Chemical identification and indexing in full-text articles: an overview of the NLM-Chem track at BioCreative VII.

The BioCreative National Library of Medicine (NLM)-Chem track calls for a community effort to fine-tune automated recognition of chemical names in the biomedical literature. Chemicals are one of the most searched biomedical entities in PubMed, and-as highlighted during the coronavirus disease 2019 pandemic-their identification may significantly advance research in multiple biomedical subfields. While previous community challenges focused on identifying chemical names mentioned in titles and abstracts, the full text contains valuable additional detail. We, therefore, organized the BioCreative NLM-Chem track as a community effort to address automated chemical entity recognition in full-text articles. The track consisted of two tasks: (i) chemical identification and (ii) chemical indexing. The chemical identification task required predicting all chemicals mentioned in recently published full-text articles, both span [i.e. named entity recognition (NER)] and normalization (i.e. entity linking), using Medical Subject Headings (MeSH). The chemical indexing task required identifying which chemicals reflect topics for each article and should therefore appear in the listing of MeSH terms for the document in the MEDLINE article indexing. This manuscript summarizes the BioCreative NLM-Chem track and post-challenge experiments. We received a total of 85 submissions from 17 teams worldwide. The highest performance achieved for the chemical identification task was 0.8672 F-score (0.8759 precision and 0.8587 recall) for strict NER performance and 0.8136 F-score (0.8621 precision and 0.7702 recall) for strict normalization performance. The highest performance achieved for the chemical indexing task was 0.6073 F-score (0.7417 precision and 0.5141 recall). This community challenge demonstrated that (i) the current substantial achievements in deep learning technologies can be utilized to improve automated prediction accuracy further and (ii) the chemical indexing task is substantially more challenging. We look forward to further developing biomedical text-mining methods to respond to the rapid growth of biomedical literature. The NLM-Chem track dataset and other challenge materials are publicly available at https://ftp.ncbi.nlm.nih.gov/pub/lu/BC7-NLM-Chem-track/. Database URL https://ftp.ncbi.nlm.nih.gov/pub/lu/BC7-NLM-Chem-track/.

Opinions and options about COVID-19: Personality correlates and sex differences in two European countries.

In the initial months of the COVID-19 pandemic in 2020, we collected data (N = 1,420) from Portugal and Spain in relation to personality (i.e., Dark Triad traits, Big Five traits, religiousness, and negative affect) and attitudes related to COVID-19 about its origins, opinions on how to deal with it, and fear of it. The most pervasive patterns we found were: (1) neurotic-type dispositions were associated with stronger opinions about the origins of the virus and leave people to have more fear of the virus but also more trust in tested establishments to provide help. (2): religious people were less trusting of science, thought prayer was answer, and attributed the existence of the virus to an act of God. We also found that sex differences and country differences in attitudes towards COVID-19 were mediate by sex/country differences in personality traits like emotional stability, religiousness, and negative affect. For instance, women reported more fear of COVID-19 than men did, and this was verified by women's greater tendency to have negative affect and low emotional stability relative to men. Results point to the central role of neuroticism in accounting for variance in broad-spectrum attitudes towards COVID-19.

COVID-19 and their outcomes: how personality, place, and sex of people play a role in the psychology of COVID-19 beliefs

Background There is considerable variation in people's attitudes towards the COVID-19 pandemic. One way to understand why people differ in their attitudes is to examine how personality traits predict the degree to which people hold different attitudes. Methods We collected data (N = 1420) from Portugal and Spain using Facebook advertising. We measured the Dark Triad and Big Five traits, and negative affect, along with ad hoc items for religiousness, and attitudes towards and fear of COVID. Results Neuroticism and Negative affect was linked to various domains of insecurity or fear and provides insights into how personality predicts concerns and behaviors related to the COVID-19 pandemic. Religious people were less trusting in science, thought prayer was answer, and attributed the existence of the virus to an act of God. Women reported more fear of COVID-19 than men did, and this was enabled by women's greater tendency to have Negative Affect and higher Neuroticism than men. Conclusions Neurotic people and those with more Negative Affect appear to be more fearful, more trusting in others and systems likely to protect them (e.g. scientists), and less likely to trust in systems shown to not help them (e.g. prayer). We found other effects for the Dark Triad traits and the Big Five traits. In total, we highlight some of the reasons that people may be in such disagreements about what to do about the virus at the individual and institutional levels. Personality, place, and participant's sex all appear to play a role in the psychology of COVID-19 beliefs.