip()
|
||||
new_parent = doc.add_list_item(text=text, parent=parent)
|
||||
stop_walk = True
|
||||
elif child.tag == "fig":
|
||||
self._add_figure_captions(doc, parent, child)
|
||||
stop_walk = True
|
||||
elif child.tag == "table-wrap":
|
||||
self._add_tables(doc, parent, child)
|
||||
stop_walk = True
|
||||
elif child.tag == "suplementary-material":
|
||||
stop_walk = True
|
||||
elif child.tag == "fn-group":
|
||||
# header = child.xpath(".//title") or child.xpath(".//label")
|
||||
# if header:
|
||||
# text = JatsDocumentBackend._get_text(header[0])
|
||||
# fn_parent = doc.add_heading(text=text, parent=new_parent)
|
||||
# self._add_footnote_group(doc, fn_parent, child)
|
||||
stop_walk = True
|
||||
elif child.tag == "ref-list" and node.tag != "ref-list":
|
||||
header = child.xpath("title|label")
|
||||
text = (
|
||||
JatsDocumentBackend._get_text(header[0])
|
||||
if len(header) > 0
|
||||
else DEFAULT_HEADER_REFERENCES
|
||||
)
|
||||
new_parent = doc.add_heading(text=text, parent=parent)
|
||||
new_parent = doc.add_group(
|
||||
parent=new_parent, label=GroupLabel.LIST, name="list"
|
||||
)
|
||||
elif child.tag == "element-citation":
|
||||
text = self._parse_element_citation(child)
|
||||
self._add_citation(doc, parent, text)
|
||||
stop_walk = True
|
||||
elif child.tag == "mixed-citation":
|
||||
text = JatsDocumentBackend._get_text(child).strip()
|
||||
self._add_citation(doc, parent, text)
|
||||
stop_walk = True
|
||||
elif child.tag == "tex-math":
|
||||
self._add_equation(doc, parent, child)
|
||||
stop_walk = True
|
||||
elif child.tag == "inline-formula":
|
||||
# TODO: address inline formulas when supported by docling-core
|
||||
stop_walk = True
|
||||
|
||||
# step into child
|
||||
if not stop_walk:
|
||||
new_text = self._walk_linear(doc, new_parent, child)
|
||||
if not (node.getparent().tag == "p" and node.tag in flush_tags):
|
||||
node_text += new_text
|
||||
|
||||
# pick up the tail text
|
||||
node_text += child.tail.replace("\n", " ") if child.tail else ""
|
||||
|
||||
# create paragraph
|
||||
if node.tag == "p" and node_text.strip():
|
||||
doc.add_text(label=DocItemLabel.TEXT, text=node_text.strip(), parent=parent)
|
||||
return ""
|
||||
else:
|
||||
# backpropagate the text
|
||||
return node_text
|
||||
@ -1,592 +0,0 @@
|
||||
import logging
|
||||
from io import BytesIO
|
||||
from pathlib import Path
|
||||
from typing import Any, Set, Union
|
||||
|
||||
import lxml
|
||||
from bs4 import BeautifulSoup
|
||||
from docling_core.types.doc import (
|
||||
DocItemLabel,
|
||||
DoclingDocument,
|
||||
DocumentOrigin,
|
||||
GroupLabel,
|
||||
TableCell,
|
||||
TableData,
|
||||
)
|
||||
from lxml import etree
|
||||
from typing_extensions import TypedDict, override
|
||||
|
||||
from docling.backend.abstract_backend import DeclarativeDocumentBackend
|
||||
from docling.datamodel.base_models import InputFormat
|
||||
from docling.datamodel.document import InputDocument
|
||||
|
||||
_log = logging.getLogger(__name__)
|
||||
|
||||
|
||||
class Paragraph(TypedDict):
|
||||
text: str
|
||||
headers: list[str]
|
||||
|
||||
|
||||
class Author(TypedDict):
|
||||
name: str
|
||||
affiliation_names: list[str]
|
||||
|
||||
|
||||
class Table(TypedDict):
|
||||
label: str
|
||||
caption: str
|
||||
content: str
|
||||
|
||||
|
||||
class FigureCaption(TypedDict):
|
||||
label: str
|
||||
caption: str
|
||||
|
||||
|
||||
class Reference(TypedDict):
|
||||
author_names: str
|
||||
title: str
|
||||
journal: str
|
||||
year: str
|
||||
|
||||
|
||||
class XMLComponents(TypedDict):
|
||||
title: str
|
||||
authors: list[Author]
|
||||
abstract: str
|
||||
paragraphs: list[Paragraph]
|
||||
tables: list[Table]
|
||||
figure_captions: list[FigureCaption]
|
||||
references: list[Reference]
|
||||
|
||||
|
||||
class PubMedDocumentBackend(DeclarativeDocumentBackend):
|
||||
"""
|
||||
The code from this document backend has been developed by modifying parts of the PubMed Parser library (version 0.5.0, released on 12.08.2024):
|
||||
Achakulvisut et al., (2020).
|
||||
Pubmed Parser: A Python Parser for PubMed Open-Access XML Subset and MEDLINE XML Dataset XML Dataset.
|
||||
Journal of Open Source Software, 5(46), 1979,
|
||||
https://doi.org/10.21105/joss.01979
|
||||
"""
|
||||
|
||||
@override
|
||||
def __init__(self, in_doc: "InputDocument", path_or_stream: Union[BytesIO, Path]):
|
||||
super().__init__(in_doc, path_or_stream)
|
||||
self.path_or_stream = path_or_stream
|
||||
|
||||
# Initialize parents for the document hierarchy
|
||||
self.parents: dict = {}
|
||||
|
||||
self.valid = False
|
||||
try:
|
||||
if isinstance(self.path_or_stream, BytesIO):
|
||||
self.path_or_stream.seek(0)
|
||||
self.tree: lxml.etree._ElementTree = etree.parse(self.path_or_stream)
|
||||
if "/NLM//DTD JATS" in self.tree.docinfo.public_id:
|
||||
self.valid = True
|
||||
except Exception as exc:
|
||||
raise RuntimeError(
|
||||
f"Could not initialize PubMed backend for file with hash {self.document_hash}."
|
||||
) from exc
|
||||
|
||||
@override
|
||||
def is_valid(self) -> bool:
|
||||
return self.valid
|
||||
|
||||
@classmethod
|
||||
@override
|
||||
def supports_pagination(cls) -> bool:
|
||||
return False
|
||||
|
||||
@override
|
||||
def unload(self):
|
||||
if isinstance(self.path_or_stream, BytesIO):
|
||||
self.path_or_stream.close()
|
||||
self.path_or_stream = None
|
||||
|
||||
@classmethod
|
||||
@override
|
||||
def supported_formats(cls) -> Set[InputFormat]:
|
||||
return {InputFormat.XML_PUBMED}
|
||||
|
||||
@override
|
||||
def convert(self) -> DoclingDocument:
|
||||
# Create empty document
|
||||
origin = DocumentOrigin(
|
||||
filename=self.file.name or "file",
|
||||
mimetype="application/xml",
|
||||
binary_hash=self.document_hash,
|
||||
)
|
||||
doc = DoclingDocument(name=self.file.stem or "file", origin=origin)
|
||||
|
||||
_log.debug("Trying to convert PubMed XML document...")
|
||||
|
||||
# Get parsed XML components
|
||||
xml_components: XMLComponents = self._parse()
|
||||
|
||||
# Add XML components to the document
|
||||
doc = self._populate_document(doc, xml_components)
|
||||
return doc
|
||||
|
||||
def _parse_title(self) -> str:
|
||||
title: str = " ".join(
|
||||
[
|
||||
t.replace("\n", "")
|
||||
for t in self.tree.xpath(".//title-group/article-title")[0].itertext()
|
||||
]
|
||||
)
|
||||
return title
|
||||
|
||||
def _parse_authors(self) -> list[Author]:
|
||||
# Get mapping between affiliation ids and names
|
||||
affiliation_names = []
|
||||
for affiliation_node in self.tree.xpath(".//aff[@id]"):
|
||||
affiliation_names.append(
|
||||
": ".join([t for t in affiliation_node.itertext() if t != "\n"])
|
||||
)
|
||||
affiliation_ids_names = {
|
||||
id: name
|
||||
for id, name in zip(self.tree.xpath(".//aff[@id]/@id"), affiliation_names)
|
||||
}
|
||||
|
||||
# Get author names and affiliation names
|
||||
authors: list[Author] = []
|
||||
for author_node in self.tree.xpath(
|
||||
'.//contrib-group/contrib[@contrib-type="author"]'
|
||||
):
|
||||
author: Author = {
|
||||
"name": "",
|
||||
"affiliation_names": [],
|
||||
}
|
||||
|
||||
# Affiliation names
|
||||
affiliation_ids = [
|
||||
a.attrib["rid"] for a in author_node.xpath('xref[@ref-type="aff"]')
|
||||
]
|
||||
for id in affiliation_ids:
|
||||
if id in affiliation_ids_names:
|
||||
author["affiliation_names"].append(affiliation_ids_names[id])
|
||||
|
||||
# Name
|
||||
author["name"] = (
|
||||
author_node.xpath("name/surname")[0].text
|
||||
+ " "
|
||||
+ author_node.xpath("name/given-names")[0].text
|
||||
)
|
||||
|
||||
authors.append(author)
|
||||
return authors
|
||||
|
||||
def _parse_abstract(self) -> str:
|
||||
texts = []
|
||||
for abstract_node in self.tree.xpath(".//abstract"):
|
||||
for text in abstract_node.itertext():
|
||||
texts.append(text.replace("\n", ""))
|
||||
abstract: str = "".join(texts)
|
||||
return abstract
|
||||
|
||||
def _parse_main_text(self) -> list[Paragraph]:
|
||||
paragraphs: list[Paragraph] = []
|
||||
for paragraph_node in self.tree.xpath("//body//p"):
|
||||
# Skip captions
|
||||
if "/caption" in paragraph_node.getroottree().getpath(paragraph_node):
|
||||
continue
|
||||
|
||||
paragraph: Paragraph = {"text": "", "headers": []}
|
||||
|
||||
# Text
|
||||
paragraph["text"] = "".join(
|
||||
[t.replace("\n", "") for t in paragraph_node.itertext()]
|
||||
)
|
||||
|
||||
# Header
|
||||
path = "../title"
|
||||
while len(paragraph_node.xpath(path)) > 0:
|
||||
paragraph["headers"].append(
|
||||
"".join(
|
||||
[
|
||||
t.replace("\n", "")
|
||||
for t in paragraph_node.xpath(path)[0].itertext()
|
||||
]
|
||||
)
|
||||
)
|
||||
path = "../" + path
|
||||
|
||||
paragraphs.append(paragraph)
|
||||
|
||||
return paragraphs
|
||||
|
||||
def _parse_tables(self) -> list[Table]:
|
||||
tables: list[Table] = []
|
||||
for table_node in self.tree.xpath(".//body//table-wrap"):
|
||||
table: Table = {"label": "", "caption": "", "content": ""}
|
||||
|
||||
# Content
|
||||
if len(table_node.xpath("table")) > 0:
|
||||
table_content_node = table_node.xpath("table")[0]
|
||||
elif len(table_node.xpath("alternatives/table")) > 0:
|
||||
table_content_node = table_node.xpath("alternatives/table")[0]
|
||||
else:
|
||||
table_content_node = None
|
||||
if table_content_node != None:
|
||||
table["content"] = etree.tostring(table_content_node).decode("utf-8")
|
||||
|
||||
# Caption
|
||||
if len(table_node.xpath("caption/p")) > 0:
|
||||
caption_node = table_node.xpath("caption/p")[0]
|
||||
elif len(table_node.xpath("caption/title")) > 0:
|
||||
caption_node = table_node.xpath("caption/title")[0]
|
||||
else:
|
||||
caption_node = None
|
||||
if caption_node != None:
|
||||
table["caption"] = "".join(
|
||||
[t.replace("\n", "") for t in caption_node.itertext()]
|
||||
)
|
||||
|
||||
# Label
|
||||
if len(table_node.xpath("label")) > 0:
|
||||
table["label"] = table_node.xpath("label")[0].text
|
||||
|
||||
tables.append(table)
|
||||
return tables
|
||||
|
||||
def _parse_figure_captions(self) -> list[FigureCaption]:
|
||||
figure_captions: list[FigureCaption] = []
|
||||
|
||||
if not (self.tree.xpath(".//fig")):
|
||||
return figure_captions
|
||||
|
||||
for figure_node in self.tree.xpath(".//fig"):
|
||||
figure_caption: FigureCaption = {
|
||||
"caption": "",
|
||||
"label": "",
|
||||
}
|
||||
|
||||
# Label
|
||||
if figure_node.xpath("label"):
|
||||
figure_caption["label"] = "".join(
|
||||
[
|
||||
t.replace("\n", "")
|
||||
for t in figure_node.xpath("label")[0].itertext()
|
||||
]
|
||||
)
|
||||
|
||||
# Caption
|
||||
if figure_node.xpath("caption"):
|
||||
caption = ""
|
||||
for caption_node in figure_node.xpath("caption")[0].getchildren():
|
||||
caption += (
|
||||
"".join([t.replace("\n", "") for t in caption_node.itertext()])
|
||||
+ "\n"
|
||||
)
|
||||
figure_caption["caption"] = caption
|
||||
|
||||
figure_captions.append(figure_caption)
|
||||
|
||||
return figure_captions
|
||||
|
||||
def _parse_references(self) -> list[Reference]:
|
||||
references: list[Reference] = []
|
||||
for reference_node_abs in self.tree.xpath(".//ref-list/ref"):
|
||||
reference: Reference = {
|
||||
"author_names": "",
|
||||
"title": "",
|
||||
"journal": "",
|
||||
"year": "",
|
||||
}
|
||||
reference_node: Any = None
|
||||
for tag in ["mixed-citation", "element-citation", "citation"]:
|
||||
if len(reference_node_abs.xpath(tag)) > 0:
|
||||
reference_node = reference_node_abs.xpath(tag)[0]
|
||||
break
|
||||
|
||||
if reference_node is None:
|
||||
continue
|
||||
|
||||
if all(
|
||||
not (ref_type in ["citation-type", "publication-type"])
|
||||
for ref_type in reference_node.attrib.keys()
|
||||
):
|
||||
continue
|
||||
|
||||
# Author names
|
||||
names = []
|
||||
if len(reference_node.xpath("name")) > 0:
|
||||
for name_node in reference_node.xpath("name"):
|
||||
name_str = " ".join(
|
||||
[t.text for t in name_node.getchildren() if (t.text != None)]
|
||||
)
|
||||
names.append(name_str)
|
||||
elif len(reference_node.xpath("person-group")) > 0:
|
||||
for name_node in reference_node.xpath("person-group")[0]:
|
||||
name_str = (
|
||||
name_node.xpath("given-names")[0].text
|
||||
+ " "
|
||||
+ name_node.xpath("surname")[0].text
|
||||
)
|
||||
names.append(name_str)
|
||||
reference["author_names"] = "; ".join(names)
|
||||
|
||||
# Title
|
||||
if len(reference_node.xpath("article-title")) > 0:
|
||||
reference["title"] = " ".join(
|
||||
[
|
||||
t.replace("\n", " ")
|
||||
for t in reference_node.xpath("article-title")[0].itertext()
|
||||
]
|
||||
)
|
||||
|
||||
# Journal
|
||||
if len(reference_node.xpath("source")) > 0:
|
||||
reference["journal"] = reference_node.xpath("source")[0].text
|
||||
|
||||
# Year
|
||||
if len(reference_node.xpath("year")) > 0:
|
||||
reference["year"] = reference_node.xpath("year")[0].text
|
||||
|
||||
if (
|
||||
not (reference_node.xpath("article-title"))
|
||||
and not (reference_node.xpath("journal"))
|
||||
and not (reference_node.xpath("year"))
|
||||
):
|
||||
reference["title"] = reference_node.text
|
||||
|
||||
references.append(reference)
|
||||
return references
|
||||
|
||||
def _parse(self) -> XMLComponents:
|
||||
"""Parsing PubMed document."""
|
||||
xml_components: XMLComponents = {
|
||||
"title": self._parse_title(),
|
||||
"authors": self._parse_authors(),
|
||||
"abstract": self._parse_abstract(),
|
||||
"paragraphs": self._parse_main_text(),
|
||||
"tables": self._parse_tables(),
|
||||
"figure_captions": self._parse_figure_captions(),
|
||||
"references": self._parse_references(),
|
||||
}
|
||||
return xml_components
|
||||
|
||||
def _populate_document(
|
||||
self, doc: DoclingDocument, xml_components: XMLComponents
|
||||
) -> DoclingDocument:
|
||||
self._add_title(doc, xml_components)
|
||||
self._add_authors(doc, xml_components)
|
||||
self._add_abstract(doc, xml_components)
|
||||
self._add_main_text(doc, xml_components)
|
||||
|
||||
if xml_components["tables"]:
|
||||
self._add_tables(doc, xml_components)
|
||||
|
||||
if xml_components["figure_captions"]:
|
||||
self._add_figure_captions(doc, xml_components)
|
||||
|
||||
self._add_references(doc, xml_components)
|
||||
return doc
|
||||
|
||||
def _add_figure_captions(
|
||||
self, doc: DoclingDocument, xml_components: XMLComponents
|
||||
) -> None:
|
||||
self.parents["Figures"] = doc.add_heading(
|
||||
parent=self.parents["Title"], text="Figures"
|
||||
)
|
||||
for figure_caption_xml_component in xml_components["figure_captions"]:
|
||||
figure_caption_text = (
|
||||
figure_caption_xml_component["label"]
|
||||
+ ": "
|
||||
+ figure_caption_xml_component["caption"].strip()
|
||||
)
|
||||
fig_caption = doc.add_text(
|
||||
label=DocItemLabel.CAPTION, text=figure_caption_text
|
||||
)
|
||||
doc.add_picture(
|
||||
parent=self.parents["Figures"],
|
||||
caption=fig_caption,
|
||||
)
|
||||
return
|
||||
|
||||
def _add_title(self, doc: DoclingDocument, xml_components: XMLComponents) -> None:
|
||||
self.parents["Title"] = doc.add_text(
|
||||
parent=None,
|
||||
text=xml_components["title"],
|
||||
label=DocItemLabel.TITLE,
|
||||
)
|
||||
return
|
||||
|
||||
def _add_authors(self, doc: DoclingDocument, xml_components: XMLComponents) -> None:
|
||||
authors_affiliations: list = []
|
||||
for author in xml_components["authors"]:
|
||||
authors_affiliations.append(author["name"])
|
||||
authors_affiliations.append(", ".join(author["affiliation_names"]))
|
||||
authors_affiliations_str = "; ".join(authors_affiliations)
|
||||
|
||||
doc.add_text(
|
||||
parent=self.parents["Title"],
|
||||
text=authors_affiliations_str,
|
||||
label=DocItemLabel.PARAGRAPH,
|
||||
)
|
||||
return
|
||||
|
||||
def _add_abstract(
|
||||
self, doc: DoclingDocument, xml_components: XMLComponents
|
||||
) -> None:
|
||||
abstract_text: str = xml_components["abstract"]
|
||||
self.parents["Abstract"] = doc.add_heading(
|
||||
parent=self.parents["Title"], text="Abstract"
|
||||
)
|
||||
doc.add_text(
|
||||
parent=self.parents["Abstract"],
|
||||
text=abstract_text,
|
||||
label=DocItemLabel.TEXT,
|
||||
)
|
||||
return
|
||||
|
||||
def _add_main_text(
|
||||
self, doc: DoclingDocument, xml_components: XMLComponents
|
||||
) -> None:
|
||||
added_headers: list = []
|
||||
for paragraph in xml_components["paragraphs"]:
|
||||
if not (paragraph["headers"]):
|
||||
continue
|
||||
|
||||
# Header
|
||||
for i, header in enumerate(reversed(paragraph["headers"])):
|
||||
if header in added_headers:
|
||||
continue
|
||||
added_headers.append(header)
|
||||
|
||||
if ((i - 1) >= 0) and list(reversed(paragraph["headers"]))[
|
||||
i - 1
|
||||
] in self.parents:
|
||||
parent = self.parents[list(reversed(paragraph["headers"]))[i - 1]]
|
||||
else:
|
||||
parent = self.parents["Title"]
|
||||
|
||||
self.parents[header] = doc.add_heading(parent=parent, text=header)
|
||||
|
||||
# Paragraph text
|
||||
if paragraph["headers"][0] in self.parents:
|
||||
parent = self.parents[paragraph["headers"][0]]
|
||||
else:
|
||||
parent = self.parents["Title"]
|
||||
|
||||
doc.add_text(parent=parent, label=DocItemLabel.TEXT, text=paragraph["text"])
|
||||
return
|
||||
|
||||
def _add_references(
|
||||
self, doc: DoclingDocument, xml_components: XMLComponents
|
||||
) -> None:
|
||||
self.parents["References"] = doc.add_heading(
|
||||
parent=self.parents["Title"], text="References"
|
||||
)
|
||||
current_list = doc.add_group(
|
||||
parent=self.parents["References"], label=GroupLabel.LIST, name="list"
|
||||
)
|
||||
for reference in xml_components["references"]:
|
||||
reference_text: str = ""
|
||||
if reference["author_names"]:
|
||||
reference_text += reference["author_names"] + ". "
|
||||
|
||||
if reference["title"]:
|
||||
reference_text += reference["title"]
|
||||
if reference["title"][-1] != ".":
|
||||
reference_text += "."
|
||||
reference_text += " "
|
||||
|
||||
if reference["journal"]:
|
||||
reference_text += reference["journal"]
|
||||
|
||||
if reference["year"]:
|
||||
reference_text += " (" + reference["year"] + ")"
|
||||
|
||||
if not (reference_text):
|
||||
_log.debug(f"Skipping reference for: {str(self.file)}")
|
||||
continue
|
||||
|
||||
doc.add_list_item(
|
||||
text=reference_text, enumerated=False, parent=current_list
|
||||
)
|
||||
return
|
||||
|
||||
def _add_tables(self, doc: DoclingDocument, xml_components: XMLComponents) -> None:
|
||||
self.parents["Tables"] = doc.add_heading(
|
||||
parent=self.parents["Title"], text="Tables"
|
||||
)
|
||||
for table_xml_component in xml_components["tables"]:
|
||||
try:
|
||||
self._add_table(doc, table_xml_component)
|
||||
except Exception as e:
|
||||
_log.debug(f"Skipping unsupported table for: {str(self.file)}")
|
||||
pass
|
||||
return
|
||||
|
||||
def _add_table(self, doc: DoclingDocument, table_xml_component: Table) -> None:
|
||||
soup = BeautifulSoup(table_xml_component["content"], "html.parser")
|
||||
table_tag = soup.find("table")
|
||||
|
||||
nested_tables = table_tag.find("table")
|
||||
if nested_tables:
|
||||
_log.debug(f"Skipping nested table for: {str(self.file)}")
|
||||
return
|
||||
|
||||
# Count the number of rows (number of <tr> elements)
|
||||
num_rows = len(table_tag.find_all("tr"))
|
||||
|
||||
# Find the number of columns (taking into account colspan)
|
||||
num_cols = 0
|
||||
for row in table_tag.find_all("tr"):
|
||||
col_count = 0
|
||||
for cell in row.find_all(["td", "th"]):
|
||||
colspan = int(cell.get("colspan", 1))
|
||||
col_count += colspan
|
||||
num_cols = max(num_cols, col_count)
|
||||
|
||||
grid = [[None for _ in range(num_cols)] for _ in range(num_rows)]
|
||||
|
||||
data = TableData(num_rows=num_rows, num_cols=num_cols, table_cells=[])
|
||||
|
||||
# Iterate over the rows in the table
|
||||
for row_idx, row in enumerate(table_tag.find_all("tr")):
|
||||
# For each row, find all the column cells (both <td> and <th>)
|
||||
cells = row.find_all(["td", "th"])
|
||||
|
||||
# Check if each cell in the row is a header -> means it is a column header
|
||||
col_header = True
|
||||
for j, html_cell in enumerate(cells):
|
||||
if html_cell.name == "td":
|
||||
col_header = False
|
||||
|
||||
# Extract and print the text content of each cell
|
||||
col_idx = 0
|
||||
for _, html_cell in enumerate(cells):
|
||||
text = html_cell.text
|
||||
|
||||
col_span = int(html_cell.get("colspan", 1))
|
||||
row_span = int(html_cell.get("rowspan", 1))
|
||||
|
||||
while grid[row_idx][col_idx] != None:
|
||||
col_idx += 1
|
||||
for r in range(row_span):
|
||||
for c in range(col_span):
|
||||
grid[row_idx + r][col_idx + c] = text
|
||||
|
||||
cell = TableCell(
|
||||
text=text,
|
||||
row_span=row_span,
|
||||
col_span=col_span,
|
||||
start_row_offset_idx=row_idx,
|
||||
end_row_offset_idx=row_idx + row_span,
|
||||
start_col_offset_idx=col_idx,
|
||||
end_col_offset_idx=col_idx + col_span,
|
||||
col_header=col_header,
|
||||
row_header=((not col_header) and html_cell.name == "th"),
|
||||
)
|
||||
data.table_cells.append(cell)
|
||||
|
||||
table_caption = doc.add_text(
|
||||
label=DocItemLabel.CAPTION,
|
||||
text=table_xml_component["label"] + ": " + table_xml_component["caption"],
|
||||
)
|
||||
doc.add_table(data=data, parent=self.parents["Tables"], caption=table_caption)
|
||||
return
|
||||
@ -34,7 +34,6 @@ class InputFormat(str, Enum):
|
||||
DOCX = "docx"
|
||||
PPTX = "pptx"
|
||||
HTML = "html"
|
||||
XML_PUBMED = "xml_pubmed"
|
||||
IMAGE = "image"
|
||||
PDF = "pdf"
|
||||
ASCIIDOC = "asciidoc"
|
||||
@ -42,6 +41,7 @@ class InputFormat(str, Enum):
|
||||
CSV = "csv"
|
||||
XLSX = "xlsx"
|
||||
XML_USPTO = "xml_uspto"
|
||||
XML_JATS = "xml_jats"
|
||||
JSON_DOCLING = "json_docling"
|
||||
|
||||
|
||||
@ -59,7 +59,7 @@ FormatToExtensions: Dict[InputFormat, List[str]] = {
|
||||
InputFormat.PDF: ["pdf"],
|
||||
InputFormat.MD: ["md"],
|
||||
InputFormat.HTML: ["html", "htm", "xhtml"],
|
||||
InputFormat.XML_PUBMED: ["xml", "nxml"],
|
||||
InputFormat.XML_JATS: ["xml", "nxml"],
|
||||
InputFormat.IMAGE: ["jpg", "jpeg", "png", "tif", "tiff", "bmp"],
|
||||
InputFormat.ASCIIDOC: ["adoc", "asciidoc", "asc"],
|
||||
InputFormat.CSV: ["csv"],
|
||||
@ -79,7 +79,7 @@ FormatToMimeType: Dict[InputFormat, List[str]] = {
|
||||
"application/vnd.openxmlformats-officedocument.presentationml.presentation",
|
||||
],
|
||||
InputFormat.HTML: ["text/html", "application/xhtml+xml"],
|
||||
InputFormat.XML_PUBMED: ["application/xml"],
|
||||
InputFormat.XML_JATS: ["application/xml"],
|
||||
InputFormat.IMAGE: [
|
||||
"image/png",
|
||||
"image/jpeg",
|
||||
|
||||
@ -333,11 +333,11 @@ class _DocumentConversionInput(BaseModel):
|
||||
):
|
||||
input_format = InputFormat.XML_USPTO
|
||||
|
||||
if (
|
||||
InputFormat.XML_PUBMED in formats
|
||||
and "/NLM//DTD JATS" in xml_doctype
|
||||
if InputFormat.XML_JATS in formats and (
|
||||
"JATS-journalpublishing" in xml_doctype
|
||||
or "JATS-archive" in xml_doctype
|
||||
):
|
||||
input_format = InputFormat.XML_PUBMED
|
||||
input_format = InputFormat.XML_JATS
|
||||
|
||||
elif mime == "text/plain":
|
||||
if InputFormat.XML_USPTO in formats and content_str.startswith("PATN\r\n"):
|
||||
|
||||
@ -18,7 +18,7 @@ from docling.backend.md_backend import MarkdownDocumentBackend
|
||||
from docling.backend.msexcel_backend import MsExcelDocumentBackend
|
||||
from docling.backend.mspowerpoint_backend import MsPowerpointDocumentBackend
|
||||
from docling.backend.msword_backend import MsWordDocumentBackend
|
||||
from docling.backend.xml.pubmed_backend import PubMedDocumentBackend
|
||||
from docling.backend.xml.jats_backend import JatsDocumentBackend
|
||||
from docling.backend.xml.uspto_backend import PatentUsptoDocumentBackend
|
||||
from docling.datamodel.base_models import (
|
||||
ConversionStatus,
|
||||
@ -102,9 +102,9 @@ class PatentUsptoFormatOption(FormatOption):
|
||||
backend: Type[PatentUsptoDocumentBackend] = PatentUsptoDocumentBackend
|
||||
|
||||
|
||||
class XMLPubMedFormatOption(FormatOption):
|
||||
class XMLJatsFormatOption(FormatOption):
|
||||
pipeline_cls: Type = SimplePipeline
|
||||
backend: Type[AbstractDocumentBackend] = PubMedDocumentBackend
|
||||
backend: Type[AbstractDocumentBackend] = JatsDocumentBackend
|
||||
|
||||
|
||||
class ImageFormatOption(FormatOption):
|
||||
@ -143,8 +143,8 @@ def _get_default_option(format: InputFormat) -> FormatOption:
|
||||
InputFormat.XML_USPTO: FormatOption(
|
||||
pipeline_cls=SimplePipeline, backend=PatentUsptoDocumentBackend
|
||||
),
|
||||
InputFormat.XML_PUBMED: FormatOption(
|
||||
pipeline_cls=SimplePipeline, backend=PubMedDocumentBackend
|
||||
InputFormat.XML_JATS: FormatOption(
|
||||
pipeline_cls=SimplePipeline, backend=JatsDocumentBackend
|
||||
),
|
||||
InputFormat.IMAGE: FormatOption(
|
||||
pipeline_cls=StandardPdfPipeline, backend=DoclingParseV2DocumentBackend
|
||||
|
||||
@ -82,7 +82,7 @@
|
||||
"from docling.document_converter import DocumentConverter\n",
|
||||
"\n",
|
||||
"# a sample PMC article:\n",
|
||||
"source = \"../../tests/data/pubmed/elife-56337.nxml\"\n",
|
||||
"source = \"../../tests/data/jats/elife-56337.nxml\"\n",
|
||||
"converter = DocumentConverter()\n",
|
||||
"result = converter.convert(source)\n",
|
||||
"print(result.status)"
|
||||
@ -97,7 +97,7 @@
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 29,
|
||||
"execution_count": 2,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
@ -106,11 +106,11 @@
|
||||
"text": [
|
||||
"# KRAB-zinc finger protein gene expansion in response to active retrotransposons in the murine lineage\n",
|
||||
"\n",
|
||||
"Wolf Gernot; 1: The Eunice Kennedy Shriver National Institute of Child Health and Human Development, The National Institutes of Health: Bethesda: United States; de Iaco Alberto; 2: School of Life Sciences, École Polytechnique Fédérale de Lausanne (EPFL): Lausanne: Switzerland; Sun Ming-An; 1: The Eunice Kennedy Shriver National Institute of Child Health and Human Development, The National Institutes of Health: Bethesda: United States; Bruno Melania; 1: The Eunice Kennedy Shriver National Institute of Child Health and Human Development, The National Institutes of Health: Bethesda: United States; Tinkham Matthew; 1: The Eunice Kennedy Shriver National Institute of Child Health and Human Development, The National Institutes of Health: Bethesda: United States; Hoang Don; 1: The Eunice Kennedy Shriver National Institute of Child Health and Human Development, The National Institutes of Health: Bethesda: United States; Mitra Apratim; 1: The Eunice Kennedy Shriver National Institute of Child Health and Human Development, The National Institutes of Health: Bethesda: United States; Ralls Sherry; 1: The Eunice Kennedy Shriver National Institute of Child Health and Human Development, The National Institutes of Health: Bethesda: United States; Trono Didier; 2: School of Life Sciences, École Polytechnique Fédérale de Lausanne (EPFL): Lausanne: Switzerland; Macfarlan Todd S; 1: The Eunice Kennedy Shriver National Institute of Child Health and Human Development, The National Institutes of Health: Bethesda: United States\n",
|
||||
"Gernot Wolf, Alberto de Iaco, Ming-An Sun, Melania Bruno, Matthew Tinkham, Don Hoang, Apratim Mitra, Sherry Ralls, Didier Trono, Todd S Macfarlan\n",
|
||||
"\n",
|
||||
"The Eunice Kennedy Shriver National Institute of Child Health and Human Development, The National Institutes of Health, Bethesda, United States; School of Life Sciences, École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland\n",
|
||||
"\n",
|
||||
"## Abstract\n",
|
||||
"\n",
|
||||
"The Krüppel-associated box zinc finger protein (KRAB-ZFP) family diversified in mammals. The majority of human KRAB-ZFPs bind transposable elements (TEs), however, since most TEs are inactive in humans it is unclear whether KRAB-ZFPs emerged to suppress TEs. We demonstrate that many recently emerged murine KRAB-ZFPs also bind to TEs, including the active ETn, IAP, and L1 families. Using a CRISPR/Cas9-based engineering approach, we genetically deleted five large clusters of KRAB-ZFPs and demonstrate that target TEs are de-repressed, unleashing TE-encoded enhancers. Homozygous knockout mice lacking one of two KRAB-ZFP gene clusters on chromosome 2 and chromosome 4 were nonetheless viable. In pedigrees of chromosome 4 cluster KRAB-ZFP mutants, we identified numerous novel ETn insertions with a modest increase in mutants. Our data strongly support the current model that recent waves of retrotransposon activity drove the expansion of KRAB-ZFP genes in mice and that many KRAB-ZFPs play a redundant role restricting TE activity.\n",
|
||||
"\n"
|
||||
]
|
||||
}
|
||||
@ -131,7 +131,7 @@
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 2,
|
||||
"execution_count": 3,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
@ -198,7 +198,7 @@
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 3,
|
||||
"execution_count": 4,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
@ -224,7 +224,7 @@
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 4,
|
||||
"execution_count": 5,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
@ -261,7 +261,7 @@
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 5,
|
||||
"execution_count": 6,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
@ -313,7 +313,7 @@
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 6,
|
||||
"execution_count": 7,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
@ -359,9 +359,18 @@
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"execution_count": 8,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"Downloading https://bulkdata.uspto.gov/data/patent/grant/redbook/fulltext/2024/ipg241217.zip...\n",
|
||||
"Parsing zip file, splitting into XML sections, and exporting to files...\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"import zipfile\n",
|
||||
"\n",
|
||||
@ -407,7 +416,7 @@
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 8,
|
||||
"execution_count": 9,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
@ -435,7 +444,7 @@
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 9,
|
||||
"execution_count": 11,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
@ -449,7 +458,7 @@
|
||||
{
|
||||
"data": {
|
||||
"application/vnd.jupyter.widget-view+json": {
|
||||
"model_id": "3964d1ff30f74588a2f6b53ca8865a9f",
|
||||
"model_id": "316241ca89a843bda3170f2a5c76c639",
|
||||
"version_major": 2,
|
||||
"version_minor": 0
|
||||
},
|
||||
@ -471,7 +480,7 @@
|
||||
"source": [
|
||||
"from tqdm.notebook import tqdm\n",
|
||||
"\n",
|
||||
"from docling.backend.xml.pubmed_backend import PubMedDocumentBackend\n",
|
||||
"from docling.backend.xml.jats_backend import JatsDocumentBackend\n",
|
||||
"from docling.backend.xml.uspto_backend import PatentUsptoDocumentBackend\n",
|
||||
"from docling.datamodel.base_models import InputFormat\n",
|
||||
"from docling.datamodel.document import InputDocument\n",
|
||||
@ -479,10 +488,10 @@
|
||||
"# check PMC\n",
|
||||
"in_doc = InputDocument(\n",
|
||||
" path_or_stream=TEMP_DIR / \"nihpp-2024.12.26.630351v1.nxml\",\n",
|
||||
" format=InputFormat.XML_PUBMED,\n",
|
||||
" backend=PubMedDocumentBackend,\n",
|
||||
" format=InputFormat.XML_JATS,\n",
|
||||
" backend=JatsDocumentBackend,\n",
|
||||
")\n",
|
||||
"backend = PubMedDocumentBackend(\n",
|
||||
"backend = JatsDocumentBackend(\n",
|
||||
" in_doc=in_doc, path_or_stream=TEMP_DIR / \"nihpp-2024.12.26.630351v1.nxml\"\n",
|
||||
")\n",
|
||||
"print(f\"Document {in_doc.file.name} is a valid PMC article? {backend.is_valid()}\")\n",
|
||||
@ -521,7 +530,7 @@
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 10,
|
||||
"execution_count": 12,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
@ -543,7 +552,7 @@
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"✏️ **Tip**: in general, there is no need to use the backend converters to parse USPTO or PubMed XML files. The generic `DocumentConverter` object tries to guess the input document format and applies the corresponding backend parser. The conversion shown in [Simple Conversion](#simple-conversion) is the recommended usage for the supported XML files."
|
||||
"✏️ **Tip**: in general, there is no need to use the backend converters to parse USPTO or JATS (PubMed) XML files. The generic `DocumentConverter` object tries to guess the input document format and applies the corresponding backend parser. The conversion shown in [Simple Conversion](#simple-conversion) is the recommended usage for the supported XML files."
|
||||
]
|
||||
},
|
||||
{
|
||||
@ -579,7 +588,7 @@
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 11,
|
||||
"execution_count": 13,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
@ -607,7 +616,7 @@
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 12,
|
||||
"execution_count": 14,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
@ -625,144 +634,9 @@
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 13,
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stderr",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"2025-01-24 16:49:57,108 [DEBUG][_create_connection]: Created new connection using: 2d58fad6c63448a486c0c0ffe3b7b28c (async_milvus_client.py:600)\n",
|
||||
"Loading files: 51%|█████ | 51/100 [00:00<00:00, 67.88file/s]Input document ipg241217-1050.xml does not match any allowed format.\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"Failed to load file /var/folders/2r/b2sdj1512g1_0m7wzzy7sftr0000gn/T/tmp11rjcdj8/ipg241217-1050.xml with error: File format not allowed: /var/folders/2r/b2sdj1512g1_0m7wzzy7sftr0000gn/T/tmp11rjcdj8/ipg241217-1050.xml. Skipping...\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"name": "stderr",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"Loading files: 100%|██████████| 100/100 [00:01<00:00, 58.05file/s]\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"data": {
|
||||
"application/vnd.jupyter.widget-view+json": {
|
||||
"model_id": "e9208639f1a4418d97267a28305d18fa",
|
||||
"version_major": 2,
|
||||
"version_minor": 0
|
||||
},
|
||||
"text/plain": [
|
||||
"Parsing nodes: 0%| | 0/99 [00:00<?, ?it/s]"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
"output_type": "display_data"
|
||||
},
|
||||
{
|
||||
"data": {
|
||||
"application/vnd.jupyter.widget-view+json": {
|
||||
"model_id": "88026613f6f44f0c8476dceaa1cb78cd",
|
||||
"version_major": 2,
|
||||
"version_minor": 0
|
||||
},
|
||||
"text/plain": [
|
||||
"Generating embeddings: 0%| | 0/2048 [00:00<?, ?it/s]"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
"output_type": "display_data"
|
||||
},
|
||||
{
|
||||
"data": {
|
||||
"application/vnd.jupyter.widget-view+json": {
|
||||
"model_id": "7522b8b434b54616b4cfc3d71e9556d7",
|
||||
"version_major": 2,
|
||||
"version_minor": 0
|
||||
},
|
||||
"text/plain": [
|
||||
"Generating embeddings: 0%| | 0/2048 [00:00<?, ?it/s]"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
"output_type": "display_data"
|
||||
},
|
||||
{
|
||||
"data": {
|
||||
"application/vnd.jupyter.widget-view+json": {
|
||||
"model_id": "5879d8161c2041f5b100959e69ff9017",
|
||||
"version_major": 2,
|
||||
"version_minor": 0
|
||||
},
|
||||
"text/plain": [
|
||||
"Generating embeddings: 0%| | 0/2048 [00:00<?, ?it/s]"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
"output_type": "display_data"
|
||||
},
|
||||
{
|
||||
"data": {
|
||||
"application/vnd.jupyter.widget-view+json": {
|
||||
"model_id": "557912b5e3c741f3a06127156bc46379",
|
||||
"version_major": 2,
|
||||
"version_minor": 0
|
||||
},
|
||||
"text/plain": [
|
||||
"Generating embeddings: 0%| | 0/2048 [00:00<?, ?it/s]"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
"output_type": "display_data"
|
||||
},
|
||||
{
|
||||
"data": {
|
||||
"application/vnd.jupyter.widget-view+json": {
|
||||
"model_id": "843bb145942b449aa55fc5b8208da734",
|
||||
"version_major": 2,
|
||||
"version_minor": 0
|
||||
},
|
||||
"text/plain": [
|
||||
"Generating embeddings: 0%| | 0/2048 [00:00<?, ?it/s]"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
"output_type": "display_data"
|
||||
},
|
||||
{
|
||||
"data": {
|
||||
"application/vnd.jupyter.widget-view+json": {
|
||||
"model_id": "c7dba09a4aed422998e9b9c2c3a70317",
|
||||
"version_major": 2,
|
||||
"version_minor": 0
|
||||
},
|
||||
"text/plain": [
|
||||
"Generating embeddings: 0%| | 0/2048 [00:00<?, ?it/s]"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
"output_type": "display_data"
|
||||
},
|
||||
{
|
||||
"data": {
|
||||
"application/vnd.jupyter.widget-view+json": {
|
||||
"model_id": "0bd031356c7e4e879dcbe1d04e6c4a4e",
|
||||
"version_major": 2,
|
||||
"version_minor": 0
|
||||
},
|
||||
"text/plain": [
|
||||
"Generating embeddings: 0%| | 0/425 [00:00<?, ?it/s]"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
"output_type": "display_data"
|
||||
}
|
||||
],
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"from llama_index.core import StorageContext, VectorStoreIndex\n",
|
||||
"from llama_index.vector_stores.milvus import MilvusVectorStore\n",
|
||||
|
||||
@ -21,7 +21,7 @@ Schema-specific support:
|
||||
| Format | Description |
|
||||
|--------|-------------|
|
||||
| USPTO XML | XML format followed by [USPTO](https://www.uspto.gov/patents) patents |
|
||||
| PMC XML | XML format followed by [PubMed Central®](https://pmc.ncbi.nlm.nih.gov/) articles |
|
||||
| JATS XML | XML format followed by [JATS](https://jats.nlm.nih.gov/) articles |
|
||||
| Docling JSON | JSON-serialized [Docling Document](./concepts/docling_document.md) |
|
||||
|
||||
## Supported output formats
|
||||
|
||||
70
tests/data/groundtruth/docling_v2/bmj_sample.xml.itxt
Normal file
70
tests/data/groundtruth/docling_v2/bmj_sample.xml.itxt
Normal file
@ -0,0 +1,70 @@
|
||||
item-0 at level 0: unspecified: group _root_
|
||||
item-1 at level 1: title: Evolving general practice consul ... Britain: issues of length and context
|
||||
item-2 at level 2: paragraph: George K Freeman, John P Horder, ... on P Hill, Nayan C Shah, Andrew Wilson
|
||||
item-3 at level 2: paragraph: Centre for Primary Care and Soci ... ersity of Leicester, Leicester LE5 4PW
|
||||
item-4 at level 2: text: In 1999 Shah1 and others said th ... per consultation in general practice?
|
||||
item-5 at level 2: text: We report on the outcome of exte ... review identified 14 relevant papers.
|
||||
item-6 at level 2: section_header: Summary points
|
||||
item-7 at level 3: list: group list
|
||||
item-8 at level 4: list_item: Longer consultations are associa ... ith a range of better patient outcomes
|
||||
item-9 at level 4: list_item: Modern consultations in general ... th more serious and chronic conditions
|
||||
item-10 at level 4: list_item: Increasing patient participation ... interaction, which demands extra time
|
||||
item-11 at level 4: list_item: Difficulties with access and wit ... e and lead to further pressure on time
|
||||
item-12 at level 4: list_item: Longer consultations should be a ... t to maximise interpersonal continuity
|
||||
item-13 at level 4: list_item: Research on implementation is needed
|
||||
item-14 at level 2: section_header: Longer consultations: benefits for patients
|
||||
item-15 at level 3: text: The systematic review consistent ... ther some doctors insist on more time.
|
||||
item-16 at level 3: text: A national survey in 1998 report ... s the effects of their own experience.
|
||||
item-17 at level 2: section_header: Context of modern consultations
|
||||
item-18 at level 3: text: Shorter consultations were more ... potential length of the consultation.
|
||||
item-19 at level 2: section_header: Participatory consultation style
|
||||
item-20 at level 3: text: The most effective consultations ... style usually lengthens consultations.
|
||||
item-21 at level 2: section_header: Extended professional agenda
|
||||
item-22 at level 3: text: The traditional consultation in ... agerial expectations of good practice.
|
||||
item-23 at level 3: text: Adequate time is essential. It m ... inevitably leads to pressure on time.
|
||||
item-24 at level 2: section_header: Access problems
|
||||
item-25 at level 3: text: In a service free at the point o ... ort notice squeeze consultation times.
|
||||
item-26 at level 3: text: While appointment systems can an ... for the inadequate access to doctors.
|
||||
item-27 at level 3: text: In response to perception of del ... ntation is currently being negotiated.
|
||||
item-28 at level 3: text: Virtually all patients think tha ... e that is free at the point of access.
|
||||
item-29 at level 3: text: A further government initiative ... ealth advice and first line treatment.
|
||||
item-30 at level 2: section_header: Loss of interpersonal continuity
|
||||
item-31 at level 3: text: If a patient has to consult seve ... unning and professional frustration.18
|
||||
item-32 at level 3: text: Mechanic described how loss of l ... patient and professional satisfaction.
