Interaction of threat expressions and eye gaze: an event-related potential study

he current study examined the interaction of fearful, angry, happy, and neutral expressions with left, straight, and right eye gaze directions. Human participants viewed faces consisting of various expression and eye gaze combinations while event-related potential (ERP) data were collected. The results showed that angry expressions modulated the mean amplitude of the P1, whereas fearful and happy expressions modulated the mean amplitude of the N170. No influence of eye gaze on mean amplitudes for the P1 and N170 emerged. Fearful, angry, and happy expressions began to interact with eye gaze to influence mean amplitudes in the time window of 200–400 ms. The results suggest early processing of expression influence ERPs independent of eye gaze, whereas expression and gaze interact to influence later ERPs

The Bioinformatics Open Source Conference (BOSC) 2013

Contact: [email protected]

On the neural networks of empathy: A principal component analysis of an fMRI study

© 2008 Nomi et al; licensee BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution Licens

BOSC 2019, the 20th annual Bioinformatics Open Source Conference.

The Bioinformatics Open Source Conference is a volunteer-organized meeting that covers open source software development and open science in bioinformatics. Launched in 2000, BOSC has been held every year since. BOSC 2019, the 20th annual BOSC, took place as one of the Communities of Special Interest (COSIs) at the Intelligent Systems for Molecular Biology meeting (ISMB/ECCB 2019). The two-day meeting included a total of 46 talks and 55 posters, as well as eight Birds of a Feather interest groups. The keynote speaker was University of Cape Town professor Dr. Nicola Mulder, who spoke on "Building infrastructure for responsible open science in Africa". Immediately after BOSC 2019, about 50 people participated in the two-day CollaborationFest (CoFest for short), an open and free community-driven event at which participants work together to contribute to bioinformatics software, documentation, training materials, and use cases

Memorandum on Mississippi House Bill 1523

As legal scholars with expertise in matters of religious freedom, civil rights, and the interaction between those fields, we offer our opinion on the scope and meaning of Mississippi House Bill 1523, which was signed into law today by Governor Phil Bryant. Specifically, we wish to call attention to language in the law that we believe conflicts with the Establishment Clause of the U.S. Constitution. We share the view of Justice Kennedy when he expressed that “a bare . . . desire to harm a politically unpopular group cannot constitute a legitimate governmental interest,” and would add that neither can such a desire be justified in the name of religious liberty. HB 1523 presents a conflict with First Amendment religious freedom doctrine by providing for religious exemptions that will meaningfully harm the rights of others, particularly LGBT Mississippians

KG-COVID-19: A Framework to Produce Customized Knowledge Graphs for COVID-19 Response.

Integrated, up-to-date data about SARS-CoV-2 and COVID-19 is crucial for the ongoing response to the COVID-19 pandemic by the biomedical research community. While rich biological knowledge exists for SARS-CoV-2 and related viruses (SARS-CoV, MERS-CoV), integrating this knowledge is difficult and time-consuming, since much of it is in siloed databases or in textual format. Furthermore, the data required by the research community vary drastically for different tasks; the optimal data for a machine learning task, for example, is much different from the data used to populate a browsable user interface for clinicians. To address these challenges, we created KG-COVID-19, a flexible framework that ingests and integrates heterogeneous biomedical data to produce knowledge graphs (KGs), and applied it to create a KG for COVID-19 response. This KG framework also can be applied to other problems in which siloed biomedical data must be quickly integrated for different research applications, including future pandemics

NSAID use and clinical outcomes in COVID-19 patients: a 38-center retrospective cohort study.

