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Chambers, B.

Publications and source records attributed to Chambers, B..

2 recordsLinked to original sources

FOXO1 and FOXO3 cooperatively regulate innate lymphoid cell development

The natural killer (NK) and non-cytotoxic innate lymphoid cells (ILC) lineages play vital role in the regulation of the immune system. Yet understanding of mechanisms controlling NK/ILC development remains incomplete. The evolutionary conserved FOXO family of forkhead transcription factors are critical regulators of cellular processes. We found that the loss of FOXO1 and FOXO3 together caused impaired activation of the NK gene expression program and reduced ETS binding already at the common lymphoid progenitor (CLP) level and a block at the ILC progenitor (ILCP) to NK progenitor transition. FOXO controlled NK cell maturation in organ specific manner and their ability to respond to IL-15. At the ILCP level, disruption of the ILC lineage specific gene programs was associated with broad perturbation of the generation of the non-cytotoxic ILC subsets. We concluded that FOXO1 and FOXO3 cooperatively regulate ILC lineage specification at the progenitor level as well as the generation of mature ILCs.

cell biology

NERO: A Biomedical Named-entity (Recognition) Ontology with a Large, Annotated Corpus Reveals Meaningful Associations Through Text Embedding

Machine reading is essential for unlocking valuable knowledge contained in the millions of existing biomedical documents. Over the last two decades 1,2, the most dramatic advances in machine-reading have followed in the wake of critical corpus development3. Large, well-annotated corpora have been associated with punctuated advances in machine reading methodology and automated knowledge extraction systems in the same way that ImageNet 4 was fundamental for developing machine vision techniques. This study contributes six components to an advanced, named-entity analysis tool for biomedicine: (a) a new, Named-Entity Recognition Ontology (NERO) developed specifically for describing entities in biomedical texts, which accounts for diverse levels of ambiguity, bridging the scientific sublanguages of molecular biology, genetics, biochemistry, and medicine; (b) detailed guidelines for human experts annotating hundreds of named-entity classes; (c) pictographs for all named entities, to simplify the burden of annotation for curators; (d) an original, annotated corpus comprising 35,865 sentences, which encapsulate 190,679 named entities and 43,438 events connecting two or more entities; (e) validated, off-the-shelf, named-entity recognition automated extraction, and; (f) embedding models that demonstrate the promise of biomedical associations embedded within this corpus.

systems biology