|
||||
item-33 at level 2: section_header: Health service reforms
|
||||
item-34 at level 3: text: Finally, for the past 15 years t ... ents and staff) and what is delivered.
|
||||
item-35 at level 2: section_header: The future
|
||||
item-36 at level 3: text: We think that the way ahead must ... p further the care of chronic disease.
|
||||
item-37 at level 3: text: The challenge posed to general p ... ermedicalisation need to be exploited.
|
||||
item-38 at level 3: text: We must ensure better communicat ... between planned and ad hoc consulting.
|
||||
item-39 at level 2: section_header: Next steps
|
||||
item-40 at level 3: text: General practitioners do not beh ... ailable time in complex consultations.
|
||||
item-41 at level 3: text: Devising appropriate incentives ... and interpersonal knowledge and trust.
|
||||
item-42 at level 2: section_header: Acknowledgments
|
||||
item-43 at level 3: text: We thank the other members of th ... Practitioners for administrative help.
|
||||
item-44 at level 2: section_header: References
|
||||
item-45 at level 3: list: group list
|
||||
item-46 at level 4: list_item: Shah NC. Viewpoint: Consultation ... y men!”. Br J Gen Pract 49:497 (1999).
|
||||
item-47 at level 4: list_item: Mechanic D. How should hamsters ... BMJ 323:266–268 (2001). PMID: 11485957
|
||||
item-48 at level 4: list_item: Howie JGR, Porter AMD, Heaney DJ ... n Pract 41:48–54 (1991). PMID: 2031735
|
||||
item-49 at level 4: list_item: Howie JGR, Heaney DJ, Maxwell M, ... BMJ 319:738–743 (1999). PMID: 10487999
|
||||
item-50 at level 4: list_item: Kaplan SH, Greenfield S, Ware JE ... c disease. Med Care 27:110–125 (1989).
|
||||
item-51 at level 4: list_item: Airey C, Erens B. National surve ... e, 1998. London: NHS Executive (1999).
|
||||
item-52 at level 4: list_item: Hart JT. Expectations of health ... h Expect 1:3–13 (1998). PMID: 11281857
|
||||
item-53 at level 4: list_item: Tuckett D, Boulton M, Olson C, W ... London: Tavistock Publications (1985).
|
||||
item-54 at level 4: list_item: General Medical Council. Draft r ... ctors/index.htm (accessed 2 Jan 2002).
|
||||
item-55 at level 4: list_item: Balint M. The doctor, his patien ... the illness. London: Tavistock (1957).
|
||||
item-56 at level 4: list_item: Stott NCH, Davies RH. The except ... J R Coll Gen Pract 29:210–205 (1979).
|
||||
item-57 at level 4: list_item: Hill AP, Hill AP. Challenges for ... nium. London: King's Fund75–86 (2000).
|
||||
item-58 at level 4: list_item: National service framework for c ... . London: Department of Health (2000).
|
||||
item-59 at level 4: list_item: Hart JT. A new kind of doctor: t ... ommunity. London: Merlin Press (1988).
|
||||
item-60 at level 4: list_item: Morrison I, Smith R. Hamster hea ... J 321:1541–1542 (2000). PMID: 11124164
|
||||
item-61 at level 4: list_item: Arber S, Sawyer L. Do appointmen ... BMJ 284:478–480 (1982). PMID: 6800503
|
||||
item-62 at level 4: list_item: Hjortdahl P, Borchgrevink CF. Co ... MJ 303:1181–1184 (1991). PMID: 1747619
|
||||
item-63 at level 4: list_item: Howie JGR, Hopton JL, Heaney DJ, ... Pract 42:181–185 (1992). PMID: 1389427
|
||||
item-64 at level 4: list_item: Freeman G, Shepperd S, Robinson ... ), Summer 2000. London: NCCSDO (2001).
|
||||
item-65 at level 4: list_item: Wilson A, McDonald P, Hayes L, C ... Pract 41:184–187 (1991). PMID: 1878267
|
||||
item-66 at level 4: list_item: De Maeseneer J, Hjortdahl P, Sta ... J 320:1616–1617 (2000). PMID: 10856043
|
||||
item-67 at level 4: list_item: Freeman G, Hjortdahl P. What fut ... MJ 314:1870–1873 (1997). PMID: 9224130
|
||||
item-68 at level 4: list_item: Kibbe DC, Bentz E, McLaughlin CP ... Pract 36:304–308 (1993). PMID: 8454977
|
||||
item-69 at level 4: list_item: Williams M, Neal RD. Time for a ... ct 48:1783–1786 (1998). PMID: 10198490
|
||||
1080
tests/data/groundtruth/docling_v2/bmj_sample.xml.json
Normal file
1080
tests/data/groundtruth/docling_v2/bmj_sample.xml.json
Normal file
File diff suppressed because it is too large
Load Diff
105
tests/data/groundtruth/docling_v2/bmj_sample.xml.md
Normal file
105
tests/data/groundtruth/docling_v2/bmj_sample.xml.md
Normal file
@ -0,0 +1,105 @@
|
||||
# Evolving general practice consultation in Britain: issues of length and context
|
||||
|
||||
George K Freeman, John P Horder, John G R Howie, A Pali Hungin, Alison P Hill, Nayan C Shah, Andrew Wilson
|
||||
|
||||
Centre for Primary Care and Social Medicine, Imperial College of Science, Technology and Medicine, London W6 8RP; Royal College of General Practitioners, London SW7 1PU; Department of General Practice, University of Edinburgh, Edinburgh EH8 9DX; Centre for Health Studies, University of Durham, Durham DH1 3HN; Kilburn Park Medical Centre, London NW6; Department of General Practice and Primary Health Care, University of Leicester, Leicester LE5 4PW
|
||||
|
||||
In 1999 Shah1 and others said that the Royal College of General Practitioners should advocate longer consultations in general practice as a matter of policy. The college set up a working group chaired by A P Hungin, and a systematic review of literature on consultation length in general practice was commissioned. The working group agreed that the available evidence would be hard to interpret without discussion of the changing context within which consultations now take place. For many years general practitioners and those who have surveyed patients' opinions in the United Kingdom have complained about short consultation time, despite a steady increase in actual mean length. Recently Mechanic pointed out that this is also true in the United States.2 Is there any justification for a further increase in mean time allocated per consultation in general practice?
|
||||
|
||||
We report on the outcome of extensive debate among a group of general practitioners with an interest in the process of care, with reference to the interim findings of the commissioned systematic review and our personal databases. The review identified 14 relevant papers.
|
||||
|
||||
## Summary points
|
||||
|
||||
- Longer consultations are associated with a range of better patient outcomes
|
||||
- Modern consultations in general practice deal with patients with more serious and chronic conditions
|
||||
- Increasing patient participation means more complex interaction, which demands extra time
|
||||
- Difficulties with access and with loss of continuity add to perceived stress and poor performance and lead to further pressure on time
|
||||
- Longer consultations should be a professional priority, combined with increased use of technology and more flexible practice management to maximise interpersonal continuity
|
||||
- Research on implementation is needed
|
||||
|
||||
## Longer consultations: benefits for patients
|
||||
|
||||
The systematic review consistently showed that doctors with longer consultation times prescribe less and offer more advice on lifestyle and other health promoting activities. Longer consultations have been significantly associated with better recognition and handling of psychosocial problems3 and with better patient enablement.4 Also clinical care for some chronic illnesses is better in practices with longer booked intervals between one appointment and the next.5 It is not clear whether time is itself the main influence or whether some doctors insist on more time.
|
||||
|
||||
A national survey in 1998 reported that most (87%) patients were satisfied with the length of their most recent consultation.6 Satisfaction with any service will be high if expectations are met or exceeded. But expectations are modified by previous experience.7 The result is that primary care patients are likely to be satisfied with what they are used to unless the context modifies the effects of their own experience.
|
||||
|
||||
## Context of modern consultations
|
||||
|
||||
Shorter consultations were more appropriate when the population was younger, when even a brief absence from employment due to sickness required a doctor's note, and when many simple remedies were available only on prescription. Recently at least five important influences have increased the content and hence the potential length of the consultation.
|
||||
|
||||
## Participatory consultation style
|
||||
|
||||
The most effective consultations are those in which doctors most directly acknowledge and perhaps respond to patients' problems and concerns. In addition, for patients to be committed to taking advantage of medical advice they must agree with both the goals and methods proposed. A landmark publication in the United Kingdom was Meetings Between Experts, which argued that while doctors are the experts about medical problems in general patients are the experts on how they themselves experience these problems.8 New emphasis on teaching consulting skills in general practice advocated specific attention to the patient's agenda, beliefs, understanding, and agreement. Currently the General Medical Council, aware that communication difficulties underlie many complaints about doctors, has further emphasised the importance of involving patients in consultations in its revised guidance to medical schools.9 More patient involvement should give a better outcome, but this participatory style usually lengthens consultations.
|
||||
|
||||
## Extended professional agenda
|
||||
|
||||
The traditional consultation in general practice was brief.2 The patient presented symptoms and the doctor prescribed treatment. In 1957 Balint gave new insights into the meaning of symptoms.10 By 1979 an enhanced model of consultation was presented, in which the doctors dealt with ongoing as well as presenting problems and added health promotion and education about future appropriate use of services.11 Now, with an ageing population and more community care of chronic illness, there are more issues to be considered at each consultation. Ideas of what constitutes good general practice are more complex.12 Good practice now includes both extended care of chronic medical problems—for example, coronary heart disease13—and a public health role. At first this model was restricted to those who lead change (“early adopters”) and enthusiasts14 but now it is embedded in professional and managerial expectations of good practice.
|
||||
|
||||
Adequate time is essential. It may be difficult for an elderly patient with several active problems to undress, be examined, and get adequate professional consideration in under 15 minutes. Here the doctor is faced with the choice of curtailing the consultation or of reducing the time available for the next patient. Having to cope with these situations often contributes to professional dissatisfaction.15 This combination of more care, more options, and more genuine discussion of those options with informed patient choice inevitably leads to pressure on time.
|
||||
|
||||
## Access problems
|
||||
|
||||
In a service free at the point of access, rising demand will tend to increase rationing by delay. But attempts to improve access by offering more consultations at short notice squeeze consultation times.
|
||||
|
||||
While appointment systems can and should reduce queuing time for consultations, they have long tended to be used as a brake on total demand.16 This may seriously erode patients' confidence in being able to see their doctor or nurse when they need to. Patients are offered appointments further ahead but may keep these even if their symptoms have remitted “just in case.” Availability of consultations is thus blocked. Receptionists are then inappropriately blamed for the inadequate access to doctors.
|
||||
|
||||
In response to perception of delay, the government has set targets in the NHS plan of “guaranteed access to a primary care professional within 24 hours and to a primary care doctor within 48 hours.” Implementation is currently being negotiated.
|
||||
|
||||
Virtually all patients think that they would not consult unless it was absolutely necessary. They do not think they are wasting NHS time and do not like being made to feel so. But underlying general practitioners' willingness to make patients wait several days is their perception that few of the problems are urgent. Patients and general practitioners evidently do not agree about the urgency of so called minor problems. To some extent general practice in the United Kingdom may have scored an “own goal” by setting up perceived access barriers (appointment systems and out of hours cooperatives) in the attempt to increase professional standards and control demand in a service that is free at the point of access.
|
||||
|
||||
A further government initiative has been to bypass general practice with new services—notably, walk-in centres (primary care clinics in which no appointment is needed) and NHS Direct (a professional telephone helpline giving advice on simple remedies and access to services). Introduced widely and rapidly, these services each potentially provide significant features of primary care—namely, quick access to skilled health advice and first line treatment.
|
||||
|
||||
## Loss of interpersonal continuity
|
||||
|
||||
If a patient has to consult several different professionals, particularly over a short period of time, there is inevitable duplication of stories, risk of naive diagnoses, potential for conflicting advice, and perhaps loss of trust. Trust is essential if patients are to accept the “wait and see” management policy which is, or should be, an important part of the management of self limiting conditions, which are often on the boundary between illness and non-illness.17 Such duplication again increases pressure for more extra (unscheduled) consultations resulting in late running and professional frustration.18
|
||||
|
||||
Mechanic described how loss of longitudinal (and perhaps personal and relational19) continuity influences the perception and use of time through an inability to build on previous consultations.2 Knowing the doctor well, particularly in smaller practices, is associated with enhanced patient enablement in shorter time.4 Though Mechanic pointed out that three quarters of UK patients have been registered with their general practitioner five years or more, this may be misleading. Practices are growing, with larger teams and more registered patients. Being registered with a doctor in a larger practice is usually no guarantee that the patient will be able to see the same doctor or the doctor of his or her choice, who may be different. Thus the system does not encourage adequate personal continuity. This adds to pressure on time and reduces both patient and professional satisfaction.
|
||||
|
||||
## Health service reforms
|
||||
|
||||
Finally, for the past 15 years the NHS has experienced unprecedented change with a succession of major administrative reforms. Recent reforms have focused on an NHS led by primary care, including the aim of shifting care from the secondary specialist sector to primary care. One consequence is increased demand for primary care of patients with more serious and less stable problems. With the limited piloting of reforms we do not know whether such major redirection can be achieved without greatly altering the delicate balance between expectations (of both patients and staff) and what is delivered.
|
||||
|
||||
## The future
|
||||
|
||||
We think that the way ahead must embrace both longer mean consultation times and more flexibility. More time is needed for high quality consultations with patients with major and complex problems of all kinds. But patients also need access to simpler services and advice. This should be more appropriate (and cost less) when it is given by professionals who know the patient and his or her medical history and social circumstances. For doctors, the higher quality associated with longer consultations may lead to greater professional satisfaction and, if these longer consultations are combined with more realistic scheduling, to reduced levels of stress.20 They will also find it easier to develop further the care of chronic disease.
|
||||
|
||||
The challenge posed to general practice by walk-in centres and NHS Direct is considerable, and the diversion of funding from primary care is large. The risk of waste and duplication increases as more layers of complexity are added to a primary care service that started out as something familiar, simple, and local and which is still envied in other developed countries.21 Access needs to be simple, and the advantages of personal knowledge and trust in minimising duplication and overmedicalisation need to be exploited.
|
||||
|
||||
We must ensure better communication and access so that patients can more easily deal with minor issues and queries with someone they know and trust and avoid the formality and inconvenience of a full face to face consultation. Too often this has to be with a different professional, unfamiliar with the nuances of the case. There should be far more managerial emphasis on helping patients to interact with their chosen practitioner22; such a programme has been described.23 Modern information systems make it much easier to record which doctor(s) a patient prefers to see and to monitor how often this is achieved. The telephone is hardly modern but is underused. Email avoids the problems inherent in arranging simultaneous availability necessary for telephone consultations but at the cost of reducing the communication of emotions. There is a place for both.2 Access without prior appointment is a valued feature of primary care, and we need to know more about the right balance between planned and ad hoc consulting.
|
||||
|
||||
## Next steps
|
||||
|
||||
General practitioners do not behave in a uniform way. They can be categorised as slow, medium, and fast and react in different ways to changes in consulting speed.18 They are likely to have differing views about a widespread move to lengthen consultation time. We do not need further confirmation that longer consultations are desirable and necessary, but research could show us the best way to learn how to introduce them with minimal disruption to the way in which patients and practices like primary care to be provided.24 We also need to learn how to make the most of available time in complex consultations.
|
||||
|
||||
Devising appropriate incentives and helping practices move beyond just reacting to demand in the traditional way by working harder and faster is perhaps our greatest challenge in the United Kingdom. The new primary are trusts need to work together with the growing primary care research networks to carry out the necessary development work. In particular, research is needed on how a primary care team can best provide the right balance of quick access and interpersonal knowledge and trust.
|
||||
|
||||
## Acknowledgments
|
||||
|
||||
We thank the other members of the working group: Susan Childs, Paul Freeling, Iona Heath, Marshall Marinker, and Bonnie Sibbald. We also thank Fenny Green of the Royal College of General Practitioners for administrative help.
|
||||
|
||||
## References
|
||||
|
||||
- Shah NC. Viewpoint: Consultation time—time for a change? Still the “perfunctory work of perfunctory men!”. Br J Gen Pract 49:497 (1999).
|
||||
- Mechanic D. How should hamsters run? Some observations about sufficient patient time in primary care. BMJ 323:266–268 (2001). PMID: 11485957
|
||||
- Howie JGR, Porter AMD, Heaney DJ, Hopton JL. Long to short consultation ratio: a proxy measure of quality of care for general practice. Br J Gen Pract 41:48–54 (1991). PMID: 2031735
|
||||
- Howie JGR, Heaney DJ, Maxwell M, Walker JJ, Freeman GK, Rai H. Quality at general practice consultations: cross-sectional survey. BMJ 319:738–743 (1999). PMID: 10487999
|
||||
- Kaplan SH, Greenfield S, Ware JE. Assessing the effects of physician-patient interactions on the outcome of chronic disease. Med Care 27:110–125 (1989).
|
||||
- Airey C, Erens B. National surveys of NHS patients: general practice, 1998. London: NHS Executive (1999).
|
||||
- Hart JT. Expectations of health care: promoted, managed or shared?. Health Expect 1:3–13 (1998). PMID: 11281857
|
||||
- Tuckett D, Boulton M, Olson C, Williams A. Meetings between experts: an approach to sharing ideas in medical consultations. London: Tavistock Publications (1985).
|
||||
- General Medical Council. Draft recommendations on undergraduate medical education. July 2001. www.gmc-uk.org/med\_ed/tomorrowsdoctors/index.htm (accessed 2 Jan 2002).
|
||||
- Balint M. The doctor, his patient and the illness. London: Tavistock (1957).
|
||||
- Stott NCH, Davies RH. The exceptional potential in each primary care consultation. J R Coll Gen Pract 29:210–205 (1979).
|
||||
- Hill AP, Hill AP. Challenges for primary care. What's gone wrong with health care? Challenges for the new millennium. London: King's Fund75–86 (2000).
|
||||
- National service framework for coronary heart disease. London: Department of Health (2000).
|
||||
- Hart JT. A new kind of doctor: the general practitioner's part in the health of the community. London: Merlin Press (1988).
|
||||
- Morrison I, Smith R. Hamster health care. BMJ 321:1541–1542 (2000). PMID: 11124164
|
||||
- Arber S, Sawyer L. Do appointment systems work?. BMJ 284:478–480 (1982). PMID: 6800503
|
||||
- Hjortdahl P, Borchgrevink CF. Continuity of care: influence of general practitioners' knowledge about their patients on use of resources in consultations. BMJ 303:1181–1184 (1991). PMID: 1747619
|
||||
- Howie JGR, Hopton JL, Heaney DJ, Porter AMD. Attitudes to medical care, the organization of work, and stress among general practitioners. Br J Gen Pract 42:181–185 (1992). PMID: 1389427
|
||||
- Freeman G, Shepperd S, Robinson I, Ehrich K, Richards SC, Pitman P. Continuity of care: report of a scoping exercise for the national co-ordinating centre for NHS Service Delivery and Organisation R&D (NCCSDO), Summer 2000. London: NCCSDO (2001).
|
||||
- Wilson A, McDonald P, Hayes L, Cooney J. Longer booking intervals in general practice: effects on doctors' stress and arousal. Br J Gen Pract 41:184–187 (1991). PMID: 1878267
|
||||
- De Maeseneer J, Hjortdahl P, Starfield B. Fix what's wrong, not what's right, with general practice in Britain. BMJ 320:1616–1617 (2000). PMID: 10856043
|
||||
- Freeman G, Hjortdahl P. What future for continuity of care in general practice?. BMJ 314:1870–1873 (1997). PMID: 9224130
|
||||
- Kibbe DC, Bentz E, McLaughlin CP. Continuous quality improvement for continuity of care. J Fam Pract 36:304–308 (1993). PMID: 8454977
|
||||
- Williams M, Neal RD. Time for a change? The process of lengthening booking intervals in general practice. Br J Gen Pract 48:1783–1786 (1998). PMID: 10198490
|
||||
@ -1,165 +1,149 @@
|
||||
item-0 at level 0: unspecified: group _root_
|
||||
item-1 at level 1: title: KRAB-zinc finger protein gene ex ... retrotransposons in the murine lineage
|
||||
item-2 at level 2: paragraph: Wolf Gernot; 1: The Eunice Kenne ... tes of Health: Bethesda: United States
|
||||
item-3 at level 2: section_header: Abstract
|
||||
item-4 at level 3: text: The Krüppel-associated box zinc ... edundant role restricting TE activity.
|
||||
item-5 at level 2: section_header: Introduction
|
||||
item-6 at level 3: text: Nearly half of the human and mou ... s are active beyond early development.
|
||||
item-7 at level 3: text: TEs, especially long terminal re ... f evolutionarily young KRAB-ZFP genes.
|
||||
item-8 at level 2: section_header: Results
|
||||
item-9 at level 3: section_header: Mouse KRAB-ZFPs target retrotransposons
|
||||
item-10 at level 4: text: We analyzed the RNA expression p ... duplications (Kauzlaric et al., 2017).
|
||||
item-11 at level 4: text: To determine the binding sites o ... ctive in the early embryo (Figure 1A).
|
||||
item-12 at level 4: text: We generally observed that KRAB- ... responsible for this silencing effect.
|
||||
item-13 at level 4: text: To further test the hypothesis t ... t easily evade repression by mutation.
|
||||
item-14 at level 4: text: Our KRAB-ZFP ChIP-seq dataset al ... ntirely shift the mode of DNA binding.
|
||||
item-15 at level 3: section_header: Genetic deletion of KRAB-ZFP gen ... leads to retrotransposon reactivation
|
||||
item-16 at level 4: text: The majority of KRAB-ZFP genes a ... ung et al., 2014; Deniz et al., 2018).
|
||||
item-17 at level 3: section_header: KRAB-ZFP cluster deletions license TE-borne enhancers
|
||||
item-18 at level 4: text: We next used our RNA-seq dataset ... vating effects of TEs on nearby genes.
|
||||
item-19 at level 4: text: While we generally observed that ... he internal region and not on the LTR.
|
||||
item-20 at level 3: section_header: ETn retrotransposition in Chr4-cl KO and WT mice
|
||||
item-21 at level 4: text: IAP, ETn/ETnERV and MuLV/RLTR4 r ... s may contribute to reduced viability.
|
||||
item-22 at level 4: text: We reasoned that retrotransposon ... Tn insertions at a high recovery rate.
|
||||
item-23 at level 4: text: Using this dataset, we first con ... nsertions in our pedigree (Figure 4A).
|
||||
item-24 at level 4: text: To validate some of the novel ET ... ess might have truncated this element.
|
||||
item-25 at level 4: text: Besides novel ETn insertions tha ... tions (Figure 4—figure supplement 3D).
|
||||
item-26 at level 4: text: Finally, we asked whether there ... s clearly also play an important role.
|
||||
item-27 at level 2: section_header: Discussion
|
||||
item-28 at level 3: text: C2H2 zinc finger proteins, about ... ) depending upon their insertion site.
|
||||
item-29 at level 3: text: Despite a lack of widespread ETn ... ion of the majority of KRAB-ZFP genes.
|
||||
item-30 at level 2: section_header: Materials and methods
|
||||
item-31 at level 3: section_header: Cell lines and transgenic mice
|
||||
item-32 at level 4: text: Mouse ES cells and F9 EC cells w ... KO/KO and KO/WT (B6/129 F2) offspring.
|
||||
item-33 at level 3: section_header: Generation of KRAB-ZFP expressing cell lines
|
||||
item-34 at level 4: text: KRAB-ZFP ORFs were PCR-amplified ... led and further expanded for ChIP-seq.
|
||||
item-35 at level 3: section_header: CRISPR/Cas9 mediated deletion of KRAB-ZFP clusters and an MMETn insertion
|
||||
item-36 at level 4: text: All gRNAs were expressed from th ... PCR genotyping (Supplementary file 3).
|
||||
item-37 at level 3: section_header: ChIP-seq analysis
|
||||
item-38 at level 4: text: For ChIP-seq analysis of KRAB-ZF ... 010 or Khil et al., 2012 respectively.
|
||||
item-39 at level 4: text: ChIP-seq libraries were construc ... were re-mapped using Bowtie (--best).
|
||||
item-40 at level 3: section_header: Luciferase reporter assays
|
||||
item-41 at level 4: text: For KRAB-ZFP repression assays, ... after transfection as described above.
|
||||
item-42 at level 3: section_header: RNA-seq analysis
|
||||
item-43 at level 4: text: Whole RNA was purified using RNe ... lemented in the R function p.adjust().
|
||||
item-44 at level 3: section_header: Reduced representation bisulfite sequencing (RRBS-seq)
|
||||
item-45 at level 4: text: For RRBS-seq analysis, Chr4-cl W ... h sample were considered for analysis.
|
||||
item-46 at level 3: section_header: Retrotransposition assay
|
||||
item-47 at level 4: text: The retrotransposition vectors p ... were stained with Amido Black (Sigma).
|
||||
item-48 at level 3: section_header: Capture-seq screen
|
||||
item-49 at level 4: text: To identify novel retrotransposo ... assembly using the Unicycler software.
|
||||
item-50 at level 2: section_header: Tables
|
||||
item-51 at level 3: table with [9x5]
|
||||
item-51 at level 4: caption: Table 1.: * Number of protein-coding KRAB-ZFP genes identified in a previously published screen (Imbeault et al., 2017) and the ChIP-seq data column indicates the number of KRAB-ZFPs for which ChIP-seq was performed in this study.
|
||||
item-52 at level 3: table with [31x5]
|
||||
item-52 at level 4: caption: Key resources table:
|
||||
item-53 at level 2: section_header: Figures
|
||||
item-54 at level 3: picture
|
||||
item-54 at level 4: caption: Figure 1.: Genome-wide binding patterns of mouse KRAB-ZFPs.
|
||||
(A) Probability heatmap of KRAB-ZFP binding to TEs. Blue color intensity (main field) corresponds to -log10 (adjusted p-value) enrichment of ChIP-seq peak overlap with TE groups (Fisher’s exact test). The green/red color intensity (top panel) represents mean KAP1 (GEO accession: GSM1406445) and H3K9me3 (GEO accession: GSM1327148) enrichment (respectively) at peaks overlapping significantly targeted TEs (adjusted p-value<1e-5) in WT ES cells. (B) Summarized ChIP-seq signal for indicated KRAB-ZFPs and previously published KAP1 and H3K9me3 in WT ES cells across 127 intact ETn elements. (C) Heatmaps of KRAB-ZFP ChIP-seq signal at ChIP-seq peaks. For better comparison, peaks for all three KRAB-ZFPs were called with the same parameters (p<1e-10, peak enrichment >20). The top panel shows a schematic of the arrangement of the contact amino acid composition of each zinc finger. Zinc fingers are grouped and colored according to similarity, with amino acid differences relative to the five consensus fingers highlighted in white.
|
||||
Figure 1—source data 1.KRAB-ZFP expression in 40 mouse tissues and cell lines (ENCODE).Mean values of replicates are shown as log2 transcripts per million.
|
||||
Figure 1—source data 2.Probability heatmap of KRAB-ZFP binding to TEs.Values corresponds to -log10 (adjusted p-value) enrichment of ChIP-seq peak overlap with TE groups (Fisher’s exact test).
|
||||
item-55 at level 3: picture
|
||||
item-55 at level 4: caption: Figure 1—figure supplement 1.: ES cell-specific expression of KRAB-ZFP gene clusters.
|
||||
(A) Heatmap showing expression patterns of mouse KRAB-ZFPs in 40 mouse tissues and cell lines (ENCODE). Heatmap colors indicate gene expression levels in log2 transcripts per million (TPM). The asterisk indicates a group of 30 KRAB-ZFPs that are exclusively expressed in ES cells. (B) Physical location of the genes encoding for the 30 KRAB-ZFPs that are exclusively expressed in ES cells. (C) Phylogenetic (Maximum likelihood) tree of the KRAB domains of mouse KRAB-ZFPs. KRAB-ZFPs encoded on the gene clusters on chromosome 2 and 4 are highlighted. The scale bar at the bottom indicates amino acid substitutions per site.
|
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item-56 at level 3: picture
|
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item-56 at level 4: caption: Figure 1—figure supplement 2.: KRAB-ZFP binding motifs and their repression activity.
|
||||
(A) Comparison of computationally predicted (bottom) and experimentally determined (top) KRAB-ZFP binding motifs. Only significant pairs are shown (FDR < 0.1). (B) Luciferase reporter assays to confirm KRAB-ZFP repression of the identified target sites. Bars show the luciferase activity (normalized to Renilla luciferase) of reporter plasmids containing the indicated target sites cloned upstream of the SV40 promoter. Reporter plasmids were co-transfected into 293 T cells with a Renilla luciferase plasmid for normalization and plasmids expressing the targeting KRAB-ZFP. Normalized mean luciferase activity (from three replicates) is shown relative to luciferase activity of the reporter plasmid co-transfected with an empty pcDNA3.1 vector.
|
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item-57 at level 3: picture
|
||||
item-57 at level 4: caption: Figure 1—figure supplement 3.: KRAB-ZFP binding to ETn retrotransposons.
|
||||
(A) Comparison of the PBSLys1,2 sequence with Zfp961 binding motifs in nonrepetitive peaks (Nonrep) and peaks at ETn elements. (B) Retrotransposition assays of original (ETnI1-neoTNF and MusD2-neoTNF Ribet et al., 2004) and modified reporter vectors where the Rex2 or Gm13051 binding motifs where removed. Schematic of reporter vectors are displayed at the top. HeLa cells were transfected as described in the Materials and Methods section and neo-resistant colonies, indicating retrotransposition events, were selected and stained. (C) Stem-loop structure of the ETn RNA export signal, the Gm13051 motif on the corresponding DNA is marked with red circles, the part of the motif that was deleted is indicated with grey crosses (adapted from Legiewicz et al., 2010).
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item-58 at level 3: picture
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item-58 at level 4: caption: Figure 2.: Retrotransposon reactivation in KRAB-ZFP cluster KO ES cells.
|
||||
(A) RNA-seq analysis of TE expression in five KRAB-ZFP cluster KO ES cells. Green and grey squares on top of the panel represent KRAB-ZFPs with or without ChIP-seq data, respectively, within each deleted gene cluster. Reactivated TEs that are bound by one or several KRAB-ZFPs are indicated by green squares in the panel. Significantly up- and downregulated elements (adjusted p-value<0.05) are highlighted in red and green, respectively. (B) Differential KAP1 binding and H3K9me3 enrichment at TE groups (summarized across all insertions) in Chr2-cl and Chr4-cl KO ES cells. TE groups targeted by one or several KRAB-ZFPs encoded within the deleted clusters are highlighted in blue (differential enrichment over the entire TE sequences) and red (differential enrichment at TE regions that overlap with KRAB-ZFP ChIP-seq peaks). (C) DNA methylation status of CpG sites at indicated TE groups in WT and Chr4-cl KO ES cells grown in serum containing media or in hypomethylation-inducing media (2i + Vitamin C). P-values were calculated using paired t-test.
|
||||
Figure 2—source data 1.Differential H3K9me3 and KAP1 distribution in WT and KRAB-ZFP cluster KO ES cells at TE families and KRAB-ZFP bound TE insertions.Differential read counts and statistical testing were determined by DESeq2.
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item-59 at level 3: picture
|
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item-59 at level 4: caption: Figure 2—figure supplement 1.: Epigenetic changes at TEs and TE-borne enhancers in KRAB-ZFP cluster KO ES cells.
|
||||
(A) Differential analysis of summative (all individual insertions combined) H3K9me3 enrichment at TE groups in Chr10-cl, Chr13.1-cl and Chr13.2-cl KO ES cells. TE groups targeted by one or several KRAB-ZFPs encoded within the deleted clusters are highlighted in orange (differential enrichment over the entire TE sequences) and red (differential enrichment at TE regions that overlap with KRAB-ZFP ChIP-seq peaks). (B) Top: Schematic view of the Cd59a/Cd59b locus with a 5’ truncated ETn insertion. ChIP-seq (Input subtracted from ChIP) data for overexpressed epitope-tagged Gm13051 (a Chr4-cl KRAB-ZFP) in F9 EC cells, and re-mapped KAP1 (GEO accession: GSM1406445) and H3K9me3 (GEO accession: GSM1327148) in WT ES cells are shown together with RNA-seq data from Chr4-cl WT and KO ES cells (mapped using Bowtie (-a -m 1 --strata -v 2) to exclude reads that cannot be uniquely mapped). Bottom: Transcriptional activity of a 5 kb fragment with or without fragments of the ETn insertion was tested by luciferase reporter assay in Chr4-cl WT and KO ES cells.
|
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item-60 at level 3: picture
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||||
item-60 at level 4: caption: Figure 3.: TE-dependent gene activation in KRAB-ZFP cluster KO ES cells.
|
||||
(A) Differential gene expression in Chr2-cl and Chr4-cl KO ES cells. Significantly up- and downregulated genes (adjusted p-value<0.05) are highlighted in red and green, respectively, KRAB-ZFP genes within the deleted clusters are shown in blue. (B) Correlation of TEs and gene deregulation. Plots show enrichment of TE groups within 100 kb of up- and downregulated genes relative to all genes. Significantly overrepresented LTR and LINE groups (adjusted p-value<0.1) are highlighted in blue and red, respectively. (C) Schematic view of the downstream region of Chst1 where a 5’ truncated ETn insertion is located. ChIP-seq (Input subtracted from ChIP) data for overexpressed epitope-tagged Gm13051 (a Chr4-cl KRAB-ZFP) in F9 EC cells, and re-mapped KAP1 (GEO accession: GSM1406445) and H3K9me3 (GEO accession: GSM1327148) in WT ES cells are shown together with RNA-seq data from Chr4-cl WT and KO ES cells (mapped using Bowtie (-a -m 1 --strata -v 2) to exclude reads that cannot be uniquely mapped). (D) RT-qPCR analysis of Chst1 mRNA expression in Chr4-cl WT and KO ES cells with or without the CRISPR/Cas9 deleted ETn insertion near Chst1. Values represent mean expression (normalized to Gapdh) from three biological replicates per sample (each performed in three technical replicates) in arbitrary units. Error bars represent standard deviation and asterisks indicate significance (p<0.01, Student’s t-test). n.s.: not significant. (E) Mean coverage of ChIP-seq data (Input subtracted from ChIP) in Chr4-cl WT and KO ES cells over 127 full-length ETn insertions. The binding sites of the Chr4-cl KRAB-ZFPs Rex2 and Gm13051 are indicated by dashed lines.
|
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item-61 at level 3: picture
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||||
item-61 at level 4: caption: Figure 4.: ETn retrotransposition in Chr4-cl KO mice.
|
||||
(A) Pedigree of mice used for transposon insertion screening by capture-seq in mice of different strain backgrounds. The number of novel ETn insertions (only present in one animal) are indicated. For animals whose direct ancestors have not been screened, the ETn insertions are shown in parentheses since parental inheritance cannot be excluded in that case. Germ line insertions are indicated by asterisks. All DNA samples were prepared from tail tissues unless noted (-S: spleen, -E: ear, -B:Blood) (B) Statistical analysis of ETn insertion frequency in tail tissue from 30 Chr4-cl KO, KO/WT and WT mice that were derived from one Chr4-c KO x KO/WT and two Chr4-cl KO/WT x KO/WT matings. Only DNA samples that were collected from juvenile tails were considered for this analysis. P-values were calculated using one-sided Wilcoxon Rank Sum Test. In the last panel, KO, WT and KO/WT mice derived from all matings were combined for the statistical analysis.
|
||||
Figure 4—source data 1.Coordinates of identified novel ETn insertions and supporting capture-seq read counts.Genomic regions indicate cluster of supporting reads.
|
||||
Figure 4—source data 2.Sequences of capture-seq probes used to enrich genomic DNA for ETn and MuLV (RLTR4) insertions.
|
||||
item-62 at level 3: picture
|
||||
item-62 at level 4: caption: Figure 4—figure supplement 1.: Birth statistics of KRAB-ZFP cluster KO mice and TE reactivation in adult tissues.
|
||||
(A) Birth statistics of Chr4- and Chr2-cl mice derived from KO/WT x KO/WT matings in different strain backgrounds. (B) RNA-seq analysis of TE expression in Chr2- (left) and Chr4-cl (right) KO tissues. TE groups with the highest reactivation phenotype in ES cells are shown separately. Significantly up- and downregulated elements (adjusted p-value<0.05) are highlighted in red and green, respectively. Experiments were performed in at least two biological replicates.
|
||||
item-63 at level 3: picture
|
||||
item-63 at level 4: caption: Figure 4—figure supplement 2.: Identification of polymorphic ETn and MuLV retrotransposon insertions in Chr4-cl KO and WT mice.
|
||||
Heatmaps show normalized capture-seq read counts in RPM (Read Per Million) for identified polymorphic ETn (A) and MuLV (B) loci in different mouse strains. Only loci with strong support for germ line ETn or MuLV insertions (at least 100 or 3000 ETn or MuLV RPM, respectively) in at least two animals are shown. Non-polymorphic insertion loci with high read counts in all screened mice were excluded for better visibility. The sample information (sample name and cell type/tissue) is annotated at the bottom, with the strain information indicated by color at the top. The color gradient indicates log10(RPM+1).
|
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item-64 at level 3: picture
|
||||
item-64 at level 4: caption: Figure 4—figure supplement 3.: Confirmation of novel ETn insertions identified by capture-seq.
|
||||
(A) PCR validation of novel ETn insertions in genomic DNA of three littermates (IDs: T09673, T09674 and T00436) and their parents (T3913 and T3921). Primer sequences are shown in Supplementary file 3. (B) ETn capture-seq read counts (RPM) at putative novel somatic (loci identified exclusively in one single animal), novel germ line (loci identified in several littermates) insertions, and at B6 reference ETn elements. (C) Heatmap shows capture-seq read counts (RPM) of a Chr4-cl KO mouse (ID: C6733) as determined in different tissues. Each row represents a novel ETn locus that was identified in at least one tissue. The color gradient indicates log10(RPM+1). (D) Heatmap shows the capture-seq RPM in technical replicates using the same Chr4-cl KO DNA sample (rep1/rep2) or replicates with DNA samples prepared from different sections of the tail from the same mouse at different ages (tail1/tail2). Each row represents a novel ETn locus that was identified in at least one of the displayed samples. The color gradient indicates log10(RPM+1).
|
||||
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item-123 at level 1: caption: Table 1.: * Number of protein-co ... ChIP-seq was performed in this study.
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item-124 at level 1: caption: Key resources table:
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||||
item-125 at level 1: caption: Figure 1.: Genome-wide binding p ... with TE groups (Fisher’s exact test).
|
||||
item-126 at level 1: caption: Figure 1—figure supplement 1.: E ... tes amino acid substitutions per site.
|
||||
item-127 at level 1: caption: Figure 1—figure supplement 2.: K ... sfected with an empty pcDNA3.1 vector.
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||||
item-128 at level 1: caption: Figure 1—figure supplement 3.: K ... (adapted from Legiewicz et al., 2010).
|
||||
item-129 at level 1: caption: Figure 2.: Retrotransposon react ... cal testing were determined by DESeq2.
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||||
item-130 at level 1: caption: Figure 2—figure supplement 1.: E ... r assay in Chr4-cl WT and KO ES cells.
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||||
item-131 at level 1: caption: Figure 3.: TE-dependent gene act ... Gm13051 are indicated by dashed lines.
|
||||
item-132 at level 1: caption: Figure 4.: ETn retrotranspositio ... A for ETn and MuLV (RLTR4) insertions.
|
||||
item-133 at level 1: caption: Figure 4—figure supplement 1.: B ... in at least two biological replicates.
|
||||
item-134 at level 1: caption: Figure 4—figure supplement 2.: I ... color gradient indicates log10(RPM+1).
|
||||
item-135 at level 1: caption: Figure 4—figure supplement 3.: C ... color gradient indicates log10(RPM+1).
|
||||
item-2 at level 2: paragraph: Gernot Wolf, Alberto de Iaco, Mi ... Ralls, Didier Trono, Todd S Macfarlan
|
||||
item-3 at level 2: paragraph: The Eunice Kennedy Shriver Natio ... Lausanne (EPFL), Lausanne, Switzerland
|
||||
item-4 at level 2: section_header: Abstract
|
||||
item-5 at level 3: text: The Krüppel-associated box zinc ... edundant role restricting TE activity.
|
||||
item-6 at level 2: section_header: Introduction
|
||||
item-7 at level 3: text: Nearly half of the human and mou ... s are active beyond early development.
|
||||
item-8 at level 3: text: TEs, especially long terminal re ... f evolutionarily young KRAB-ZFP genes.
|
||||
item-9 at level 2: section_header: Results
|
||||
item-10 at level 3: section_header: Mouse KRAB-ZFPs target retrotransposons
|
||||
item-11 at level 4: text: We analyzed the RNA expression p ... duplications (Kauzlaric et al., 2017).
|
||||
item-12 at level 4: text: To determine the binding sites o ... ctive in the early embryo (Figure 1A).
|
||||
item-13 at level 4: picture
|
||||
item-13 at level 5: caption: Figure 1. Genome-wide binding patterns of mouse KRAB-ZFPs. (A) Probability heatmap of KRAB-ZFP binding to TEs. Blue color intensity (main field) corresponds to -log10 (adjusted p-value) enrichment of ChIP-seq peak overlap with TE groups (Fisher’s exact test). The green/red color intensity (top panel) represents mean KAP1 (GEO accession: GSM1406445) and H3K9me3 (GEO accession: GSM1327148) enrichment (respectively) at peaks overlapping significantly targeted TEs (adjusted p-value<1e-5) in WT ES cells. (B) Summarized ChIP-seq signal for indicated KRAB-ZFPs and previously published KAP1 and H3K9me3 in WT ES cells across 127 intact ETn elements. (C) Heatmaps of KRAB-ZFP ChIP-seq signal at ChIP-seq peaks. For better comparison, peaks for all three KRAB-ZFPs were called with the same parameters (p<1e-10, peak enrichment >20). The top panel shows a schematic of the arrangement of the contact amino acid composition of each zinc finger. Zinc fingers are grouped and colored according to similarity, with amino acid differences relative to the five consensus fingers highlighted in white.
|
||||
item-14 at level 4: table with [9x5]
|
||||
item-14 at level 5: caption: Table 1. KRAB-ZFP genes clusters in the mouse genome that were investigated in this study. * Number of protein-coding KRAB-ZFP genes identified in a previously published screen (Imbeault et al., 2017) and the ChIP-seq data column indicates the number of KRAB-ZFPs for which ChIP-seq was performed in this study.
|
||||
item-15 at level 4: text: We generally observed that KRAB- ... responsible for this silencing effect.
|
||||
item-16 at level 4: text: To further test the hypothesis t ... t easily evade repression by mutation.
|
||||
item-17 at level 4: text: Our KRAB-ZFP ChIP-seq dataset al ... ntirely shift the mode of DNA binding.
|
||||
item-18 at level 3: section_header: Genetic deletion of KRAB-ZFP gen ... leads to retrotransposon reactivation
|
||||
item-19 at level 4: text: The majority of KRAB-ZFP genes a ... ung et al., 2014; Deniz et al., 2018).
|
||||
item-20 at level 4: picture
|
||||
item-20 at level 5: caption: Figure 2. Retrotransposon reactivation in KRAB-ZFP cluster KO ES cells. (A) RNA-seq analysis of TE expression in five KRAB-ZFP cluster KO ES cells. Green and grey squares on top of the panel represent KRAB-ZFPs with or without ChIP-seq data, respectively, within each deleted gene cluster. Reactivated TEs that are bound by one or several KRAB-ZFPs are indicated by green squares in the panel. Significantly up- and downregulated elements (adjusted p-value<0.05) are highlighted in red and green, respectively. (B) Differential KAP1 binding and H3K9me3 enrichment at TE groups (summarized across all insertions) in Chr2-cl and Chr4-cl KO ES cells. TE groups targeted by one or several KRAB-ZFPs encoded within the deleted clusters are highlighted in blue (differential enrichment over the entire TE sequences) and red (differential enrichment at TE regions that overlap with KRAB-ZFP ChIP-seq peaks). (C) DNA methylation status of CpG sites at indicated TE groups in WT and Chr4-cl KO ES cells grown in serum containing media or in hypomethylation-inducing media (2i + Vitamin C). P-values were calculated using paired t-test.
|
||||
item-21 at level 3: section_header: KRAB-ZFP cluster deletions license TE-borne enhancers
|
||||
item-22 at level 4: text: We next used our RNA-seq dataset ... vating effects of TEs on nearby genes.
|
||||
item-23 at level 4: picture
|
||||
item-23 at level 5: caption: Figure 3. TE-dependent gene activation in KRAB-ZFP cluster KO ES cells. (A) Differential gene expression in Chr2-cl and Chr4-cl KO ES cells. Significantly up- and downregulated genes (adjusted p-value<0.05) are highlighted in red and green, respectively, KRAB-ZFP genes within the deleted clusters are shown in blue. (B) Correlation of TEs and gene deregulation. Plots show enrichment of TE groups within 100 kb of up- and downregulated genes relative to all genes. Significantly overrepresented LTR and LINE groups (adjusted p-value<0.1) are highlighted in blue and red, respectively. (C) Schematic view of the downstream region of Chst1 where a 5’ truncated ETn insertion is located. ChIP-seq (Input subtracted from ChIP) data for overexpressed epitope-tagged Gm13051 (a Chr4-cl KRAB-ZFP) in F9 EC cells, and re-mapped KAP1 (GEO accession: GSM1406445) and H3K9me3 (GEO accession: GSM1327148) in WT ES cells are shown together with RNA-seq data from Chr4-cl WT and KO ES cells (mapped using Bowtie (-a -m 1 --strata -v 2) to exclude reads that cannot be uniquely mapped). (D) RT-qPCR analysis of Chst1 mRNA expression in Chr4-cl WT and KO ES cells with or without the CRISPR/Cas9 deleted ETn insertion near Chst1. Values represent mean expression (normalized to Gapdh) from three biological replicates per sample (each performed in three technical replicates) in arbitrary units. Error bars represent standard deviation and asterisks indicate significance (p<0.01, Student’s t-test). n.s.: not significant. (E) Mean coverage of ChIP-seq data (Input subtracted from ChIP) in Chr4-cl WT and KO ES cells over 127 full-length ETn insertions. The binding sites of the Chr4-cl KRAB-ZFPs Rex2 and Gm13051 are indicated by dashed lines.