BACKGROUND: Non-steroidal anti-inflammatory drugs (NSAIDs) are commonly used to reduce pain, fever, and inflammation but have been associated with complications in community-acquired pneumonia. Observations shortly after the start of the COVID-19 pandemic in 2020 suggested that ibuprofen was associated with an increased risk of adverse events in COVID-19 patients, but subsequent observational studies failed to demonstrate increased risk and in one case showed reduced risk associated with NSAID use. METHODS: A 38-center retrospective cohort study was performed that leveraged the harmonized, high-granularity electronic health record data of the National COVID Cohort Collaborative. A propensity-matched cohort of 19,746 COVID-19 inpatients was constructed by matching cases (treated with NSAIDs at the time of admission) and 19,746 controls (not treated) from 857,061 patients with COVID-19 available for analysis. The primary outcome of interest was COVID-19 severity in hospitalized patients, which was classified as: moderate, severe, or mortality/hospice. Secondary outcomes were acute kidney injury (AKI), extracorporeal membrane oxygenation (ECMO), invasive ventilation, and all-cause mortality at any time following COVID-19 diagnosis. RESULTS: Logistic regression showed that NSAID use was not associated with increased COVID-19 severity (OR: 0.57 95% CI: 0.53-0.61). Analysis of secondary outcomes using logistic regression showed that NSAID use was not associated with increased risk of all-cause mortality (OR 0.51 95% CI: 0.47-0.56), invasive ventilation (OR: 0.59 95% CI: 0.55-0.64), AKI (OR: 0.67 95% CI: 0.63-0.72), or ECMO (OR: 0.51 95% CI: 0.36-0.7). In contrast, the odds ratios indicate reduced risk of these outcomes, but our quantitative bias analysis showed E-values of between 1.9 and 3.3 for these associations, indicating that comparatively weak or moderate confounder associations could explain away the observed associations. CONCLUSIONS: Study interpretation is limited by the observational design. Recording of NSAID use may have been incomplete. Our study demonstrates that NSAID use is not associated with increased COVID-19 severity, all-cause mortality, invasive ventilation, AKI, or ECMO in COVID-19 inpatients. A conservative interpretation in light of the quantitative bias analysis is that there is no evidence that NSAID use is associated with risk of increased severity or the other measured outcomes. Our results confirm and extend analogous findings in previous observational studies using a large cohort of patients drawn from 38 centers in a nationally representative multicenter database

Testimony Regarding the First Amendment Defense Act (FADA)

My testimony today is delivered on behalf of twenty leading legal scholars who have joined me in providing an in depth analysis of the meaning and likely effects of the First Amendment Defense Act (FADA), were it to become law. We feel particularly compelled to provide testimony to this Committee because the first legislative finding set out in FADA declares that: “Leading legal scholars concur that conflicts between same-sex marriage and religious liberty are real and should be addressed through legislation.” As leading legal scholars we must correct this statement: we do not concur that conflicts between same-sex marriage and religious liberty are real, nor do we hold the view that any such conflict should be addressed through legislation. On the contrary, we maintain that religious liberty rights are already well protected in the U.S. Constitution and in existing federal and state legislation, rendering FADA both unnecessary and harmful

KG-Hub-building and exchanging biological knowledge graphs.

MOTIVATION: Knowledge graphs (KGs) are a powerful approach for integrating heterogeneous data and making inferences in biology and many other domains, but a coherent solution for constructing, exchanging, and facilitating the downstream use of KGs is lacking. RESULTS: Here we present KG-Hub, a platform that enables standardized construction, exchange, and reuse of KGs. Features include a simple, modular extract-transform-load pattern for producing graphs compliant with Biolink Model (a high-level data model for standardizing biological data), easy integration of any OBO (Open Biological and Biomedical Ontologies) ontology, cached downloads of upstream data sources, versioned and automatically updated builds with stable URLs, web-browsable storage of KG artifacts on cloud infrastructure, and easy reuse of transformed subgraphs across projects. Current KG-Hub projects span use cases including COVID-19 research, drug repurposing, microbial-environmental interactions, and rare disease research. KG-Hub is equipped with tooling to easily analyze and manipulate KGs. KG-Hub is also tightly integrated with graph machine learning (ML) tools which allow automated graph ML, including node embeddings and training of models for link prediction and node classification. AVAILABILITY AND IMPLEMENTATION: https://kghub.org