|
||||
item-24 at level 4: text: While we generally observed that ... he internal region and not on the LTR.
|
||||
item-25 at level 3: section_header: ETn retrotransposition in Chr4-cl KO and WT mice
|
||||
item-26 at level 4: text: IAP, ETn/ETnERV and MuLV/RLTR4 r ... s may contribute to reduced viability.
|
||||
item-27 at level 4: text: We reasoned that retrotransposon ... Tn insertions at a high recovery rate.
|
||||
item-28 at level 4: text: Using this dataset, we first con ... nsertions in our pedigree (Figure 4A).
|
||||
item-29 at level 4: picture
|
||||
item-29 at level 5: caption: Figure 4. ETn retrotransposition in Chr4-cl KO mice. (A) Pedigree of mice used for transposon insertion screening by capture-seq in mice of different strain backgrounds. The number of novel ETn insertions (only present in one animal) are indicated. For animals whose direct ancestors have not been screened, the ETn insertions are shown in parentheses since parental inheritance cannot be excluded in that case. Germ line insertions are indicated by asterisks. All DNA samples were prepared from tail tissues unless noted (-S: spleen, -E: ear, -B:Blood) (B) Statistical analysis of ETn insertion frequency in tail tissue from 30 Chr4-cl KO, KO/WT and WT mice that were derived from one Chr4-c KO x KO/WT and two Chr4-cl KO/WT x KO/WT matings. Only DNA samples that were collected from juvenile tails were considered for this analysis. P-values were calculated using one-sided Wilcoxon Rank Sum Test. In the last panel, KO, WT and KO/WT mice derived from all matings were combined for the statistical analysis.
|
||||
item-30 at level 4: text: To validate some of the novel ET ... ess might have truncated this element.
|
||||
item-31 at level 4: text: Besides novel ETn insertions tha ... tions (Figure 4—figure supplement 3D).
|
||||
item-32 at level 4: text: Finally, we asked whether there ... s clearly also play an important role.
|
||||
item-33 at level 2: section_header: Discussion
|
||||
item-34 at level 3: text: C2H2 zinc finger proteins, about ... ) depending upon their insertion site.
|
||||
item-35 at level 3: text: Despite a lack of widespread ETn ... ion of the majority of KRAB-ZFP genes.
|
||||
item-36 at level 2: section_header: Materials and methods
|
||||
item-37 at level 3: table with [31x5]
|
||||
item-37 at level 4: caption: Key resources table
|
||||
item-38 at level 3: section_header: Cell lines and transgenic mice
|
||||
item-39 at level 4: text: Mouse ES cells and F9 EC cells w ... KO/KO and KO/WT (B6/129 F2) offspring.
|
||||
item-40 at level 3: section_header: Generation of KRAB-ZFP expressing cell lines
|
||||
item-41 at level 4: text: KRAB-ZFP ORFs were PCR-amplified ... led and further expanded for ChIP-seq.
|
||||
item-42 at level 3: section_header: CRISPR/Cas9 mediated deletion of KRAB-ZFP clusters and an MMETn insertion
|
||||
item-43 at level 4: text: All gRNAs were expressed from th ... PCR genotyping (Supplementary file 3).
|
||||
item-44 at level 3: section_header: ChIP-seq analysis
|
||||
item-45 at level 4: text: For ChIP-seq analysis of KRAB-ZF ... 010 or Khil et al., 2012 respectively.
|
||||
item-46 at level 4: text: ChIP-seq libraries were construc ... were re-mapped using Bowtie (--best).
|
||||
item-47 at level 3: section_header: Luciferase reporter assays
|
||||
item-48 at level 4: text: For KRAB-ZFP repression assays, ... after transfection as described above.
|
||||
item-49 at level 3: section_header: RNA-seq analysis
|
||||
item-50 at level 4: text: Whole RNA was purified using RNe ... lemented in the R function p.adjust().
|
||||
item-51 at level 3: section_header: Reduced representation bisulfite sequencing (RRBS-seq)
|
||||
item-52 at level 4: text: For RRBS-seq analysis, Chr4-cl W ... h sample were considered for analysis.
|
||||
item-53 at level 3: section_header: Retrotransposition assay
|
||||
item-54 at level 4: text: The retrotransposition vectors p ... were stained with Amido Black (Sigma).
|
||||
item-55 at level 3: section_header: Capture-seq screen
|
||||
item-56 at level 4: text: To identify novel retrotransposo ... assembly using the Unicycler software.
|
||||
item-57 at level 2: section_header: Funding Information
|
||||
item-58 at level 3: text: This paper was supported by the following grants:
|
||||
item-59 at level 3: list: group list
|
||||
item-60 at level 4: list_item: http://dx.doi.org/10.13039/10000 ... ment 1ZIAHD008933 to Todd S Macfarlan.
|
||||
item-61 at level 4: list_item: http://dx.doi.org/10.13039/50110 ... ndation 310030_152879 to Didier Trono.
|
||||
item-62 at level 4: list_item: http://dx.doi.org/10.13039/50110 ... dation 310030B_173337 to Didier Trono.
|
||||
item-63 at level 4: list_item: http://dx.doi.org/10.13039/50110 ... ch Council No. 268721 to Didier Trono.
|
||||
item-64 at level 4: list_item: http://dx.doi.org/10.13039/50110 ... rch Council No 694658 to Didier Trono.
|
||||
item-65 at level 2: section_header: Acknowledgements
|
||||
item-66 at level 3: text: We thank Alex Grinberg, Jeanne Y ... 268721; Transpos-X, No. 694658) (DT).
|
||||
item-67 at level 2: section_header: Additional information
|
||||
item-68 at level 2: section_header: Additional files
|
||||
item-69 at level 2: section_header: Data availability
|
||||
item-70 at level 3: text: All NGS data has been deposited ... GenBank database (MH449667- MH449669).
|
||||
item-71 at level 3: text: The following datasets were generated:
|
||||
item-72 at level 3: text: Wolf G. Retrotransposon reactiva ... ession Omnibus (2019). NCBI: GSE115291
|
||||
item-73 at level 3: text: Wolf G. Mus musculus musculus st ... e. NCBI GenBank (2019). NCBI: MH449667
|
||||
item-74 at level 3: text: Wolf G. Mus musculus musculus st ... e. NCBI GenBank (2019). NCBI: MH449668
|
||||
item-75 at level 3: text: Wolf G. Mus musculus musculus st ... e. NCBI GenBank (2019). NCBI: MH449669
|
||||
item-76 at level 3: text: The following previously published datasets were used:
|
||||
item-77 at level 3: text: Castro-Diaz N, Ecco G, Coluccio ... ssion Omnibus (2014). NCBI: GSM1406445
|
||||
item-78 at level 3: text: Andrew ZX. H3K9me3_ChIPSeq (Ctrl ... ssion Omnibus (2014). NCBI: GSM1327148
|
||||
item-79 at level 2: section_header: References
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item-137 at level 1: caption: Figure 1. Genome-wide binding pa ... onsensus fingers highlighted in white.
|
||||
item-138 at level 1: caption: Table 1. KRAB-ZFP genes clusters ... ChIP-seq was performed in this study.
|
||||
item-139 at level 1: caption: Figure 2. Retrotransposon reacti ... s were calculated using paired t-test.
|
||||
item-140 at level 1: caption: Figure 3. TE-dependent gene acti ... Gm13051 are indicated by dashed lines.
|
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item-141 at level 1: caption: Figure 4. ETn retrotransposition ... combined for the statistical analysis.
|
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item-142 at level 1: caption: Key resources table
|
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File diff suppressed because it is too large
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@ -1,6 +1,8 @@
|
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# KRAB-zinc finger protein gene expansion in response to active retrotransposons in the murine lineage
|
||||
|
||||
Wolf Gernot; 1: The Eunice Kennedy Shriver National Institute of Child Health and Human Development, The National Institutes of Health: Bethesda: United States; de Iaco Alberto; 2: School of Life Sciences, École Polytechnique Fédérale de Lausanne (EPFL): Lausanne: Switzerland; Sun Ming-An; 1: The Eunice Kennedy Shriver National Institute of Child Health and Human Development, The National Institutes of Health: Bethesda: United States; Bruno Melania; 1: The Eunice Kennedy Shriver National Institute of Child Health and Human Development, The National Institutes of Health: Bethesda: United States; Tinkham Matthew; 1: The Eunice Kennedy Shriver National Institute of Child Health and Human Development, The National Institutes of Health: Bethesda: United States; Hoang Don; 1: The Eunice Kennedy Shriver National Institute of Child Health and Human Development, The National Institutes of Health: Bethesda: United States; Mitra Apratim; 1: The Eunice Kennedy Shriver National Institute of Child Health and Human Development, The National Institutes of Health: Bethesda: United States; Ralls Sherry; 1: The Eunice Kennedy Shriver National Institute of Child Health and Human Development, The National Institutes of Health: Bethesda: United States; Trono Didier; 2: School of Life Sciences, École Polytechnique Fédérale de Lausanne (EPFL): Lausanne: Switzerland; Macfarlan Todd S; 1: The Eunice Kennedy Shriver National Institute of Child Health and Human Development, The National Institutes of Health: Bethesda: United States
|
||||
Gernot Wolf, Alberto de Iaco, Ming-An Sun, Melania Bruno, Matthew Tinkham, Don Hoang, Apratim Mitra, Sherry Ralls, Didier Trono, Todd S Macfarlan
|
||||
|
||||
The Eunice Kennedy Shriver National Institute of Child Health and Human Development, The National Institutes of Health, Bethesda, United States; School of Life Sciences, École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland
|
||||
|
||||
## Abstract
|
||||
|
||||
@ -20,6 +22,23 @@ We analyzed the RNA expression profiles of mouse KRAB-ZFPs across a wide range o
|
||||
|
||||
To determine the binding sites of the KRAB-ZFPs within these and other gene clusters, we expressed epitope-tagged KRAB-ZFPs using stably integrating vectors in mouse embryonic carcinoma (EC) or ES cells (Table 1, Supplementary file 1) and performed chromatin immunoprecipitation followed by deep sequencing (ChIP-seq). We then determined whether the identified binding sites are significantly enriched over annotated TEs and used the non-repetitive peak fraction to identify binding motifs. We discarded 7 of 68 ChIP-seq datasets because we could not obtain a binding motif or a target TE and manual inspection confirmed low signal to noise ratio. Of the remaining 61 KRAB-ZFPs, 51 significantly overlapped at least one TE subfamily (adjusted p-value<1e-5). Altogether, 81 LTR retrotransposon, 18 LINE, 10 SINE and one DNA transposon subfamilies were targeted by at least one of the 51 KRAB-ZFPs (Figure 1A and Supplementary file 1). Chr2-cl KRAB-ZFPs preferably bound IAPEz retrotransposons and L1-type LINEs, while Chr4-cl KRAB-ZFPs targeted various retrotransposons, including the closely related MMETn (hereafter referred to as ETn) and ETnERV (also known as MusD) elements (Figure 1A). ETn elements are non-autonomous LTR retrotransposons that require trans-complementation by the fully coding ETnERV elements that contain Gag, Pro and Pol genes (Ribet et al., 2004). These elements have accumulated to ~240 and~100 copies in the reference C57BL/6 genome, respectively, with ~550 solitary LTRs (Baust et al., 2003). Both ETn and ETnERVs are still active, generating polymorphisms and mutations in several mouse strains (Gagnier et al., 2019). The validity of our ChIP-seq screen was confirmed by the identification of binding motifs - which often resembled the computationally predicted motifs (Figure 1—figure supplement 2A) - for the majority of screened KRAB-ZFPs (Supplementary file 1). Moreover, predicted and experimentally determined motifs were found in targeted TEs in most cases (Supplementary file 1), and reporter repression assays confirmed KRAB-ZFP induced silencing for all the tested sequences (Figure 1—figure supplement 2B). Finally, we observed KAP1 and H3K9me3 enrichment at most of the targeted TEs in wild type ES cells, indicating that most of these KRAB-ZFPs are functionally active in the early embryo (Figure 1A).
|
||||
|
||||
Figure 1. Genome-wide binding patterns of mouse KRAB-ZFPs. (A) Probability heatmap of KRAB-ZFP binding to TEs. Blue color intensity (main field) corresponds to -log10 (adjusted p-value) enrichment of ChIP-seq peak overlap with TE groups (Fisher’s exact test). The green/red color intensity (top panel) represents mean KAP1 (GEO accession: GSM1406445) and H3K9me3 (GEO accession: GSM1327148) enrichment (respectively) at peaks overlapping significantly targeted TEs (adjusted p-value<1e-5) in WT ES cells. (B) Summarized ChIP-seq signal for indicated KRAB-ZFPs and previously published KAP1 and H3K9me3 in WT ES cells across 127 intact ETn elements. (C) Heatmaps of KRAB-ZFP ChIP-seq signal at ChIP-seq peaks. For better comparison, peaks for all three KRAB-ZFPs were called with the same parameters (p<1e-10, peak enrichment >20). The top panel shows a schematic of the arrangement of the contact amino acid composition of each zinc finger. Zinc fingers are grouped and colored according to similarity, with amino acid differences relative to the five consensus fingers highlighted in white.
|
||||
|
||||
<!-- image -->
|
||||
|
||||
Table 1. KRAB-ZFP genes clusters in the mouse genome that were investigated in this study. * Number of protein-coding KRAB-ZFP genes identified in a previously published screen (Imbeault et al., 2017) and the ChIP-seq data column indicates the number of KRAB-ZFPs for which ChIP-seq was performed in this study.
|
||||
|
||||
| Cluster | Location | Size (Mb) | # of KRAB-ZFPs* | ChIP-seq data |
|
||||
|-----------|------------|-------------|-------------------|-----------------|
|
||||
| Chr2 | Chr2 qH4 | 3.1 | 40 | 17 |
|
||||
| Chr4 | Chr4 qE1 | 2.3 | 21 | 19 |
|
||||
| Chr10 | Chr10 qC1 | 0.6 | 6 | 1 |
|
||||
| Chr13.1 | Chr13 qB3 | 1.2 | 6 | 2 |
|
||||
| Chr13.2 | Chr13 qB3 | 0.8 | 26 | 12 |
|
||||
| Chr8 | Chr8 qB3.3 | 0.1 | 4 | 4 |
|
||||
| Chr9 | Chr9 qA3 | 0.1 | 4 | 2 |
|
||||
| Other | - | - | 248 | 4 |
|
||||
|
||||
We generally observed that KRAB-ZFPs present exclusively in mouse target TEs that are restricted to the mouse genome, indicating KRAB-ZFPs and their targets emerged together. For example, several mouse-specific KRAB-ZFPs in Chr2-cl and Chr4-cl target IAP and ETn elements which are only found in the mouse genome and are highly active. This is the strongest data to date supporting that recent KRAB-ZFP expansions in these young clusters is a response to recent TE activity. Likewise, ZFP599 and ZFP617, both conserved in Muroidea, bind to various ORR1-type LTRs which are present in the rat genome (Supplementary file 1). However, ZFP961, a KRAB-ZFP encoded on a small gene cluster on chromosome 8 that is conserved in Muroidea targets TEs that are only found in the mouse genome (e.g. ETn), a paradox we have previously observed with ZFP809, which also targets TEs that are evolutionarily younger than itself (Wolf et al., 2015b). The ZFP961 binding site is located at the 5’ end of the internal region of ETn and ETnERV elements, a sequence that usually contains the primer binding site (PBS), which is required to prime retroviral reverse transcription. Indeed, the ZFP961 motif closely resembles the PBSLys1,2 (Figure 1—figure supplement 3A), which had been previously identified as a KAP1-dependent target of retroviral repression (Yamauchi et al., 1995; Wolf et al., 2008). Repression of the PBSLys1,2 by ZFP961 was also confirmed in reporter assays (Figure 1—figure supplement 2B), indicating that ZFP961 is likely responsible for this silencing effect.
|
||||
|
||||
To further test the hypothesis that KRAB-ZFPs target sites necessary for retrotransposition, we utilized previously generated ETn and ETnERV retrotransposition reporters in which we mutated KRAB-ZFP binding sites (Ribet et al., 2004). Whereas the ETnERV reporters are sufficient for retrotransposition, the ETn reporter requires ETnERV genes supplied in trans. We tested and confirmed that the REX2/ZFP600 and GM13051 binding sites within these TEs are required for efficient retrotransposition (Figure 1—figure supplement 3B). REX2 and ZFP600 both bind a target about 200 bp from the start of the internal region (Figure 1B), a region that often encodes the packaging signal. GM13051 binds a target coding for part of a highly structured mRNA export signal (Legiewicz et al., 2010) near the 3’ end of the internal region of ETn (Figure 1—figure supplement 3C). Both signals are characterized by stem-loop intramolecular base-pairing in which a single mutation can disrupt loop formation. This indicates that at least some KRAB-ZFPs evolved to bind functionally essential target sequences which cannot easily evade repression by mutation.
|
||||
@ -30,10 +49,18 @@ Our KRAB-ZFP ChIP-seq dataset also provided unique insights into the emergence o
|
||||
|
||||
The majority of KRAB-ZFP genes are harbored in large, highly repetitive clusters that have formed by successive complex segmental duplications (Kauzlaric et al., 2017), rendering them inaccessible to conventional gene targeting. We therefore developed a strategy to delete entire KRAB-ZFP gene clusters in ES cells (including the Chr2-cl and Chr4-cl as well as two clusters on chromosome 13 and a cluster on chromosome 10) using two CRISPR/Cas9 gRNAs targeting unique regions flanking each cluster, and short single-stranded repair oligos with homologies to both sides of the projected cut sites. Using this approach, we generated five cluster KO ES cell lines in at least two biological replicates and performed RNA sequencing (RNA-seq) to determine TE expression levels. Strikingly, four of the five cluster KO ES cells exhibited distinct TE reactivation phenotypes (Figure 2A). Chr2-cl KO resulted in reactivation of several L1 subfamilies as well as RLTR10 (up to more than 100-fold as compared to WT) and IAPEz ERVs. In contrast, the most strongly upregulated TEs in Chr4-cl KO cells were ETn/ETnERV (up to 10-fold as compared to WT), with several other ERV groups modestly reactivated. ETn/ETnERV elements were also upregulated in Chr13.2-cl KO ES cells while the only upregulated ERVs in Chr13.1-cl KO ES cells were MMERVK10C elements (Figure 2A). Most reactivated retrotransposons were targeted by at least one KRAB-ZFP that was encoded in the deleted cluster (Figure 2A and Supplementary file 1), indicating a direct effect of these KRAB-ZFPs on TE expression levels. Furthermore, we observed a loss of KAP1 binding and H3K9me3 at several TE subfamilies that are targeted by at least one KRAB-ZFP within the deleted Chr2-cl and Chr4-cl (Figure 2B, Figure 2—figure supplement 1A), including L1, ETn and IAPEz elements. Using reduced representation bisulfite sequencing (RRBS-seq), we found that a subset of KRAB-ZFP bound TEs were partially hypomethylated in Chr4-cl KO ES cells, but only when grown in genome-wide hypomethylation-inducing conditions (Blaschke et al., 2013; Figure 2C and Supplementary file 2). These data are consistent with the hypothesis that KRAB-ZFPs/KAP1 are not required to establish DNA methylation, but under certain conditions they protect specific TEs and imprint control regions from genome-wide demethylation (Leung et al., 2014; Deniz et al., 2018).
|
||||
|
||||
Figure 2. Retrotransposon reactivation in KRAB-ZFP cluster KO ES cells. (A) RNA-seq analysis of TE expression in five KRAB-ZFP cluster KO ES cells. Green and grey squares on top of the panel represent KRAB-ZFPs with or without ChIP-seq data, respectively, within each deleted gene cluster. Reactivated TEs that are bound by one or several KRAB-ZFPs are indicated by green squares in the panel. Significantly up- and downregulated elements (adjusted p-value<0.05) are highlighted in red and green, respectively. (B) Differential KAP1 binding and H3K9me3 enrichment at TE groups (summarized across all insertions) in Chr2-cl and Chr4-cl KO ES cells. TE groups targeted by one or several KRAB-ZFPs encoded within the deleted clusters are highlighted in blue (differential enrichment over the entire TE sequences) and red (differential enrichment at TE regions that overlap with KRAB-ZFP ChIP-seq peaks). (C) DNA methylation status of CpG sites at indicated TE groups in WT and Chr4-cl KO ES cells grown in serum containing media or in hypomethylation-inducing media (2i + Vitamin C). P-values were calculated using paired t-test.
|
||||
|
||||
<!-- image -->
|
||||
|
||||
### KRAB-ZFP cluster deletions license TE-borne enhancers
|
||||
|
||||
We next used our RNA-seq datasets to determine the effect of KRAB-ZFP cluster deletions on gene expression. We identified 195 significantly upregulated and 130 downregulated genes in Chr4-cl KO ES cells, and 108 upregulated and 59 downregulated genes in Chr2-cl KO ES cells (excluding genes on the deleted cluster) (Figure 3A). To address whether gene deregulation in Chr2-cl and Chr4-cl KO ES cells is caused by nearby TE reactivation, we determined whether genes near certain TE subfamilies are more frequently deregulated than random genes. We found a strong correlation of gene upregulation and TE proximity for several TE subfamilies, of which many became transcriptionally activated themselves (Figure 3B). For example, nearly 10% of genes that are located within 100 kb (up- or downstream of the TSS) of an ETn element are upregulated in Chr4-cl KO ES cells, as compared to 0.8% of all genes. In Chr2-cl KO ES cells, upregulated genes were significantly enriched near various LINE groups but also IAPEz-int and RLTR10-int elements, indicating that TE-binding KRAB-ZFPs in these clusters limit the potential activating effects of TEs on nearby genes.
|
||||
|
||||
Figure 3. TE-dependent gene activation in KRAB-ZFP cluster KO ES cells. (A) Differential gene expression in Chr2-cl and Chr4-cl KO ES cells. Significantly up- and downregulated genes (adjusted p-value<0.05) are highlighted in red and green, respectively, KRAB-ZFP genes within the deleted clusters are shown in blue. (B) Correlation of TEs and gene deregulation. Plots show enrichment of TE groups within 100 kb of up- and downregulated genes relative to all genes. Significantly overrepresented LTR and LINE groups (adjusted p-value<0.1) are highlighted in blue and red, respectively. (C) Schematic view of the downstream region of Chst1 where a 5’ truncated ETn insertion is located. ChIP-seq (Input subtracted from ChIP) data for overexpressed epitope-tagged Gm13051 (a Chr4-cl KRAB-ZFP) in F9 EC cells, and re-mapped KAP1 (GEO accession: GSM1406445) and H3K9me3 (GEO accession: GSM1327148) in WT ES cells are shown together with RNA-seq data from Chr4-cl WT and KO ES cells (mapped using Bowtie (-a -m 1 --strata -v 2) to exclude reads that cannot be uniquely mapped). (D) RT-qPCR analysis of Chst1 mRNA expression in Chr4-cl WT and KO ES cells with or without the CRISPR/Cas9 deleted ETn insertion near Chst1. Values represent mean expression (normalized to Gapdh) from three biological replicates per sample (each performed in three technical replicates) in arbitrary units. Error bars represent standard deviation and asterisks indicate significance (p<0.01, Student’s t-test). n.s.: not significant. (E) Mean coverage of ChIP-seq data (Input subtracted from ChIP) in Chr4-cl WT and KO ES cells over 127 full-length ETn insertions. The binding sites of the Chr4-cl KRAB-ZFPs Rex2 and Gm13051 are indicated by dashed lines.
|
||||
|
||||
<!-- image -->
|
||||
|
||||
While we generally observed that TE-associated gene reactivation is not caused by elongated or spliced transcription starting at the retrotransposons, we did observe that the strength of the effect of ETn elements on gene expression is stronger on genes in closer proximity. About 25% of genes located within 20 kb of an ETn element, but only 5% of genes located at a distance between 50 and 100 kb from the nearest ETn insertion, become upregulated in Chr4-cl KO ES cells. Importantly however, the correlation is still significant for genes that are located at distances between 50 and 100 kb from the nearest ETn insertion, indicating that ETn elements can act as long-range enhancers of gene expression in the absence of KRAB-ZFPs that target them. To confirm that Chr4-cl KRAB-ZFPs such as GM13051 block ETn-borne enhancers, we tested the ability of a putative ETn enhancer to activate transcription in a reporter assay. For this purpose, we cloned a 5 kb fragment spanning from the GM13051 binding site within the internal region of a truncated ETn insertion to the first exon of the Cd59a gene, which is strongly activated in Chr4-cl KO ES cells (Figure 2—figure supplement 1B). We observed strong transcriptional activity of this fragment which was significantly higher in Chr4-cl KO ES cells. Surprisingly, this activity was reduced to background when the internal segment of the ETn element was not included in the fragment, suggesting the internal segment of the ETn element, but not its LTR, contains a Chr4-cl KRAB-ZFP sensitive enhancer. To further corroborate these findings, we genetically deleted an ETn element that is located about 60 kb from the TSS of Chst1, one of the top-upregulated genes in Chr4-cl KO ES cells (Figure 3C). RT-qPCR analysis revealed that the Chst1 upregulation phenotype in Chr4-cl KO ES cells diminishes when the ETn insertion is absent, providing direct evidence that a KRAB-ZFP controlled ETn-borne enhancer regulates Chst1 expression (Figure 3D). Furthermore, ChIP-seq confirmed a general increase of H3K4me3, H3K4me1 and H3K27ac marks at ETn elements in Chr4-cl KO ES cells (Figure 3E). Notably, enhancer marks were most pronounced around the GM13051 binding site near the 3’ end of the internal region, confirming that the enhancer activity of ETn is located on the internal region and not on the LTR.
|
||||
|
||||
### ETn retrotransposition in Chr4-cl KO and WT mice
|
||||
@ -44,6 +71,10 @@ We reasoned that retrotransposon activation could account for the reduced viabil
|
||||
|
||||
Using this dataset, we first confirmed the polymorphic nature of both ETn and MuLV retrotransposons in laboratory mouse strains (Figure 4—figure supplement 2A), highlighting the potential of these elements to retrotranspose. To identify novel insertions, we filtered out insertions that were supported by ETn/MuLV-paired reads in more than one animal. While none of the 54 ancestry-controlled mice showed a single novel MuLV insertion, we observed greatly varying numbers of up to 80 novel ETn insertions in our pedigree (Figure 4A).
|
||||
|
||||
Figure 4. ETn retrotransposition in Chr4-cl KO mice. (A) Pedigree of mice used for transposon insertion screening by capture-seq in mice of different strain backgrounds. The number of novel ETn insertions (only present in one animal) are indicated. For animals whose direct ancestors have not been screened, the ETn insertions are shown in parentheses since parental inheritance cannot be excluded in that case. Germ line insertions are indicated by asterisks. All DNA samples were prepared from tail tissues unless noted (-S: spleen, -E: ear, -B:Blood) (B) Statistical analysis of ETn insertion frequency in tail tissue from 30 Chr4-cl KO, KO/WT and WT mice that were derived from one Chr4-c KO x KO/WT and two Chr4-cl KO/WT x KO/WT matings. Only DNA samples that were collected from juvenile tails were considered for this analysis. P-values were calculated using one-sided Wilcoxon Rank Sum Test. In the last panel, KO, WT and KO/WT mice derived from all matings were combined for the statistical analysis.
|
||||
|
||||
<!-- image -->
|
||||
|
||||
To validate some of the novel ETn insertions, we designed specific PCR primers for five of the insertions and screened genomic DNA of the mice in which they were identified as well as their parents. For all tested insertions, we were able to amplify their flanking sequence and show that these insertions are absent in their parents (Figure 4—figure supplement 3A). To confirm their identity, we amplified and sequenced three of the novel full-length ETn insertions. Two of these elements (Genbank accession: MH449667-68) resembled typical ETnII elements with identical 5’ and 3’ LTRs and target site duplications (TSD) of 4 or 6 bp, respectively. The third sequenced element (MH449669) represented a hybrid element that contains both ETnI and MusD (ETnERV) sequences. Similar insertions can be found in the B6 reference genome; however, the identified novel insertion has a 2.5 kb deletion of the 5’ end of the internal region. Additionally, the 5’ and 3’ LTR of this element differ in one nucleotide near the start site and contain an unusually large 248 bp TSD (containing a SINE repeat) indicating that an improper integration process might have truncated this element.
|
||||
|
||||
Besides novel ETn insertions that were only identified in one specific animal, we also observed three ETn insertions that could be detected in several siblings but not in their parents or any of the other screened mice. This strongly indicates that these retrotransposition events occurred in the germ line of the parents from which they were passed on to some of their offspring. One of these germ line insertions was evidently passed on from the offspring to the next generation (Figure 4A). As expected, the read numbers supporting these novel germ line insertions were comparable to the read numbers that were found in the flanking regions of annotated B6 ETn insertions (Figure 4—figure supplement 3B). In contrast, virtually all novel insertions that were only found in one animal were supported by significantly fewer reads (Figure 4—figure supplement 3B). This indicates that these elements resulted from retrotransposition events in the developing embryo and not in the zygote or parental germ cells. Indeed, we detected different sets of insertions in various tissues from the same animal (Figure 4—figure supplement 3C). Even between tail samples that were collected from the same animal at different ages, only a fraction of the new insertions were present in both samples, while technical replicates from the same genomic DNA samples showed a nearly complete overlap in insertions (Figure 4—figure supplement 3D).
|
||||
@ -58,6 +89,41 @@ Despite a lack of widespread ETn activation in Chr4-cl KO mice, it still remains
|
||||
|
||||
## Materials and methods
|
||||
|
||||
Key resources table
|
||||
|
||||
| Reagent type (species) or resource | Designation | Source or reference | Identifiers | Additional information |
|
||||
|------------------------------------------|----------------------------------------|-----------------------------------|-------------------------------------|------------------------------------------------------|
|
||||
| Strain, strain background (Mus musculus) | 129 × 1/SvJ | The Jackson Laboratory | 000691 | Mice used to generate mixed strain Chr4-cl KO mice |
|
||||
| Cell line (Homo-sapiens) | HeLa | ATCC | ATCC CCL-2 | |
|
||||
| Cell line (Mus musculus) | JM8A3.N1 C57BL/6N-Atm1Brd | KOMP Repository | PL236745 | B6 ES cells used to generate KO cell lines and mice |
|
||||
| Cell line (Mus musculus) | B6;129‐ Gt(ROSA)26Sortm1(cre/ERT)Nat/J | The Jackson Laboratory | 004847 | ES cells used to generate KO cell lines and mice |
|
||||
| Cell line (Mus musculus) | R1 ES cells | Andras Nagy lab | R1 | 129 ES cells used to generate KO cell lines and mice |
|
||||
| Cell line (Mus musculus) | F9 Embryonic carcinoma cells | ATCC | ATCC CRL-1720 | |
|
||||
| Antibody | Mouse monoclonal ANTI-FLAG M2 antibody | Sigma-Aldrich | Cat# F1804, RRID:AB\_262044 | ChIP (1 µg/107 cells) |
|
||||
| Antibody | Rabbit polyclonal anti-HA | Abcam | Cat# ab9110, RRID:AB\_307019 | ChIP (1 µg/107 cells) |
|
||||
| Antibody | Mouse monoclonal anti-HA | Covance | Cat# MMS-101P-200, RRID:AB\_10064068 | |
|
||||
| Antibody | Rabbit polyclonal anti-H3K9me3 | Active Motif | Cat# 39161, RRID:AB\_2532132 | ChIP (3 µl/107 cells) |
|
||||
| Antibody | Rabbit polyclonal anti-GFP | Thermo Fisher Scientific | Cat# A-11122, RRID:AB\_221569 | ChIP (1 µg/107 cells) |
|
||||
| Antibody | Rabbit polyclonal anti- H3K4me3 | Abcam | Cat# ab8580, RRID:AB\_306649 | ChIP (1 µg/107 cells) |
|
||||
| Antibody | Rabbit polyclonal anti- H3K4me1 | Abcam | Cat# ab8895, RRID:AB\_306847 | ChIP (1 µg/107 cells) |
|
||||
| Antibody | Rabbit polyclonal anti- H3K27ac | Abcam | Cat# ab4729, RRID:AB\_2118291 | ChIP (1 µg/107 cells) |
|
||||
| Recombinant DNA reagent | pCW57.1 | Addgene | RRID:Addgene\_41393 | Inducible lentiviral expression vector |
|
||||
| Recombinant DNA reagent | pX330-U6-Chimeric\_BB-CBh-hSpCas9 | Addgene | RRID:Addgene\_42230 | CRISPR/Cas9 expression construct |
|
||||
| Sequence-based reagent | Chr2-cl KO gRNA.1 | This paper | Cas9 gRNA | GCCGTTGCTCAGTCCAAATG |
|
||||
| Sequenced-based reagent | Chr2-cl KO gRNA.2 | This paper | Cas9 gRNA | GATACCAGAGGTGGCCGCAAG |
|
||||
| Sequenced-based reagent | Chr4-cl KO gRNA.1 | This paper | Cas9 gRNA | GCAAAGGGGCTCCTCGATGGA |
|
||||
| Sequence-based reagent | Chr4-cl KO gRNA.2 | This paper | Cas9 gRNA | GTTTATGGCCGTGCTAAGGTC |
|
||||
| Sequenced-based reagent | Chr10-cl KO gRNA.1 | This paper | Cas9 gRNA | GTTGCCTTCATCCCACCGTG |
|
||||
| Sequenced-based reagent | Chr10-cl KO gRNA.2 | This paper | Cas9 gRNA | GAAGTTCGACTTGGACGGGCT |
|
||||
| Sequenced-based reagent | Chr13.1-cl KO gRNA.1 | This paper | Cas9 gRNA | GTAACCCATCATGGGCCCTAC |
|
||||
| Sequenced-based reagent | Chr13.1-cl KO gRNA.2 | This paper | Cas9 gRNA | GGACAGGTTATAGGTTTGAT |
|
||||
| Sequenced-based reagent | Chr13.2-cl KO gRNA.1 | This paper | Cas9 gRNA | GGGTTTCTGAGAAACGTGTA |
|
||||
| Sequenced-based reagent | Chr13.2-cl KO gRNA.2 | This paper | Cas9 gRNA | GTGTAATGAGTTCTTATATC |
|
||||
| Commercial assay or kit | SureSelectQXT Target Enrichment kit | Agilent | G9681-90000 | |
|
||||
| Software, algorithm | Bowtie | http://bowtie-bio.sourceforge.net | RRID:SCR\_005476 | |
|
||||
| Software, algorithm | MACS14 | https://bio.tools/macs | RRID:SCR\_013291 | |
|
||||
| Software, algorithm | Tophat | https://ccb.jhu.edu | RRID:SCR\_013035 | |
|
||||
|
||||
### Cell lines and transgenic mice
|
||||
|
||||
Mouse ES cells and F9 EC cells were cultivated as described previously (Wolf et al., 2015b) unless stated otherwise. Chr4-cl KO ES cells originate from B6;129‐ Gt(ROSA)26Sortm1(cre/ERT)Nat/J mice (Jackson lab), all other KRAB-ZFP cluster KO ES cell lines originate from JM8A3.N1 C57BL/6N-Atm1Brd ES cells (KOMP Repository). Chr2-cl KO and WT ES cells were initially grown in serum-containing media (Wolf et al., 2015b) but changed to 2i media (De Iaco et al., 2017) for several weeks before analysis. To generate Chr4-cl and Chr2-cl KO mice, the cluster deletions were repeated in B6 ES (KOMP repository) or R1 (Nagy lab) ES cells, respectively, and heterozygous clones were injected into B6 albino blastocysts. Chr2-cl KO mice were therefore kept on a mixed B6/Svx129/Sv-CP strain background while Chr4-cl KO mice were initially derived on a pure C57BL/6 background. For capture-seq screens, Chr4-cl KO mice were crossed with 129 × 1/SvJ mice (Jackson lab) to produce the founder mice for Chr4-cl KO and WT (B6/129 F1) offspring. Chr4-cl KO/WT (B6/129 F1) were also crossed with 129 × 1/SvJ mice to get Chr4-cl KO/WT (B6/129 F1) mice, which were intercrossed to give rise to the parents of Chr4-cl KO/KO and KO/WT (B6/129 F2) offspring.
|
||||
@ -96,173 +162,99 @@ The retrotransposition vectors pCMV-MusD2, pCMV-MusD2-neoTNF and pCMV-ETnI1-neoT
|
||||
|
||||
To identify novel retrotransposon insertions, genomic DNA from various tissues (Supplementary file 4) was purified and used for library construction with target enrichment using the SureSelectQXT Target Enrichment kit (Agilent). Custom RNA capture probes were designed to hybridize with the 120 bp 5’ ends of the 5’ LTRs and the 120 bp 3’ ends of the 3’ LTR of about 600 intact (internal region flanked by two LTRs) MMETn/RLTRETN retrotransposons or of 140 RLTR4\_MM/RLTR4 retrotransposons that were upregulated in Chr4-cl KO ES cells (Figure 4—source data 2). Enriched libraries were sequenced on an Illumina HiSeq as paired-end 50 bp reads. R1 and R2 reads were mapped to the mm9 genome separately, using settings that only allow non-duplicated, uniquely mappable reads (Bowtie -m 1 --best --strata; samtools rmdup -s) and under settings that allow multimapping and duplicated reads (Bowtie --best). Of the latter, only reads that overlap (min. 50% of read) with RLTRETN, MMETn-int, ETnERV-int, ETnERV2-int or ETnERV3-int repeats (ETn) or RLTR4, RLTR4\_MM-int or MuLV-int repeats (RLTR4) were kept. Only uniquely mappable reads whose paired reads were overlapping with the repeats mentioned above were used for further analysis. All ETn- and RLTR4-paired reads were then clustered (as bed files) using BEDTools (bedtools merge -i -n -d 1000) to receive a list of all potential annotated and non-annotated new ETn or RLTR4 insertion sites and all overlapping ETn- or RLTR4-paired reads were counted for each sample at each locus. Finally, all regions that were located within 1 kb of an annotated RLTRETN, MMETn-int, ETnERV-int, ETnERV2-int or ETnERV3-int repeat as well as regions overlapping with previously identified polymorphic ETn elements (Nellåker et al., 2012) were removed. Genomic loci with at least 10 reads per million unique ETn- or RLTR4-paired reads were considered as insertion sites. To qualify for a de-novo insertion, we allowed no called insertions in any of the other screened mice at the locus and not a single read at the locus in the ancestors of the mouse. Insertions at the same locus in at least two siblings from the same offspring were considered as germ line insertions, if the insertion was absent in the parents and mice who were not direct descendants from these siblings. Full-length sequencing of new ETn insertions was done by Sanger sequencing of short PCR products in combination with Illumina sequencing of a large PCR product (Supplementary file 3), followed by de-novo assembly using the Unicycler software.
|
||||
|
||||
## Tables
|
||||
## Funding Information
|
||||
|
||||
Table 1.: * Number of protein-coding KRAB-ZFP genes identified in a previously published screen (Imbeault et al., 2017) and the ChIP-seq data column indicates the number of KRAB-ZFPs for which ChIP-seq was performed in this study.
|
||||
This paper was supported by the following grants:
|
||||
|
||||
| Cluster | Location | Size (Mb) | # of KRAB-ZFPs* | ChIP-seq data |
|
||||
|-----------|------------|-------------|-------------------|-----------------|
|
||||
| Chr2 | Chr2 qH4 | 3.1 | 40 | 17 |
|
||||
| Chr4 | Chr4 qE1 | 2.3 | 21 | 19 |
|
||||
| Chr10 | Chr10 qC1 | 0.6 | 6 | 1 |
|
||||
| Chr13.1 | Chr13 qB3 | 1.2 | 6 | 2 |
|
||||
| Chr13.2 | Chr13 qB3 | 0.8 | 26 | 12 |
|
||||
| Chr8 | Chr8 qB3.3 | 0.1 | 4 | 4 |
|
||||
| Chr9 | Chr9 qA3 | 0.1 | 4 | 2 |
|
||||
| Other | - | - | 248 | 4 |
|
||||
- http://dx.doi.org/10.13039/100009633Eunice Kennedy Shriver National Institute of Child Health and Human Development 1ZIAHD008933 to Todd S Macfarlan.
|
||||
- http://dx.doi.org/10.13039/501100001711Swiss National Science Foundation 310030\_152879 to Didier Trono.
|
||||
- http://dx.doi.org/10.13039/501100001711Swiss National Science Foundation 310030B\_173337 to Didier Trono.
|
||||
- http://dx.doi.org/10.13039/501100000781European Research Council No. 268721 to Didier Trono.
|
||||
- http://dx.doi.org/10.13039/501100000781European Research Council No 694658 to Didier Trono.
|
||||
|
||||
Key resources table:
|
||||
## Acknowledgements
|
||||
|
||||
| Reagent type (species) or resource | Designation | Source or reference | Identifiers | Additional information |
|
||||
|------------------------------------------|----------------------------------------|-----------------------------------|-------------------------------------|------------------------------------------------------|
|
||||
| Strain, strain background (Mus musculus) | 129 × 1/SvJ | The Jackson Laboratory | 000691 | Mice used to generate mixed strain Chr4-cl KO mice |
|
||||
| Cell line (Homo-sapiens) | HeLa | ATCC | ATCC CCL-2 | |
|
||||
| Cell line (Mus musculus) | JM8A3.N1 C57BL/6N-Atm1Brd | KOMP Repository | PL236745 | B6 ES cells used to generate KO cell lines and mice |
|
||||
| Cell line (Mus musculus) | B6;129‐ Gt(ROSA)26Sortm1(cre/ERT)Nat/J | The Jackson Laboratory | 004847 | ES cells used to generate KO cell lines and mice |
|
||||
| Cell line (Mus musculus) | R1 ES cells | Andras Nagy lab | R1 | 129 ES cells used to generate KO cell lines and mice |
|
||||
| Cell line (Mus musculus) | F9 Embryonic carcinoma cells | ATCC | ATCC CRL-1720 | |
|
||||
| Antibody | Mouse monoclonal ANTI-FLAG M2 antibody | Sigma-Aldrich | Cat# F1804, RRID:AB\_262044 | ChIP (1 µg/107 cells) |
|
||||
| Antibody | Rabbit polyclonal anti-HA | Abcam | Cat# ab9110, RRID:AB\_307019 | ChIP (1 µg/107 cells) |
|
||||
| Antibody | Mouse monoclonal anti-HA | Covance | Cat# MMS-101P-200, RRID:AB\_10064068 | |
|
||||
| Antibody | Rabbit polyclonal anti-H3K9me3 | Active Motif | Cat# 39161, RRID:AB\_2532132 | ChIP (3 µl/107 cells) |
|
||||
| Antibody | Rabbit polyclonal anti-GFP | Thermo Fisher Scientific | Cat# A-11122, RRID:AB\_221569 | ChIP (1 µg/107 cells) |
|
||||
| Antibody | Rabbit polyclonal anti- H3K4me3 | Abcam | Cat# ab8580, RRID:AB\_306649 | ChIP (1 µg/107 cells) |
|
||||
| Antibody | Rabbit polyclonal anti- H3K4me1 | Abcam | Cat# ab8895, RRID:AB\_306847 | ChIP (1 µg/107 cells) |
|
||||
| Antibody | Rabbit polyclonal anti- H3K27ac | Abcam | Cat# ab4729, RRID:AB\_2118291 | ChIP (1 µg/107 cells) |
|
||||
| Recombinant DNA reagent | pCW57.1 | Addgene | RRID:Addgene\_41393 | Inducible lentiviral expression vector |
|
||||
| Recombinant DNA reagent | pX330-U6-Chimeric\_BB-CBh-hSpCas9 | Addgene | RRID:Addgene\_42230 | CRISPR/Cas9 expression construct |
|
||||
| Sequence-based reagent | Chr2-cl KO gRNA.1 | This paper | Cas9 gRNA | GCCGTTGCTCAGTCCAAATG |
|
||||
| Sequenced-based reagent | Chr2-cl KO gRNA.2 | This paper | Cas9 gRNA | GATACCAGAGGTGGCCGCAAG |
|
||||
| Sequenced-based reagent | Chr4-cl KO gRNA.1 | This paper | Cas9 gRNA | GCAAAGGGGCTCCTCGATGGA |
|
||||
| Sequence-based reagent | Chr4-cl KO gRNA.2 | This paper | Cas9 gRNA | GTTTATGGCCGTGCTAAGGTC |
|
||||
| Sequenced-based reagent | Chr10-cl KO gRNA.1 | This paper | Cas9 gRNA | GTTGCCTTCATCCCACCGTG |
|
||||
| Sequenced-based reagent | Chr10-cl KO gRNA.2 | This paper | Cas9 gRNA | GAAGTTCGACTTGGACGGGCT |
|
||||
| Sequenced-based reagent | Chr13.1-cl KO gRNA.1 | This paper | Cas9 gRNA | GTAACCCATCATGGGCCCTAC |
|
||||
| Sequenced-based reagent | Chr13.1-cl KO gRNA.2 | This paper | Cas9 gRNA | GGACAGGTTATAGGTTTGAT |
|
||||
| Sequenced-based reagent | Chr13.2-cl KO gRNA.1 | This paper | Cas9 gRNA | GGGTTTCTGAGAAACGTGTA |
|
||||
| Sequenced-based reagent | Chr13.2-cl KO gRNA.2 | This paper | Cas9 gRNA | GTGTAATGAGTTCTTATATC |
|
||||
| Commercial assay or kit | SureSelectQXT Target Enrichment kit | Agilent | G9681-90000 | |
|
||||
| Software, algorithm | Bowtie | http://bowtie-bio.sourceforge.net | RRID:SCR\_005476 | |
|
||||
| Software, algorithm | MACS14 | https://bio.tools/macs | RRID:SCR\_013291 | |
|
||||
| Software, algorithm | Tophat | https://ccb.jhu.edu | RRID:SCR\_013035 | |
|
||||
We thank Alex Grinberg, Jeanne Yimdjo and Victoria Carter for generating and maintaining transgenic mice. We also thank members of the Macfarlan and Trono labs for useful discussion, Steven Coon, James Iben, Tianwei Li and Anna Malawska for NGS and computational support. This work was supported by NIH grant 1ZIAHD008933 and the NIH DDIR Innovation Award program (TSM), and by subsidies from the Swiss National Science Foundation (310030\_152879 and 310030B\_173337) and the European Research Council (KRABnKAP, No. 268721; Transpos-X, No. 694658) (DT).
|
||||
|
||||
## Figures
|
||||
## Additional information
|
||||
|
||||
Figure 1.: Genome-wide binding patterns of mouse KRAB-ZFPs.
|
||||
(A) Probability heatmap of KRAB-ZFP binding to TEs. Blue color intensity (main field) corresponds to -log10 (adjusted p-value) enrichment of ChIP-seq peak overlap with TE groups (Fisher’s exact test). The green/red color intensity (top panel) represents mean KAP1 (GEO accession: GSM1406445) and H3K9me3 (GEO accession: GSM1327148) enrichment (respectively) at peaks overlapping significantly targeted TEs (adjusted p-value<1e-5) in WT ES cells. (B) Summarized ChIP-seq signal for indicated KRAB-ZFPs and previously published KAP1 and H3K9me3 in WT ES cells across 127 intact ETn elements. (C) Heatmaps of KRAB-ZFP ChIP-seq signal at ChIP-seq peaks. For better comparison, peaks for all three KRAB-ZFPs were called with the same parameters (p<1e-10, peak enrichment >20). The top panel shows a schematic of the arrangement of the contact amino acid composition of each zinc finger. Zinc fingers are grouped and colored according to similarity, with amino acid differences relative to the five consensus fingers highlighted in white.
|
||||
Figure 1—source data 1.KRAB-ZFP expression in 40 mouse tissues and cell lines (ENCODE).Mean values of replicates are shown as log2 transcripts per million.
|
||||
Figure 1—source data 2.Probability heatmap of KRAB-ZFP binding to TEs.Values corresponds to -log10 (adjusted p-value) enrichment of ChIP-seq peak overlap with TE groups (Fisher’s exact test).
|
||||
## Additional files
|
||||
|
||||
<!-- image -->
|
||||
## Data availability
|
||||
|
||||
Figure 1—figure supplement 1.: ES cell-specific expression of KRAB-ZFP gene clusters.
|
||||
(A) Heatmap showing expression patterns of mouse KRAB-ZFPs in 40 mouse tissues and cell lines (ENCODE). Heatmap colors indicate gene expression levels in log2 transcripts per million (TPM). The asterisk indicates a group of 30 KRAB-ZFPs that are exclusively expressed in ES cells. (B) Physical location of the genes encoding for the 30 KRAB-ZFPs that are exclusively expressed in ES cells. (C) Phylogenetic (Maximum likelihood) tree of the KRAB domains of mouse KRAB-ZFPs. KRAB-ZFPs encoded on the gene clusters on chromosome 2 and 4 are highlighted. The scale bar at the bottom indicates amino acid substitutions per site.
|
||||
All NGS data has been deposited in GEO (GSE115291). Sequences of full-length de novo ETn insertions have been deposited in the GenBank database (MH449667- MH449669).
|
||||
|
||||
<!-- image -->
|
||||
The following datasets were generated:
|
||||
|
||||
Figure 1—figure supplement 2.: KRAB-ZFP binding motifs and their repression activity.
|
||||
(A) Comparison of computationally predicted (bottom) and experimentally determined (top) KRAB-ZFP binding motifs. Only significant pairs are shown (FDR < 0.1). (B) Luciferase reporter assays to confirm KRAB-ZFP repression of the identified target sites. Bars show the luciferase activity (normalized to Renilla luciferase) of reporter plasmids containing the indicated target sites cloned upstream of the SV40 promoter. Reporter plasmids were co-transfected into 293 T cells with a Renilla luciferase plasmid for normalization and plasmids expressing the targeting KRAB-ZFP. Normalized mean luciferase activity (from three replicates) is shown relative to luciferase activity of the reporter plasmid co-transfected with an empty pcDNA3.1 vector.
|
||||
Wolf G. Retrotransposon reactivation and mobilization upon deletions of megabase scale KRAB zinc finger gene clusters in mice. NCBI Gene Expression Omnibus (2019). NCBI: GSE115291
|
||||
|
||||
<!-- image -->
|
||||
Wolf G. Mus musculus musculus strain C57BL/6x129X1/SvJ retrotransposon MMETn-int, complete sequence. NCBI GenBank (2019). NCBI: MH449667
|
||||
|
||||
Figure 1—figure supplement 3.: KRAB-ZFP binding to ETn retrotransposons.
|
||||
(A) Comparison of the PBSLys1,2 sequence with Zfp961 binding motifs in nonrepetitive peaks (Nonrep) and peaks at ETn elements. (B) Retrotransposition assays of original (ETnI1-neoTNF and MusD2-neoTNF Ribet et al., 2004) and modified reporter vectors where the Rex2 or Gm13051 binding motifs where removed. Schematic of reporter vectors are displayed at the top. HeLa cells were transfected as described in the Materials and Methods section and neo-resistant colonies, indicating retrotransposition events, were selected and stained. (C) Stem-loop structure of the ETn RNA export signal, the Gm13051 motif on the corresponding DNA is marked with red circles, the part of the motif that was deleted is indicated with grey crosses (adapted from Legiewicz et al., 2010).
|
||||
Wolf G. Mus musculus musculus strain C57BL/6x129X1/SvJ retrotransposon MMETn-int, complete sequence. NCBI GenBank (2019). NCBI: MH449668
|
||||
|
||||
<!-- image -->
|
||||
Wolf G. Mus musculus musculus strain C57BL/6x129X1/SvJ retrotransposon MMETn-int, complete sequence. NCBI GenBank (2019). NCBI: MH449669
|
||||
|
||||
Figure 2.: Retrotransposon reactivation in KRAB-ZFP cluster KO ES cells.
|
||||
(A) RNA-seq analysis of TE expression in five KRAB-ZFP cluster KO ES cells. Green and grey squares on top of the panel represent KRAB-ZFPs with or without ChIP-seq data, respectively, within each deleted gene cluster. Reactivated TEs that are bound by one or several KRAB-ZFPs are indicated by green squares in the panel. Significantly up- and downregulated elements (adjusted p-value<0.05) are highlighted in red and green, respectively. (B) Differential KAP1 binding and H3K9me3 enrichment at TE groups (summarized across all insertions) in Chr2-cl and Chr4-cl KO ES cells. TE groups targeted by one or several KRAB-ZFPs encoded within the deleted clusters are highlighted in blue (differential enrichment over the entire TE sequences) and red (differential enrichment at TE regions that overlap with KRAB-ZFP ChIP-seq peaks). (C) DNA methylation status of CpG sites at indicated TE groups in WT and Chr4-cl KO ES cells grown in serum containing media or in hypomethylation-inducing media (2i + Vitamin C). P-values were calculated using paired t-test.
|
||||
Figure 2—source data 1.Differential H3K9me3 and KAP1 distribution in WT and KRAB-ZFP cluster KO ES cells at TE families and KRAB-ZFP bound TE insertions.Differential read counts and statistical testing were determined by DESeq2.
|
||||
The following previously published datasets were used:
|
||||
|
||||
<!-- image -->
|
||||
Castro-Diaz N, Ecco G, Coluccio A, Kapopoulou A, Duc J, Trono D. Evollutionally dynamic L1 regulation in embryonic stem cells. NCBI Gene Expression Omnibus (2014). NCBI: GSM1406445
|
||||
|
||||
Figure 2—figure supplement 1.: Epigenetic changes at TEs and TE-borne enhancers in KRAB-ZFP cluster KO ES cells.
|
||||
(A) Differential analysis of summative (all individual insertions combined) H3K9me3 enrichment at TE groups in Chr10-cl, Chr13.1-cl and Chr13.2-cl KO ES cells. TE groups targeted by one or several KRAB-ZFPs encoded within the deleted clusters are highlighted in orange (differential enrichment over the entire TE sequences) and red (differential enrichment at TE regions that overlap with KRAB-ZFP ChIP-seq peaks). (B) Top: Schematic view of the Cd59a/Cd59b locus with a 5’ truncated ETn insertion. ChIP-seq (Input subtracted from ChIP) data for overexpressed epitope-tagged Gm13051 (a Chr4-cl KRAB-ZFP) in F9 EC cells, and re-mapped KAP1 (GEO accession: GSM1406445) and H3K9me3 (GEO accession: GSM1327148) in WT ES cells are shown together with RNA-seq data from Chr4-cl WT and KO ES cells (mapped using Bowtie (-a -m 1 --strata -v 2) to exclude reads that cannot be uniquely mapped). Bottom: Transcriptional activity of a 5 kb fragment with or without fragments of the ETn insertion was tested by luciferase reporter assay in Chr4-cl WT and KO ES cells.
|
||||
|
||||
<!-- image -->
|
||||
|
||||
Figure 3.: TE-dependent gene activation in KRAB-ZFP cluster KO ES cells.
|
||||
(A) Differential gene expression in Chr2-cl and Chr4-cl KO ES cells. Significantly up- and downregulated genes (adjusted p-value<0.05) are highlighted in red and green, respectively, KRAB-ZFP genes within the deleted clusters are shown in blue. (B) Correlation of TEs and gene deregulation. Plots show enrichment of TE groups within 100 kb of up- and downregulated genes relative to all genes. Significantly overrepresented LTR and LINE groups (adjusted p-value<0.1) are highlighted in blue and red, respectively. (C) Schematic view of the downstream region of Chst1 where a 5’ truncated ETn insertion is located. ChIP-seq (Input subtracted from ChIP) data for overexpressed epitope-tagged Gm13051 (a Chr4-cl KRAB-ZFP) in F9 EC cells, and re-mapped KAP1 (GEO accession: GSM1406445) and H3K9me3 (GEO accession: GSM1327148) in WT ES cells are shown together with RNA-seq data from Chr4-cl WT and KO ES cells (mapped using Bowtie (-a -m 1 --strata -v 2) to exclude reads that cannot be uniquely mapped). (D) RT-qPCR analysis of Chst1 mRNA expression in Chr4-cl WT and KO ES cells with or without the CRISPR/Cas9 deleted ETn insertion near Chst1. Values represent mean expression (normalized to Gapdh) from three biological replicates per sample (each performed in three technical replicates) in arbitrary units. Error bars represent standard deviation and asterisks indicate significance (p<0.01, Student’s t-test). n.s.: not significant. (E) Mean coverage of ChIP-seq data (Input subtracted from ChIP) in Chr4-cl WT and KO ES cells over 127 full-length ETn insertions. The binding sites of the Chr4-cl KRAB-ZFPs Rex2 and Gm13051 are indicated by dashed lines.
|
||||
|
||||
<!-- image -->
|
||||
|
||||
Figure 4.: ETn retrotransposition in Chr4-cl KO mice.
|
||||
(A) Pedigree of mice used for transposon insertion screening by capture-seq in mice of different strain backgrounds. The number of novel ETn insertions (only present in one animal) are indicated. For animals whose direct ancestors have not been screened, the ETn insertions are shown in parentheses since parental inheritance cannot be excluded in that case. Germ line insertions are indicated by asterisks. All DNA samples were prepared from tail tissues unless noted (-S: spleen, -E: ear, -B:Blood) (B) Statistical analysis of ETn insertion frequency in tail tissue from 30 Chr4-cl KO, KO/WT and WT mice that were derived from one Chr4-c KO x KO/WT and two Chr4-cl KO/WT x KO/WT matings. Only DNA samples that were collected from juvenile tails were considered for this analysis. P-values were calculated using one-sided Wilcoxon Rank Sum Test. In the last panel, KO, WT and KO/WT mice derived from all matings were combined for the statistical analysis.
|
||||
Figure 4—source data 1.Coordinates of identified novel ETn insertions and supporting capture-seq read counts.Genomic regions indicate cluster of supporting reads.
|
||||
Figure 4—source data 2.Sequences of capture-seq probes used to enrich genomic DNA for ETn and MuLV (RLTR4) insertions.
|
||||
|
||||
<!-- image -->
|
||||
|
||||
Figure 4—figure supplement 1.: Birth statistics of KRAB-ZFP cluster KO mice and TE reactivation in adult tissues.
|
||||
(A) Birth statistics of Chr4- and Chr2-cl mice derived from KO/WT x KO/WT matings in different strain backgrounds. (B) RNA-seq analysis of TE expression in Chr2- (left) and Chr4-cl (right) KO tissues. TE groups with the highest reactivation phenotype in ES cells are shown separately. Significantly up- and downregulated elements (adjusted p-value<0.05) are highlighted in red and green, respectively. Experiments were performed in at least two biological replicates.
|
||||
|
||||
<!-- image -->
|
||||
|
||||
Figure 4—figure supplement 2.: Identification of polymorphic ETn and MuLV retrotransposon insertions in Chr4-cl KO and WT mice.
|
||||
Heatmaps show normalized capture-seq read counts in RPM (Read Per Million) for identified polymorphic ETn (A) and MuLV (B) loci in different mouse strains. Only loci with strong support for germ line ETn or MuLV insertions (at least 100 or 3000 ETn or MuLV RPM, respectively) in at least two animals are shown. Non-polymorphic insertion loci with high read counts in all screened mice were excluded for better visibility. The sample information (sample name and cell type/tissue) is annotated at the bottom, with the strain information indicated by color at the top. The color gradient indicates log10(RPM+1).
|
||||
|
||||
<!-- image -->
|
||||
|
||||
Figure 4—figure supplement 3.: Confirmation of novel ETn insertions identified by capture-seq.
|
||||
(A) PCR validation of novel ETn insertions in genomic DNA of three littermates (IDs: T09673, T09674 and T00436) and their parents (T3913 and T3921). Primer sequences are shown in Supplementary file 3. (B) ETn capture-seq read counts (RPM) at putative novel somatic (loci identified exclusively in one single animal), novel germ line (loci identified in several littermates) insertions, and at B6 reference ETn elements. (C) Heatmap shows capture-seq read counts (RPM) of a Chr4-cl KO mouse (ID: C6733) as determined in different tissues. Each row represents a novel ETn locus that was identified in at least one tissue. The color gradient indicates log10(RPM+1). (D) Heatmap shows the capture-seq RPM in technical replicates using the same Chr4-cl KO DNA sample (rep1/rep2) or replicates with DNA samples prepared from different sections of the tail from the same mouse at different ages (tail1/tail2). Each row represents a novel ETn locus that was identified in at least one of the displayed samples. The color gradient indicates log10(RPM+1).
|
||||
|
||||
<!-- image -->
|
||||
Andrew ZX. H3K9me3\_ChIPSeq (Ctrl). NCBI Gene Expression Omnibus (2014). NCBI: GSM1327148
|
||||
|
||||
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148
tests/data/groundtruth/docling_v2/pnas_sample.xml.itxt
Normal file
148
tests/data/groundtruth/docling_v2/pnas_sample.xml.itxt
Normal file
@ -0,0 +1,148 @@
|
||||
item-0 at level 0: unspecified: group _root_
|
||||
item-1 at level 1: title: The coreceptor mutation CCR5Δ32 ... V epidemics and is selected for by HIV
|
||||
item-2 at level 2: paragraph: Amy D. Sullivan, Janis Wigginton, Denise Kirschner
|
||||
item-3 at level 2: paragraph: Department of Microbiology and I ... dical School, Ann Arbor, MI 48109-0620
|
||||
item-4 at level 2: section_header: Abstract
|
||||
item-5 at level 3: text: We explore the impact of a host ... creasing the frequency of this allele.
|
||||
item-6 at level 2: text: Nineteen million people have die ... factors such as host genetics (4, 5).
|
||||
item-7 at level 2: text: To exemplify the contribution of ... follow the CCR5Δ32 allelic frequency.
|
||||
item-8 at level 2: text: We hypothesize that CCR5Δ32 limi ... g the frequency of this mutant allele.
|
||||
item-9 at level 2: text: CCR5 is a host-cell chemokine re ... iral strain (such as X4 or R5X4) (30).
|
||||
item-10 at level 2: section_header: The Model
|
||||
item-11 at level 3: text: Because we are most concerned wi ... t both economic and social conditions.
|
||||
item-12 at level 3: picture
|
||||
item-12 at level 4: caption: Figure 1 A schematic representation of the basic compartmental HIV epidemic model. The criss-cross lines indicate the sexual mixing between different compartments. Each of these interactions has a positive probability of taking place; they also incorporate individual rates of transmission indicated as λ, but in full notation is λ î,,→i,j, where i,j,k is the phenotype of the infected partner and î, is the phenotype of the susceptible partner. Also shown are the different rates of disease progression, γ i,j,k , that vary according to genotype, gender, and stage. Thus, the interactions between different genotypes, genders, and stages are associated with a unique probability of HIV infection. M, male; F, female.
|
||||
item-13 at level 3: table with [6x5]
|
||||
item-13 at level 4: caption: Table 1 Children's genotype
|
||||
item-14 at level 3: section_header: Parameter Estimates for the Model.
|
||||
item-15 at level 4: text: Estimates for rates that govern ... d in Fig. 1 are summarized as follows:
|
||||
item-16 at level 4: formula: \frac{dS_{i,j}(t)}{dt}={\chi}_{ ... ,\hat {k}{\rightarrow}i,j}S_{i,j}(t),
|
||||
item-17 at level 4: formula: \hspace{1em}\hspace{1em}\hspace ... j,A}(t)-{\gamma}_{i,j,A}I_{i,j,A}(t),
|
||||
item-18 at level 4: formula: \frac{dI_{i,j,B}(t)}{dt}={\gamm ... j,B}(t)-{\gamma}_{i,j,B}I_{i,j,B}(t),
|
||||
item-19 at level 4: formula: \frac{dA(t)}{dt}={\gamma}_{i,j, ... \right) -{\mu}_{A}A(t)-{\delta}A(t),
|
||||
item-20 at level 4: text: where, in addition to previously ... on of the infected partner, and j ≠ .
|
||||
item-21 at level 4: table with [14x5]
|
||||
item-21 at level 5: caption: Table 2 Transmission probabilities
|
||||
item-22 at level 4: table with [8x3]
|
||||
item-22 at level 5: caption: Table 3 Progression rates
|
||||
item-23 at level 4: table with [20x3]
|
||||
item-23 at level 5: caption: Table 4 Parameter values
|
||||
item-24 at level 4: text: The effects of the CCR5 W/Δ32 an ... nting this probability of infection is
|
||||
item-25 at level 4: formula: {\lambda}_{\hat {i},\hat {j},\h ... \hat {i},\hat {j},\hat {k}} \right] ,
|
||||
item-26 at level 4: text: where j ≠ is either male or fe ... e those with AIDS in the simulations).
|
||||
item-27 at level 4: text: The average rate of partner acqu ... owing the male rates to vary (36, 37).
|
||||
item-28 at level 4: section_header: Transmission probabilities.
|
||||
item-29 at level 5: text: The effect of a genetic factor i ... reported; ref. 42) (ref. 43, Table 2).
|
||||
item-30 at level 5: text: Given the assumption of no treat ... ases during the end stage of disease).
|
||||
item-31 at level 4: section_header: Disease progression.
|
||||
item-32 at level 5: text: We assume three stages of HIV in ... ssion rates are summarized in Table 3.
|
||||
item-33 at level 3: section_header: Demographic Setting.
|
||||
item-34 at level 4: text: Demographic parameters are based ... [suppressing (t) notation]: χ1,j 1,j =
|
||||
item-35 at level 4: formula: B_{r}\hspace{.167em}{ \,\substa ... }+I_{2,M,k})}{N_{M}} \right] + \right
|
||||
item-36 at level 4: formula: p_{v} \left \left( \frac{(I_{1, ... ght] \right) \right] ,\hspace{.167em}
|
||||
item-37 at level 4: text: where the probability of HIV ver ... heir values are summarized in Table 4.
|
||||
item-38 at level 2: section_header: Prevalence of HIV
|
||||
item-39 at level 3: section_header: Demographics and Model Validation.
|
||||
item-40 at level 4: text: The model was validated by using ... 5% to capture early epidemic behavior.
|
||||
item-41 at level 4: text: In deciding on our initial value ... n within given subpopulations (2, 49).
|
||||
item-42 at level 4: text: In the absence of HIV infection, ... those predicted by our model (Fig. 2).
|
||||
item-43 at level 4: picture
|
||||
item-43 at level 5: caption: Figure 2 Model simulation of HIV infection in a population lacking the protective CCR5Δ32 allele compared with national data from Kenya (healthy adults) and Mozambique (blood donors, ref. 17). The simulated population incorporates parameter estimates from sub-Saharan African demographics. Note the two outlier points from the Mozambique data were likely caused by underreporting in the early stages of the epidemic.
|
||||
item-44 at level 3: section_header: Effects of the Allele on Prevalence.
|
||||
item-45 at level 4: text: After validating the model in th ... among adults for total HIV/AIDS cases.
|
||||
item-46 at level 4: text: Although CCR5Δ32/Δ32 homozygosit ... frequency of the mutation as 0.105573.
|
||||
item-47 at level 4: text: Fig. 3 shows the prevalence of H ... mic, reaching 18% before leveling off.
|
||||
item-48 at level 4: picture
|
||||
item-48 at level 5: caption: Figure 3 Prevalence of HIV/AIDS in the adult population as predicted by the model. The top curve (○) indicates prevalence in a population lacking the protective allele. We compare that to a population with 19% heterozygous and 1% homozygous for the allele (implying an allelic frequency of 0.105573. Confidence interval bands (light gray) are shown around the median simulation () providing a range of uncertainty in evaluating parameters for the effect of the mutation on the infectivity and the duration of asymptomatic HIV for heterozygotes.
|
||||
item-49 at level 4: text: In contrast, when a proportion o ... gins to decline slowly after 70 years.
|
||||
item-50 at level 4: text: In the above simulations we assu ... in the presence of the CCR5 mutation.
|
||||
item-51 at level 4: text: Because some parameters (e.g., r ... s a major influence on disease spread.
|
||||
item-52 at level 2: section_header: HIV Induces Selective Pressure on Genotype Frequency
|
||||
item-53 at level 3: text: To observe changes in the freque ... for ≈1,600 years before leveling off.
|
||||
item-54 at level 3: picture
|
||||
item-54 at level 4: caption: Figure 4 Effects of HIV-1 on selection of the CCR5Δ32 allele. The Hardy-Weinberg equilibrium level is represented in the no-infection simulation (solid lines) for each population. Divergence from the original Hardy-Weinberg equilibrium is shown to occur in the simulations that include HIV infection (dashed lines). Fraction of the total subpopulations are presented: (A) wild types (W/W), (B) heterozygotes (W/Δ32), and (C) homozygotes (Δ32/Δ32). Note that we initiate this simulation with a much lower allelic frequency (0.00105) than used in the rest of the study to better exemplify the actual selective effect over a 1,000-year time scale. (D) The allelic selection effect over a 2,000-year time scale.
|
||||
item-55 at level 2: section_header: Discussion
|
||||
item-56 at level 3: text: This study illustrates how popul ... pulations where the allele is present.
|
||||
item-57 at level 3: text: We also observed that HIV can pr ... is) have been present for much longer.
|
||||
item-58 at level 3: text: Two mathematical models have con ... ce of the pathogen constant over time.
|
||||
item-59 at level 3: text: Even within our focus on host pr ... f a protective allele such as CCR5Δ32.
|
||||
item-60 at level 3: text: Although our models demonstrate ... f the population to epidemic HIV (16).
|
||||
item-61 at level 3: text: In assessing the HIV/AIDS epidem ... for education and prevention programs.
|
||||
item-62 at level 2: section_header: Acknowledgments
|
||||
item-63 at level 3: text: We thank Mark Krosky, Katia Koel ... ers for extremely insightful comments.
|
||||
item-64 at level 2: section_header: References
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||||
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|
||||
item-132 at level 1: caption: Figure 1 A schematic representat ... of HIV infection. M, male; F, female.
|
||||
item-133 at level 1: caption: Table 1 Children's genotype
|
||||
item-134 at level 1: caption: Table 2 Transmission probabilities
|
||||
item-135 at level 1: caption: Table 3 Progression rates
|
||||
item-136 at level 1: caption: Table 4 Parameter values
|
||||
item-137 at level 1: caption: Figure 2 Model simulation of HIV ... g in the early stages of the epidemic.
|
||||
item-138 at level 1: caption: Figure 3 Prevalence of HIV/AIDS ... of asymptomatic HIV for heterozygotes.
|
||||
item-139 at level 1: caption: Figure 4 Effects of HIV-1 on sel ... n effect over a 2,000-year time scale.
|
||||
6353
tests/data/groundtruth/docling_v2/pnas_sample.xml.json
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6353
tests/data/groundtruth/docling_v2/pnas_sample.xml.json
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File diff suppressed because it is too large
Load Diff
258
tests/data/groundtruth/docling_v2/pnas_sample.xml.md
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258
tests/data/groundtruth/docling_v2/pnas_sample.xml.md
Normal file
@ -0,0 +1,258 @@
|
||||
# The coreceptor mutation CCR5Δ32 influences the dynamics of HIV epidemics and is selected for by HIV
|
||||
|
||||
Amy D. Sullivan, Janis Wigginton, Denise Kirschner
|
||||
|
||||
Department of Microbiology and Immunology, University of Michigan Medical School, Ann Arbor, MI 48109-0620
|
||||
|
||||
## Abstract
|
||||
|
||||
We explore the impact of a host genetic factor on heterosexual HIV epidemics by using a deterministic mathematical model. A protective allele unequally distributed across populations is exemplified in our models by the 32-bp deletion in the host-cell chemokine receptor CCR5, CCR5Δ32. Individuals homozygous for CCR5Δ32 are protected against HIV infection whereas those heterozygous for CCR5Δ32 have lower pre-AIDS viral loads and delayed progression to AIDS. CCR5Δ32 may limit HIV spread by decreasing the probability of both risk of infection and infectiousness. In this work, we characterize epidemic HIV within three dynamic subpopulations: CCR5/CCR5 (homozygous, wild type), CCR5/CCR5Δ32 (heterozygous), and CCR5Δ32/CCR5Δ32 (homozygous, mutant). Our results indicate that prevalence of HIV/AIDS is greater in populations lacking the CCR5Δ32 alleles (homozygous wild types only) as compared with populations that include people heterozygous or homozygous for CCR5Δ32. Also, we show that HIV can provide selective pressure for CCR5Δ32, increasing the frequency of this allele.
|
||||
|
||||
Nineteen million people have died of AIDS since the discovery of HIV in the 1980s. In 1999 alone, 5.4 million people were newly infected with HIV (ref. 1 and http://www.unaids.org/epidemicupdate/report/Epireport.html). (For brevity, HIV-1 is referred to as HIV in this paper.) Sub-Saharan Africa has been hardest hit, with more than 20% of the general population HIV-positive in some countries (2, 3). In comparison, heterosexual epidemics in developed, market-economy countries have not reached such severe levels. Factors contributing to the severity of the epidemic in economically developing countries abound, including economic, health, and social differences such as high levels of sexually transmitted diseases and a lack of prevention programs. However, the staggering rate at which the epidemic has spread in sub-Saharan Africa has not been adequately explained. The rate and severity of this epidemic also could indicate a greater underlying susceptibility to HIV attributable not only to sexually transmitted disease, economics, etc., but also to other more ubiquitous factors such as host genetics (4, 5).
|
||||
|
||||
To exemplify the contribution of such a host genetic factor to HIV prevalence trends, we consider a well-characterized 32-bp deletion in the host-cell chemokine receptor CCR5, CCR5Δ32. When HIV binds to host cells, it uses the CD4 receptor on the surface of host immune cells together with a coreceptor, mainly the CCR5 and CXCR4 chemokine receptors (6). Homozygous mutations for this 32-bp deletion offer almost complete protection from HIV infection, and heterozygous mutations are associated with lower pre-AIDS viral loads and delayed progression to AIDS (7–14). CCR5Δ32 generally is found in populations of European descent, with allelic frequencies ranging from 0 to 0.29 (13). African and Asian populations studied outside the United States or Europe appear to lack the CCR5Δ32 allele, with an allelic frequency of almost zero (5, 13). Thus, to understand the effects of a protective allele, we use a mathematical model to track prevalence of HIV in populations with or without CCR5Δ32 heterozygous and homozygous people and also to follow the CCR5Δ32 allelic frequency.
|
||||
|
||||
We hypothesize that CCR5Δ32 limits epidemic HIV by decreasing infection rates, and we evaluate the relative contributions to this by the probability of infection and duration of infectivity. To capture HIV infection as a chronic infectious disease together with vertical transmission occurring in untreated mothers, we model a dynamic population (i.e., populations that vary in growth rates because of fluctuations in birth or death rates) based on realistic demographic characteristics (18). This scenario also allows tracking of the allelic frequencies over time. This work considers how a specific host genetic factor affecting HIV infectivity and viremia at the individual level might influence the epidemic in a dynamic population and how HIV exerts selective pressure, altering the frequency of this mutant allele.
|
||||
|
||||
CCR5 is a host-cell chemokine receptor, which is also used as a coreceptor by R5 strains of HIV that are generally acquired during sexual transmission (6, 19–25). As infection progresses to AIDS the virus expands its repertoire of potential coreceptors to include other CC-family and CXC-family receptors in roughly 50% of patients (19, 26, 27). CCR5Δ32 was identified in HIV-resistant people (28). Benefits to individuals from the mutation in this allele are as follows. Persons homozygous for the CCR5Δ32 mutation are almost nonexistent in HIV-infected populations (11, 12) (see ref. 13 for review). Persons heterozygous for the mutant allele (CCR5 W/Δ32) tend to have lower pre-AIDS viral loads. Aside from the beneficial effects that lower viral loads may have for individuals, there is also an altruistic effect, as transmission rates are reduced for individuals with low viral loads (as compared with, for example, AZT and other studies; ref. 29). Finally, individuals heterozygous for the mutant allele (CCR5 W/Δ32) also have a slower progression to AIDS than those homozygous for the wild-type allele (CCR5 W/W) (7–10), remaining in the population 2 years longer, on average. Interestingly, the dearth of information on HIV disease progression in people homozygous for the CCR5Δ32 allele (CCR5 Δ32/Δ32) stems from the rarity of HIV infection in this group (4, 12, 28). However, in case reports of HIV-infected CCR5 Δ32/Δ32 homozygotes, a rapid decline in CD4+ T cells and a high viremia are observed, likely because of initial infection with a more aggressive viral strain (such as X4 or R5X4) (30).
|
||||
|
||||
## The Model
|
||||
|
||||
Because we are most concerned with understanding the severity of the epidemic in developing countries where the majority of infection is heterosexual, we consider a purely heterosexual model. To model the effects of the allele in the population, we examine the rate of HIV spread by using an enhanced susceptible-infected-AIDS model of epidemic HIV (for review see ref. 31). Our model compares two population scenarios: a CCR5 wild-type population and one with CCR5Δ32 heterozygotes and homozygotes in addition to the wild type. To model the scenario where there are only wild-type individuals present in the population (i.e., CCR5 W/W), we track the sexually active susceptibles at time t [Si,j (t)], where i = 1 refers to genotype (CCR5 W/W only in this case) and j is either the male or female subpopulation. We also track those who are HIV-positive at time t not yet having AIDS in Ii,j,k (t) where k refers to stage of HIV infection [primary (A) or asymptomatic (B)]. The total number of individuals with AIDS at time t are tracked in A(t). The source population are children, χ i,j (t), who mature into the sexually active population at time t (Fig. 1, Table 1). We compare the model of a population lacking the CCR5Δ32 allele to a demographically similar population with a high frequency of the allele. When genetic heterogeneity is included, male and female subpopulations are each further divided into three distinct genotypic groups, yielding six susceptible subpopulations, [Si,j (t), where i ranges from 1 to 3, where 1 = CCR5W/W; 2 = CCR5 W/Δ32; 3 = CCR5 Δ32/Δ32]. The infected classes, Ii,j,k (t), also increase in number to account for these new genotype compartments. In both settings we assume there is no treatment available and no knowledge of HIV status by people in the early acute and middle asymptomatic stages (both conditions exist in much of sub-Saharan Africa). In addition, we assume that sexual mixing in the population occurs randomly with respect to genotype and HIV disease status, all HIV-infected people eventually progress to AIDS, and no barrier contraceptives are used. These last assumptions reflect both economic and social conditions.
|
||||
|
||||
Figure 1 A schematic representation of the basic compartmental HIV epidemic model. The criss-cross lines indicate the sexual mixing between different compartments. Each of these interactions has a positive probability of taking place; they also incorporate individual rates of transmission indicated as λ, but in full notation is λ î,,→i,j, where i,j,k is the phenotype of the infected partner and î, is the phenotype of the susceptible partner. Also shown are the different rates of disease progression, γ i,j,k , that vary according to genotype, gender, and stage. Thus, the interactions between different genotypes, genders, and stages are associated with a unique probability of HIV infection. M, male; F, female.
|
||||
|
||||
<!-- image -->
|
||||
|
||||
Table 1 Children's genotype
|
||||
|
||||
| Parents | Mother | Mother | Mother | Mother |
|
||||
|-----------|----------|--------------------|------------------------------|--------------------|
|
||||
| | | | | |
|
||||
| Father | | W/W | W/Δ32 | Δ32/Δ32 |
|
||||
| | W/W | χ1,j 1,j | χ1,j 1,j, χ2,j 2,j | χ2,j 2,j |
|
||||
| | W/Δ32 | χ1,j 1,j, χ2,j 2,j | χ1,j 1,j, χ2,j 2,j, χ3,j 3,j | χ2,j 2,j, χ3,j 3,j |
|
||||
| | Δ32/Δ32 | χ2,j 2,j | χ2,j 2,j, χ3,j 3,j | χ3,j 3,j |
|
||||
|
||||
### Parameter Estimates for the Model.
|
||||
|
||||
Estimates for rates that govern the interactions depicted in Fig. 1 were derived from the extensive literature on HIV. Our parameters and their estimates are summarized in Tables 2–4. The general form of the equations describing the rates of transition between population classes as depicted in Fig. 1 are summarized as follows:
|
||||
|
||||
$$ \frac{dS_{i,j}(t)}{dt}={\chi}_{i,j}(t)-{\mu}_{j}S_{i,j}(t)-{\lambda}_{\hat {\imath},\hat {},\hat {k}{\rightarrow}i,j}S_{i,j}(t), $$
|
||||
|
||||
$$ \hspace{1em}\hspace{1em}\hspace{.167em}\frac{dI_{i,j,A}(t)}{dt}={\lambda}_{\hat {\imath},\hat {},\hat {k}{\rightarrow}i,j}S_{i,j}(t)-{\mu}_{j}I_{i,j,A}(t)-{\gamma}_{i,j,A}I_{i,j,A}(t), $$
|
||||
|
||||
$$ \frac{dI_{i,j,B}(t)}{dt}={\gamma}_{i,j,A}I_{i,j,A}(t)-{\mu}_{j}I_{i,j,B}(t)-{\gamma}_{i,j,B}I_{i,j,B}(t), $$
|
||||
|
||||
$$ \frac{dA(t)}{dt}={\gamma}_{i,j,B} \left( { \,\substack{ ^{3} \\ {\sum} \\ _{i=1} }\, }I_{i,F,B}(t)+I_{i,M,B}(t) \right) -{\mu}_{A}A(t)-{\delta}A(t), $$
|
||||
|
||||
where, in addition to previously defined populations and rates (with i equals genotype, j equals gender, and k equals stage of infection, either A or B), μ j , represents the non-AIDS (natural) death rate for males and females respectively, and μA is estimated by the average (μF + μM/2). This approximation allows us to simplify the model (only one AIDS compartment) without compromising the results, as most people with AIDS die of AIDS (δAIDS) and very few of other causes (μA). These estimates include values that affect infectivity (λ î,,→i,j ), transmission (β î,,→i,j ), and disease progression (γ i , j , k ) where the î,, notation represents the genotype, gender, and stage of infection of the infected partner, and j ≠ .
|
||||
|
||||
Table 2 Transmission probabilities
|
||||
|
||||
| HIV-infected partner (îıı^^, ^^, k k^^) | Susceptible partner (i, j) | Susceptible partner (i, j) | Susceptible partner (i, j) | Susceptible partner (i, j) |
|
||||
|-----------------------------------------------|------------------------------|------------------------------|------------------------------|------------------------------|
|
||||
| HIV-infected partner (îıı^^, ^^, k k^^) | | | | |
|
||||
| HIV-infected partner (îıı^^, ^^, k k^^) | (^^ to j) | W/W | W/Δ32 | Δ32/Δ32 |
|
||||
| | | | | |
|
||||
| Acute/primary | | | | |
|
||||
| W/W or Δ32/Δ32 | M to F | 0.040 | 0.040 | 0.00040 |
|
||||
| | F to M | 0.020 | 0.020 | 0.00020 |
|
||||
| W/Δ32 | M to F | 0.030 | 0.030 | 0.00030 |
|
||||
| | F to M | 0.015 | 0.015 | 0.00015 |
|
||||
| Asymptomatic | | | | |
|
||||
| W/W or Δ32/Δ32 | M to F | 0.0010 | 0.0010 | 10 × 10−6 |
|
||||
| | F to M | 0.0005 | 0.0005 | 5 × 10−6 |
|
||||
| W/Δ32 | M to F | 0.0005 | 0.0005 | 5 × 10−6 |
|
||||
| | F to M | 0.00025 | 0.00025 | 2.5 × 10−6 |
|
||||
|
||||
Table 3 Progression rates
|
||||
|
||||
| Genotype | Disease stage | Males/females |
|
||||
|------------|-----------------|------------------|
|
||||
| | | |
|
||||
| W/W | A | 3.5 |
|
||||
| | B | 0.16667 |
|
||||
| W/Δ32 | A | 3.5 |
|
||||
| | B | 0.125 |
|
||||
| Δ32/Δ32 | A | 3.5 |
|
||||
| | B | 0.16667 |
|
||||
|
||||
Table 4 Parameter values
|
||||
|
||||
| Parameter | Definition | Value |
|
||||
|-----------------------------------------|----------------------------------------------------------|-------------------------|
|
||||
| | | |
|
||||
| μ F F, μ M M | All-cause mortality for adult females (males) | 0.015 (0.016) per year |
|
||||
| μχχ | All-cause childhood mortality (<15 years of age) | 0.01 per year |
|
||||
| B r r | Birthrate | 0.25 per woman per year |
|
||||
| SA F F | Percent females acquiring new partners (sexual activity) | 10% |
|
||||
| SA M M | Percent males acquiring new partners (sexual activity) | 25% |
|
||||
| m F F(ς$$ {\mathrm{_{{F}}^{{2}}}} $$) | Mean (variance) no. of new partners for females | 1.8 (1.2) per year |
|
||||
| ς$$ {\mathrm{_{{M}}^{{2}}}} $$ | Variance in no. of new partners for males | 5.5 per year |
|
||||
| 1 − p v v | Probability of vertical transmission | 0.30 per birth |
|
||||
| I i,j,k i,j,k(0) | Initial total population HIV-positive | 0.50% |
|
||||
| χ i,j i,j(0) | Initial total children in population (<15 years of age) | 45% |
|
||||
| W/W (0) | Initial total wild types (W/W) in population | 80% |
|
||||
| W/Δ32(0) | Initial total heterozygotes (W/Δ32) in population | 19% |
|
||||
| Δ32/Δ32(0) | Initial total homozygotes (Δ32/Δ32) in population | 1% |
|
||||
| r M M(r F F) | Initial percent males (females) in total population | 49% (51%) |
|
||||
| ϕ F F, ϕ M M | Number of sexual contacts a female (male) has | 30 (24) per partner |
|
||||
| ɛ i,j,k i,j,k | % effect of mutation on transmission rates (see Table 2) | 0 < ɛ i,j,k i,j,k < 1 |
|
||||
| δ | Death rate for AIDS population | 1.0 per year |
|
||||
| q | Allelic frequency of Δ32 allele | 0.105573 |
|
||||
|
||||
The effects of the CCR5 W/Δ32 and CCR5 Δ32/Δ32 genotypes are included in our model through both the per-capita probabilities of infection, λ î,,→i,j , and the progression rates, γ i , j , k . The infectivity coefficients, λ î,,→i,j , are calculated for each population subgroup based on the following: likelihood of HIV transmission in a sexual encounter between a susceptible and an infected (βîıı^^,j,k k^^→i,j ) person; formation of new partnerships (c j j); number of contacts in a given partnership (ϕ j ); and probability of encountering an infected individual (I î,, /N ). The formula representing this probability of infection is
|
||||
|
||||
$$ {\lambda}_{\hat {i},\hat {j},\hat {k}{\rightarrow}i,j}=\frac{C_{j}{\cdot}{\phi}_{j}}{N_{\hat {j}}}\hspace{.167em} \left[ { \,\substack{ \\ {\sum} \\ _{\hat {i},\hat {k}} }\, }{\beta}_{\hat {i},\hat {j},\hat {k}{\rightarrow}i,j}{\cdot}I_{\hat {i},\hat {j},\hat {k}} \right] , $$
|
||||
|
||||
where j ≠ is either male or female. N represents the total population of gender (this does not include those with AIDS in the simulations).
|
||||
|
||||
The average rate of partner acquisition, cj , includes the mean plus the variance to mean ratio of the relevant distribution of partner-change rates to capture the small number of high-risk people: cj = mj + (ς/m j) where the mean (mj ) and variance (ς) are annual figures for new partnerships only (32). These means are estimated from Ugandan data for the number of heterosexual partners in the past year (33) and the number of nonregular heterosexual partners (i.e., spouses or long-term partners) in the past year (34). In these sexual activity surveys, men invariably have more new partnerships; thus, we assumed that they would have fewer average contacts per partnership than women (a higher rate of new partner acquisition means fewer sexual contacts with a given partner; ref. 35). To incorporate this assumption in our model, the male contacts/partnership, ϕ M , was reduced by 20%. In a given population, the numbers of heterosexual interactions must equate between males and females. The balancing equation applied here is SA F·m F·N F = SA M·m M·N M, where SAj are the percent sexually active and Nj are the total in the populations for gender j. To specify changes in partner acquisition, we apply a male flexibility mechanism, holding the female rate of acquisition constant and allowing the male rates to vary (36, 37).
|
||||
|
||||
#### Transmission probabilities.
|
||||
|
||||
The effect of a genetic factor in a model of HIV transmission can be included by reducing the transmission coefficient. The probabilities of transmission per contact with an infected partner, βîıı^^,^^,k k^^→i,j , have been estimated in the literature (see ref. 38 for estimates in minimally treated groups). We want to capture a decreased risk in transmission based on genotype (ref. 39, Table 2). No studies have directly evaluated differences in infectivity between HIV-infected CCR5 W/Δ32 heterozygotes and HIV-infected CCR5 wild types. Thus, we base estimates for reduced transmission on studies of groups with various HIV serum viral loads (40), HTLV-I/II viral loads (41), and a study of the effect of AZT treatment on transmission (29). We decrease transmission probabilities for infecting CCR5Δ32/Δ32 persons by 100-fold to reflect the rarity of infections in these persons. However, we assume that infected CCR5Δ32/Δ32 homozygotes can infect susceptibles at a rate similar to CCR5W/W homozygotes, as the former generally have high viremias (ref. 30, Table 2). We also assume that male-to-female transmission is twice as efficient as female-to-male transmission (up to a 9-fold difference has been reported; ref. 42) (ref. 43, Table 2).
|
||||
|
||||
Given the assumption of no treatment, the high burden of disease in people with AIDS is assumed to greatly limit their sexual activity. Our initial model excludes people with AIDS from the sexually active groups. Subsequently, we allow persons with AIDS to be sexually active, fixing their transmission rates (βAIDS) to be the same across all CCR5 genotypes, and lower than transmission rates for primary-stage infection (as the viral burden on average is not as high as during the acute phase), and larger than transmission rates for asymptomatic-stage infection (as the viral burden characteristically increases during the end stage of disease).
|
||||
|
||||
#### Disease progression.
|
||||
|
||||
We assume three stages of HIV infection: primary (acute, stage A), asymptomatic HIV (stage B), and AIDS. The rates of transition through the first two stages are denoted by γ i,j,k i,j,k, where i represents genotype, j is male/female, and k represents either stage A or stage B. Transition rates through each of these stages are assumed to be inversely proportional to the duration of that stage; however, other distributions are possible (31, 44, 45). Although viral loads generally peak in the first 2 months of infection, steady-state viral loads are established several months beyond this (46). For group A, the primary HIV-infecteds, duration is assumed to be 3.5 months. Based on results from European cohort studies (7–10), the beneficial effects of the CCR5 W/Δ32 genotype are observed mainly in the asymptomatic years of HIV infection; ≈7 years after seroconversion survival rates appear to be quite similar between heterozygous and homozygous individuals. We also assume that CCR5Δ32/Δ32-infected individuals and wild-type individuals progress similarly, and that men and women progress through each disease stage at the same rate. Given these observations, and that survival after infection may be shorter in untreated populations, we choose the duration time in stage B to be 6 years for wild-type individuals and 8 years for heterozygous individuals. Transition through AIDS, δAIDS, is inversely proportional to the duration of AIDS. We estimate this value to be 1 year for the time from onset of AIDS to death. The progression rates are summarized in Table 3.
|
||||
|
||||
### Demographic Setting.
|
||||
|
||||
Demographic parameters are based on data from Malawi, Zimbabwe, and Botswana (3, 47). Estimated birth and child mortality rates are used to calculate the annual numbers of children (χ i,j i,j) maturing into the potentially sexually active, susceptible group at the age of 15 years (3). For example, in the case where the mother is CCR5 wild type and the father is CCR5 wild type or heterozygous, the number of CCR5 W/W children is calculated as follows [suppressing (t) notation]: χ1,j 1,j =
|
||||
|
||||
$$ B_{r}\hspace{.167em}{ \,\substack{ \\ {\sum} \\ _{k} }\, } \left[ S_{1,F}\frac{(S_{1,M}+I_{1,M,k})}{N_{M}}+ \left[ (0.5)S_{1,F}\frac{(S_{2,M}+I_{2,M,k})}{N_{M}} \right] + \right $$
|
||||
|
||||
$$ p_{v} \left \left( \frac{(I_{1,F,k}(S_{1,M}+I_{1,M,k}))}{N_{M}}+ \left[ (0.5)I_{1,F,k}\frac{(S_{2,M}+I_{2,M,k})}{N_{M}} \right] \right) \right] ,\hspace{.167em} $$
|
||||
|
||||
where the probability of HIV vertical transmission, 1 − pv , and the birthrate, Br , are both included in the equations together with the Mendelian inheritance values as presented in Table 1. The generalized version of this equation (i.e., χ i,j i,j) can account for six categories of children (including gender and genotype). We assume that all children of all genotypes are at risk, although we can relax this condition if data become available to support vertical protection (e.g., ref. 48). All infected children are assumed to die before age 15. Before entering the susceptible group at age 15, there is additional loss because of mortality from all non-AIDS causes occurring less than 15 years of age at a rate of μχχ × χ i,j i,j (where μχ is the mortality under 15 years of age). Children then enter the population as susceptibles at an annual rate, ς j j × χ i,j i,j/15, where ς j distributes the children 51% females and 49% males. All parameters and their values are summarized in Table 4.
|
||||
|
||||
## Prevalence of HIV
|
||||
|
||||
### Demographics and Model Validation.
|
||||
|
||||
The model was validated by using parameters estimated from available demographic data. Simulations were run in the absence of HIV infection to compare the model with known population growth rates. Infection was subsequently introduced with an initial low HIV prevalence of 0.5% to capture early epidemic behavior.
|
||||
|
||||
In deciding on our initial values for parameters during infection, we use Joint United Nations Programme on HIV/AIDS national prevalence data for Malawi, Zimbabwe, and Botswana. Nationwide seroprevalence of HIV in these countries varies from ≈11% to over 20% (3), although there may be considerable variation within given subpopulations (2, 49).
|
||||
|
||||
In the absence of HIV infection, the annual percent population growth rate in the model is ≈2.5%, predicting the present-day values for an average of sub-Saharan African cities (data not shown). To validate the model with HIV infection, we compare our simulation of the HIV epidemic to existing prevalence data for Kenya and Mozambique (http://www.who.int/emc-hiv/fact-sheets/pdfs/kenya.pdf and ref. 51). Prevalence data collected from these countries follow similar trajectories to those predicted by our model (Fig. 2).
|
||||
|
||||
Figure 2 Model simulation of HIV infection in a population lacking the protective CCR5Δ32 allele compared with national data from Kenya (healthy adults) and Mozambique (blood donors, ref. 17). The simulated population incorporates parameter estimates from sub-Saharan African demographics. Note the two outlier points from the Mozambique data were likely caused by underreporting in the early stages of the epidemic.
|
||||
|
||||
<!-- image -->
|
||||
|
||||
### Effects of the Allele on Prevalence.
|
||||
|
||||
After validating the model in the wild type-only population, both CCR5Δ32 heterozygous and homozygous people are included. Parameter values for HIV transmission, duration of illness, and numbers of contacts per partner are assumed to be the same within both settings. We then calculate HIV/AIDS prevalence among adults for total HIV/AIDS cases.
|
||||
|
||||
Although CCR5Δ32/Δ32 homozygosity is rarely seen in HIV-positive populations (prevalence ranges between 0 and 0.004%), 1–20% of people in HIV-negative populations of European descent are homozygous. Thus, to evaluate the potential impact of CCR5Δ32, we estimate there are 19% CCR5 W/Δ32 heterozygous and 1% CCR5 Δ32/Δ32 homozygous people in our population. These values are in Hardy-Weinberg equilibrium with an allelic frequency of the mutation as 0.105573.
|
||||
|
||||
Fig. 3 shows the prevalence of HIV in two populations: one lacking the mutant CCR5 allele and another carrying that allele. In the population lacking the protective mutation, prevalence increases logarithmically for the first 35 years of the epidemic, reaching 18% before leveling off.
|
||||
|
||||
Figure 3 Prevalence of HIV/AIDS in the adult population as predicted by the model. The top curve (○) indicates prevalence in a population lacking the protective allele. We compare that to a population with 19% heterozygous and 1% homozygous for the allele (implying an allelic frequency of 0.105573. Confidence interval bands (light gray) are shown around the median simulation () providing a range of uncertainty in evaluating parameters for the effect of the mutation on the infectivity and the duration of asymptomatic HIV for heterozygotes.
|
||||
|
||||
<!-- image -->
|
||||
|
||||
In contrast, when a proportion of the population carries the CCR5Δ32 allele, the epidemic increases more slowly, but still logarithmically, for the first 50 years, and HIV/AIDS prevalence reaches ≈12% (Fig. 3). Prevalence begins to decline slowly after 70 years.
|
||||
|
||||
In the above simulations we assume that people with AIDS are not sexually active. However, when these individuals are included in the sexually active population the severity of the epidemic increases considerably (data not shown). Consistent with our initial simulations, prevalences are still relatively lower in the presence of the CCR5 mutation.
|
||||
|
||||
Because some parameters (e.g., rate constants) are difficult to estimate based on available data, we implement an uncertainty analysis to assess the variability in the model outcomes caused by any inaccuracies in estimates of the parameter values with regard to the effect of the allelic mutation. For these analyses we use Latin hypercube sampling, as described in refs. 52–56, Our uncertainty and sensitivity analyses focus on infectivity vs. duration of infectiousness. To this end, we assess the effects on the dynamics of the epidemic for a range of values of the parameters governing transmission and progression rates: βîıı^^,^^,k k^^→i,j and γ i,j,k i,j,k. All other parameters are held constant. These results are presented as an interval band about the average simulation for the population carrying the CCR5Δ32 allele (Fig. 3). Although there is variability in the model outcomes, the analysis indicates that the overall model predictions are consistent for a wide range of transmission and progression rates. Further, most of the variation observed in the outcome is because of the transmission rates for both heterosexual males and females in the primary stage of infection (β2,M,A → i ,F, β2,F,A → i ,M). As mentioned above, we assume lower viral loads correlate with reduced infectivity; thus, the reduction in viral load in heterozygotes has a major influence on disease spread.
|
||||
|
||||
## HIV Induces Selective Pressure on Genotype Frequency
|
||||
|
||||
To observe changes in the frequency of the CCR5Δ32 allele in a setting with HIV infection as compared with the Hardy-Weinberg equilibrium in the absence of HIV, we follow changes in the total number of CCR5Δ32 heterozygotes and homozygotes over 1,000 years (Fig. 4). We initially perform simulations in the absence of HIV infection as a negative control to show there is not significant selection of the allele in the absence of infection. To determine how long it would take for the allelic frequency to reach present-day levels (e.g., q = 0.105573), we initiate this simulation for 1,000 years with a very small allelic frequency (q = 0.00105). In the absence of HIV, the allelic frequency is maintained in equilibrium as shown by the constant proportions of CCR5Δ32 heterozygotes and homozygotes (Fig. 4, solid lines). The selection for CCR5Δ32 in the presence of HIV is seen in comparison (Fig. 4, dashed lines). We expand the time frame of this simulation to 2,000 years to view the point at which the frequency reaches present levels (where q ∼0.105573 at year = 1200). Note that the allelic frequency increases for ≈1,600 years before leveling off.
|
||||
|
||||
Figure 4 Effects of HIV-1 on selection of the CCR5Δ32 allele. The Hardy-Weinberg equilibrium level is represented in the no-infection simulation (solid lines) for each population. Divergence from the original Hardy-Weinberg equilibrium is shown to occur in the simulations that include HIV infection (dashed lines). Fraction of the total subpopulations are presented: (A) wild types (W/W), (B) heterozygotes (W/Δ32), and (C) homozygotes (Δ32/Δ32). Note that we initiate this simulation with a much lower allelic frequency (0.00105) than used in the rest of the study to better exemplify the actual selective effect over a 1,000-year time scale. (D) The allelic selection effect over a 2,000-year time scale.
|
||||
|
||||
<!-- image -->
|
||||
|
||||
## Discussion
|
||||
|
||||
This study illustrates how populations can differ in susceptibility to epidemic HIV/AIDS depending on a ubiquitous attribute such as a prevailing genotype. We have examined heterosexual HIV epidemics by using mathematical models to assess HIV transmission in dynamic populations either with or without CCR5Δ32 heterozygous and homozygous persons. The most susceptible population lacks the protective mutation in CCR5. In less susceptible populations, the majority of persons carrying the CCR5Δ32 allele are heterozygotes. We explore the hypothesis that lower viral loads (CCR5Δ32 heterozygotes) or resistance to infection (CCR5Δ32 homozygotes) observed in persons with this coreceptor mutation ultimately can influence HIV epidemic trends. Two contrasting influences of the protective CCR5 allele are conceivable: it may limit the epidemic by decreasing the probability of infection because of lower viral loads in infected heterozygotes, or it may exacerbate the epidemic by extending the time that infectious individuals remain in the sexually active population. Our results strongly suggest the former. Thus, the absence of this allele in Africa could explain the severity of HIV disease as compared with populations where the allele is present.
|
||||
|
||||
We also observed that HIV can provide selective pressure for the CCR5Δ32 allele within a population, increasing the allelic frequency. Other influences may have additionally selected for this allele. Infectious diseases such as plague and small pox have been postulated to select for CCR5Δ32 (57, 58). For plague, relatively high levels of CCR5Δ32 are believed to have arisen within ≈4,000 years, accounting for the prevalence of the mutation only in populations of European descent. Smallpox virus uses the CC-coreceptor, indicating that direct selection for mutations in CCR5 may have offered resistance to smallpox. Given the differences in the epidemic rates of plague (59), smallpox, and HIV, it is difficult to directly compare our results to these findings. However, our model suggests that the CCR5Δ32 mutation could have reached its present allelic frequency in Northern Europe within this time frame if selected for by a disease with virulence patterns similar to HIV. Our results further support the idea that HIV has been only recently introduced as a pathogen into African populations, as the frequency of the protective allele is almost zero, and our model predicts that selection of the mutant allele in this population by HIV alone takes at least 1,000 years. This prediction is distinct from the frequency of the CCR5Δ32 allele in European populations, where pathogens that may have influenced its frequency (e.g., Yersinia pestis) have been present for much longer.
|
||||
|
||||
Two mathematical models have considered the role of parasite and host genetic heterogeneity with regard to susceptibility to another pathogen, namely malaria (60, 61). In each it was determined that heterogeneity of host resistance facilitates the maintenance of diversity in parasite virulence. Given our underlying interest in the coevolution of pathogen and host, we focus on changes in a host protective mutation, holding the virulence of the pathogen constant over time.
|
||||
|
||||
Even within our focus on host protective mutations, numerous genetic factors, beneficial or detrimental, could potentially influence epidemics. Other genetically determined host factors affecting HIV susceptibility and disease progression include a CCR5 A/A to G/G promoter polymorphism (62), a CCR2 point mutation (11, 63), and a mutation in the CXCR4 ligand (64). The CCR2b mutation, CCR264I, is found in linkage with at least one CCR5 promoter polymorphism (65) and is prevalent in populations where CCR5Δ32 is nonexistent, such as sub-Saharan Africa (63). However, as none of these mutations have been consistently shown to be as protective as the CCR5Δ32 allele, we simplified our model to incorporate only the effect of CCR5Δ32. Subsequent models could be constructed from our model to account for the complexity of multiple protective alleles. It is interesting to note that our model predicts that even if CCR264I is present at high frequencies in Africa, its protective effects may not augment the lack of a protective allele such as CCR5Δ32.
|
||||
|
||||
Although our models demonstrate that genetic factors can contribute to the high prevalence of HIV in sub-Saharan Africa, demographic factors are also clearly important in this region. Our models explicitly incorporated such factors, for example, lack of treatment availability. Additional factors were implicitly controlled for by varying only the presence of the CCR5Δ32 allele. More complex models eventually could include interactions with infectious diseases that serve as cofactors in HIV transmission. The role of high sexually transmitted disease prevalences in HIV infection has long been discussed, especially in relation to core populations (15, 50, 66). Malaria, too, might influence HIV transmission, as it is associated with transient increases in semen HIV viral loads and thus could increase the susceptibility of the population to epidemic HIV (16).
|
||||
|
||||
In assessing the HIV/AIDS epidemic, considerable attention has been paid to the influence of core groups in driving sexually transmitted disease epidemics. Our results also highlight how characteristics more uniformly distributed in a population can affect susceptibility. We observed that the genotypic profile of a population affects its susceptibility to epidemic HIV/AIDS. Additional studies are needed to better characterize the influence of these genetic determinants on HIV transmission, as they may be crucial in estimating the severity of the epidemic in some populations. This information can influence the design of treatment strategies as well as point to the urgency for education and prevention programs.
|
||||
|
||||
## Acknowledgments
|
||||
|
||||
We thank Mark Krosky, Katia Koelle, and Kevin Chung for programming and technical assistance. We also thank Drs. V. J. DiRita, P. Kazanjian, and S. M. Blower for helpful comments and discussions. We thank the reviewers for extremely insightful comments.
|
||||
|
||||
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||||
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|
||||
@ -1,132 +1,135 @@
|
||||
item-0 at level 0: unspecified: group _root_
|
||||
item-1 at level 1: title: Risk factors associated with fai ... s: Results of a multi-country analysis
|
||||
item-2 at level 2: paragraph: Burgert-Brucker Clara R.; 1: Glo ... shington, DC, United States of America
|
||||
item-3 at level 2: section_header: Abstract
|
||||
item-4 at level 3: text: Achieving elimination of lymphat ... ine prevalence and/or lower elevation.
|
||||
item-5 at level 2: section_header: Introduction
|
||||
item-6 at level 3: text: Lymphatic filariasis (LF), a dis ... 8 countries remain endemic for LF [3].
|
||||
item-7 at level 3: text: The road to elimination as a pub ... t elimination be officially validated.
|
||||
item-8 at level 3: text: Pre-TAS include at least one sen ... me of day that blood can be taken [5].
|
||||
item-9 at level 3: text: When a country fails to meet the ... o ensure rounds of MDA are not missed.
|
||||
item-10 at level 3: text: This study aims to understand wh ... e of limited LF elimination resources.
|
||||
item-11 at level 2: section_header: Methods
|
||||
item-12 at level 3: text: This is a secondary data analysi ... rch; no ethical approval was required.
|
||||
item-13 at level 3: text: Building on previous work, we de ... available global geospatial data sets.
|
||||
item-14 at level 3: section_header: Data sources
|
||||
item-15 at level 4: text: Information on baseline prevalen ... publicly available sources (Table 1).
|
||||
item-16 at level 3: section_header: Outcome and covariate variables
|
||||
item-17 at level 4: text: The outcome of interest for this ... r than or equal to 1% Mf or 2% Ag [4].
|
||||
item-18 at level 4: text: Potential covariates were derive ... is and the final categorizations used.
|
||||
item-19 at level 4: section_header: Baseline prevalence
|
||||
item-20 at level 5: text: Baseline prevalence can be assum ... (2) using the cut-off of <10% or ≥10%.
|
||||
item-21 at level 4: section_header: Agent
|
||||
item-22 at level 5: text: In terms of differences in trans ... dazole (DEC-ALB)] from the MDA domain.
|
||||
item-23 at level 4: section_header: Environment
|
||||
item-24 at level 5: text: LF transmission intensity is inf ... dicates a higher level of “greenness.”
|
||||
item-25 at level 5: text: We included the socio-economic v ... proxy for socio-economic status [33].
|
||||
item-26 at level 5: text: Finally, all or parts of distric ... s were co-endemic with onchocerciasis.
|
||||
item-27 at level 4: section_header: MDA
|
||||
item-28 at level 5: text: Treatment effectiveness depends ... esent a threat to elimination [41,42].
|
||||
item-29 at level 5: text: We considered three approaches w ... unds ever documented in that district.
|
||||
item-30 at level 4: section_header: Pre-TAS implementation
|
||||
item-31 at level 5: text: Pre-TAS results can be influence ... d throughout the time period of study.
|
||||
item-32 at level 3: section_header: Data inclusion criteria
|
||||
item-33 at level 4: text: The dataset, summarized at the d ... al analysis dataset had 554 districts.
|
||||
item-34 at level 3: section_header: Statistical analysis and modeling
|
||||
item-35 at level 4: text: Statistical analysis and modelin ... d the number of variables accordingly.
|
||||
item-36 at level 4: text: Sensitivity analysis was perform ... ot have been truly LF-endemic [43,44].
|
||||
item-37 at level 2: section_header: Results
|
||||
item-38 at level 3: text: The overall pre-TAS pass rate fo ... ts had baseline prevalences below 20%.
|
||||
item-39 at level 3: text: Fig 3 shows the unadjusted analy ... overage, and sufficient rounds of MDA.
|
||||
item-40 at level 3: text: The final log-binomial model inc ... igh baseline and diagnostic test used.
|
||||
item-41 at level 3: text: Fig 4 shows the risk ratio resul ... of failing pre-TAS (95% CI 1.95–4.83).
|
||||
item-42 at level 3: text: Sensitivity analyses were conduc ... gnified by large confidence intervals.
|
||||
item-43 at level 3: text: Overall 74 districts in the data ... or 51% of all the failures (38 of 74).
|
||||
item-44 at level 2: section_header: Discussion
|
||||
item-45 at level 3: text: This paper reports for the first ... ctors associated with TAS failure [7].
|
||||
item-46 at level 3: text: Though diagnostic test used was ... FTS was more sensitive than ICT [45].
|
||||
item-47 at level 3: text: Elevation was the only environme ... ich impact vector chances of survival.
|
||||
item-48 at level 3: text: The small number of failures ove ... search has shown the opposite [15,16].
|
||||
item-49 at level 3: text: All other variables included in ... are not necessary to lower prevalence.
|
||||
item-50 at level 3: text: Limitations to this study includ ... reducing LF prevalence [41,48,51–53].
|
||||
item-51 at level 3: text: Fourteen districts were excluded ... ta to extreme outliners in a district.
|
||||
item-52 at level 3: text: As this analysis used data acros ... of individuals included in the survey.
|
||||
item-53 at level 3: text: This paper provides evidence fro ... th high baseline and/or low elevation.
|
||||
item-54 at level 2: section_header: Tables
|
||||
item-55 at level 3: table with [18x8]
|
||||
item-55 at level 4: caption: Table 1: Categorization of potential factors influencing pre-TAS results.
|
||||
item-56 at level 3: table with [11x6]
|
||||
item-56 at level 4: caption: Table 2: Adjusted risk ratios for pre-TAS failure from log-binomial model sensitivity analysis.
|
||||
item-57 at level 2: section_header: Figures
|
||||
item-58 at level 3: picture
|
||||
item-58 at level 4: caption: Fig 1: Number of pre-TAS by country.
|
||||
item-59 at level 3: picture
|
||||
item-59 at level 4: caption: Fig 2: District-level baseline prevalence by country.
|
||||
item-60 at level 3: picture
|
||||
item-60 at level 4: caption: Fig 3: Percent pre-TAS failure by each characteristic (unadjusted).
|
||||
item-61 at level 3: picture
|
||||
item-61 at level 4: caption: Fig 4: Adjusted risk ratios for pre-TAS failure with 95% Confidence Interval from log-binomial model.
|
||||
item-62 at level 3: picture
|
||||
item-62 at level 4: caption: Fig 5: Analysis of failures by model combinations.
|
||||
item-63 at level 2: section_header: References
|
||||
item-64 at level 3: list: group list
|
||||
item-65 at level 4: list_item: World Health Organization. Lymph ... rategic plan 2010–2020. Geneva; 2010.
|
||||
item-66 at level 4: list_item: World Health Organization. Valid ... public health problem. Geneva; 2017.
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item-67 at level 4: list_item: Global programme to eliminate ly ... eport, 2018. Wkly Epidemiol Rec (2019)
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item-68 at level 4: list_item: World Health Organization. Globa ... ss drug administration. Geneva; 2011.
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item-69 at level 4: list_item: World Health Organization. Stren ... isease-specific Indicators. 2016; 42.
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item-70 at level 4: list_item: Kyelem D; Biswas G; Bockarie MJ; ... search needs. Am J Trop Med Hyg (2008)
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item-71 at level 4: list_item: Goldberg EM; King JD; Mupfasoni ... c filariasis. Am J Trop Med Hyg (2019)
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item-72 at level 4: list_item: Cano J; Rebollo MP; Golding N; P ... present. Parasites and Vectors (2014)
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item-73 at level 4: list_item: CGIAR-CSI. CGIAR-CSI SRTM 90m DEM Digital Elevation Database. In: .
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item-74 at level 4: list_item: USGS NASA. Vegetation indices 16 ... et]. [cited 1 May 2018]. Available: .
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item-77 at level 4: list_item: Elvidge CD; Baugh KE; Zhizhin M; ... hts. Proc Asia-Pacific Adv Netw (2013)
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item-79 at level 4: list_item: Michael E; Malecela-Lazaro MN; S ... c filariasis. Lancet Infect Dis (2004)
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item-82 at level 4: list_item: Evans D; McFarland D; Adamani W; ... Nigeria. Ann Trop Med Parasitol (2011)
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item-83 at level 4: list_item: Richards FO; Eigege A; Miri ES; ... in Nigeria. PLoS Negl Trop Dis (2011)
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item-95 at level 4: list_item: Ngwira BM; Tambala P; Perez a M; ... infection in Malawi. Filaria J (2007)
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item-96 at level 4: list_item: Simonsen PE; Mwakitalu ME. Urban ... hatic filariasis. Parasitol Res (2013)
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item-97 at level 4: list_item: Proville J; Zavala-Araiza D; Wag ... socio-economic trends. PLoS One (2017)
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item-98 at level 4: list_item: Endeshaw T; Taye A; Tadesse Z; K ... st Ethiopia. Pathog Glob Health (2015)
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item-104 at level 4: list_item: Boyd A; Won KY; McClintock SK; D ... gane, Haiti. PLoS Negl Trop Dis (2010)
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item-105 at level 4: list_item: Irvine MA; Reimer LJ; Njenga SM; ... mination. Parasites and Vectors (2015)
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item-106 at level 4: list_item: Irvine MA; Stolk WA; Smith ME; S ... elling study. Lancet Infect Dis (2017)
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item-107 at level 4: list_item: Pion SD; Montavon C; Chesnais CB ... crofilaremia. Am J Trop Med Hyg (2016)
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item-108 at level 4: list_item: Wanji S; Esum ME; Njouendou AJ; ... in Cameroon. PLoS Negl Trop Dis (2018)
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item-109 at level 4: list_item: Chesnais CB; Awaca-Uvon NP; Bola ... a in Africa. PLoS Negl Trop Dis (2017)
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item-110 at level 4: list_item: Silumbwe A; Zulu JM; Halwindi H; ... haran Africa. BMC Public Health (2017)
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item-111 at level 4: list_item: Adams AM; Vuckovic M; Birch E; B ... nistration. Trop Med Infect Dis (2018)
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item-112 at level 4: list_item: Rao RU; Samarasekera SD; Nagodav ... n Sri Lanka. PLoS Negl Trop Dis (2017)
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item-113 at level 4: list_item: Xu Z; Graves PM; Lau CL; Clement ... is in American Samoa. Epidemics (2018)
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item-114 at level 4: list_item: Id CM; Tettevi EJ; Mechan F; Idu ... rural Ghana. PLoS Negl Trop Dis (2019)
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item-115 at level 4: list_item: Eigege A; Kal A; Miri E; Sallau ... in Nigeria. PLoS Negl Trop Dis (2013)
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item-116 at level 4: list_item: Van den Berg H; Kelly-Hope LA; L ... r management. Lancet Infect Dis (2013)
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item-117 at level 4: list_item: Webber R.. Eradication of Wucher ... ntrol. Trans R Soc Trop Med Hyg (1979)
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item-118 at level 1: caption: Table 1: Categorization of potential factors influencing pre-TAS results.
|
||||
item-119 at level 1: caption: Table 2: Adjusted risk ratios fo ... g-binomial model sensitivity analysis.
|
||||
item-120 at level 1: caption: Fig 1: Number of pre-TAS by country.
|
||||
item-121 at level 1: caption: Fig 2: District-level baseline prevalence by country.
|
||||
item-122 at level 1: caption: Fig 3: Percent pre-TAS failure by each characteristic (unadjusted).
|
||||
item-123 at level 1: caption: Fig 4: Adjusted risk ratios for ... ence Interval from log-binomial model.
|
||||
item-124 at level 1: caption: Fig 5: Analysis of failures by model combinations.
|
||||
item-2 at level 2: paragraph: Clara R. Burgert-Brucker, Kathry ... garet Baker, John Kraemer, Molly Brady
|
||||
item-3 at level 2: paragraph: Global Health Division, RTI Inte ... shington, DC, United States of America
|
||||
item-4 at level 2: section_header: Abstract
|
||||
item-5 at level 3: text: Achieving elimination of lymphat ... as at highest risk of failing pre-TAS.
|
||||
item-6 at level 2: section_header: Author summary
|
||||
item-7 at level 3: text: Achieving elimination of lymphat ... ine prevalence and/or lower elevation.
|
||||
item-8 at level 2: section_header: Introduction
|
||||
item-9 at level 3: text: Lymphatic filariasis (LF), a dis ... 8 countries remain endemic for LF [3].
|
||||
item-10 at level 3: text: The road to elimination as a pub ... t elimination be officially validated.
|
||||
item-11 at level 3: text: Pre-TAS include at least one sen ... me of day that blood can be taken [5].
|
||||
item-12 at level 3: text: When a country fails to meet the ... o ensure rounds of MDA are not missed.
|
||||
item-13 at level 3: text: This study aims to understand wh ... e of limited LF elimination resources.
|
||||
item-14 at level 2: section_header: Methods
|
||||
item-15 at level 3: text: This is a secondary data analysi ... rch; no ethical approval was required.
|
||||
item-16 at level 3: text: Building on previous work, we de ... available global geospatial data sets.
|
||||
item-17 at level 3: table with [18x8]
|
||||
item-17 at level 4: caption: Table 1 Categorization of potential factors influencing pre-TAS results.
|
||||
item-18 at level 3: section_header: Data sources
|
||||
item-19 at level 4: text: Information on baseline prevalen ... publicly available sources (Table 1).
|
||||
item-20 at level 3: section_header: Outcome and covariate variables
|
||||
item-21 at level 4: text: The outcome of interest for this ... r than or equal to 1% Mf or 2% Ag [4].
|
||||
item-22 at level 4: text: Potential covariates were derive ... is and the final categorizations used.
|
||||
item-23 at level 4: section_header: Baseline prevalence
|
||||
item-24 at level 5: text: Baseline prevalence can be assum ... (2) using the cut-off of <10% or ≥10%.
|
||||
item-25 at level 4: section_header: Agent
|
||||
item-26 at level 5: text: In terms of differences in trans ... dazole (DEC-ALB)] from the MDA domain.
|
||||
item-27 at level 4: section_header: Environment
|
||||
item-28 at level 5: text: LF transmission intensity is inf ... dicates a higher level of “greenness.”
|
||||
item-29 at level 5: text: We included the socio-economic v ... proxy for socio-economic status [33].
|
||||
item-30 at level 5: text: Finally, all or parts of distric ... s were co-endemic with onchocerciasis.
|
||||
item-31 at level 4: section_header: MDA
|
||||
item-32 at level 5: text: Treatment effectiveness depends ... esent a threat to elimination [41,42].
|
||||
item-33 at level 5: text: We considered three approaches w ... unds ever documented in that district.
|
||||
item-34 at level 4: section_header: Pre-TAS implementation
|
||||
item-35 at level 5: text: Pre-TAS results can be influence ... d throughout the time period of study.
|
||||
item-36 at level 3: section_header: Data inclusion criteria
|
||||
item-37 at level 4: text: The dataset, summarized at the d ... al analysis dataset had 554 districts.
|
||||
item-38 at level 3: section_header: Statistical analysis and modeling
|
||||
item-39 at level 4: text: Statistical analysis and modelin ... d the number of variables accordingly.
|
||||
item-40 at level 4: text: Sensitivity analysis was perform ... ot have been truly LF-endemic [43,44].
|
||||
item-41 at level 2: section_header: Results
|
||||
item-42 at level 3: text: The overall pre-TAS pass rate fo ... ts had baseline prevalences below 20%.
|
||||
item-43 at level 3: picture
|
||||
item-43 at level 4: caption: Fig 1 Number of pre-TAS by country.
|
||||
item-44 at level 3: picture
|
||||
item-44 at level 4: caption: Fig 2 District-level baseline prevalence by country.
|
||||
item-45 at level 3: text: Fig 3 shows the unadjusted analy ... overage, and sufficient rounds of MDA.
|
||||
item-46 at level 3: picture
|
||||
item-46 at level 4: caption: Fig 3 Percent pre-TAS failure by each characteristic (unadjusted).
|
||||
item-47 at level 3: text: The final log-binomial model inc ... igh baseline and diagnostic test used.
|
||||
item-48 at level 3: text: Fig 4 shows the risk ratio resul ... of failing pre-TAS (95% CI 1.95–4.83).
|
||||
item-49 at level 3: picture
|
||||
item-49 at level 4: caption: Fig 4 Adjusted risk ratios for pre-TAS failure with 95% Confidence Interval from log-binomial model.
|
||||
item-50 at level 3: text: Sensitivity analyses were conduc ... gnified by large confidence intervals.
|
||||
item-51 at level 3: table with [11x6]
|
||||
item-51 at level 4: caption: Table 2 Adjusted risk ratios for pre-TAS failure from log-binomial model sensitivity analysis.
|
||||
item-52 at level 3: text: Overall 74 districts in the data ... or 51% of all the failures (38 of 74).
|
||||
item-53 at level 3: picture
|
||||
item-53 at level 4: caption: Fig 5 Analysis of failures by model combinations.
|
||||
item-54 at level 2: section_header: Discussion
|
||||
item-55 at level 3: text: This paper reports for the first ... ctors associated with TAS failure [7].
|
||||
item-56 at level 3: text: Though diagnostic test used was ... FTS was more sensitive than ICT [45].
|
||||
item-57 at level 3: text: Elevation was the only environme ... ich impact vector chances of survival.
|
||||
item-58 at level 3: text: The small number of failures ove ... search has shown the opposite [15,16].
|
||||
item-59 at level 3: text: All other variables included in ... are not necessary to lower prevalence.
|
||||
item-60 at level 3: text: Limitations to this study includ ... reducing LF prevalence [41,48,51–53].
|
||||
item-61 at level 3: text: Fourteen districts were excluded ... ta to extreme outliners in a district.
|
||||
item-62 at level 3: text: As this analysis used data acros ... of individuals included in the survey.
|
||||
item-63 at level 3: text: This paper provides evidence fro ... th high baseline and/or low elevation.
|
||||
item-64 at level 2: section_header: Acknowledgments
|
||||
item-65 at level 3: text: The authors would like to thank ... e surveys financially and technically.
|
||||
item-66 at level 2: section_header: References
|
||||
item-67 at level 3: list: group list
|
||||
item-68 at level 4: list_item: World Health Organization. Lymph ... trategic plan 2010–2020. Geneva; 2010.
|
||||
item-69 at level 4: list_item: World Health Organization. Valid ... a public health problem. Geneva; 2017.
|
||||
item-70 at level 4: list_item: World Health Organization. Globa ... Wkly Epidemiol Rec. 2019;94: 457–472.
|
||||
item-71 at level 4: list_item: World Health Organization. Globa ... ass drug administration. Geneva; 2011.
|
||||
item-72 at level 4: list_item: World Health Organization. Stren ... Disease-specific Indicators. 2016; 42.
|
||||
item-73 at level 4: list_item: KyelemD, BiswasG, BockarieMJ, Br ... Trop Med Hyg. 2008;79: 480–4. 18840733
|
||||
item-74 at level 4: list_item: GoldbergEM, KingJD, MupfasoniD, ... . 2019; 10.4269/ajtmh.18-0721 31115301
|
||||
item-75 at level 4: list_item: CanoJ, RebolloMP, GoldingN, Pull ... : 1–19. 10.1186/1756-3305-7-1 24411014
|
||||
item-76 at level 4: list_item: CGIAR-CSI. CGIAR-CSI SRTM 90m DE ... Available: http://srtm.csi.cgiar.org/
|
||||
item-77 at level 4: list_item: USGS NASA. Vegetation indices 16 ... /lpdaac.usgs.gov/products/myd13a1v006/
|
||||
item-78 at level 4: list_item: FunkC, PetersonP, LandsfeldM, Pe ... 2015;2 10.1038/sdata.2015.66 26646728
|
||||
item-79 at level 4: list_item: LloydCT, SorichettaA, TatemAJ. H ... : 170001 10.1038/sdata.2017.1 28140386
|
||||
item-80 at level 4: list_item: ElvidgeCD, BaughKE, ZhizhinM, Hs ... Network; 2013;35: 62 10.7125/apan.35.7
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||||
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item-121 at level 1: caption: Table 1 Categorization of potential factors influencing pre-TAS results.
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item-122 at level 1: caption: Fig 1 Number of pre-TAS by country.
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item-123 at level 1: caption: Fig 2 District-level baseline prevalence by country.
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item-124 at level 1: caption: Fig 3 Percent pre-TAS failure by each characteristic (unadjusted).
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item-125 at level 1: caption: Fig 4 Adjusted risk ratios for p ... ence Interval from log-binomial model.
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item-126 at level 1: caption: Table 2 Adjusted risk ratios for ... g-binomial model sensitivity analysis.
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item-127 at level 1: caption: Fig 5 Analysis of failures by model combinations.
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# Risk factors associated with failing pre-transmission assessment surveys (pre-TAS) in lymphatic filariasis elimination programs: Results of a multi-country analysis
|
||||
|
||||
Burgert-Brucker Clara R.; 1: Global Health Division, RTI International, Washington, DC, United States of America; Zoerhoff Kathryn L.; 1: Global Health Division, RTI International, Washington, DC, United States of America; Headland Maureen; 1: Global Health Division, RTI International, Washington, DC, United States of America, 2: Global Health, Population, and Nutrition, FHI 360, Washington, DC, United States of America; Shoemaker Erica A.; 1: Global Health Division, RTI International, Washington, DC, United States of America; Stelmach Rachel; 1: Global Health Division, RTI International, Washington, DC, United States of America; Karim Mohammad Jahirul; 3: Department of Disease Control, Ministry of Health and Family Welfare, Dhaka, Bangladesh; Batcho Wilfrid; 4: National Control Program of Communicable Diseases, Ministry of Health, Cotonou, Benin; Bougouma Clarisse; 5: Lymphatic Filariasis Elimination Program, Ministère de la Santé, Ouagadougou, Burkina Faso; Bougma Roland; 5: Lymphatic Filariasis Elimination Program, Ministère de la Santé, Ouagadougou, Burkina Faso; Benjamin Didier Biholong; 6: National Onchocerciasis and Lymphatic Filariasis Control Program, Ministry of Health, Yaounde, Cameroon; Georges Nko'Ayissi; 6: National Onchocerciasis and Lymphatic Filariasis Control Program, Ministry of Health, Yaounde, Cameroon; Marfo Benjamin; 7: Neglected Tropical Diseases Programme, Ghana Health Service, Accra, Ghana; Lemoine Jean Frantz; 8: Ministry of Health, Port-au-Prince, Haiti; Pangaribuan Helena Ullyartha; 9: National Institute Health Research & Development, Ministry of Health, Jakarta, Indonesia; Wijayanti Eksi; 9: National Institute Health Research & Development, Ministry of Health, Jakarta, Indonesia; Coulibaly Yaya Ibrahim; 10: Filariasis Unit, International Center of Excellence in Research, Faculty of Medicine and Odontostomatology, Bamako, Mali; Doumbia Salif Seriba; 10: Filariasis Unit, International Center of Excellence in Research, Faculty of Medicine and Odontostomatology, Bamako, Mali; Rimal Pradip; 11: Epidemiology and Disease Control Division, Department of Health Service, Kathmandu, Nepal; Salissou Adamou Bacthiri; 12: Programme Onchocercose et Filariose Lymphatique, Ministère de la Santé, Niamey, Niger; Bah Yukaba; 13: National Neglected Tropical Disease Program, Ministry of Health and Sanitation, Freetown, Sierra Leone; Mwingira Upendo; 14: Neglected Tropical Disease Control Programme, National Institute for Medical Research, Dar es Salaam, Tanzania; Nshala Andreas; 15: IMA World Health/Tanzania NTD Control Programme, Uppsala University, & TIBA Fellow, Dar es Salaam, Tanzania; Muheki Edridah; 16: Programme to Eliminate Lymphatic Filariasis, Ministry of Health, Kampala, Uganda; Shott Joseph; 17: Division of Neglected Tropical Diseases, Office of Infectious Diseases, Bureau for Global Health, USAID, Washington, DC, United States of America; Yevstigneyeva Violetta; 17: Division of Neglected Tropical Diseases, Office of Infectious Diseases, Bureau for Global Health, USAID, Washington, DC, United States of America; Ndayishimye Egide; 2: Global Health, Population, and Nutrition, FHI 360, Washington, DC, United States of America; Baker Margaret; 1: Global Health Division, RTI International, Washington, DC, United States of America; Kraemer John; 1: Global Health Division, RTI International, Washington, DC, United States of America, 18: Georgetown University, Washington, DC, United States of America; Brady Molly; 1: Global Health Division, RTI International, Washington, DC, United States of America
|
||||
Clara R. Burgert-Brucker, Kathryn L. Zoerhoff, Maureen Headland, Erica A. Shoemaker, Rachel Stelmach, Mohammad Jahirul Karim, Wilfrid Batcho, Clarisse Bougouma, Roland Bougma, Biholong Benjamin Didier, Nko'Ayissi Georges, Benjamin Marfo, Jean Frantz Lemoine, Helena Ullyartha Pangaribuan, Eksi Wijayanti, Yaya Ibrahim Coulibaly, Salif Seriba Doumbia, Pradip Rimal, Adamou Bacthiri Salissou, Yukaba Bah, Upendo Mwingira, Andreas Nshala, Edridah Muheki, Joseph Shott, Violetta Yevstigneyeva, Egide Ndayishimye, Margaret Baker, John Kraemer, Molly Brady
|
||||
|
||||
Global Health Division, RTI International, Washington, DC, United States of America; Global Health, Population, and Nutrition, FHI 360, Washington, DC, United States of America; Department of Disease Control, Ministry of Health and Family Welfare, Dhaka, Bangladesh; National Control Program of Communicable Diseases, Ministry of Health, Cotonou, Benin; Lymphatic Filariasis Elimination Program, Ministère de la Santé, Ouagadougou, Burkina Faso; National Onchocerciasis and Lymphatic Filariasis Control Program, Ministry of Health, Yaounde, Cameroon; Neglected Tropical Diseases Programme, Ghana Health Service, Accra, Ghana; Ministry of Health, Port-au-Prince, Haiti; National Institute Health Research & Development, Ministry of Health, Jakarta, Indonesia; Filariasis Unit, International Center of Excellence in Research, Faculty of Medicine and Odontostomatology, Bamako, Mali; Epidemiology and Disease Control Division, Department of Health Service, Kathmandu, Nepal; Programme Onchocercose et Filariose Lymphatique, Ministère de la Santé, Niamey, Niger; National Neglected Tropical Disease Program, Ministry of Health and Sanitation, Freetown, Sierra Leone; Neglected Tropical Disease Control Programme, National Institute for Medical Research, Dar es Salaam, Tanzania; IMA World Health/Tanzania NTD Control Programme, Uppsala University, & TIBA Fellow, Dar es Salaam, Tanzania; Programme to Eliminate Lymphatic Filariasis, Ministry of Health, Kampala, Uganda; Division of Neglected Tropical Diseases, Office of Infectious Diseases, Bureau for Global Health, USAID, Washington, DC, United States of America; Georgetown University, Washington, DC, United States of America
|
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|
||||
## Abstract
|
||||
|
||||
Achieving elimination of lymphatic filariasis (LF) as a public health problem requires a minimum of five effective rounds of mass drug administration (MDA) and demonstrating low prevalence in subsequent assessments. The first assessments recommended by the World Health Organization (WHO) are sentinel and spot-check sites—referred to as pre-transmission assessment surveys (pre-TAS)—in each implementation unit after MDA. If pre-TAS shows that prevalence in each site has been lowered to less than 1% microfilaremia or less than 2% antigenemia, the implementation unit conducts a TAS to determine whether MDA can be stopped. Failure to pass pre-TAS means that further rounds of MDA are required. This study aims to understand factors influencing pre-TAS results using existing programmatic data from 554 implementation units, of which 74 (13%) failed, in 13 countries. Secondary data analysis was completed using existing data from Bangladesh, Benin, Burkina Faso, Cameroon, Ghana, Haiti, Indonesia, Mali, Nepal, Niger, Sierra Leone, Tanzania, and Uganda. Additional covariate data were obtained from spatial raster data sets. Bivariate analysis and multilinear regression were performed to establish potential relationships between variables and the pre-TAS result. Higher baseline prevalence and lower elevation were significant in the regression model. Variables statistically significantly associated with failure (p-value ≤0.05) in the bivariate analyses included baseline prevalence at or above 5% or 10%, use of Filariasis Test Strips (FTS), primary vector of Culex, treatment with diethylcarbamazine-albendazole, higher elevation, higher population density, higher enhanced vegetation index (EVI), higher annual rainfall, and 6 or more rounds of MDA. This paper reports for the first time factors associated with pre-TAS results from a multi-country analysis. This information can help countries more effectively forecast program activities, such as the potential need for more rounds of MDA, and prioritize resources to ensure adequate coverage of all persons in areas at highest risk of failing pre-TAS.Author summaryAchieving elimination of lymphatic filariasis (LF) as a public health problem requires a minimum of five rounds of mass drug administration (MDA) and being able to demonstrate low prevalence in several subsequent assessments. LF elimination programs implement sentinel and spot-check site assessments, called pre-TAS, to determine whether districts are eligible to implement more rigorous population-based surveys to determine whether MDA can be stopped or if further rounds are required. Reasons for failing pre-TAS are not well understood and have not previously been examined with data compiled from multiple countries. For this analysis, we analyzed data from routine USAID and WHO reports from Bangladesh, Benin, Burkina Faso, Cameroon, Ghana, Haiti, Indonesia, Mali, Nepal, Niger, Sierra Leone, Tanzania, and Uganda. In a model that included multiple variables, high baseline prevalence and lower elevation were significant. In models comparing only one variable to the outcome, the following were statistically significantly associated with failure: higher baseline prevalence at or above 5% or 10%, use of the FTS, primary vector of Culex, treatment with diethylcarbamazine-albendazole, lower elevation, higher population density, higher Enhanced Vegetation Index, higher annual rainfall, and six or more rounds of mass drug administration. These results can help national programs plan MDA more effectively, e.g., by focusing resources on areas with higher baseline prevalence and/or lower elevation.
|
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Achieving elimination of lymphatic filariasis (LF) as a public health problem requires a minimum of five effective rounds of mass drug administration (MDA) and demonstrating low prevalence in subsequent assessments. The first assessments recommended by the World Health Organization (WHO) are sentinel and spot-check sites—referred to as pre-transmission assessment surveys (pre-TAS)—in each implementation unit after MDA. If pre-TAS shows that prevalence in each site has been lowered to less than 1% microfilaremia or less than 2% antigenemia, the implementation unit conducts a TAS to determine whether MDA can be stopped. Failure to pass pre-TAS means that further rounds of MDA are required. This study aims to understand factors influencing pre-TAS results using existing programmatic data from 554 implementation units, of which 74 (13%) failed, in 13 countries. Secondary data analysis was completed using existing data from Bangladesh, Benin, Burkina Faso, Cameroon, Ghana, Haiti, Indonesia, Mali, Nepal, Niger, Sierra Leone, Tanzania, and Uganda. Additional covariate data were obtained from spatial raster data sets. Bivariate analysis and multilinear regression were performed to establish potential relationships between variables and the pre-TAS result. Higher baseline prevalence and lower elevation were significant in the regression model. Variables statistically significantly associated with failure (p-value ≤0.05) in the bivariate analyses included baseline prevalence at or above 5% or 10%, use of Filariasis Test Strips (FTS), primary vector of Culex, treatment with diethylcarbamazine-albendazole, higher elevation, higher population density, higher enhanced vegetation index (EVI), higher annual rainfall, and 6 or more rounds of MDA. This paper reports for the first time factors associated with pre-TAS results from a multi-country analysis. This information can help countries more effectively forecast program activities, such as the potential need for more rounds of MDA, and prioritize resources to ensure adequate coverage of all persons in areas at highest risk of failing pre-TAS.
|
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|
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## Author summary
|
||||
|
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Achieving elimination of lymphatic filariasis (LF) as a public health problem requires a minimum of five rounds of mass drug administration (MDA) and being able to demonstrate low prevalence in several subsequent assessments. LF elimination programs implement sentinel and spot-check site assessments, called pre-TAS, to determine whether districts are eligible to implement more rigorous population-based surveys to determine whether MDA can be stopped or if further rounds are required. Reasons for failing pre-TAS are not well understood and have not previously been examined with data compiled from multiple countries. For this analysis, we analyzed data from routine USAID and WHO reports from Bangladesh, Benin, Burkina Faso, Cameroon, Ghana, Haiti, Indonesia, Mali, Nepal, Niger, Sierra Leone, Tanzania, and Uganda. In a model that included multiple variables, high baseline prevalence and lower elevation were significant. In models comparing only one variable to the outcome, the following were statistically significantly associated with failure: higher baseline prevalence at or above 5% or 10%, use of the FTS, primary vector of Culex, treatment with diethylcarbamazine-albendazole, lower elevation, higher population density, higher Enhanced Vegetation Index, higher annual rainfall, and six or more rounds of mass drug administration. These results can help national programs plan MDA more effectively, e.g., by focusing resources on areas with higher baseline prevalence and/or lower elevation.
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## Introduction
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@ -24,6 +30,28 @@ This is a secondary data analysis using existing data, collected for programmati
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Building on previous work, we delineated five domains of variables that could influence pre-TAS outcomes: prevalence, agent, environment, MDA, and pre-TAS implementation (Table 1) [6–8]. We prioritized key concepts that could be measured through our data or captured through publicly available global geospatial data sets.
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Table 1 Categorization of potential factors influencing pre-TAS results.
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| Domain | Factor | Covariate | Description | Reference Group | Summary statistic | Temporal Resolution | Source |
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|------------------------|-----------------------|-------------------------------|-----------------------------------------------------------------|----------------------|---------------------|-----------------------|--------------------|
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| Prevalence | Baseline prevalence | 5% cut off | Maximum reported mapping or baseline sentinel site prevalence | <5% | Maximum | Varies | Programmatic data |
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| Prevalence | Baseline prevalence | 10% cut off | Maximum reported mapping or baseline sentinel site prevalence | <10% | Maximum | Varies | Programmatic data |
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| Agent | Parasite | Parasite | Predominate parasite in district | W. bancrofti & mixed | Binary value | 2018 | Programmatic data |
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| Environment | Vector | Vector | Predominate vector in district | Anopheles & Mansonia | Binary value | 2018 | Country expert |
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| Environment | Geography | Elevation | Elevation measured in meters | >350 | Mean | 2000 | CGIAR-CSI SRTM [9] |
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| Environment | Geography | District area | Area measured in km2 | >2,500 | Maximum sum | Static | Programmatic data |
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| Environment | Climate | EVI | Enhanced vegetation index | > 0.3 | Mean | 2015 | MODIS [10] |
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| Environment | Climate | Rainfall | Annual rainfall measured in mm | ≤ 700 | Mean | 2015 | CHIRPS [11] |
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| Environment | Socio-economic | Population density | Number of people per km2 | ≤ 100 | Mean | 2015 | WorldPop [12] |
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| Environment | Socio-economic | Nighttime lights | Nighttime light index from 0 to 63 | >1.5 | Mean | 2015 | VIIRS [13] |
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| Environment | Co-endemicity | Co-endemic for onchocerciasis | Part or all of district is also endemic for onchocerciases | Non-endemic | Binary value | 2018 | Programmatic data |
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| MDA | Drug efficacy | Drug package | DEC-ALB or IVM-ALB | DEC-ALB | Binary value | 2018 | Programmatic data |
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| MDA | Implementation of MDA | Coverage | Median MDA coverage for last 5 rounds | ≥ 65% | Median | Varies | Programmatic data |
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| MDA | Implementation of MDA | Sufficient rounds | Number of rounds of sufficient (≥ 65% coverage) in last 5 years | ≥ 3 | Count | Varies | Programmatic data |
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| MDA | Implementation of MDA | Number of rounds | Maximum number of recorded rounds of MDA | ≥ 6 | Maximum | Varies | Programmatic data |
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| Pre-TAS implementation | Quality of survey | Diagnostic method | Using Mf or Ag | Mf | Binary value | Varies | Programmatic data |
|
||||
| Pre-TAS implementation | Quality of survey | Diagnostic test | Using Mf, ICT, or FTS | Mf | Categorical | Varies | Programmatic data |
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### Data sources
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Information on baseline prevalence, MDA coverage, the number of MDA rounds, and pre-TAS information (month and year of survey, district, site name, and outcome) was gathered through regular reporting for the USAID-funded NTD programs (ENVISION, END in Africa, and END in Asia). These data were augmented by other reporting data such as the country’s dossier data annexes, the WHO Preventive Chemotherapy and Transmission Control Databank, and WHO reporting forms. Data were then reviewed by country experts, including the Ministry of Health program staff and implementing program staff, and updated as necessary. Data on vectors were also obtained from country experts. The district geographic boundaries were matched to geospatial shapefiles from the ENVISION project geospatial data repository, while other geospatial data were obtained through publicly available sources (Table 1).
|
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@ -74,16 +102,51 @@ Sensitivity analysis was performed for the final log-binomial model to test for
|
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The overall pre-TAS pass rate for the districts included in this analysis was 87% (74 failures in 554 districts). Nearly 40% of the 554 districts were from Cameroon (134) and Tanzania (87) (Fig 1). No districts in Bangladesh, Cameroon, Mali, or Uganda failed a pre-TAS in this data set; over 25% of districts in Burkina Faso, Ghana, Haiti, Nepal, and Sierra Leone failed pre-TAS in this data set. Baseline prevalence varied widely within and between the 13 countries. Fig 2 shows the highest, lowest, and median baseline prevalence in the study districts by country. Burkina Faso had the highest median baseline prevalence at 52% and Burkina Faso, Tanzania, and Ghana all had at least one district with a very high baseline of over 70%. In Mali, Indonesia, Benin, and Bangladesh, all districts had baseline prevalences below 20%.
|
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Fig 1 Number of pre-TAS by country.
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<!-- image -->
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Fig 2 District-level baseline prevalence by country.
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<!-- image -->
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Fig 3 shows the unadjusted analysis for key variables by pre-TAS result. Variables statistically significantly associated with failure (p-value ≤0.05) included higher baseline prevalence at or above 5% or 10%, FTS diagnostic test, primary vector of Culex, treatment with DEC-ALB, higher elevation, higher population density, higher EVI, higher annual rainfall, and six or more rounds of MDA. Variables that were not significantly associated with pre-TAS failure included diagnostic method used (Ag or Mf), parasite, co-endemicity for onchocerciasis, median MDA coverage, and sufficient rounds of MDA.
|
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Fig 3 Percent pre-TAS failure by each characteristic (unadjusted).
|
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<!-- image -->
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The final log-binomial model included the variables of baseline prevalence ≥10%, the diagnostic test used (FTS and ICT), and elevation. The final model also included a significant interaction term between high baseline and diagnostic test used.
|
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|
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Fig 4 shows the risk ratio results with their corresponding confidence intervals. In a model with interaction between baseline and diagnostic test the baseline parameter was significant while diagnostic test and the interaction term were not. Districts with high baseline had a statistically significant (p-value ≤0.05) 2.52 times higher risk of failure (95% CI 1.37–4.64) compared to those with low baseline prevalence. The FTS diagnostic test or ICT diagnostic test alone were not significant nor was the interaction term. Additionally, districts with an elevation below 350 meters had a statistically significant (p-value ≤0.05) 3.07 times higher risk of failing pre-TAS (95% CI 1.95–4.83).
|
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||||
Fig 4 Adjusted risk ratios for pre-TAS failure with 95% Confidence Interval from log-binomial model.
|
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<!-- image -->
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|
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Sensitivity analyses were conducted using the same model with different subsets of the dataset including (1) all districts except for districts in Cameroon (134 total with no failures), (2) only districts in Africa, (3) only districts with W. bancrofti, and (4) only districts with Anopheles as primary vector. The results of the sensitivity models (Table 2) indicate an overall robust model. High baseline and lower elevation remained significant across all the models. The ICT diagnostic test used remains insignificant across all models. The FTS diagnostic test was positively significant in model 1 and negatively significant in model 4. The interaction term of baseline prevalence and FTS diagnostic test was significant in three models though the estimate was unstable in the W. bancrofti-only and Anopheles-only models (models 3 and 4 respectively), as signified by large confidence intervals.
|
||||
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Table 2 Adjusted risk ratios for pre-TAS failure from log-binomial model sensitivity analysis.
|
||||
|
||||
| | | (1) | (2) | (3) | (4) |
|
||||
|---------------------------------------------|------------------|----------------------------|--------------------------|--------------------------------------|---------------------------------|
|
||||
| | Full Model | Without Cameroon districts | Only districts in Africa | Only W. bancrofti parasite districts | Only Anopheles vector districts |
|
||||
| Number of Failures | 74 | 74 | 44 | 72 | 46 |
|
||||
| Number of total districts | (N = 554) | (N = 420) | (N = 407) | (N = 518) | (N = 414) |
|
||||
| Covariate | RR (95% CI) | RR (95% CI) | RR (95% CI) | RR (95% CI) | RR (95% CI) |
|
||||
| Baseline prevalence > = 10% & used FTS test | 2.38 (0.96–5.90) | 1.23 (0.52–2.92) | 14.52 (1.79–117.82) | 2.61 (1.03–6.61) | 15.80 (1.95–127.67) |
|
||||
| Baseline prevalence > = 10% & used ICT test | 0.80 (0.20–3.24) | 0.42 (0.11–1.68) | 1.00 (0.00–0.00) | 0.88 (0.21–3.60) | 1.00 (0.00–0.00) |
|
||||
| +Used FTS test | 1.16 (0.52–2.59) | 2.40 (1.12–5.11) | 0.15 (0.02–1.11) | 1.03 (0.45–2.36) | 0.13 (0.02–0.96) |
|
||||
| +Used ICT test | 0.92 (0.32–2.67) | 1.47 (0.51–4.21) | 0.33 (0.04–2.54) | 0.82 (0.28–2.43) | 0.27 (0.03–2.04) |
|
||||
| +Baseline prevalence > = 10% | 2.52 (1.37–4.64) | 2.42 (1.31–4.47) | 2.03 (1.06–3.90) | 2.30 (1.21–4.36) | 2.01 (1.07–3.77) |
|
||||
| Elevation < 350m | 3.07 (1.95–4.83) | 2.21 (1.42–3.43) | 4.68 (2.22–9.87) | 3.04 (1.93–4.79) | 3.76 (1.92–7.37) |
|
||||
|
||||
Overall 74 districts in the dataset failed pre-TAS. Fig 5 summarizes the likelihood of failure by variable combinations identified in the log-binomial model. For those districts with a baseline prevalence ≥10% that used a FTS diagnostic test and have an average elevation below 350 meters (Combination C01), 87% of the 23 districts failed. Of districts with high baseline that used an ICT diagnostic test and have a low average elevation (C02) 45% failed. Overall, combinations with high baseline and low elevation C01, C02, and C04 accounted for 51% of all the failures (38 of 74).
|
||||
|
||||
Fig 5 Analysis of failures by model combinations.
|
||||
|
||||
<!-- image -->
|
||||
|
||||
## Discussion
|
||||
|
||||
This paper reports for the first time factors associated with pre-TAS results from a multi-country analysis. Variables significantly associated with failure were higher baseline prevalence and lower elevation. Districts with a baseline prevalence of 10% or more were at 2.52 times higher risk to fail pre-TAS in the final log-binomial model. In the bivariate analysis, baseline prevalence above 5% was also significantly more likely to fail compared to lower baselines, which indicates that the threshold for higher baseline prevalence may be as little as 5%, similar to what was found in Goldberg et al., which explored ecological and socioeconomic factors associated with TAS failure [7].
|
||||
@ -104,119 +167,62 @@ As this analysis used data across a variety of countries and epidemiological sit
|
||||
|
||||
This paper provides evidence from analysis of 554 districts and 13 countries on the factors associated with pre-TAS results. Baseline prevalence, elevation, vector, population density, EVI, rainfall, and number of MDA rounds were all significant in either bivariate or multivariate analyses. This information along with knowledge of local context can help countries more effectively plan pre-TAS and forecast program activities, such as the potential need for more than five rounds of MDA in areas with high baseline and/or low elevation.
|
||||
|
||||
## Tables
|
||||
## Acknowledgments
|
||||
|
||||
Table 1: Categorization of potential factors influencing pre-TAS results.
|
||||
|
||||
| Domain | Factor | Covariate | Description | Reference Group | Summary statistic | Temporal Resolution | Source |
|
||||
|------------------------|-----------------------|-------------------------------|-----------------------------------------------------------------|----------------------|---------------------|-----------------------|--------------------|
|
||||
| Prevalence | Baseline prevalence | 5% cut off | Maximum reported mapping or baseline sentinel site prevalence | <5% | Maximum | Varies | Programmatic data |
|
||||
| Prevalence | Baseline prevalence | 10% cut off | Maximum reported mapping or baseline sentinel site prevalence | <10% | Maximum | Varies | Programmatic data |
|
||||
| Agent | Parasite | Parasite | Predominate parasite in district | W. bancrofti & mixed | Binary value | 2018 | Programmatic data |
|
||||
| Environment | Vector | Vector | Predominate vector in district | Anopheles & Mansonia | Binary value | 2018 | Country expert |
|
||||
| Environment | Geography | Elevation | Elevation measured in meters | >350 | Mean | 2000 | CGIAR-CSI SRTM [9] |
|
||||
| Environment | Geography | District area | Area measured in km2 | >2,500 | Maximum sum | Static | Programmatic data |
|
||||
| Environment | Climate | EVI | Enhanced vegetation index | > 0.3 | Mean | 2015 | MODIS [10] |
|
||||
| Environment | Climate | Rainfall | Annual rainfall measured in mm | ≤ 700 | Mean | 2015 | CHIRPS [11] |
|
||||
| Environment | Socio-economic | Population density | Number of people per km2 | ≤ 100 | Mean | 2015 | WorldPop [12] |
|
||||
| Environment | Socio-economic | Nighttime lights | Nighttime light index from 0 to 63 | >1.5 | Mean | 2015 | VIIRS [13] |
|
||||
| Environment | Co-endemicity | Co-endemic for onchocerciasis | Part or all of district is also endemic for onchocerciases | Non-endemic | Binary value | 2018 | Programmatic data |
|
||||
| MDA | Drug efficacy | Drug package | DEC-ALB or IVM-ALB | DEC-ALB | Binary value | 2018 | Programmatic data |
|
||||
| MDA | Implementation of MDA | Coverage | Median MDA coverage for last 5 rounds | ≥ 65% | Median | Varies | Programmatic data |
|
||||
| MDA | Implementation of MDA | Sufficient rounds | Number of rounds of sufficient (≥ 65% coverage) in last 5 years | ≥ 3 | Count | Varies | Programmatic data |
|
||||
| MDA | Implementation of MDA | Number of rounds | Maximum number of recorded rounds of MDA | ≥ 6 | Maximum | Varies | Programmatic data |
|
||||
| Pre-TAS implementation | Quality of survey | Diagnostic method | Using Mf or Ag | Mf | Binary value | Varies | Programmatic data |
|
||||
| Pre-TAS implementation | Quality of survey | Diagnostic test | Using Mf, ICT, or FTS | Mf | Categorical | Varies | Programmatic data |
|
||||
|
||||
Table 2: Adjusted risk ratios for pre-TAS failure from log-binomial model sensitivity analysis.
|
||||
|
||||
| | | (1) | (2) | (3) | (4) |
|
||||
|---------------------------------------------|------------------|----------------------------|--------------------------|--------------------------------------|---------------------------------|
|
||||
| | Full Model | Without Cameroon districts | Only districts in Africa | Only W. bancrofti parasite districts | Only Anopheles vector districts |
|
||||
| Number of Failures | 74 | 74 | 44 | 72 | 46 |
|
||||
| Number of total districts | (N = 554) | (N = 420) | (N = 407) | (N = 518) | (N = 414) |
|
||||
| Covariate | RR (95% CI) | RR (95% CI) | RR (95% CI) | RR (95% CI) | RR (95% CI) |
|
||||
| Baseline prevalence > = 10% & used FTS test | 2.38 (0.96–5.90) | 1.23 (0.52–2.92) | 14.52 (1.79–117.82) | 2.61 (1.03–6.61) | 15.80 (1.95–127.67) |
|
||||
| Baseline prevalence > = 10% & used ICT test | 0.80 (0.20–3.24) | 0.42 (0.11–1.68) | 1.00 (0.00–0.00) | 0.88 (0.21–3.60) | 1.00 (0.00–0.00) |
|
||||
| +Used FTS test | 1.16 (0.52–2.59) | 2.40 (1.12–5.11) | 0.15 (0.02–1.11) | 1.03 (0.45–2.36) | 0.13 (0.02–0.96) |
|
||||
| +Used ICT test | 0.92 (0.32–2.67) | 1.47 (0.51–4.21) | 0.33 (0.04–2.54) | 0.82 (0.28–2.43) | 0.27 (0.03–2.04) |
|
||||
| +Baseline prevalence > = 10% | 2.52 (1.37–4.64) | 2.42 (1.31–4.47) | 2.03 (1.06–3.90) | 2.30 (1.21–4.36) | 2.01 (1.07–3.77) |
|
||||
| Elevation < 350m | 3.07 (1.95–4.83) | 2.21 (1.42–3.43) | 4.68 (2.22–9.87) | 3.04 (1.93–4.79) | 3.76 (1.92–7.37) |
|
||||
|
||||
## Figures
|
||||
|
||||
Fig 1: Number of pre-TAS by country.
|
||||
|
||||
<!-- image -->
|
||||
|
||||
Fig 2: District-level baseline prevalence by country.
|
||||
|
||||
<!-- image -->
|
||||
|
||||
Fig 3: Percent pre-TAS failure by each characteristic (unadjusted).
|
||||
|
||||
<!-- image -->
|
||||
|
||||
Fig 4: Adjusted risk ratios for pre-TAS failure with 95% Confidence Interval from log-binomial model.
|
||||
|
||||
<!-- image -->
|
||||
|
||||
Fig 5: Analysis of failures by model combinations.
|
||||
|
||||
<!-- image -->
|
||||
The authors would like to thank all those involved from the Ministries of Health, volunteers and community members in the sentinel and spot-check site surveys for their tireless commitment to ridding the world of LF. In addition, gratitude is given to Joseph Koroma and all the partners, including USAID, RTI International, FHI 360, IMA World Health, and Helen Keller International, who supported the surveys financially and technically.
|
||||
|
||||
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@ -1,177 +1,176 @@
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item-0 at level 0: unspecified: group _root_
|
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item-1 at level 1: title: Potential to reduce greenhouse g ... cattle systems in subtropical regions
|
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item-2 at level 2: paragraph: Ribeiro-Filho Henrique M. N.; 1: ... , California, United States of America
|
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item-3 at level 2: section_header: Abstract
|
||||
item-4 at level 3: text: Carbon (C) footprint of dairy pr ... uce the C footprint to a small extent.
|
||||
item-5 at level 2: section_header: Introduction
|
||||
item-6 at level 3: text: Greenhouse gas (GHG) emissions f ... suitable for food crop production [4].
|
||||
item-7 at level 3: text: Considering the key role of live ... anagement to mitigate the C footprint.
|
||||
item-8 at level 3: text: In subtropical climate zones, co ... t in tropical pastures (e.g. [17–19]).
|
||||
item-9 at level 3: text: It has been shown that dairy cow ... sions from crop and reduced DM intake.
|
||||
item-10 at level 3: text: The aim of this work was to quan ... uring lactation periods was evaluated.
|
||||
item-11 at level 2: section_header: Materials and methods
|
||||
item-12 at level 3: text: An LCA was developed according t ... 90816 - https://www.udesc.br/cav/ceua.
|
||||
item-13 at level 3: section_header: System boundary
|
||||
item-14 at level 4: text: The goal of the study was to ass ... n were outside of the system boundary.
|
||||
item-15 at level 3: section_header: Functional unit
|
||||
item-16 at level 4: text: The functional unit was one kilo ... tein according to NRC [20] as follows:
|
||||
item-17 at level 4: text: ECM = Milk production × (0.0929 ... characteristics described in Table 1.
|
||||
item-18 at level 3: section_header: Data sources and livestock system description
|
||||
item-19 at level 4: text: The individual feed requirements ... ed to the ad libitum TMR intake group.
|
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item-20 at level 4: text: Using experimental data, three s ... med during an entire lactation period.
|
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item-21 at level 3: section_header: Impact assessment
|
||||
item-22 at level 4: text: The CO2e emissions were calculat ... 65 for CO2, CH4 and N2O, respectively.
|
||||
item-23 at level 3: section_header: Feed production
|
||||
item-24 at level 4: section_header: Diets composition
|
||||
item-25 at level 5: text: The DM intake of each ingredient ... collected throughout the experiments.
|
||||
item-26 at level 4: section_header: GHG emissions from crop and pasture production
|
||||
item-27 at level 5: text: GHG emission factors used for of ... onsume 70% of pastures during grazing.
|
||||
item-28 at level 5: text: Emissions from on-farm feed prod ... factors described by Rotz et al. [42].
|
||||
item-29 at level 3: section_header: Animal husbandry
|
||||
item-30 at level 4: text: The CH4 emissions from enteric f ... 1) = 13.8 + 0.185 × NDF (% DM intake).
|
||||
item-31 at level 3: section_header: Manure from confined cows and urine and dung from grazing animals
|
||||
item-32 at level 4: text: The CH4 emission from manure (kg ... for dietary GE per kg of DM (MJ kg-1).
|
||||
item-33 at level 4: text: The OM digestibility was estimat ... h were 31%, 26% and 46%, respectively.
|
||||
item-34 at level 4: text: The N2O-N emissions from urine a ... using the IPCC [38] emission factors.
|
||||
item-35 at level 3: section_header: Farm management
|
||||
item-36 at level 4: text: Emissions due to farm management ... crop and pasture production’ section.
|
||||
item-37 at level 4: text: The amount of fuel use for manur ... me that animals stayed on confinement.
|
||||
item-38 at level 4: text: The emissions from fuel were est ... × kg CO2e (kg machinery mass)-1 [42].
|
||||
item-39 at level 4: text: Emissions from electricity for m ... ws in naturally ventilated barns [47].
|
||||
item-40 at level 4: text: The lower impact of emissions fr ... greater than 5% of total C footprint.
|
||||
item-41 at level 4: text: Emissions from farm management d ... gas and hard coal, respectively [46].
|
||||
item-42 at level 3: section_header: Co-product allocation
|
||||
item-43 at level 4: text: The C footprint for milk produce ... directly assigned to milk production.
|
||||
item-44 at level 3: section_header: Sensitivity analysis
|
||||
item-45 at level 4: text: A sensitivity index was calculat ... ses a similar change in the footprint.
|
||||
item-46 at level 2: section_header: Results and discussion
|
||||
item-47 at level 3: text: The study has assessed the impac ... , feed production and electricity use.
|
||||
item-48 at level 3: section_header: Greenhouse gas emissions
|
||||
item-49 at level 4: text: Depending on emission factors us ... more than 5% of overall GHG emissions.
|
||||
item-50 at level 4: text: Considering IPCC emission factor ... the C footprint of the dairy systems.
|
||||
item-51 at level 4: text: The similarity of C footprint be ... of TMR was replaced by pasture access.
|
||||
item-52 at level 4: text: The lower C footprint in scenari ... r, averaging 0.004 kg N2O-N kg-1 [37].
|
||||
item-53 at level 3: section_header: Methane emissions
|
||||
item-54 at level 4: text: The enteric CH4 intensity was si ... ], which did not happen in this study.
|
||||
item-55 at level 4: text: The lack of difference in enteri ... same scenarios as in this study [26].
|
||||
item-56 at level 3: section_header: Emissions from excreta and feed production
|
||||
item-57 at level 4: text: Using IPCC emission factors for ... may not be captured by microbes [65].
|
||||
item-58 at level 4: text: Using local emission factors for ... be revised for the subtropical region.
|
||||
item-59 at level 4: text: Emissions for feed production de ... act, particularly in confinements [9].
|
||||
item-60 at level 3: section_header: Assumptions and limitations
|
||||
item-61 at level 4: text: The milk production and composit ... ions as a function of soil management.
|
||||
item-62 at level 3: section_header: Further considerations
|
||||
item-63 at level 4: text: The potential for using pasture ... g ECM)-1 in case of foot lesions [72].
|
||||
item-64 at level 4: text: Grazing lands may also improve b ... hange of CO2 would be negligible [76].
|
||||
item-65 at level 2: section_header: Conclusions
|
||||
item-66 at level 3: text: This study assessed the C footpr ... on with or without access to pastures.
|
||||
item-67 at level 2: section_header: Tables
|
||||
item-68 at level 3: table with [13x3]
|
||||
item-68 at level 4: caption: Table 1: Descriptive characteristics of the herd.
|
||||
item-69 at level 3: table with [21x11]
|
||||
item-69 at level 4: caption: Table 2: Dairy cows’ diets in different scenariosa.
|
||||
item-70 at level 3: table with [9x5]
|
||||
item-70 at level 4: caption: Table 3: GHG emission factors for Off- and On-farm feed production.
|
||||
item-71 at level 3: table with [28x5]
|
||||
item-71 at level 4: caption: Table 4: GHG emissions from On-farm feed production.
|
||||
item-72 at level 3: table with [12x4]
|
||||
item-72 at level 4: caption: Table 5: Factors for major resource inputs in farm management.
|
||||
item-73 at level 2: section_header: Figures
|
||||
item-74 at level 3: picture
|
||||
item-74 at level 4: caption: Fig 1: Overview of the milk production system boundary considered in the study.
|
||||
item-75 at level 3: picture
|
||||
item-75 at level 4: caption: Fig 2: Overall greenhouse gas emissions in dairy cattle systems under various scenarios.
|
||||
TMR = ad libitum TMR intake, 75TMR = 75% of ad libitum TMR intake with access to pasture, 50TMR = 50% of ad libitum TMR intake with access to pasture. (a) N2O emission factors for urine and dung from IPCC [38], feed production emission factors from Table 3 without accounting for sequestered CO2-C from perennial pasture, production of electricity = 0.73 kg CO2e kWh-1 [41]. (b) N2O emission factors for urine and dung from IPCC [38], feed production emission factors from Table 3 without accounting for sequestered CO2-C from perennial pasture, production of electricity = 0.205 kg CO2e kWh-1 [46]; (c) N2O emission factors for urine and dung from local data [37], feed production EF from Table 4 without accounting for sequestered CO2-C from perennial pasture, production of electricity = 0.205 kg CO2e kWh-1 [46]. (d) N2O emission factors for urine and dung from local data [37], feed production emission factors from Table 4 accounting for sequestered CO2-C from perennial pasture, production of electricity = 0.205 kg CO2e kWh-1 [46].
|
||||
item-76 at level 3: picture
|
||||
item-76 at level 4: caption: Fig 3: Sensitivity of the C footprint.
|
||||
Sensitivity index = percentage change in C footprint for a 10% change in the given emission source divided by 10% of. (a) N2O emission factors for urine and dung from IPCC [38], feed production emission factors from Table 3, production of electricity = 0.73 kg CO2e kWh-1 [41]. (b) N2O emission factors for urine and dung from IPCC [38], feed production emission factors from Table 3, production of electricity = 0.205 kg CO2e kWh-1 [46]; (c) N2O emission factors for urine and dung from local data [37], feed production EF from Table 4 without accounting sequestered CO2-C from perennial pasture, production of electricity = 0.205 kg CO2e kWh-1 [46]. (d) N2O emission factors for urine and dung from local data [37], feed production emission factors from Table 4 accounting sequestered CO2-C from perennial pasture, production of electricity = 0.205 kg CO2e kWh-1 [46].
|
||||
item-77 at level 3: picture
|
||||
item-77 at level 4: caption: Fig 4: Greenhouse gas emissions (GHG) from manure and feed production in dairy cattle systems.
|
||||
TMR = ad libitum TMR intake, 75TMR = 75% of ad libitum TMR intake with access to pasture, 50TMR = 50% of ad libitum TMR intake with access to pasture. (a) N2O emission factors for urine and dung from IPCC [38]. (b) Feed production emission factors from Table 3. (c) N2O emission factors for urine and dung from local data [37]. (d) Feed production emission factors from Table 4 accounting sequestered CO2-C from perennial pasture.
|
||||
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item-141 at level 4: list_item: Niu M; Appuhamy JADRN; Leytem AB ... s simultaneously. Anim Prod Sci (2016)
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item-142 at level 4: list_item: Waghorn GC; Law N; Bryant M; Pac ... with fodder beet. Anim Prod Sci (2019)
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item-144 at level 4: list_item: Schwab CG; Broderick GA. A 100-Y ... tion in dairy cows. J Dairy Sci (2017)
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item-145 at level 4: list_item: Sordi A; Dieckow J; Bayer C; Alb ... tureland. Agric Ecosyst Environ (2014)
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||||
item-146 at level 4: list_item: Simon PL; Dieckow J; de Klein CA ... pastures. Agric Ecosyst Environ (2018)
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||||
item-147 at level 4: list_item: Wang X; Ledgard S; Luo J; Guo Y; ... e assessment. Sci Total Environ (2018)
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||||
item-148 at level 4: list_item: Pirlo G; Lolli S. Environmental ... Lombardy (Italy). J Clean Prod (2019)
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||||
item-149 at level 4: list_item: Herzog A; Winckler C; Zollitsch ... tigation. Agric Ecosyst Environ (2018)
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||||
item-150 at level 4: list_item: Mostert PF; van Middelaar CE; Bo ... f milk production. J Clean Prod (2018)
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||||
item-151 at level 4: list_item: Mostert PF; van Middelaar CE; de ... of milk production. Agric Syst (2018)
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||||
item-152 at level 4: list_item: Foley JA; Ramankutty N; Brauman ... for a cultivated planet. Nature (2011)
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||||
item-153 at level 4: list_item: Lal R.. Soil Carbon Sequestratio ... nd Food Security. Science (80-) (2004)
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||||
item-154 at level 4: list_item: Boddey RM; Jantalia CP; Conceiça ... al agriculture. Glob Chang Biol (2010)
|
||||
item-155 at level 4: list_item: McConkey B; Angers D; Bentham M; ... he LULUCF sector for NIR 2014. (2014)
|
||||
item-156 at level 1: caption: Table 1: Descriptive characteristics of the herd.
|
||||
item-157 at level 1: caption: Table 2: Dairy cows’ diets in different scenariosa.
|
||||
item-158 at level 1: caption: Table 3: GHG emission factors for Off- and On-farm feed production.
|
||||
item-159 at level 1: caption: Table 4: GHG emissions from On-farm feed production.
|
||||
item-160 at level 1: caption: Table 5: Factors for major resource inputs in farm management.
|
||||
item-161 at level 1: caption: Fig 1: Overview of the milk prod ... stem boundary considered in the study.
|
||||
item-162 at level 1: caption: Fig 2: Overall greenhouse gas em ... lectricity = 0.205 kg CO2e kWh-1 [46].
|
||||
item-163 at level 1: caption: Fig 3: Sensitivity of the C foot ... lectricity = 0.205 kg CO2e kWh-1 [46].
|
||||
item-164 at level 1: caption: Fig 4: Greenhouse gas emissions ... uestered CO2-C from perennial pasture.
|
||||
item-2 at level 2: paragraph: Henrique M. N. Ribeiro-Filho, Maurício Civiero, Ermias Kebreab
|
||||
item-3 at level 2: paragraph: Department of Animal Science, Un ... atarina, Lages, Santa Catarina, Brazil
|
||||
item-4 at level 2: section_header: Abstract
|
||||
item-5 at level 3: text: Carbon (C) footprint of dairy pr ... uce the C footprint to a small extent.
|
||||
item-6 at level 2: section_header: Introduction
|
||||
item-7 at level 3: text: Greenhouse gas (GHG) emissions f ... suitable for food crop production [4].
|
||||
item-8 at level 3: text: Considering the key role of live ... anagement to mitigate the C footprint.
|
||||
item-9 at level 3: text: In subtropical climate zones, co ... t in tropical pastures (e.g. [17–19]).
|
||||
item-10 at level 3: text: It has been shown that dairy cow ... sions from crop and reduced DM intake.
|
||||
item-11 at level 3: text: The aim of this work was to quan ... uring lactation periods was evaluated.
|
||||
item-12 at level 2: section_header: Materials and methods
|
||||
item-13 at level 3: text: An LCA was developed according t ... 90816 - https://www.udesc.br/cav/ceua.
|
||||
item-14 at level 3: section_header: System boundary
|
||||
item-15 at level 4: text: The goal of the study was to ass ... n were outside of the system boundary.
|
||||
item-16 at level 4: picture
|
||||
item-16 at level 5: caption: Fig 1 Overview of the milk production system boundary considered in the study.
|
||||
item-17 at level 3: section_header: Functional unit
|
||||
item-18 at level 4: text: The functional unit was one kilo ... tein according to NRC [20] as follows:
|
||||
item-19 at level 4: text: ECM = Milk production × (0.0929 ... characteristics described in Table 1.
|
||||
item-20 at level 4: table with [13x3]
|
||||
item-20 at level 5: caption: Table 1 Descriptive characteristics of the herd.
|
||||
item-21 at level 3: section_header: Data sources and livestock system description
|
||||
item-22 at level 4: text: The individual feed requirements ... ed to the ad libitum TMR intake group.
|
||||
item-23 at level 4: text: Using experimental data, three s ... med during an entire lactation period.
|
||||
item-24 at level 3: section_header: Impact assessment
|
||||
item-25 at level 4: text: The CO2e emissions were calculat ... 65 for CO2, CH4 and N2O, respectively.
|
||||
item-26 at level 3: section_header: Feed production
|
||||
item-27 at level 4: section_header: Diets composition
|
||||
item-28 at level 5: text: The DM intake of each ingredient ... collected throughout the experiments.
|
||||
item-29 at level 5: table with [21x11]
|
||||
item-29 at level 6: caption: Table 2 Dairy cows’ diets in different scenariosa.
|
||||
item-30 at level 4: section_header: GHG emissions from crop and pasture production
|
||||
item-31 at level 5: text: GHG emission factors used for of ... onsume 70% of pastures during grazing.
|
||||
item-32 at level 5: table with [9x5]
|
||||
item-32 at level 6: caption: Table 3 GHG emission factors for Off- and On-farm feed production.
|
||||
item-33 at level 5: text: Emissions from on-farm feed prod ... factors described by Rotz et al. [42].
|
||||
item-34 at level 5: table with [28x5]
|
||||
item-34 at level 6: caption: Table 4 GHG emissions from On-farm feed production.
|
||||
item-35 at level 3: section_header: Animal husbandry
|
||||
item-36 at level 4: text: The CH4 emissions from enteric f ... 1) = 13.8 + 0.185 × NDF (% DM intake).
|
||||
item-37 at level 3: section_header: Manure from confined cows and urine and dung from grazing animals
|
||||
item-38 at level 4: text: The CH4 emission from manure (kg ... for dietary GE per kg of DM (MJ kg-1).
|
||||
item-39 at level 4: text: The OM digestibility was estimat ... h were 31%, 26% and 46%, respectively.
|
||||
item-40 at level 4: text: The N2O-N emissions from urine a ... using the IPCC [38] emission factors.
|
||||
item-41 at level 3: section_header: Farm management
|
||||
item-42 at level 4: text: Emissions due to farm management ... crop and pasture production’ section.
|
||||
item-43 at level 4: table with [12x4]
|
||||
item-43 at level 5: caption: Table 5 Factors for major resource inputs in farm management.
|
||||
item-44 at level 4: text: The amount of fuel use for manur ... me that animals stayed on confinement.
|
||||
item-45 at level 4: text: The emissions from fuel were est ... × kg CO2e (kg machinery mass)-1 [42].
|
||||
item-46 at level 4: text: Emissions from electricity for m ... ws in naturally ventilated barns [47].
|
||||
item-47 at level 3: section_header: Co-product allocation
|
||||
item-48 at level 4: text: The C footprint for milk produce ... directly assigned to milk production.
|
||||
item-49 at level 3: section_header: Sensitivity analysis
|
||||
item-50 at level 4: text: A sensitivity index was calculat ... ses a similar change in the footprint.
|
||||
item-51 at level 2: section_header: Results and discussion
|
||||
item-52 at level 3: text: The study has assessed the impac ... , feed production and electricity use.
|
||||
item-53 at level 3: section_header: Greenhouse gas emissions
|
||||
item-54 at level 4: text: Depending on emission factors us ... more than 5% of overall GHG emissions.
|
||||
item-55 at level 4: picture
|
||||
item-55 at level 5: caption: Fig 2 Overall greenhouse gas emissions in dairy cattle systems under various scenarios. TMR = ad libitum TMR intake, 75TMR = 75% of ad libitum TMR intake with access to pasture, 50TMR = 50% of ad libitum TMR intake with access to pasture. (a) N2O emission factors for urine and dung from IPCC [38], feed production emission factors from Table 3 without accounting for sequestered CO2-C from perennial pasture, production of electricity = 0.73 kg CO2e kWh-1 [41]. (b) N2O emission factors for urine and dung from IPCC [38], feed production emission factors from Table 3 without accounting for sequestered CO2-C from perennial pasture, production of electricity = 0.205 kg CO2e kWh-1 [46]; (c) N2O emission factors for urine and dung from local data [37], feed production EF from Table 4 without accounting for sequestered CO2-C from perennial pasture, production of electricity = 0.205 kg CO2e kWh-1 [46]. (d) N2O emission factors for urine and dung from local data [37], feed production emission factors from Table 4 accounting for sequestered CO2-C from perennial pasture, production of electricity = 0.205 kg CO2e kWh-1 [46].
|
||||
item-56 at level 4: text: Considering IPCC emission factor ... the C footprint of the dairy systems.
|
||||
item-57 at level 4: text: The similarity of C footprint be ... of TMR was replaced by pasture access.
|
||||
item-58 at level 4: text: The lower C footprint in scenari ... r, averaging 0.004 kg N2O-N kg-1 [37].
|
||||
item-59 at level 3: section_header: Methane emissions
|
||||
item-60 at level 4: text: The enteric CH4 intensity was si ... ], which did not happen in this study.
|
||||
item-61 at level 4: picture
|
||||
item-61 at level 5: caption: Fig 3 Sensitivity of the C footprint. Sensitivity index = percentage change in C footprint for a 10% change in the given emission source divided by 10% of. (a) N2O emission factors for urine and dung from IPCC [38], feed production emission factors from Table 3, production of electricity = 0.73 kg CO2e kWh-1 [41]. (b) N2O emission factors for urine and dung from IPCC [38], feed production emission factors from Table 3, production of electricity = 0.205 kg CO2e kWh-1 [46]; (c) N2O emission factors for urine and dung from local data [37], feed production EF from Table 4 without accounting sequestered CO2-C from perennial pasture, production of electricity = 0.205 kg CO2e kWh-1 [46]. (d) N2O emission factors for urine and dung from local data [37], feed production emission factors from Table 4 accounting sequestered CO2-C from perennial pasture, production of electricity = 0.205 kg CO2e kWh-1 [46].
|
||||
item-62 at level 4: text: The lack of difference in enteri ... same scenarios as in this study [26].
|
||||
item-63 at level 3: section_header: Emissions from excreta and feed production
|
||||
item-64 at level 4: text: Using IPCC emission factors for ... may not be captured by microbes [65].
|
||||
item-65 at level 4: picture
|
||||
item-65 at level 5: caption: Fig 4 Greenhouse gas emissions (GHG) from manure and feed production in dairy cattle systems. TMR = ad libitum TMR intake, 75TMR = 75% of ad libitum TMR intake with access to pasture, 50TMR = 50% of ad libitum TMR intake with access to pasture. (a) N2O emission factors for urine and dung from IPCC [38]. (b) Feed production emission factors from Table 3. (c) N2O emission factors for urine and dung from local data [37]. (d) Feed production emission factors from Table 4 accounting sequestered CO2-C from perennial pasture.
|
||||
item-66 at level 4: text: Using local emission factors for ... be revised for the subtropical region.
|
||||
item-67 at level 4: text: Emissions for feed production de ... act, particularly in confinements [9].
|
||||
item-68 at level 3: section_header: Farm management
|
||||
item-69 at level 4: text: The lower impact of emissions fr ... greater than 5% of total C footprint.
|
||||
item-70 at level 4: text: Emissions from farm management d ... gas and hard coal, respectively [46].
|
||||
item-71 at level 3: section_header: Assumptions and limitations
|
||||
item-72 at level 4: text: The milk production and composit ... ions as a function of soil management.
|
||||
item-73 at level 3: section_header: Further considerations
|
||||
item-74 at level 4: text: The potential for using pasture ... g ECM)-1 in case of foot lesions [72].
|
||||
item-75 at level 4: text: Grazing lands may also improve b ... hange of CO2 would be negligible [76].
|
||||
item-76 at level 2: section_header: Conclusions
|
||||
item-77 at level 3: text: This study assessed the C footpr ... on with or without access to pastures.
|
||||
item-78 at level 2: section_header: Acknowledgments
|
||||
item-79 at level 3: text: Thanks to Anna Naranjo for helpf ... of the herd considered in this study.
|
||||
item-80 at level 2: section_header: References
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||||
item-155 at level 4: list_item: LalR. Soil Carbon Sequestration ... 1627. 10.1126/science.1097396 15192216
|
||||
item-156 at level 4: list_item: BoddeyRM, JantaliaCP, ConceiçaoP ... –795. 10.1111/j.1365-2486.2009.02020.x
|
||||
item-157 at level 4: list_item: McConkeyB, AngersD, BenthamM, Bo ... the LULUCF sector for NIR 2014. 2014.
|
||||
item-158 at level 1: caption: Fig 1 Overview of the milk produ ... stem boundary considered in the study.
|
||||
item-159 at level 1: caption: Table 1 Descriptive characteristics of the herd.
|
||||
item-160 at level 1: caption: Table 2 Dairy cows’ diets in different scenariosa.
|
||||
item-161 at level 1: caption: Table 3 GHG emission factors for Off- and On-farm feed production.
|
||||
item-162 at level 1: caption: Table 4 GHG emissions from On-farm feed production.
|
||||
item-163 at level 1: caption: Table 5 Factors for major resource inputs in farm management.
|
||||
item-164 at level 1: caption: Fig 2 Overall greenhouse gas emi ... lectricity = 0.205 kg CO2e kWh-1 [46].
|
||||
item-165 at level 1: caption: Fig 3 Sensitivity of the C footp ... lectricity = 0.205 kg CO2e kWh-1 [46].
|
||||
item-166 at level 1: caption: Fig 4 Greenhouse gas emissions ( ... uestered CO2-C from perennial pasture.
|
||||
File diff suppressed because it is too large
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@ -1,6 +1,8 @@
|
||||
# Potential to reduce greenhouse gas emissions through different dairy cattle systems in subtropical regions
|
||||
|
||||
Ribeiro-Filho Henrique M. N.; 1: Department of Animal Science, University of California, Davis, California, United States of America, 2: Programa de Pós-graduação em Ciência Animal, Universidade do Estado de Santa Catarina, Lages, Santa Catarina, Brazil; Civiero Maurício; 2: Programa de Pós-graduação em Ciência Animal, Universidade do Estado de Santa Catarina, Lages, Santa Catarina, Brazil; Kebreab Ermias; 1: Department of Animal Science, University of California, Davis, California, United States of America
|
||||
Henrique M. N. Ribeiro-Filho, Maurício Civiero, Ermias Kebreab
|
||||
|
||||
Department of Animal Science, University of California, Davis, California, United States of America; Programa de Pós-graduação em Ciência Animal, Universidade do Estado de Santa Catarina, Lages, Santa Catarina, Brazil
|
||||
|
||||
## Abstract
|
||||
|
||||
@ -26,12 +28,33 @@ An LCA was developed according to the ISO standards [23,24] and Food and Agricul
|
||||
|
||||
The goal of the study was to assess the C footprint of annual tropical and temperate pastures in lactating dairy cow diets. The production system was divided into four main processes: (i) animal husbandry, (ii) manure management and urine and dung deposited by grazing animals, (iii) production of feed ingredients and (iv) farm management (Fig 1). The study boundary included all processes up to the animal farm gate (cradle to gate), including secondary sources such as GHG emissions during the production of fuel, electricity, machinery, manufacturing of fertilizer, pesticides, seeds and plastic used in silage production. Fuel combustion and machinery (manufacture and repairs) for manure handling and electricity for milking and confinement were accounted as emissions from farm management. Emissions post milk production were assumed to be similar for all scenarios, therefore, activities including milk processing, distribution, retail or consumption were outside of the system boundary.
|
||||
|
||||
Fig 1 Overview of the milk production system boundary considered in the study.
|
||||
|
||||
<!-- image -->
|
||||
|
||||
### Functional unit
|
||||
|
||||
The functional unit was one kilogram of energy-corrected milk (ECM) at the farm gate. All processes in the system were calculated based on one kilogram ECM. The ECM was calculated by multiplying milk production by the ratio of the energy content of the milk to the energy content of standard milk with 4% fat and 3.3% true protein according to NRC [20] as follows:
|
||||
|
||||
ECM = Milk production × (0.0929 × fat% + 0.0588× true protein% + 0.192) / (0.0929 × (4%) + 0.0588 × (3.3%) + 0.192), where fat% and protein% are fat and protein percentages in milk, respectively. The average milk production and composition were recorded from the University of Santa Catarina State (Brazil) herd, considering 165 lactations between 2009 and 2018. The herd is predominantly Holstein × Jersey cows, with key characteristics described in Table 1.
|
||||
|
||||
Table 1 Descriptive characteristics of the herd.
|
||||
|
||||
| Item | Unit | Average |
|
||||
|-------------------------------|-----------|-----------|
|
||||
| Milking cows | # | 165 |
|
||||
| Milk production | kg year-1 | 7,015 |
|
||||
| Milk fat | % | 4.0 |
|
||||
| Milk protein | % | 3.3 |
|
||||
| Length of lactation | days | 305 |
|
||||
| Body weight | kg | 553 |
|
||||
| Lactations per cow | # | 4 |
|
||||
| Replacement rate | % | 25 |
|
||||
| Cull rate | % | 25 |
|
||||
| First artificial insemination | months | 16 |
|
||||
| Weaned | days | 60 |
|
||||
| Mortality | % | 3.0 |
|
||||
|
||||
### Data sources and livestock system description
|
||||
|
||||
The individual feed requirements, as well as the milk production responses based on feed strategies were based on data recorded from the herd described above and two experiments performed using lactating cows from the same herd. Due to the variation on herbage production throughout the year, feed requirements were estimated taking into consideration that livestock systems have a calving period in April, which represents the beginning of fall season in the southern Hemisphere. The experiments have shown a 10% reduction in ECM production in dairy cows that received both 75 and 50% of ad libitum TMR intake with access to grazing a tropical pasture (pearl-millet, Pennisetum glaucum ‘Campeiro’) compared to cows receiving ad libitum TMR intake. Cows grazing on a temperate pasture (ryegrass, Lolium multiflorum ‘Maximus’) did not need changes to ECM production compared to the ad libitum TMR intake group.
|
||||
@ -48,108 +71,7 @@ The CO2e emissions were calculated by multiplying the emissions of CO2, CH4 and
|
||||
|
||||
The DM intake of each ingredient throughout the entire life of animals during lactation periods was calculated for each scenario: cows receiving only TMR, cows receiving 75% of TMR with annual pastures and cows receiving 50% of TMR with annual pastures (Table 2). In each of other phases of life (calf, heifer, dry cow), animals received the same diet, including a perennial tropical pasture (kikuyu grass, Pennisetum clandestinum). The DM intake of calves, heifers and dry cows was calculated assuming 2.8, 2.5 and 1.9% body weight, respectively [20]. In each case, the actual DM intake of concentrate and corn silage was recorded, and pasture DM intake was estimated by the difference between daily expected DM intake and actual DM intake of concentrate and corn silage. For lactating heifers and cows, TMR was formulated to meet the net energy for lactation (NEL) and metabolizable protein (MP) requirements of experimental animals, according to [28]. The INRA system was used because it is possible to estimate pasture DM intake taking into account the TMR intake, pasture management and the time of access to pasture using the GrazeIn model [29], which was integrated in the software INRAtion 4.07 (https://www.inration.educagri.fr/fr/forum.php). The nutrient intake was calculated as a product of TMR and pasture intake and the nutrient contents of TMR and pasture, respectively, which were determined in feed samples collected throughout the experiments.
|
||||
|
||||
#### GHG emissions from crop and pasture production
|
||||
|
||||
GHG emission factors used for off- and on-farm feed production were based on literature values, and are presented in Table 3. The emission factor used for corn grain is the average of emission factors observed in different levels of synthetic N fertilization [30]. The emission factor used for soybean is based on Brazilian soybean production [31]. The emissions used for corn silage, including feed processing (cutting, crushing and mixing), and annual or perennial grass productions were 3300 and 1500 kg CO2e ha-1, respectively [32]. The DM production (kg ha-1) of corn silage and pastures were based on regional and locally recorded data [33–36], assuming that animals are able to consume 70% of pastures during grazing.
|
||||
|
||||
Emissions from on-farm feed production (corn silage and pasture) were estimated using primary and secondary sources based on the actual amount of each input (Table 4). Primary sources were direct and indirect N2O-N emissions from organic and synthetic fertilizers and crop/pasture residues, CO2-C emissions from lime and urea applications, as well as fuel combustion. The direct N2O-N emission factor (kg (kg N input)-1) is based on a local study performed previously [37]. For indirect N2O-N emissions (kg N2O-N (kg NH3-N + NOx)-1), as well as CO2-C emissions from lime + urea, default values proposed by IPCC [38] were used. For perennial pastures, a C sequestration of 0.57 t ha-1 was used based on a 9-year study conducted in southern Brazil [39]. Due to the use of conventional tillage, no C sequestration was considered for annual pastures. The amount of fuel required was 8.9 (no-tillage) and 14.3 L ha-1 (disking) for annual tropical and temperate pastures, respectively [40]. The CO2 from fuel combustion was 2.7 kg CO2 L-1 [41]. Secondary sources of emissions during the production of fuel, machinery, fertilizer, pesticides, seeds and plastic for ensilage were estimated using emission factors described by Rotz et al. [42].
|
||||
|
||||
### Animal husbandry
|
||||
|
||||
The CH4 emissions from enteric fermentation intensity (g (kg ECM)-1) was a function of estimated CH4 yield (g (kg DM intake)-1), actual DM intake and ECM. The enteric CH4 yield was estimated as a function of neutral detergent fiber (NDF) concentration on total DM intake, as proposed by Niu et al. [43], where: CH4 yield (g (kg DM intake)-1) = 13.8 + 0.185 × NDF (% DM intake).
|
||||
|
||||
### Manure from confined cows and urine and dung from grazing animals
|
||||
|
||||
The CH4 emission from manure (kg (kg ECM)-1) was a function of daily CH4 emission from manure (kg cow-1) and daily ECM (kg cow-1). The daily CH4 emission from manure was estimated according to IPCC [38], which considered daily volatile solid (VS) excreted (kg DM cow-1) in manure. The daily VS was estimated as proposed by Eugène et al. [44] as: VS = NDOMI + (UE × GE) × (OM/18.45), where: VS = volatile solid excretion on an organic matter (OM) basis (kg day-1), NDOMI = non-digestible OM intake (kg day-1): (1- OM digestibility) × OM intake, UE = urinary energy excretion as a fraction of GE (0.04), GE = gross energy intake (MJ day-1), OM = organic matter (g), 18.45 = conversion factor for dietary GE per kg of DM (MJ kg-1).
|
||||
|
||||
The OM digestibility was estimated as a function of chemical composition, using equations published by INRA [21], which takes into account the effects of digestive interactions due to feeding level, the proportion of concentrate and rumen protein balance on OM digestibility. For scenarios where cows had access to grazing, the amount of calculated VS were corrected as a function of the time at pasture. The biodegradability of manure factor (0.13 for dairy cows in Latin America) and methane conversion factor (MCF) values were taken from IPCC [38]. The MCF values for pit storage below animal confinements (> 1 month) were used for the calculation, taking into account the annual average temperature (16.6ºC) or the average temperatures during the growth period of temperate (14.4ºC) or tropical (21ºC) annual pastures, which were 31%, 26% and 46%, respectively.
|
||||
|
||||
The N2O-N emissions from urine and feces were estimated considering the proportion of N excreted as manure and storage or as urine and dung deposited by grazing animals. These proportions were calculated based on the proportion of daily time that animals stayed on pasture (7 h/24 h = 0.29) or confinement (1−0.29 = 0.71). For lactating heifers and cows, the total amount of N excreted was calculated by the difference between N intake and milk N excretion. For heifers and non-lactating cows, urinary and fecal N excretion were estimated as proposed by Reed et al. [45] (Table 3: equations 10 and 12, respectively). The N2O emissions from stored manure as well as urine and dung during grazing were calculated based on the conversion of N2O-N emissions to N2O emissions, where N2O emissions = N2O-N emissions × 44/28. The emission factors were 0.002 kg N2O-N (kg N)-1 stored in a pit below animal confinements, and 0.02 kg N2O-N (kg of urine and dung)-1 deposited on pasture [38]. The indirect N2O emissions from storage manure and urine and dung deposits on pasture were also estimated using the IPCC [38] emission factors.
|
||||
|
||||
### Farm management
|
||||
|
||||
Emissions due to farm management included those from fuel and machinery for manure handling and electricity for milking and confinement (Table 5). Emissions due to feed processing such as cutting, crushing, mixing and distributing, as well as secondary sources of emissions during the production of fuel, machinery, fertilizer, pesticides, seeds and plastic for ensilage were included in ‘Emissions from crop and pasture production’ section.
|
||||
|
||||
The amount of fuel use for manure handling were estimated taking into consideration the amount of manure produced per cow and the amounts of fuel required for manure handling (L diesel t-1) [42]. The amount of manure was estimated from OM excretions (kg cow-1), assuming that the manure has 8% ash on DM basis and 60% DM content. The OM excretions were calculated by NDOMI × days in confinement × proportion of daily time that animals stayed on confinement.
|
||||
|
||||
The emissions from fuel were estimated considering the primary (emissions from fuel burned) and secondary (emissions for producing and transporting fuel) emissions. The primary emissions were calculated by the amount of fuel required for manure handling (L) × (kg CO2e L-1) [41]. The secondary emissions from fuel were calculated by the amount of fuel required for manure handling × emissions for production and transport of fuel (kg CO2e L-1) [41]. Emissions from manufacture and repair of machinery for manure handling were estimated by manure produced per cow (t) × (kg machinery mass (kg manure)-1 × 10−3) [42] × kg CO2e (kg machinery mass)-1 [42].
|
||||
|
||||
Emissions from electricity for milking and confinement were estimated using two emission factors (kg CO2 kWh-1). The first one is based on United States electricity matrix [41], and was used as a reference of an electricity matrix with less hydroelectric power than the region under study. The second is based on the Brazilian electricity matrix [46]. The electricity required for milking activities is 0.06 kWh (kg milk produced)-1 [47]. The annual electricity use for lighting was 75 kWh cow-1, which is the value considered for lactating cows in naturally ventilated barns [47].
|
||||
|
||||
The lower impact of emissions from farm management is in agreement with other studies conducted in Europe [9, 62] and USA [42, 55], where the authors found that most emissions in dairy production systems are from enteric fermentation, feed production and emissions from excreta. As emissions from fuel for on-farm feed production were accounted into the ‘emissions from crop and pasture production’, total emissions from farm management were not greater than 5% of total C footprint.
|
||||
|
||||
Emissions from farm management dropped when the emission factor for electricity generation was based on the Brazilian matrix. In this case, the emission factor for electricity generation (0.205 kg CO2e kWh-1 [46]) is much lower than that in a LCA study conducted in US (0.73 kg CO2e kWh-1 [42]). This apparent discrepancy is explained because in 2016, almost 66% of the electricity generated in Brazil was from hydropower, which has an emission factor of 0.074 kg CO2e kWh-1 against 0.382 and 0.926 kg CO2e kWh-1 produced by natural gas and hard coal, respectively [46].
|
||||
|
||||
### Co-product allocation
|
||||
|
||||
The C footprint for milk produced in the system was calculated using a biophysical allocation approach, as recommended by the International Dairy Federation [49], and described by Thoma et al. [48]. Briefly, ARmilk = 1–6.04 × BMR, where: ARmilk is the allocation ratio for milk and BMR is cow BW at the time of slaughter (kg) + calf BW sold (kg) divided by the total ECM produced during cow`s entire life (kg). The ARmilk were 0.854 and 0.849 for TMR and TMR with both pasture scenarios, respectively. The ARmilk was applied to the whole emissions, except for the electricity consumed for milking (milking parlor) and refrigerant loss, which was directly assigned to milk production.
|
||||
|
||||
### Sensitivity analysis
|
||||
|
||||
A sensitivity index was calculated as described by Rotz et al. [42]. The sensitivity index was defined for each emission source as the percentage change in the C footprint for a 10% change in the given emission source divided by 10%. Thus, a value near 0 indicates a low sensitivity, whereas an index near or greater than 1 indicates a high sensitivity because a change in this value causes a similar change in the footprint.
|
||||
|
||||
## Results and discussion
|
||||
|
||||
The study has assessed the impact of tropical and temperate pastures in dairy cows fed TMR on the C footprint of dairy production in subtropics. Different factors were taken in to consideration to estimate emissions from manure (or urine and dung) of grazing animals, feed production and electricity use.
|
||||
|
||||
### Greenhouse gas emissions
|
||||
|
||||
Depending on emission factors used for calculating emissions from urine and dung (IPCC or local data) and feed production (Tables 3 or 4), the C footprint was similar (Fig 2A and 2B) or decreased by 0.04 kg CO2e (kg ECM)-1 (Fig 2C and 2D) in scenarios that included pastures compared to ad libitum TMR intake. Due to differences in emission factors, the overall GHG emission values ranged from 0.92 to 1.04 kg CO2e (kg ECM)-1 for dairy cows receiving TMR exclusively, and from 0.88 to 1.04 kg CO2e (kg ECM)-1 for cows with access to pasture. Using IPCC emission factors [38], manure emissions increased as TMR intake went down (Fig 2A and 2B). However, using local emission factors for estimating N2O-N emissions [37], manure emissions decreased as TMR intake went down (Fig 2C and 2D). Regardless of emission factors used (Tables 3 or 4), emissions from feed production decreased to a small extent as the proportion of TMR intake decreased. Emissions from farm management did not contribute more than 5% of overall GHG emissions.
|
||||
|
||||
Considering IPCC emission factors for N2O emissions from urine and dung [38] and those from Table 3, the C footprint ranged from 0.99 to 1.04 kg CO2e (kg ECM)-1, and was close to those reported under confined based systems in California [49], Canada [50], China [8], Ireland [9], different scenarios in Australia [51,52] and Uruguay [11], which ranged from 0.98 to 1.16 kg CO2e (kg ECM)-1. When local emission factors for N2O emissions from urine and dung [37] and those from Table 4 were taking into account, the C footprint for scenarios including pasture, without accounting for sequestered CO2-C from perennial pasture—0.91 kg CO2e (kg ECM)-1—was lower than the range of values described above. However, these values were still greater than high-performance confinement systems in UK and USA [53] or grass based dairy systems in Ireland [9,53] and New Zealand [8,54], which ranged from 0.52 to 0.89 kg CO2e (kg ECM)-1. Regardless of which emission factor was used, we found a lower C footprint in all conditions compared to scenarios with lower milk production per cow or in poor conditions of manure management, which ranged from 1.4 to 2.3 kg CO2e (kg ECM)-1 [8,55]. Thus, even though differences between studies may be partially explained by various assumptions (e.g., emission factors, co-product allocation, methane emissions estimation, sequestered CO2-C, etc.), herd productivity and manure management were systematically associated with the C footprint of the dairy systems.
|
||||
|
||||
The similarity of C footprint between different scenarios using IPCC [38] for estimating emissions from manure and for emissions from feed production (Table 3) was a consequence of the trade-off between greater manure emissions and lower emissions to produce feed, as the proportion of pasture in diets increased. Additionally, the small negative effect of pasture on ECM production also contributed to the trade-off. The impact of milk production on the C footprint was reported in a meta-analysis comprising 30 studies from 15 different countries [22]. As observed in this study (Fig 2A and 2B) the authors reported no significant difference between the C footprint of pasture-based vs. confinement systems. However, they observed that an increase of 1000 kg cow-1 (5000 to 6000 kg ECM) reduced the C footprint by 0.12 kg CO2e (kg ECM)-1, which may explain an apparent discrepancy between our study and an LCA performed in south Brazilian conditions [56]. Their study compared a confinement and a grazing-based dairy system with annual average milk production of 7667 and 5535 kg cow, respectively. In this study, the same herd was used in all systems, with an annual average milk production of around 7000 kg cow-1. Experimental data showed a reduction not greater than 3% of ECM when 50% of TMR was replaced by pasture access.
|
||||
|
||||
The lower C footprint in scenarios with access to pasture, when local emission factors [37] were used for N2O emissions from urine and dung and for feed production (Table 4), may also be partially attributed to the small negative effect of pasture on ECM production. Nevertheless, local emission factors for urine and dung had a great impact on scenarios including pastures compared to ad libitum TMR intake. Whereas the IPCC [38] considers an emission of 0.02 kg N2O-N (kg N)-1 for urine and dung from grazing animals, experimental evidence shows that it may be up to five times lower, averaging 0.004 kg N2O-N kg-1 [37].
|
||||
|
||||
### Methane emissions
|
||||
|
||||
The enteric CH4 intensity was similar between different scenarios (Fig 2), showing the greatest sensitivity index, with values ranging from 0.53 to 0.62, which indicate that for a 10% change in this source, the C footprint may change between 5.3 and 6.2% (Fig 3). The large effect of enteric CH4 emissions on the whole C footprint was expected, because the impact of enteric CH4 on GHG emissions of milk production in different dairy systems has been estimated to range from 44 to 60% of the total CO2e [50,52,57,58]. However, emissions in feed production may be the most important source of GHG when emission factors for producing concentrate feeds are greater than 0.7 kg CO2e kg-1 [59], which did not happen in this study.
|
||||
|
||||
The lack of difference in enteric CH4 emissions in different systems can be explained by the narrow range of NDF content in diets (<4% difference). This non-difference is due to the lower NDF content of annual temperate pastures (495 g (kg DM)-1) compared to corn silage (550 g (kg DM)-1). Hence, an expected, increase NDF content with decreased concentrate was partially offset by an increase in the pasture proportion relatively low in NDF. This is in agreement with studies conducted in southern Brazil, which have shown that the actual enteric CH4 emissions may decrease with inclusion of temperate pastures in cows receiving corn silage and soybean meal [60] or increase enteric CH4 emissions when dairy cows grazing a temperate pasture was supplemented with corn silage [61]. Additionally, enteric CH4 emissions did not differ between dairy cows receiving TMR exclusively or grazing a tropical pasture in the same scenarios as in this study [26].
|
||||
|
||||
### Emissions from excreta and feed production
|
||||
|
||||
Using IPCC emission factors for N2O emissions from urine and dung [38] and those from Table 3, CH4 emissions from manure decreased 0.07 kg CO2e (kg ECM)-1, but N2O emissions from manure increased 0.09 kg CO2e (kg ECM)-1, as TMR intake was restricted to 50% ad libitum (Fig 4A). Emissions for pastures increased by 0.06 kg CO2e (kg ECM)-1, whereas emissions for producing concentrate feeds and corn silage decreased by 0.09 kg CO2e (kg ECM)-1, as TMR intake decreased (Fig 4B). In this situation, the lack of difference in calculated C footprints of different systems was also due to the greater emissions from manure, and offset by lower emissions from feed production with inclusion of pasture in lactating dairy cow diets. The greater N2O-N emissions from manure with pasture was a consequence of higher N2O-N emissions due to greater CP content and N urine excretion, as pasture intake increased. The effect of CP content on urine N excretion has been shown by several authors in lactating dairy cows [62–64]. For instance, by decreasing CP content from 185 to 152 g (kg DM)-1, N intake decreased by 20% and urine N excretion by 60% [62]. In this study, the CP content for lactating dairy cows ranged from 150 g (kg DM)-1 on TMR system to 198 g (kg DM)-1 on 50% TMR with pasture. Additionally, greater urine N excretion is expected with greater use of pasture. This occurs because protein utilization in pastures is inefficient, as the protein in fresh forages is highly degradable in the rumen and may not be captured by microbes [65].
|
||||
|
||||
Using local emission factors for N2O emissions from urine and dung [37] and those from Table 4, reductions in CH4 emissions from stocked manure, when pastures were included on diets, did not offset by increases in N2O emissions from excreta (Fig 4C). In this case, total emissions from manure (Fig 4C) and feed production (Fig 4D) decreased with the inclusion of pasture. The impact of greater CP content and N urine excretion with increased pasture intake was offset by the much lower emission factors used for N2O emissions from urine and dung. As suggested by other authors [66,67], these results show that IPCC default value may need to be revised for the subtropical region.
|
||||
|
||||
Emissions for feed production decreased when pasture was included due to the greater emission factor for corn grain production compared to pastures. Emissions from concentrate and silage had at least twice the sensitivity index compared to emissions from pastures. The amount of grain required per cow in a lifetime decreased from 7,300 kg to 4,000 kg when 50% of TMR was replaced by pasture access. These results are in agreement with other studies which found lower C footprint, as concentrate use is reduced and/or pasture is included [9,68,69]. Moreover, it has been demonstrated that in intensive dairy systems, after enteric fermentation, feed production is the second main contributor to C footprint [50]. There is potential to decrease the environmental impact of dairy systems by reducing the use of concentrate ingredients with high environmental impact, particularly in confinements [9].
|
||||
|
||||
### Assumptions and limitations
|
||||
|
||||
The milk production and composition data are the average for a typical herd, which might have great animal-to-animal variability. Likewise, DM yield of crops and pastures were collected from experimental observations, and may change as a function of inter-annual variation, climatic conditions, soil type, fertilization level etc. The emission factors for direct and indirect N2O emissions from urine and dung were alternatively estimated using local data, but more experiments are necessary to reduce the uncertainty. The CO2 emitted from lime and urea application was estimated from IPCC default values, which may not represent emissions in subtropical conditions. This LCA may be improved by reducing the uncertainty of factors for estimating emissions from excreta and feed production, including the C sequestration or emissions as a function of soil management.
|
||||
|
||||
### Further considerations
|
||||
|
||||
The potential for using pasture can reduce the C footprint because milk production kept pace with animal confinement. However, if milk production is to decrease with lower TMR intake and inclusion of pasture [19], the C footprint would be expected to increase. Lorenz et al. [22] showed that an increase in milk yield from 5,000 to 6,000 kg ECM reduced the C footprint by 0.12 kg CO2e (kg ECM)-1, whereas an increase from 10,000 to 11,000 kg ECM reduced the C footprint by only 0.06 kg CO2e (kg ECM)-1. Hence, the impact of increasing milk production on decreasing C footprint is not linear, and mitigation measures, such as breeding for increased genetic yield potential and increasing concentrate ratio in the diet, are potentially harmful for animal’s health and welfare [70]. For instance, increasing concentrate ratio potentially increases the occurrence of subclinical ketosis and foot lesions, and C footprint may increase by 0.03 kg CO2e (kg ECM)-1 in subclinical ketosis [71] and by 0.02 kg CO2e (kg ECM)-1 in case of foot lesions [72].
|
||||
|
||||
Grazing lands may also improve biodiversity [73]. Strategies such as zero tillage may increase stocks of soil C [74]. This study did not consider C sequestration during the growth of annual pastures, because it was assumed these grasses were planted with tillage, having a balance between C sequestration and C emissions [38]. Considering the C sequestration from no-tillage perennial pasture, the amount of C sequestration will more than compensates for C emitted. These results are in agreement with other authors who have shown that a reduction or elimination of soil tillage increases annual soil C sequestration in subtropical areas by 0.5 to 1.5 t ha-1 [75]. If 50% of tilled areas were under perennial grasslands, 1.0 t C ha-1 would be sequestered, further reducing the C footprint by 0.015 and 0.025 kg CO2e (kg ECM)-1 for the scenarios using 75 and 50% TMR, respectively. Eliminating tillage, the reduction on total GHG emissions would be 0.03 and 0.05 kg CO2e (kg ECM)-1 for 75 and 50% TMR, respectively. However, this approach may be controversial because lands which have been consistently managed for decades have approached steady state C storage, so that net exchange of CO2 would be negligible [76].
|
||||
|
||||
## Conclusions
|
||||
|
||||
This study assessed the C footprint of dairy cattle systems with or without access to pastures. Including pastures showed potential to maintain or decrease to a small extent the C footprint, which may be attributable to the evidence of low N2O emissions from urine and dung in dairy systems in subtropical areas. Even though the enteric CH4 intensity was the largest source of CO2e emissions, it did not change between different scenarios due to the narrow range of NDF content in diets and maintaining the same milk production with or without access to pastures.
|
||||
|
||||
## Tables
|
||||
|
||||
Table 1: Descriptive characteristics of the herd.
|
||||
|
||||
| Item | Unit | Average |
|
||||
|-------------------------------|-----------|-----------|
|
||||
| Milking cows | # | 165 |
|
||||
| Milk production | kg year-1 | 7,015 |
|
||||
| Milk fat | % | 4.0 |
|
||||
| Milk protein | % | 3.3 |
|
||||
| Length of lactation | days | 305 |
|
||||
| Body weight | kg | 553 |
|
||||
| Lactations per cow | # | 4 |
|
||||
| Replacement rate | % | 25 |
|
||||
| Cull rate | % | 25 |
|
||||
| First artificial insemination | months | 16 |
|
||||
| Weaned | days | 60 |
|
||||
| Mortality | % | 3.0 |
|
||||
|
||||
Table 2: Dairy cows’ diets in different scenariosa.
|
||||
Table 2 Dairy cows’ diets in different scenariosa.
|
||||
|
||||
| | Calf | Calf | Pregnant/dry | Pregnant/dry | Lactation | Lactation | Lactation | Weighted average | Weighted average | Weighted average |
|
||||
|-----------------------------------|-----------------------------------|-----------------------------------|-----------------------------------|-----------------------------------|-----------------------------------|-----------------------------------|-----------------------------------|-----------------------------------|-----------------------------------|-----------------------------------|
|
||||
@ -174,7 +96,11 @@ Table 2: Dairy cows’ diets in different scenariosa.
|
||||
| NEL, Mcal (kg DM)-1 | 1.96 | 1.69 | 1.63 | 1.44 | 1.81 | 1.78 | 1.74 | 1.8 | 1.8 | 1.7 |
|
||||
| MP, g (kg DM)-1 | 111 | 93.6 | 97.6 | 90.0 | 95.0 | 102 | 102 | 97.5 | 102 | 101 |
|
||||
|
||||
Table 3: GHG emission factors for Off- and On-farm feed production.
|
||||
#### GHG emissions from crop and pasture production
|
||||
|
||||
GHG emission factors used for off- and on-farm feed production were based on literature values, and are presented in Table 3. The emission factor used for corn grain is the average of emission factors observed in different levels of synthetic N fertilization [30]. The emission factor used for soybean is based on Brazilian soybean production [31]. The emissions used for corn silage, including feed processing (cutting, crushing and mixing), and annual or perennial grass productions were 3300 and 1500 kg CO2e ha-1, respectively [32]. The DM production (kg ha-1) of corn silage and pastures were based on regional and locally recorded data [33–36], assuming that animals are able to consume 70% of pastures during grazing.
|
||||
|
||||
Table 3 GHG emission factors for Off- and On-farm feed production.
|
||||
|
||||
| Feed | DM yield (kg ha-1) | Emission factor | Unita | References |
|
||||
|------------------|----------------------|-------------------|----------------------|--------------|
|
||||
@ -187,7 +113,9 @@ Table 3: GHG emission factors for Off- and On-farm feed production.
|
||||
| Pearl milletd | 11,000 | 0.195 | kg CO2e (kg DM)-1 | [32,35] |
|
||||
| Kikuyu grasse | 9,500 | 0.226 | kg CO2e (kg DM)-1 | [32,36] |
|
||||
|
||||
Table 4: GHG emissions from On-farm feed production.
|
||||
Emissions from on-farm feed production (corn silage and pasture) were estimated using primary and secondary sources based on the actual amount of each input (Table 4). Primary sources were direct and indirect N2O-N emissions from organic and synthetic fertilizers and crop/pasture residues, CO2-C emissions from lime and urea applications, as well as fuel combustion. The direct N2O-N emission factor (kg (kg N input)-1) is based on a local study performed previously [37]. For indirect N2O-N emissions (kg N2O-N (kg NH3-N + NOx)-1), as well as CO2-C emissions from lime + urea, default values proposed by IPCC [38] were used. For perennial pastures, a C sequestration of 0.57 t ha-1 was used based on a 9-year study conducted in southern Brazil [39]. Due to the use of conventional tillage, no C sequestration was considered for annual pastures. The amount of fuel required was 8.9 (no-tillage) and 14.3 L ha-1 (disking) for annual tropical and temperate pastures, respectively [40]. The CO2 from fuel combustion was 2.7 kg CO2 L-1 [41]. Secondary sources of emissions during the production of fuel, machinery, fertilizer, pesticides, seeds and plastic for ensilage were estimated using emission factors described by Rotz et al. [42].
|
||||
|
||||
Table 4 GHG emissions from On-farm feed production.
|
||||
|
||||
| Item | Corn silage | Annual temperate pasture | Annual tropical pasture | Perennial tropical pasture |
|
||||
|-------------------------------------------|---------------|----------------------------|---------------------------|------------------------------|
|
||||
@ -219,7 +147,23 @@ Table 4: GHG emissions from On-farm feed production.
|
||||
| kg CO2e ha-1 (emitted—sequestered) | 1833 | 964 | 1130 | -245 |
|
||||
| Emission factor, kg CO2e (kg DM)-1i | 0.115 | 0.145 | 0.147 | -0.037 |
|
||||
|
||||
Table 5: Factors for major resource inputs in farm management.
|
||||
### Animal husbandry
|
||||
|
||||
The CH4 emissions from enteric fermentation intensity (g (kg ECM)-1) was a function of estimated CH4 yield (g (kg DM intake)-1), actual DM intake and ECM. The enteric CH4 yield was estimated as a function of neutral detergent fiber (NDF) concentration on total DM intake, as proposed by Niu et al. [43], where: CH4 yield (g (kg DM intake)-1) = 13.8 + 0.185 × NDF (% DM intake).
|
||||
|
||||
### Manure from confined cows and urine and dung from grazing animals
|
||||
|
||||
The CH4 emission from manure (kg (kg ECM)-1) was a function of daily CH4 emission from manure (kg cow-1) and daily ECM (kg cow-1). The daily CH4 emission from manure was estimated according to IPCC [38], which considered daily volatile solid (VS) excreted (kg DM cow-1) in manure. The daily VS was estimated as proposed by Eugène et al. [44] as: VS = NDOMI + (UE × GE) × (OM/18.45), where: VS = volatile solid excretion on an organic matter (OM) basis (kg day-1), NDOMI = non-digestible OM intake (kg day-1): (1- OM digestibility) × OM intake, UE = urinary energy excretion as a fraction of GE (0.04), GE = gross energy intake (MJ day-1), OM = organic matter (g), 18.45 = conversion factor for dietary GE per kg of DM (MJ kg-1).
|
||||
|
||||
The OM digestibility was estimated as a function of chemical composition, using equations published by INRA [21], which takes into account the effects of digestive interactions due to feeding level, the proportion of concentrate and rumen protein balance on OM digestibility. For scenarios where cows had access to grazing, the amount of calculated VS were corrected as a function of the time at pasture. The biodegradability of manure factor (0.13 for dairy cows in Latin America) and methane conversion factor (MCF) values were taken from IPCC [38]. The MCF values for pit storage below animal confinements (> 1 month) were used for the calculation, taking into account the annual average temperature (16.6ºC) or the average temperatures during the growth period of temperate (14.4ºC) or tropical (21ºC) annual pastures, which were 31%, 26% and 46%, respectively.
|
||||
|
||||
The N2O-N emissions from urine and feces were estimated considering the proportion of N excreted as manure and storage or as urine and dung deposited by grazing animals. These proportions were calculated based on the proportion of daily time that animals stayed on pasture (7 h/24 h = 0.29) or confinement (1−0.29 = 0.71). For lactating heifers and cows, the total amount of N excreted was calculated by the difference between N intake and milk N excretion. For heifers and non-lactating cows, urinary and fecal N excretion were estimated as proposed by Reed et al. [45] (Table 3: equations 10 and 12, respectively). The N2O emissions from stored manure as well as urine and dung during grazing were calculated based on the conversion of N2O-N emissions to N2O emissions, where N2O emissions = N2O-N emissions × 44/28. The emission factors were 0.002 kg N2O-N (kg N)-1 stored in a pit below animal confinements, and 0.02 kg N2O-N (kg of urine and dung)-1 deposited on pasture [38]. The indirect N2O emissions from storage manure and urine and dung deposits on pasture were also estimated using the IPCC [38] emission factors.
|
||||
|
||||
### Farm management
|
||||
|
||||
Emissions due to farm management included those from fuel and machinery for manure handling and electricity for milking and confinement (Table 5). Emissions due to feed processing such as cutting, crushing, mixing and distributing, as well as secondary sources of emissions during the production of fuel, machinery, fertilizer, pesticides, seeds and plastic for ensilage were included in ‘Emissions from crop and pasture production’ section.
|
||||
|
||||
Table 5 Factors for major resource inputs in farm management.
|
||||
|
||||
| Item | Factor | Unita | References |
|
||||
|------------------------------------------|----------|-------------------|--------------|
|
||||
@ -235,102 +179,159 @@ Table 5: Factors for major resource inputs in farm management.
|
||||
| Electricity for milking | 0.06 | kWh (kg milk)-1 | [47] |
|
||||
| Electricity for lightingd | 75 | kWh cow-1 | [47] |
|
||||
|
||||
## Figures
|
||||
The amount of fuel use for manure handling were estimated taking into consideration the amount of manure produced per cow and the amounts of fuel required for manure handling (L diesel t-1) [42]. The amount of manure was estimated from OM excretions (kg cow-1), assuming that the manure has 8% ash on DM basis and 60% DM content. The OM excretions were calculated by NDOMI × days in confinement × proportion of daily time that animals stayed on confinement.
|
||||
|
||||
Fig 1: Overview of the milk production system boundary considered in the study.
|
||||
The emissions from fuel were estimated considering the primary (emissions from fuel burned) and secondary (emissions for producing and transporting fuel) emissions. The primary emissions were calculated by the amount of fuel required for manure handling (L) × (kg CO2e L-1) [41]. The secondary emissions from fuel were calculated by the amount of fuel required for manure handling × emissions for production and transport of fuel (kg CO2e L-1) [41]. Emissions from manufacture and repair of machinery for manure handling were estimated by manure produced per cow (t) × (kg machinery mass (kg manure)-1 × 10−3) [42] × kg CO2e (kg machinery mass)-1 [42].
|
||||
|
||||
Emissions from electricity for milking and confinement were estimated using two emission factors (kg CO2 kWh-1). The first one is based on United States electricity matrix [41], and was used as a reference of an electricity matrix with less hydroelectric power than the region under study. The second is based on the Brazilian electricity matrix [46]. The electricity required for milking activities is 0.06 kWh (kg milk produced)-1 [47]. The annual electricity use for lighting was 75 kWh cow-1, which is the value considered for lactating cows in naturally ventilated barns [47].
|
||||
|
||||
### Co-product allocation
|
||||
|
||||
The C footprint for milk produced in the system was calculated using a biophysical allocation approach, as recommended by the International Dairy Federation [49], and described by Thoma et al. [48]. Briefly, ARmilk = 1–6.04 × BMR, where: ARmilk is the allocation ratio for milk and BMR is cow BW at the time of slaughter (kg) + calf BW sold (kg) divided by the total ECM produced during cow`s entire life (kg). The ARmilk were 0.854 and 0.849 for TMR and TMR with both pasture scenarios, respectively. The ARmilk was applied to the whole emissions, except for the electricity consumed for milking (milking parlor) and refrigerant loss, which was directly assigned to milk production.
|
||||
|
||||
### Sensitivity analysis
|
||||
|
||||
A sensitivity index was calculated as described by Rotz et al. [42]. The sensitivity index was defined for each emission source as the percentage change in the C footprint for a 10% change in the given emission source divided by 10%. Thus, a value near 0 indicates a low sensitivity, whereas an index near or greater than 1 indicates a high sensitivity because a change in this value causes a similar change in the footprint.
|
||||
|
||||
## Results and discussion
|
||||
|
||||
The study has assessed the impact of tropical and temperate pastures in dairy cows fed TMR on the C footprint of dairy production in subtropics. Different factors were taken in to consideration to estimate emissions from manure (or urine and dung) of grazing animals, feed production and electricity use.
|
||||
|
||||
### Greenhouse gas emissions
|
||||
|
||||
Depending on emission factors used for calculating emissions from urine and dung (IPCC or local data) and feed production (Tables 3 or 4), the C footprint was similar (Fig 2A and 2B) or decreased by 0.04 kg CO2e (kg ECM)-1 (Fig 2C and 2D) in scenarios that included pastures compared to ad libitum TMR intake. Due to differences in emission factors, the overall GHG emission values ranged from 0.92 to 1.04 kg CO2e (kg ECM)-1 for dairy cows receiving TMR exclusively, and from 0.88 to 1.04 kg CO2e (kg ECM)-1 for cows with access to pasture. Using IPCC emission factors [38], manure emissions increased as TMR intake went down (Fig 2A and 2B). However, using local emission factors for estimating N2O-N emissions [37], manure emissions decreased as TMR intake went down (Fig 2C and 2D). Regardless of emission factors used (Tables 3 or 4), emissions from feed production decreased to a small extent as the proportion of TMR intake decreased. Emissions from farm management did not contribute more than 5% of overall GHG emissions.
|
||||
|
||||
Fig 2 Overall greenhouse gas emissions in dairy cattle systems under various scenarios. TMR = ad libitum TMR intake, 75TMR = 75% of ad libitum TMR intake with access to pasture, 50TMR = 50% of ad libitum TMR intake with access to pasture. (a) N2O emission factors for urine and dung from IPCC [38], feed production emission factors from Table 3 without accounting for sequestered CO2-C from perennial pasture, production of electricity = 0.73 kg CO2e kWh-1 [41]. (b) N2O emission factors for urine and dung from IPCC [38], feed production emission factors from Table 3 without accounting for sequestered CO2-C from perennial pasture, production of electricity = 0.205 kg CO2e kWh-1 [46]; (c) N2O emission factors for urine and dung from local data [37], feed production EF from Table 4 without accounting for sequestered CO2-C from perennial pasture, production of electricity = 0.205 kg CO2e kWh-1 [46]. (d) N2O emission factors for urine and dung from local data [37], feed production emission factors from Table 4 accounting for sequestered CO2-C from perennial pasture, production of electricity = 0.205 kg CO2e kWh-1 [46].
|
||||
|
||||
<!-- image -->
|
||||
|
||||
Fig 2: Overall greenhouse gas emissions in dairy cattle systems under various scenarios.
|
||||
TMR = ad libitum TMR intake, 75TMR = 75% of ad libitum TMR intake with access to pasture, 50TMR = 50% of ad libitum TMR intake with access to pasture. (a) N2O emission factors for urine and dung from IPCC [38], feed production emission factors from Table 3 without accounting for sequestered CO2-C from perennial pasture, production of electricity = 0.73 kg CO2e kWh-1 [41]. (b) N2O emission factors for urine and dung from IPCC [38], feed production emission factors from Table 3 without accounting for sequestered CO2-C from perennial pasture, production of electricity = 0.205 kg CO2e kWh-1 [46]; (c) N2O emission factors for urine and dung from local data [37], feed production EF from Table 4 without accounting for sequestered CO2-C from perennial pasture, production of electricity = 0.205 kg CO2e kWh-1 [46]. (d) N2O emission factors for urine and dung from local data [37], feed production emission factors from Table 4 accounting for sequestered CO2-C from perennial pasture, production of electricity = 0.205 kg CO2e kWh-1 [46].
|
||||
Considering IPCC emission factors for N2O emissions from urine and dung [38] and those from Table 3, the C footprint ranged from 0.99 to 1.04 kg CO2e (kg ECM)-1, and was close to those reported under confined based systems in California [49], Canada [50], China [8], Ireland [9], different scenarios in Australia [51,52] and Uruguay [11], which ranged from 0.98 to 1.16 kg CO2e (kg ECM)-1. When local emission factors for N2O emissions from urine and dung [37] and those from Table 4 were taking into account, the C footprint for scenarios including pasture, without accounting for sequestered CO2-C from perennial pasture—0.91 kg CO2e (kg ECM)-1—was lower than the range of values described above. However, these values were still greater than high-performance confinement systems in UK and USA [53] or grass based dairy systems in Ireland [9,53] and New Zealand [8,54], which ranged from 0.52 to 0.89 kg CO2e (kg ECM)-1. Regardless of which emission factor was used, we found a lower C footprint in all conditions compared to scenarios with lower milk production per cow or in poor conditions of manure management, which ranged from 1.4 to 2.3 kg CO2e (kg ECM)-1 [8,55]. Thus, even though differences between studies may be partially explained by various assumptions (e.g., emission factors, co-product allocation, methane emissions estimation, sequestered CO2-C, etc.), herd productivity and manure management were systematically associated with the C footprint of the dairy systems.
|
||||
|
||||
The similarity of C footprint between different scenarios using IPCC [38] for estimating emissions from manure and for emissions from feed production (Table 3) was a consequence of the trade-off between greater manure emissions and lower emissions to produce feed, as the proportion of pasture in diets increased. Additionally, the small negative effect of pasture on ECM production also contributed to the trade-off. The impact of milk production on the C footprint was reported in a meta-analysis comprising 30 studies from 15 different countries [22]. As observed in this study (Fig 2A and 2B) the authors reported no significant difference between the C footprint of pasture-based vs. confinement systems. However, they observed that an increase of 1000 kg cow-1 (5000 to 6000 kg ECM) reduced the C footprint by 0.12 kg CO2e (kg ECM)-1, which may explain an apparent discrepancy between our study and an LCA performed in south Brazilian conditions [56]. Their study compared a confinement and a grazing-based dairy system with annual average milk production of 7667 and 5535 kg cow, respectively. In this study, the same herd was used in all systems, with an annual average milk production of around 7000 kg cow-1. Experimental data showed a reduction not greater than 3% of ECM when 50% of TMR was replaced by pasture access.
|
||||
|
||||
The lower C footprint in scenarios with access to pasture, when local emission factors [37] were used for N2O emissions from urine and dung and for feed production (Table 4), may also be partially attributed to the small negative effect of pasture on ECM production. Nevertheless, local emission factors for urine and dung had a great impact on scenarios including pastures compared to ad libitum TMR intake. Whereas the IPCC [38] considers an emission of 0.02 kg N2O-N (kg N)-1 for urine and dung from grazing animals, experimental evidence shows that it may be up to five times lower, averaging 0.004 kg N2O-N kg-1 [37].
|
||||
|
||||
### Methane emissions
|
||||
|
||||
The enteric CH4 intensity was similar between different scenarios (Fig 2), showing the greatest sensitivity index, with values ranging from 0.53 to 0.62, which indicate that for a 10% change in this source, the C footprint may change between 5.3 and 6.2% (Fig 3). The large effect of enteric CH4 emissions on the whole C footprint was expected, because the impact of enteric CH4 on GHG emissions of milk production in different dairy systems has been estimated to range from 44 to 60% of the total CO2e [50,52,57,58]. However, emissions in feed production may be the most important source of GHG when emission factors for producing concentrate feeds are greater than 0.7 kg CO2e kg-1 [59], which did not happen in this study.
|
||||
|
||||
Fig 3 Sensitivity of the C footprint. Sensitivity index = percentage change in C footprint for a 10% change in the given emission source divided by 10% of. (a) N2O emission factors for urine and dung from IPCC [38], feed production emission factors from Table 3, production of electricity = 0.73 kg CO2e kWh-1 [41]. (b) N2O emission factors for urine and dung from IPCC [38], feed production emission factors from Table 3, production of electricity = 0.205 kg CO2e kWh-1 [46]; (c) N2O emission factors for urine and dung from local data [37], feed production EF from Table 4 without accounting sequestered CO2-C from perennial pasture, production of electricity = 0.205 kg CO2e kWh-1 [46]. (d) N2O emission factors for urine and dung from local data [37], feed production emission factors from Table 4 accounting sequestered CO2-C from perennial pasture, production of electricity = 0.205 kg CO2e kWh-1 [46].
|
||||
|
||||
<!-- image -->
|
||||
|
||||
Fig 3: Sensitivity of the C footprint.
|
||||
Sensitivity index = percentage change in C footprint for a 10% change in the given emission source divided by 10% of. (a) N2O emission factors for urine and dung from IPCC [38], feed production emission factors from Table 3, production of electricity = 0.73 kg CO2e kWh-1 [41]. (b) N2O emission factors for urine and dung from IPCC [38], feed production emission factors from Table 3, production of electricity = 0.205 kg CO2e kWh-1 [46]; (c) N2O emission factors for urine and dung from local data [37], feed production EF from Table 4 without accounting sequestered CO2-C from perennial pasture, production of electricity = 0.205 kg CO2e kWh-1 [46]. (d) N2O emission factors for urine and dung from local data [37], feed production emission factors from Table 4 accounting sequestered CO2-C from perennial pasture, production of electricity = 0.205 kg CO2e kWh-1 [46].
|
||||
The lack of difference in enteric CH4 emissions in different systems can be explained by the narrow range of NDF content in diets (<4% difference). This non-difference is due to the lower NDF content of annual temperate pastures (495 g (kg DM)-1) compared to corn silage (550 g (kg DM)-1). Hence, an expected, increase NDF content with decreased concentrate was partially offset by an increase in the pasture proportion relatively low in NDF. This is in agreement with studies conducted in southern Brazil, which have shown that the actual enteric CH4 emissions may decrease with inclusion of temperate pastures in cows receiving corn silage and soybean meal [60] or increase enteric CH4 emissions when dairy cows grazing a temperate pasture was supplemented with corn silage [61]. Additionally, enteric CH4 emissions did not differ between dairy cows receiving TMR exclusively or grazing a tropical pasture in the same scenarios as in this study [26].
|
||||
|
||||
### Emissions from excreta and feed production
|
||||
|
||||
Using IPCC emission factors for N2O emissions from urine and dung [38] and those from Table 3, CH4 emissions from manure decreased 0.07 kg CO2e (kg ECM)-1, but N2O emissions from manure increased 0.09 kg CO2e (kg ECM)-1, as TMR intake was restricted to 50% ad libitum (Fig 4A). Emissions for pastures increased by 0.06 kg CO2e (kg ECM)-1, whereas emissions for producing concentrate feeds and corn silage decreased by 0.09 kg CO2e (kg ECM)-1, as TMR intake decreased (Fig 4B). In this situation, the lack of difference in calculated C footprints of different systems was also due to the greater emissions from manure, and offset by lower emissions from feed production with inclusion of pasture in lactating dairy cow diets. The greater N2O-N emissions from manure with pasture was a consequence of higher N2O-N emissions due to greater CP content and N urine excretion, as pasture intake increased. The effect of CP content on urine N excretion has been shown by several authors in lactating dairy cows [62–64]. For instance, by decreasing CP content from 185 to 152 g (kg DM)-1, N intake decreased by 20% and urine N excretion by 60% [62]. In this study, the CP content for lactating dairy cows ranged from 150 g (kg DM)-1 on TMR system to 198 g (kg DM)-1 on 50% TMR with pasture. Additionally, greater urine N excretion is expected with greater use of pasture. This occurs because protein utilization in pastures is inefficient, as the protein in fresh forages is highly degradable in the rumen and may not be captured by microbes [65].
|
||||
|
||||
Fig 4 Greenhouse gas emissions (GHG) from manure and feed production in dairy cattle systems. TMR = ad libitum TMR intake, 75TMR = 75% of ad libitum TMR intake with access to pasture, 50TMR = 50% of ad libitum TMR intake with access to pasture. (a) N2O emission factors for urine and dung from IPCC [38]. (b) Feed production emission factors from Table 3. (c) N2O emission factors for urine and dung from local data [37]. (d) Feed production emission factors from Table 4 accounting sequestered CO2-C from perennial pasture.
|
||||
|
||||
<!-- image -->
|
||||
|
||||
Fig 4: Greenhouse gas emissions (GHG) from manure and feed production in dairy cattle systems.
|
||||
TMR = ad libitum TMR intake, 75TMR = 75% of ad libitum TMR intake with access to pasture, 50TMR = 50% of ad libitum TMR intake with access to pasture. (a) N2O emission factors for urine and dung from IPCC [38]. (b) Feed production emission factors from Table 3. (c) N2O emission factors for urine and dung from local data [37]. (d) Feed production emission factors from Table 4 accounting sequestered CO2-C from perennial pasture.
|
||||
Using local emission factors for N2O emissions from urine and dung [37] and those from Table 4, reductions in CH4 emissions from stocked manure, when pastures were included on diets, did not offset by increases in N2O emissions from excreta (Fig 4C). In this case, total emissions from manure (Fig 4C) and feed production (Fig 4D) decreased with the inclusion of pasture. The impact of greater CP content and N urine excretion with increased pasture intake was offset by the much lower emission factors used for N2O emissions from urine and dung. As suggested by other authors [66,67], these results show that IPCC default value may need to be revised for the subtropical region.
|
||||
|
||||
<!-- image -->
|
||||
Emissions for feed production decreased when pasture was included due to the greater emission factor for corn grain production compared to pastures. Emissions from concentrate and silage had at least twice the sensitivity index compared to emissions from pastures. The amount of grain required per cow in a lifetime decreased from 7,300 kg to 4,000 kg when 50% of TMR was replaced by pasture access. These results are in agreement with other studies which found lower C footprint, as concentrate use is reduced and/or pasture is included [9,68,69]. Moreover, it has been demonstrated that in intensive dairy systems, after enteric fermentation, feed production is the second main contributor to C footprint [50]. There is potential to decrease the environmental impact of dairy systems by reducing the use of concentrate ingredients with high environmental impact, particularly in confinements [9].
|
||||
|
||||
### Farm management
|
||||
|
||||
The lower impact of emissions from farm management is in agreement with other studies conducted in Europe [9, 62] and USA [42, 55], where the authors found that most emissions in dairy production systems are from enteric fermentation, feed production and emissions from excreta. As emissions from fuel for on-farm feed production were accounted into the ‘emissions from crop and pasture production’, total emissions from farm management were not greater than 5% of total C footprint.
|
||||
|
||||
Emissions from farm management dropped when the emission factor for electricity generation was based on the Brazilian matrix. In this case, the emission factor for electricity generation (0.205 kg CO2e kWh-1 [46]) is much lower than that in a LCA study conducted in US (0.73 kg CO2e kWh-1 [42]). This apparent discrepancy is explained because in 2016, almost 66% of the electricity generated in Brazil was from hydropower, which has an emission factor of 0.074 kg CO2e kWh-1 against 0.382 and 0.926 kg CO2e kWh-1 produced by natural gas and hard coal, respectively [46].
|
||||
|
||||
### Assumptions and limitations
|
||||
|
||||
The milk production and composition data are the average for a typical herd, which might have great animal-to-animal variability. Likewise, DM yield of crops and pastures were collected from experimental observations, and may change as a function of inter-annual variation, climatic conditions, soil type, fertilization level etc. The emission factors for direct and indirect N2O emissions from urine and dung were alternatively estimated using local data, but more experiments are necessary to reduce the uncertainty. The CO2 emitted from lime and urea application was estimated from IPCC default values, which may not represent emissions in subtropical conditions. This LCA may be improved by reducing the uncertainty of factors for estimating emissions from excreta and feed production, including the C sequestration or emissions as a function of soil management.
|
||||
|
||||
### Further considerations
|
||||
|
||||
The potential for using pasture can reduce the C footprint because milk production kept pace with animal confinement. However, if milk production is to decrease with lower TMR intake and inclusion of pasture [19], the C footprint would be expected to increase. Lorenz et al. [22] showed that an increase in milk yield from 5,000 to 6,000 kg ECM reduced the C footprint by 0.12 kg CO2e (kg ECM)-1, whereas an increase from 10,000 to 11,000 kg ECM reduced the C footprint by only 0.06 kg CO2e (kg ECM)-1. Hence, the impact of increasing milk production on decreasing C footprint is not linear, and mitigation measures, such as breeding for increased genetic yield potential and increasing concentrate ratio in the diet, are potentially harmful for animal’s health and welfare [70]. For instance, increasing concentrate ratio potentially increases the occurrence of subclinical ketosis and foot lesions, and C footprint may increase by 0.03 kg CO2e (kg ECM)-1 in subclinical ketosis [71] and by 0.02 kg CO2e (kg ECM)-1 in case of foot lesions [72].
|
||||
|
||||
Grazing lands may also improve biodiversity [73]. Strategies such as zero tillage may increase stocks of soil C [74]. This study did not consider C sequestration during the growth of annual pastures, because it was assumed these grasses were planted with tillage, having a balance between C sequestration and C emissions [38]. Considering the C sequestration from no-tillage perennial pasture, the amount of C sequestration will more than compensates for C emitted. These results are in agreement with other authors who have shown that a reduction or elimination of soil tillage increases annual soil C sequestration in subtropical areas by 0.5 to 1.5 t ha-1 [75]. If 50% of tilled areas were under perennial grasslands, 1.0 t C ha-1 would be sequestered, further reducing the C footprint by 0.015 and 0.025 kg CO2e (kg ECM)-1 for the scenarios using 75 and 50% TMR, respectively. Eliminating tillage, the reduction on total GHG emissions would be 0.03 and 0.05 kg CO2e (kg ECM)-1 for 75 and 50% TMR, respectively. However, this approach may be controversial because lands which have been consistently managed for decades have approached steady state C storage, so that net exchange of CO2 would be negligible [76].
|
||||
|
||||
## Conclusions
|
||||
|
||||
This study assessed the C footprint of dairy cattle systems with or without access to pastures. Including pastures showed potential to maintain or decrease to a small extent the C footprint, which may be attributable to the evidence of low N2O emissions from urine and dung in dairy systems in subtropical areas. Even though the enteric CH4 intensity was the largest source of CO2e emissions, it did not change between different scenarios due to the narrow range of NDF content in diets and maintaining the same milk production with or without access to pastures.
|
||||
|
||||
## Acknowledgments
|
||||
|
||||
Thanks to Anna Naranjo for helpful comments throughout the elaboration of this manuscript, and to André Thaler Neto and Roberto Kappes for providing the key characteristics of the herd considered in this study.
|
||||
|
||||
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||||
- BarrosMV, PiekarskiCM, De FranciscoAC. Carbon footprint of electricity generation in Brazil: An analysis of the 2016–2026 period. Energies. 2018;11 10.3390/en11061412
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||||
- LudingtonD, JohnsonE. Dairy Farm Energy Audit Summary. New York State Energy Res Dev Auth. 2003.
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||||
- ThomaG, JollietO, WangY. A biophysical approach to allocation of life cycle environmental burdens for fluid milk supply chain analysis. Int Dairy J. 2013;31 10.1016/j.idairyj.2012.08.012
|
||||
- NaranjoA, JohnsonA, RossowH. Greenhouse gas, water, and land footprint per unit of production of the California dairy industry over 50 years. 2020 10.3168/jds.2019-16576 32037166
|
||||
- JayasundaraS, WordenD, WeersinkA, WrightT, VanderZaagA, GordonR, et al Improving farm profitability also reduces the carbon footprint of milk production in intensive dairy production systems. J Clean Prod. 2019;229: 1018–1028. 10.1016/j.jclepro.2019.04.013
|
||||
- WilliamsSRO, FisherPD, BerrisfordT, MoatePJ, ReynardK. Reducing methane on-farm by feeding diets high in fat may not always reduce life cycle greenhouse gas emissions. Int J Life Cycle Assess. 2014;19: 69–78. 10.1007/s11367-013-0619-8
|
||||
- GollnowS, LundieS, MooreAD, McLarenJ, van BuurenN, StahleP, et al Carbon footprint of milk production from dairy cows in Australia. Int Dairy J. 2014;37: 31–38. 10.1016/j.idairyj.2014.02.005
|
||||
- O’BrienD, CapperJL, GarnsworthyPC, GraingerC, ShallooL. A case study of the carbon footprint of milk from high-performing confinement and grass-based dairy farms. J Dairy Sci. 2014 10.3168/jds.2013-7174 24440256
|
||||
- ChobtangJ, McLarenSJ, LedgardSF, DonaghyDJ. Consequential Life Cycle Assessment of Pasture-based Milk Production: A Case Study in the Waikato Region, New Zealand. J Ind Ecol. 2017;21: 1139–1152. 10.1111/jiec.12484
|
||||
- GargMR, PhondbaBT, SherasiaPL, MakkarHPS. Carbon footprint of milk production under smallholder dairying in Anand district of Western India: A cradle-to-farm gate life cycle assessment. Anim Prod Sci. 2016;56: 423–436. 10.1071/AN15464
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||||
- de LéisCM, CherubiniE, RuviaroCF, Prudêncio da SilvaV, do Nascimento LampertV, SpiesA, et al Carbon footprint of milk production in Brazil: a comparative case study. Int J Life Cycle Assess. 2015;20: 46–60. 10.1007/s11367-014-0813-3
|
||||
- O’BrienD, GeogheganA, McNamaraK, ShallooL. How can grass-based dairy farmers reduce the carbon footprint of milk? Anim Prod Sci. 2016;56: 495–500. 10.1071/AN15490
|
||||
- O’BrienD, BrennanP, HumphreysJ, RuaneE, ShallooL. An appraisal of carbon footprint of milk from commercial grass-based dairy farms in Ireland according to a certified life cycle assessment methodology. Int J Life Cycle Assess. 2014;19: 1469–1481. 10.1007/s11367-014-0755-9
|
||||
- BaekCY, LeeKM, ParkKH. Quantification and control of the greenhouse gas emissions from a dairy cow system. J Clean Prod. 2014;70: 50–60. 10.1016/j.jclepro.2014.02.010
|
||||
- Dall-OrsolettaAC, AlmeidaJGR, CarvalhoPCF, Savian JV., Ribeiro-Filho HMN. Ryegrass pasture combined with partial total mixed ration reduces enteric methane emissions and maintains the performance of dairy cows during mid to late lactation. J Dairy Sci. 2016;99: 4374–4383. 10.3168/jds.2015-10396 27016830
|
||||
- Dall-OrsolettaAC, OziemblowskiMM, BerndtA, Ribeiro-FilhoHMN. Enteric methane emission from grazing dairy cows receiving corn silage or ground corn supplementation. Anim Feed Sci Technol. 2019;253: 65–73. 10.1016/j.anifeedsci.2019.05.009
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||||
- NiuM, AppuhamyJADRN, LeytemAB, DunganRS, KebreabE. Effect of dietary crude protein and forage contents on enteric methane emissions and nitrogen excretion from dairy cows simultaneously. Anim Prod Sci. 2016;56: 312–321. 10.1071/AN15498
|
||||
- WaghornGC, LawN, BryantM, PachecoD, DalleyD. Digestion and nitrogen excretion by Holstein-Friesian cows in late lactation offered ryegrass-based pasture supplemented with fodder beet. Anim Prod Sci. 2019;59: 1261–1270. 10.1071/AN18018
|
||||
- DickhoeferU, GlowackiS, GómezCA, Castro-MontoyaJM. Forage and protein use efficiency in dairy cows grazing a mixed grass-legume pasture and supplemented with different levels of protein and starch. Livest Sci. 2018;216: 109–118. 10.1016/j.livsci.2018.08.004
|
||||
- SchwabCG, BroderickGA. A 100-Year Review: Protein and amino acid nutrition in dairy cows. J Dairy Sci. 2017;100: 10094–10112. 10.3168/jds.2017-13320 29153157
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||||
- SordiA, DieckowJ, BayerC, AlburquerqueMA, PivaJT, ZanattaJA, et al Nitrous oxide emission factors for urine and dung patches in a subtropical Brazilian pastureland. Agric Ecosyst Environ. 2014;190: 94–103. 10.1016/j.agee.2013.09.004
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||||
- SimonPL, DieckowJ, de KleinCAM, ZanattaJA, van der WeerdenTJ, RamalhoB, et al Nitrous oxide emission factors from cattle urine and dung, and dicyandiamide (DCD) as a mitigation strategy in subtropical pastures. Agric Ecosyst Environ. 2018;267: 74–82. 10.1016/j.agee.2018.08.013
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||||
- WangX, LedgardS, LuoJ, GuoY, ZhaoZ, GuoL, et al Environmental impacts and resource use of milk production on the North China Plain, based on life cycle assessment. Sci Total Environ. 2018;625: 486–495. 10.1016/j.scitotenv.2017.12.259 29291563
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||||
- PirloG, LolliS. Environmental impact of milk production from samples of organic and conventional farms in Lombardy (Italy). J Clean Prod. 2019;211: 962–971. 10.1016/j.jclepro.2018.11.070
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||||
- HerzogA, WincklerC, ZollitschW. In pursuit of sustainability in dairy farming: A review of interdependent effects of animal welfare improvement and environmental impact mitigation. Agric Ecosyst Environ. 2018;267: 174–187. 10.1016/j.agee.2018.07.029
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||||
- MostertPF, van MiddelaarCE, BokkersEAM, de BoerIJM. The impact of subclinical ketosis in dairy cows on greenhouse gas emissions of milk production. J Clean Prod. 2018 10.1016/j.jclepro.2017.10.019
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||||
- MostertPF, van MiddelaarCE, de BoerIJM, BokkersEAM. The impact of foot lesions in dairy cows on greenhouse gas emissions of milk production. Agric Syst. 2018;167: 206–212. 10.1016/j.agsy.2018.09.006
|
||||
- FoleyJA, RamankuttyN, BraumanKA, CassidyES, GerberJS, JohnstonM, et al Solutions for a cultivated planet. Nature. 2011;478: 337–342. 10.1038/nature10452 21993620
|
||||
- LalR. Soil Carbon Sequestration Impacts on Global Climate Change and Food Security. Science (80-). 2004;304: 1623–1627. 10.1126/science.1097396 15192216
|
||||
- BoddeyRM, JantaliaCP, ConceiçaoPC, ZanattaJA, BayerC, MielniczukJ, et al Carbon accumulation at depth in Ferralsols under zero-till subtropical agriculture. Glob Chang Biol. 2010;16: 784–795. 10.1111/j.1365-2486.2009.02020.x
|
||||
- McConkeyB, AngersD, BenthamM, BoehmM, BrierleyT, CerkowniakD, et al Canadian agricultural greenhouse gas monitoring accounting and reporting system: methodology and greenhouse gas estimates for agricultural land in the LULUCF sector for NIR 2014. 2014.
|
||||
842
tests/data/jats/bmj_sample.xml
Normal file
842
tests/data/jats/bmj_sample.xml
Normal file
@ -0,0 +1,842 @@
|
||||
<?xml version="1.0" encoding="UTF-8"?>
|
||||
<!DOCTYPE article PUBLIC "-//NLM//DTD JATS (Z39.96) Journal Publishing DTD v1.1 20151215//EN" "JATS-journalpublishing1.dtd">
|
||||
<article article-type="research-article" dtd-version="1.1" xml:lang="en"
|
||||
xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"
|
||||
xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" >
|
||||
<front>
|
||||
<journal-meta>
|
||||
<journal-id journal-id-type="pmc">bmj</journal-id>
|
||||
<journal-id journal-id-type="pubmed">BMJ</journal-id>
|
||||
<journal-id journal-id-type="publisher">BMJ</journal-id>
|
||||
<issn>0959-8138</issn>
|
||||
<publisher>
|
||||
<publisher-name>BMJ</publisher-name>
|
||||
</publisher>
|
||||
</journal-meta>
|
||||
<article-meta>
|
||||
<article-id pub-id-type="other">jBMJ.v324.i7342.pg880</article-id>
|
||||
<article-id pub-id-type="pmid">11950738</article-id>
|
||||
<article-categories>
|
||||
<subj-group>
|
||||
<subject>Primary care</subject>
|
||||
<subj-group>
|
||||
<subject>190</subject>
|
||||
<subject>10</subject>
|
||||
<subject>218</subject>
|
||||
<subject>219</subject>
|
||||
<subject>355</subject>
|
||||
<subject>357</subject>
|
||||
</subj-group>
|
||||
</subj-group>
|
||||
</article-categories>
|
||||
<title-group>
|
||||
<article-title>Evolving general practice consultation in Britain: issues of length and
|
||||
context</article-title>
|
||||
</title-group>
|
||||
<contrib-group>
|
||||
<contrib contrib-type="author">
|
||||
<name>
|
||||
<surname>Freeman</surname>
|
||||
<given-names>George K</given-names>
|
||||
</name>
|
||||
<role>professor of general practice</role>
|
||||
<xref ref-type="aff" rid="aff-a"/>
|
||||
</contrib>
|
||||
<contrib contrib-type="author">
|
||||
<name>
|
||||
<surname>Horder</surname>
|
||||
<given-names>John P</given-names>
|
||||
</name>
|
||||
<role>past president</role>
|
||||
<xref ref-type="aff" rid="aff-b"/>
|
||||
</contrib>
|
||||
<contrib contrib-type="author">
|
||||
<name>
|
||||
<surname>Howie</surname>
|
||||
<given-names>John G R</given-names>
|
||||
</name>
|
||||
<role>emeritus professor of general practice</role>
|
||||
<xref ref-type="aff" rid="aff-c"/>
|
||||
</contrib>
|
||||
<contrib contrib-type="author">
|
||||
<name>
|
||||
<surname>Hungin</surname>
|
||||
<given-names>A Pali</given-names>
|
||||
</name>
|
||||
<role>professor of general practice</role>
|
||||
<xref ref-type="aff" rid="aff-d"/>
|
||||
</contrib>
|
||||
<contrib contrib-type="author">
|
||||
<name>
|
||||
<surname>Hill</surname>
|
||||
<given-names>Alison P</given-names>
|
||||
</name>
|
||||
<role>general practitioner</role>
|
||||
<xref ref-type="aff" rid="aff-e"/>
|
||||
</contrib>
|
||||
<contrib contrib-type="author">
|
||||
<name>
|
||||
<surname>Shah</surname>
|
||||
<given-names>Nayan C</given-names>
|
||||
</name>
|
||||
<role>general practitioner</role>
|
||||
<xref ref-type="aff" rid="aff-b"/>
|
||||
</contrib>
|
||||
<contrib contrib-type="author">
|
||||
<name>
|
||||
<surname>Wilson</surname>
|
||||
<given-names>Andrew</given-names>
|
||||
</name>
|
||||
<role>senior lecturer</role>
|
||||
<xref ref-type="aff" rid="aff-f"/>
|
||||
</contrib>
|
||||
</contrib-group>
|
||||
<aff id="aff-a">Centre for Primary Care and Social Medicine, Imperial College of Science,
|
||||
Technology and Medicine, London W6 8RP</aff>
|
||||
<aff id="aff-b">Royal College of General Practitioners, London SW7 1PU</aff>
|
||||
<aff id="aff-c">Department of General Practice, University of Edinburgh, Edinburgh EH8 9DX</aff>
|
||||
<aff id="aff-d">Centre for Health Studies, University of Durham, Durham DH1 3HN</aff>
|
||||
<aff id="aff-e">Kilburn Park Medical Centre, London NW6</aff>
|
||||
<aff id="aff-f">Department of General Practice and Primary Health Care, University of Leicester,
|
||||
Leicester LE5 4PW</aff>
|
||||
<author-notes>
|
||||
<fn fn-type="con">
|
||||
<p>Contributors: GKF wrote the paper and revised it after repeated and detailed comments from
|
||||
all of the other authors and feedback from the first referee and from the <italic>BMJ</italic>
|
||||
editorial panel. All other authors gave detailed and repeated comments and cristicisms. GKF is
|
||||
the guarantor of the paper.</p>
|
||||
</fn>
|
||||
<fn>
|
||||
<p>Correspondence to: G Freeman <email>g.freeman@ic.ac.uk</email> </p>
|
||||
</fn>
|
||||
</author-notes>
|
||||
<pub-date date-type="pub" publication-format="print" iso-8601-date="2002-04-13">
|
||||
<day>13</day>
|
||||
<month>4</month>
|
||||
<year>2002</year>
|
||||
</pub-date>
|
||||
<volume>324</volume>
|
||||
<issue>7342</issue>
|
||||
<fpage>880</fpage>
|
||||
<lpage>882</lpage>
|
||||
<history>
|
||||
<date date-type="accepted" iso-8601-date="2002-02-07" publication-format="print">
|
||||
<day>7</day>
|
||||
<month>2</month>
|
||||
<year>2002</year>
|
||||
</date>
|
||||
</history>
|
||||
<permissions>
|
||||
<copyright-statement>Copyright © 2002, BMJ</copyright-statement>
|
||||
<copyright-year>2002, </copyright-year>
|
||||
</permissions>
|
||||
</article-meta>
|
||||
</front>
|
||||
<body>
|
||||
<p>In 1999 Shah<xref ref-type="bibr" rid="B1">1</xref> and others said that the Royal College of
|
||||
General Practitioners should advocate longer consultations in general practice as a matter of
|
||||
policy. The college set up a working group chaired by A P Hungin, and a systematic review of
|
||||
literature on consultation length in general practice was commissioned. The working group agreed
|
||||
that the available evidence would be hard to interpret without discussion of the changing context
|
||||
within which consultations now take place. For many years general practitioners and those who
|
||||
have surveyed patients' opinions in the United Kingdom have complained about short consultation
|
||||
time, despite a steady increase in actual mean length. Recently Mechanic pointed out that this is
|
||||
also true in the United States.<xref ref-type="bibr" rid="B2">2</xref> Is there any justification
|
||||
for a further increase in mean time allocated per consultation in general practice?</p>
|
||||
<p>We report on the outcome of extensive debate among a group of general practitioners with an
|
||||
interest in the process of care, with reference to the interim findings of the commissioned
|
||||
systematic review and our personal databases. The review identified 14 relevant papers. <boxed-text>
|
||||
<sec>
|
||||
<title>Summary points</title>
|
||||
<p> <list list-type="bullet">
|
||||
<list-item>
|
||||
<p>Longer consultations are associated with a range of better patient outcomes</p>
|
||||
</list-item>
|
||||
<list-item>
|
||||
<p>Modern consultations in general practice deal with patients with more serious and chronic
|
||||
conditions</p>
|
||||
</list-item>
|
||||
<list-item>
|
||||
<p>Increasing patient participation means more complex interaction, which demands extra
|
||||
time</p>
|
||||
</list-item>
|
||||
<list-item>
|
||||
<p>Difficulties with access and with loss of continuity add to perceived stress and poor
|
||||
performance and lead to further pressure on time</p>
|
||||
</list-item>
|
||||
<list-item>
|
||||
<p>Longer consultations should be a professional priority, combined with increased use of
|
||||
technology and more flexible practice management to maximise interpersonal continuity</p>
|
||||
</list-item>
|
||||
<list-item>
|
||||
<p>Research on implementation is needed</p>
|
||||
</list-item>
|
||||
</list> </p>
|
||||
</sec>
|
||||
</boxed-text> </p>
|
||||
<sec sec-type="subjects">
|
||||
<title>Longer consultations: benefits for patients</title>
|
||||
<p>The systematic review consistently showed that doctors with longer consultation times
|
||||
prescribe less and offer more advice on lifestyle and other health promoting activities. Longer
|
||||
consultations have been significantly associated with better recognition and handling of
|
||||
psychosocial problems<xref ref-type="bibr" rid="B3">3</xref> and with better patient
|
||||
enablement.<xref ref-type="bibr" rid="B4">4</xref> Also clinical care for some chronic illnesses
|
||||
is better in practices with longer booked intervals between one appointment and the next.<xref
|
||||
ref-type="bibr" rid="B5">5</xref> It is not clear whether time is itself the main influence or
|
||||
whether some doctors insist on more time.</p>
|
||||
<p>A national survey in 1998 reported that most (87%) patients were satisfied with the
|
||||
length of their most recent consultation.<xref ref-type="bibr" rid="B6">6</xref> Satisfaction
|
||||
with any service will be high if expectations are met or exceeded. But expectations are modified
|
||||
by previous experience.<xref ref-type="bibr" rid="B7">7</xref> The result is that primary care
|
||||
patients are likely to be satisfied with what they are used to unless the context modifies the
|
||||
effects of their own experience.</p>
|
||||
</sec>
|
||||
<sec>
|
||||
<title>Context of modern consultations</title>
|
||||
<p>Shorter consultations were more appropriate when the population was younger, when even a brief
|
||||
absence from employment due to sickness required a doctor's note, and when many simple remedies
|
||||
were available only on prescription. Recently at least five important influences have increased
|
||||
the content and hence the potential length of the consultation.</p>
|
||||
</sec>
|
||||
<sec>
|
||||
<title>Participatory consultation style</title>
|
||||
<p>The most effective consultations are those in which doctors most directly acknowledge and
|
||||
perhaps respond to patients' problems and concerns. In addition, for patients to be committed to
|
||||
taking advantage of medical advice they must agree with both the goals and methods proposed. A
|
||||
landmark publication in the United Kingdom was <italic>Meetings Between Experts</italic>, which
|
||||
argued that while doctors are the experts about medical problems in general patients are the
|
||||
experts on how they themselves experience these problems.<xref ref-type="bibr" rid="B8">8</xref>
|
||||
New emphasis on teaching consulting skills in general practice advocated specific attention to
|
||||
the patient's agenda, beliefs, understanding, and agreement. Currently the General Medical
|
||||
Council, aware that communication difficulties underlie many complaints about doctors, has
|
||||
further emphasised the importance of involving patients in consultations in its revised guidance
|
||||
to medical schools.<xref ref-type="bibr" rid="B9">9</xref> More patient involvement should give
|
||||
a better outcome, but this participatory style usually lengthens consultations.</p>
|
||||
</sec>
|
||||
<sec>
|
||||
<title>Extended professional agenda</title>
|
||||
<p>The traditional consultation in general practice was brief.<xref ref-type="bibr" rid="B2"
|
||||
>2</xref> The patient presented symptoms and the doctor prescribed treatment. In 1957 Balint
|
||||
gave new insights into the meaning of symptoms.<xref ref-type="bibr" rid="B10">10</xref> By 1979
|
||||
an enhanced model of consultation was presented, in which the doctors dealt with ongoing as well
|
||||
as presenting problems and added health promotion and education about future appropriate use of
|
||||
services.<xref ref-type="bibr" rid="B11">11</xref> Now, with an ageing population and more
|
||||
community care of chronic illness, there are more issues to be considered at each consultation.
|
||||
Ideas of what constitutes good general practice are more complex.<xref ref-type="bibr" rid="B12"
|
||||
>12</xref> Good practice now includes both extended care of chronic medical problems—for
|
||||
example, coronary heart disease<xref ref-type="bibr" rid="B13">13</xref>—and a public
|
||||
health role. At first this model was restricted to those who lead change (“early
|
||||
adopters”) and enthusiasts<xref ref-type="bibr" rid="B14">14</xref> but now it is
|
||||
embedded in professional and managerial expectations of good practice.</p>
|
||||
<p>Adequate time is essential. It may be difficult for an elderly patient with several active
|
||||
problems to undress, be examined, and get adequate professional consideration in under 15
|
||||
minutes. Here the doctor is faced with the choice of curtailing the consultation or of reducing
|
||||
the time available for the next patient. Having to cope with these situations often contributes
|
||||
to professional dissatisfaction.<xref ref-type="bibr" rid="B15">15</xref> This combination of
|
||||
more care, more options, and more genuine discussion of those options with informed patient
|
||||
choice inevitably leads to pressure on time.</p>
|
||||
</sec>
|
||||
<sec>
|
||||
<title>Access problems</title>
|
||||
<p>In a service free at the point of access, rising demand will tend to increase rationing by
|
||||
delay. But attempts to improve access by offering more consultations at short notice squeeze
|
||||
consultation times.</p>
|
||||
<p>While appointment systems can and should reduce queuing time for consultations, they have long
|
||||
tended to be used as a brake on total demand.<xref ref-type="bibr" rid="B16">16</xref> This may
|
||||
seriously erode patients' confidence in being able to see their doctor or nurse when they need
|
||||
to. Patients are offered appointments further ahead but may keep these even if their symptoms
|
||||
have remitted “just in case.” Availability of consultations is thus blocked.
|
||||
Receptionists are then inappropriately blamed for the inadequate access to doctors.</p>
|
||||
<p>In response to perception of delay, the government has set targets in the NHS plan of
|
||||
“guaranteed access to a primary care professional within 24 hours and to a primary care
|
||||
doctor within 48 hours.” Implementation is currently being negotiated.</p>
|
||||
<p>Virtually all patients think that they would not consult unless it was absolutely necessary.
|
||||
They do not think they are wasting NHS time and do not like being made to feel so. But
|
||||
underlying general practitioners' willingness to make patients wait several days is their
|
||||
perception that few of the problems are urgent. Patients and general practitioners evidently do
|
||||
not agree about the urgency of so called minor problems. To some extent general practice in the
|
||||
United Kingdom may have scored an “own goal” by setting up perceived access
|
||||
barriers (appointment systems and out of hours cooperatives) in the attempt to increase
|
||||
professional standards and control demand in a service that is free at the point of access.</p>
|
||||
<p>A further government initiative has been to bypass general practice with new
|
||||
services—notably, walk-in centres (primary care clinics in which no appointment is
|
||||
needed) and NHS Direct (a professional telephone helpline giving advice on simple remedies and
|
||||
access to services). Introduced widely and rapidly, these services each potentially provide
|
||||
significant features of primary care—namely, quick access to skilled health advice and
|
||||
first line treatment.</p>
|
||||
</sec>
|
||||
<sec>
|
||||
<title>Loss of interpersonal continuity</title>
|
||||
<p>If a patient has to consult several different professionals, particularly over a short period
|
||||
of time, there is inevitable duplication of stories, risk of naive diagnoses, potential for
|
||||
conflicting advice, and perhaps loss of trust. Trust is essential if patients are to accept the
|
||||
“wait and see” management policy which is, or should be, an important part of the
|
||||
management of self limiting conditions, which are often on the boundary between illness and
|
||||
non-illness.<xref ref-type="bibr" rid="B17">17</xref> Such duplication again increases pressure
|
||||
for more extra (unscheduled) consultations resulting in late running and professional
|
||||
frustration.<xref ref-type="bibr" rid="B18">18</xref> </p>
|
||||
<p>Mechanic described how loss of longitudinal (and perhaps personal and relational<xref
|
||||
ref-type="bibr" rid="B19">19</xref>) continuity influences the perception and use of time
|
||||
through an inability to build on previous consultations.<xref ref-type="bibr" rid="B2">2</xref>
|
||||
Knowing the doctor well, particularly in smaller practices, is associated with enhanced patient
|
||||
enablement in shorter time.<xref ref-type="bibr" rid="B4">4</xref> Though Mechanic pointed out
|
||||
that three quarters of UK patients have been registered with their general practitioner five
|
||||
years or more, this may be misleading. Practices are growing, with larger teams and more
|
||||
registered patients. Being registered with a doctor in a larger practice is usually no guarantee
|
||||
that the patient will be able to see the same doctor or the doctor of his or her choice, who may
|
||||
be different. Thus the system does not encourage adequate personal continuity. This adds to
|
||||
pressure on time and reduces both patient and professional satisfaction.</p>
|
||||
</sec>
|
||||
<sec>
|
||||
<title>Health service reforms</title>
|
||||
<p>Finally, for the past 15 years the NHS has experienced unprecedented change with a succession
|
||||
of major administrative reforms. Recent reforms have focused on an NHS led by primary care,
|
||||
including the aim of shifting care from the secondary specialist sector to primary care. One
|
||||
consequence is increased demand for primary care of patients with more serious and less stable
|
||||
problems. With the limited piloting of reforms we do not know whether such major redirection can
|
||||
be achieved without greatly altering the delicate balance between expectations (of both patients
|
||||
and staff) and what is delivered.</p>
|
||||
</sec>
|
||||
<sec>
|
||||
<title>The future</title>
|
||||
<p>We think that the way ahead must embrace both longer mean consultation times and more
|
||||
flexibility. More time is needed for high quality consultations with patients with major and
|
||||
complex problems of all kinds. But patients also need access to simpler services and advice.
|
||||
This should be more appropriate (and cost less) when it is given by professionals who know the
|
||||
patient and his or her medical history and social circumstances. For doctors, the higher quality
|
||||
associated with longer consultations may lead to greater professional satisfaction and, if these
|
||||
longer consultations are combined with more realistic scheduling, to reduced levels of
|
||||
stress.<xref ref-type="bibr" rid="B20">20</xref> They will also find it easier to develop
|
||||
further the care of chronic disease.</p>
|
||||
<p>The challenge posed to general practice by walk-in centres and NHS Direct is considerable, and
|
||||
the diversion of funding from primary care is large. The risk of waste and duplication increases
|
||||
as more layers of complexity are added to a primary care service that started out as something
|
||||
familiar, simple, and local and which is still envied in other developed countries.<xref
|
||||
ref-type="bibr" rid="B21">21</xref> Access needs to be simple, and the advantages of personal
|
||||
knowledge and trust in minimising duplication and overmedicalisation need to be exploited.</p>
|
||||
<p>We must ensure better communication and access so that patients can more easily deal with
|
||||
minor issues and queries with someone they know and trust and avoid the formality and
|
||||
inconvenience of a full face to face consultation. Too often this has to be with a different
|
||||
professional, unfamiliar with the nuances of the case. There should be far more managerial
|
||||
emphasis on helping patients to interact with their chosen practitioner<xref ref-type="bibr"
|
||||
rid="B22">22</xref>; such a programme has been described.<xref ref-type="bibr" rid="B23"
|
||||
>23</xref> Modern information systems make it much easier to record which doctor(s) a patient
|
||||
prefers to see and to monitor how often this is achieved. The telephone is hardly modern but is
|
||||
underused. Email avoids the problems inherent in arranging simultaneous availability necessary
|
||||
for telephone consultations but at the cost of reducing the communication of emotions. There is
|
||||
a place for both.<xref ref-type="bibr" rid="B2">2</xref> Access without prior appointment is a
|
||||
valued feature of primary care, and we need to know more about the right balance between planned
|
||||
and ad hoc consulting.</p>
|
||||
</sec>
|
||||
<sec>
|
||||
<title>Next steps</title>
|
||||
<p>General practitioners do not behave in a uniform way. They can be categorised as slow, medium,
|
||||
and fast and react in different ways to changes in consulting speed.<xref ref-type="bibr"
|
||||
rid="B18">18</xref> They are likely to have differing views about a widespread move to lengthen
|
||||
consultation time. We do not need further confirmation that longer consultations are desirable
|
||||
and necessary, but research could show us the best way to learn how to introduce them with
|
||||
minimal disruption to the way in which patients and practices like primary care to be
|
||||
provided.<xref ref-type="bibr" rid="B24">24</xref> We also need to learn how to make the most of
|
||||
available time in complex consultations.</p>
|
||||
<p>Devising appropriate incentives and helping practices move beyond just reacting to demand in
|
||||
the traditional way by working harder and faster is perhaps our greatest challenge in the United
|
||||
Kingdom. The new primary are trusts need to work together with the growing primary care research
|
||||
networks to carry out the necessary development work. In particular, research is needed on how a
|
||||
primary care team can best provide the right balance of quick access and interpersonal knowledge
|
||||
and trust.</p>
|
||||
</sec>
|
||||
</body>
|
||||
<back>
|
||||
<ack>
|
||||
<p>We thank the other members of the working group: Susan Childs, Paul Freeling, Iona Heath,
|
||||
Marshall Marinker, and Bonnie Sibbald. We also thank Fenny Green of the Royal College of General
|
||||
Practitioners for administrative help.</p>
|
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</ack>
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||||
</element-citation>
|
||||
</ref>
|
||||
<ref id="B18">
|
||||
<label>18</label>
|
||||
<element-citation publication-type="journal" publication-format="print">
|
||||
<person-group person-group-type="author"> <name>
|
||||
<surname>Howie</surname>
|
||||
<given-names>JGR</given-names>
|
||||
</name> <name>
|
||||
<surname>Hopton</surname>
|
||||
<given-names>JL</given-names>
|
||||
</name> <name>
|
||||
<surname>Heaney</surname>
|
||||
<given-names>DJ</given-names>
|
||||
</name> <name>
|
||||
<surname>Porter</surname>
|
||||
<given-names>AMD</given-names>
|
||||
</name> </person-group>
|
||||
<article-title>Attitudes to medical care, the organization of work, and stress among general
|
||||
practitioners</article-title>
|
||||
<source>Br J Gen Pract</source>
|
||||
<year iso-8601-date="1992">1992</year>
|
||||
<volume>42</volume>
|
||||
<fpage>181</fpage>
|
||||
<lpage>185</lpage>
|
||||
<pub-id pub-id-type="pmid">1389427</pub-id>
|
||||
</element-citation>
|
||||
</ref>
|
||||
<ref id="B19">
|
||||
<label>19</label>
|
||||
<element-citation publication-type="book" publication-format="web">
|
||||
<person-group person-group-type="author"> <name>
|
||||
<surname>Freeman</surname>
|
||||
<given-names>G</given-names>
|
||||
</name> <name>
|
||||
<surname>Shepperd</surname>
|
||||
<given-names>S</given-names>
|
||||
</name> <name>
|
||||
<surname>Robinson</surname>
|
||||
<given-names>I</given-names>
|
||||
</name> <name>
|
||||
<surname>Ehrich</surname>
|
||||
<given-names>K</given-names>
|
||||
</name> <name>
|
||||
<surname>Richards</surname>
|
||||
<given-names>SC</given-names>
|
||||
</name> <name>
|
||||
<surname>Pitman</surname>
|
||||
<given-names>P</given-names>
|
||||
</name> </person-group>
|
||||
<source>Continuity of care: report of a scoping exercise for the national co-ordinating centre
|
||||
for NHS Service Delivery and Organisation R&D (NCCSDO), Summer 2000</source>
|
||||
<year iso-8601-date="2001">2001</year>
|
||||
<publisher-loc>London</publisher-loc>
|
||||
<publisher-name>NCCSDO</publisher-name>
|
||||
<comment><ext-link ext-link-type="url" xmlns:xlink="http://www.w3.org/1999/xlink"
|
||||
xlink:href="http://www.sdo.lshtm.ac.uk/continuityofcare.htm"
|
||||
>www.sdo.lshtm.ac.uk/continuityofcare.htm</ext-link> (accessed 2 Jan 2002)</comment>
|
||||
</element-citation>
|
||||
</ref>
|
||||
<ref id="B20">
|
||||
<label>20</label>
|
||||
<element-citation publication-type="journal" publication-format="print">
|
||||
<person-group person-group-type="author"> <name>
|
||||
<surname>Wilson</surname>
|
||||
<given-names>A</given-names>
|
||||
</name> <name>
|
||||
<surname>McDonald</surname>
|
||||
<given-names>P</given-names>
|
||||
</name> <name>
|
||||
<surname>Hayes</surname>
|
||||
<given-names>L</given-names>
|
||||
</name> <name>
|
||||
<surname>Cooney</surname>
|
||||
<given-names>J</given-names>
|
||||
</name> </person-group>
|
||||
<article-title>Longer booking intervals in general practice: effects on doctors' stress and
|
||||
arousal</article-title>
|
||||
<source>Br J Gen Pract</source>
|
||||
<year iso-8601-date="1991">1991</year>
|
||||
<volume>41</volume>
|
||||
<fpage>184</fpage>
|
||||
<lpage>187</lpage>
|
||||
<pub-id pub-id-type="pmid">1878267</pub-id>
|
||||
</element-citation>
|
||||
</ref>
|
||||
<ref id="B21">
|
||||
<label>21</label>
|
||||
<element-citation publication-type="journal" publication-format="print">
|
||||
<person-group person-group-type="author"> <name>
|
||||
<surname>De Maeseneer</surname>
|
||||
<given-names>J</given-names>
|
||||
</name> <name>
|
||||
<surname>Hjortdahl</surname>
|
||||
<given-names>P</given-names>
|
||||
</name> <name>
|
||||
<surname>Starfield</surname>
|
||||
<given-names>B</given-names>
|
||||
</name> </person-group>
|
||||
<article-title>Fix what's wrong, not what's right, with general practice in
|
||||
Britain</article-title>
|
||||
<source>BMJ</source>
|
||||
<year iso-8601-date="2000">2000</year>
|
||||
<volume>320</volume>
|
||||
<fpage>1616</fpage>
|
||||
<lpage>1617</lpage>
|
||||
<pub-id pub-id-type="pmid">10856043</pub-id>
|
||||
</element-citation>
|
||||
</ref>
|
||||
<ref id="B22">
|
||||
<label>22</label>
|
||||
<element-citation publication-type="journal" publication-format="print">
|
||||
<person-group person-group-type="author"> <name>
|
||||
<surname>Freeman</surname>
|
||||
<given-names>G</given-names>
|
||||
</name> <name>
|
||||
<surname>Hjortdahl</surname>
|
||||
<given-names>P</given-names>
|
||||
</name> </person-group>
|
||||
<article-title>What future for continuity of care in general practice?</article-title>
|
||||
<source>BMJ</source>
|
||||
<year iso-8601-date="1997">1997</year>
|
||||
<volume>314</volume>
|
||||
<fpage>1870</fpage>
|
||||
<lpage>1873</lpage>
|
||||
<pub-id pub-id-type="pmid">9224130</pub-id>
|
||||
</element-citation>
|
||||
</ref>
|
||||
<ref id="B23">
|
||||
<label>23</label>
|
||||
<element-citation publication-type="journal" publication-format="print">
|
||||
<person-group person-group-type="author"> <name>
|
||||
<surname>Kibbe</surname>
|
||||
<given-names>DC</given-names>
|
||||
</name> <name>
|
||||
<surname>Bentz</surname>
|
||||
<given-names>E</given-names>
|
||||
</name> <name>
|
||||
<surname>McLaughlin</surname>
|
||||
<given-names>CP</given-names>
|
||||
</name> </person-group>
|
||||
<article-title>Continuous quality improvement for continuity of care</article-title>
|
||||
<source>J Fam Pract</source>
|
||||
<year iso-8601-date="1993">1993</year>
|
||||
<volume>36</volume>
|
||||
<fpage>304</fpage>
|
||||
<lpage>308</lpage>
|
||||
<pub-id pub-id-type="pmid">8454977</pub-id>
|
||||
</element-citation>
|
||||
</ref>
|
||||
<ref id="B24">
|
||||
<label>24</label>
|
||||
<element-citation publication-type="journal" publication-format="print">
|
||||
<person-group person-group-type="author"> <name>
|
||||
<surname>Williams</surname>
|
||||
<given-names>M</given-names>
|
||||
</name> <name>
|
||||
<surname>Neal</surname>
|
||||
<given-names>RD</given-names>
|
||||
</name> </person-group>
|
||||
<article-title>Time for a change? The process of lengthening booking intervals in general
|
||||
practice</article-title>
|
||||
<source>Br J Gen Pract</source>
|
||||
<year iso-8601-date="1998">1998</year>
|
||||
<volume>48</volume>
|
||||
<fpage>1783</fpage>
|
||||
<lpage>1786</lpage>
|
||||
<pub-id pub-id-type="pmid">10198490</pub-id>
|
||||
</element-citation>
|
||||
</ref>
|
||||
</ref-list>
|
||||
<fn-group>
|
||||
<fn id="fn1">
|
||||
<p>Funding: Meetings of the working group in 1999-2000 were funded by the
|
||||
<funding-source>Scientific Foundation Board of the RCGP</funding-source>.</p>
|
||||
</fn>
|
||||
<fn id="fn2">
|
||||
<p>Competing interests: None declared.</p>
|
||||
</fn>
|
||||
</fn-group>
|
||||
</back>
|
||||
</article>
|
||||
3089
tests/data/jats/pnas_sample.xml
Normal file
3089
tests/data/jats/pnas_sample.xml
Normal file
File diff suppressed because it is too large
Load Diff
@ -19,7 +19,7 @@ def get_pubmed_paths():
|
||||
|
||||
|
||||
def get_converter():
|
||||
converter = DocumentConverter(allowed_formats=[InputFormat.XML_PUBMED])
|
||||
converter = DocumentConverter(allowed_formats=[InputFormat.XML_JATS])
|
||||
return converter
|
||||
|
||||
|
||||
@ -130,24 +130,24 @@ def test_guess_format(tmp_path):
|
||||
doc_path = Path("./tests/data/uspto/pftaps057006474.txt")
|
||||
assert dci._guess_format(doc_path) == InputFormat.XML_USPTO
|
||||
|
||||
# Valid XML PubMed
|
||||
buf = BytesIO(Path("./tests/data/pubmed/elife-56337.xml").open("rb").read())
|
||||
# Valid XML JATS
|
||||
buf = BytesIO(Path("./tests/data/jats/elife-56337.xml").open("rb").read())
|
||||
stream = DocumentStream(name="elife-56337.xml", stream=buf)
|
||||
assert dci._guess_format(stream) == InputFormat.XML_PUBMED
|
||||
doc_path = Path("./tests/data/pubmed/elife-56337.xml")
|
||||
assert dci._guess_format(doc_path) == InputFormat.XML_PUBMED
|
||||
assert dci._guess_format(stream) == InputFormat.XML_JATS
|
||||
doc_path = Path("./tests/data/jats/elife-56337.xml")
|
||||
assert dci._guess_format(doc_path) == InputFormat.XML_JATS
|
||||
|
||||
buf = BytesIO(Path("./tests/data/pubmed/elife-56337.nxml").open("rb").read())
|
||||
buf = BytesIO(Path("./tests/data/jats/elife-56337.nxml").open("rb").read())
|
||||
stream = DocumentStream(name="elife-56337.nxml", stream=buf)
|
||||
assert dci._guess_format(stream) == InputFormat.XML_PUBMED
|
||||
doc_path = Path("./tests/data/pubmed/elife-56337.nxml")
|
||||
assert dci._guess_format(doc_path) == InputFormat.XML_PUBMED
|
||||
assert dci._guess_format(stream) == InputFormat.XML_JATS
|
||||
doc_path = Path("./tests/data/jats/elife-56337.nxml")
|
||||
assert dci._guess_format(doc_path) == InputFormat.XML_JATS
|
||||
|
||||
buf = BytesIO(Path("./tests/data/pubmed/elife-56337.txt").open("rb").read())
|
||||
buf = BytesIO(Path("./tests/data/jats/elife-56337.txt").open("rb").read())
|
||||
stream = DocumentStream(name="elife-56337.txt", stream=buf)
|
||||
assert dci._guess_format(stream) == InputFormat.XML_PUBMED
|
||||
doc_path = Path("./tests/data/pubmed/elife-56337.txt")
|
||||
assert dci._guess_format(doc_path) == InputFormat.XML_PUBMED
|
||||
assert dci._guess_format(stream) == InputFormat.XML_JATS
|
||||
doc_path = Path("./tests/data/jats/elife-56337.txt")
|
||||
assert dci._guess_format(doc_path) == InputFormat.XML_JATS
|
||||
|
||||
# Valid XML, non-supported flavor
|
||||
xml_content = (
|
||||
|
||||
Loading…
Reference in New Issue
Block a user