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The Chemical Functional Ontology (ChemFOnt), located at https://www.chemfont.ca, is a hierarchical, OWL-compatible ontology describing the functions and actions of >341 000 biologically important chemicals. These include primary metabolites, secondary metabolites, natural products, food chemicals, synthetic food additives, drugs, herbicides, pesticides and environmental chemicals. ChemFOnt is a FAIR-compliant resource intended to bring the same rigor, standardization and formal structure to the terms and terminology used in biochemistry, food chemistry and environmental chemistry as the gene ontology (GO) has brought to molecular biology. ChemFOnt is available as both a freely accessible, web-enabled database and a downloadable Web Ontology Language (OWL) file. Users may download and deploy ChemFOnt within their own chemical databases or integrate ChemFOnt into their own analytical software to generate machine readable relationships that can be used to make new inferences, enrich their omics data sets or make new, non-obvious connections between chemicals and their direct or indirect effects. The web version of the ChemFOnt database has been designed to be easy to search, browse and navigate. Currently ChemFOnt contains data on 341 627 chemicals, including 515 332 terms or definitions. The functional hierarchy for ChemFOnt consists of four functional ‘aspects’, 12 functional super-categories and a total of 173 705 functional terms. In addition, each of the chemicals are classified into 4825 structure-based chemical classes. ChemFOnt currently contains 3.9 million protein-chemical relationships and ∼10.3 million chemical-functional relationships. The long-term goal for ChemFOnt is for it to be adopted by databases and software tools used by the general chemistry community as well as the metabolomics, exposomics, metagenomics, genomics and proteomics communities.

The Human Microbial Metabolome Database (MiMeDB) (https://mimedb.org) is a comprehensive, multi-omic, microbiome resource that connects: (i) microbes to microbial genomes; (ii) microbial genomes to microbial metabolites; (iii) microbial metabolites to the human exposome and (iv) all of these ‘omes’ to human health. MiMeDB was established to consolidate the growing body of data connecting the human microbiome and the chemicals it produces to both health and disease. MiMeDB contains detailed taxonomic, microbiological and body-site location data on most known human microbes (bacteria and fungi). This microbial data is linked to extensive genomic and proteomic sequence data that is closely coupled to colourful interactive chromosomal maps. The database also houses detailed information about all the known metabolites generated by these microbes, their structural, chemical and spectral properties, the reactions and enzymes responsible for these metabolites and the primary exposome sources (food, drug, cosmetic, pollutant, etc.) that ultimately lead to the observed microbial metabolites in humans. Additional, extensively referenced data about the known or presumptive health effects, measured biosample concentrations and human protein targets for these compounds is provided. All of this information is housed in richly annotated, highly interactive, visually pleasing database that has been designed to be easy to search, easy to browse and easy to navigate. Currently MiMeDB contains data on 626 health effects or bioactivities, 1904 microbes, 3112 references, 22 054 reactions, 24 254 metabolites or exposure chemicals, 648 861 MS and NMR spectra, 6.4 million genes and 7.6 billion DNA bases. We believe that MiMeDB represents the kind of integrated, multi-omic or systems biology database that is needed to enable comprehensive multi-omic integration.

Background Little is currently known about vaccine effectiveness (VE) for either 2 doses of Oxford-AstraZeneca (ChAdOx1) viral vector vaccine or CoronaVac (Instituto Butantan) inactivated viral vaccine followed by a third dose of mRNA vaccine (Pfizer/BioNTech) among healthcare workers (HCWs). Methods We conducted a retrospective cohort study among HCWs (aged ≥18 years) working in a private healthcare system in Brazil from January to December 2021. VE was defined as 1 – incidence rate ratio (IRR), with IRR determined using Poisson models with the occurrence of laboratory-confirmed coronavirus disease 2019 (COVID-19) infection as the outcome, adjusting for age, sex, and job type. We compared those receiving viral vector or inactivated viral primary series (2 doses) with those who received an mRNA booster. Results A total of 11 427 HCWs met the inclusion criteria. COVID-19 was confirmed in 31.5% of HCWs receiving 2 doses of CoronaVac vaccine versus 0.9% of HCWs receiving 2 doses of CoronaVac vaccine with mRNA booster (P < .001) and 9.8% of HCWs receiving 2 doses of ChAdOx1 vaccine versus 1% among HCWs receiving 2 doses of ChAdOx1 vaccine with mRNA booster (P < .001). In the adjusted analyses, the estimated VE was 92.0% for 2 CoronaVac vaccines plus mRNA booster and 60.2% for 2 ChAdOx1 vaccines plus mRNA booster, when compared with those with no mRNA booster. Of 246 samples screened for mutations, 191 (77.6%) were Delta variants. Conclusions While 2 doses of ChAdOx1 or CoronaVac vaccines prevent COVID-19, the addition of a Pfizer/BioNTech booster provided significantly more protection.

The CFM-ID 4.0 web server (https://cfmid.wishartlab.com) is an online tool for predicting, annotating and interpreting tandem mass (MS/MS) spectra of small molecules. It is specifically designed to assist researchers pursuing studies in metabolomics, exposomics and analytical chemistry. More specifically, CFM-ID 4.0 supports the: 1) prediction of electrospray ionization quadrupole time-of-flight tandem mass spectra (ESI-QTOF-MS/MS) for small molecules over multiple collision energies (10 eV, 20 eV, and 40 eV); 2) annotation of ESI-QTOF-MS/MS spectra given the structure of the compound; and 3) identification of a small molecule that generated a given ESI-QTOF-MS/MS spectrum at one or more collision energies. The CFM-ID 4.0 web server makes use of a substantially improved MS fragmentation algorithm, a much larger database of experimental and in silico predicted MS/MS spectra and improved scoring methods to offer more accurate MS/MS spectral prediction and MS/MS-based compound identification. Compared to earlier versions of CFM-ID, this new version has an MS/MS spectral prediction performance that is ∼22% better and a compound identification accuracy that is ∼35% better on a standard (CASMI 2016) testing dataset. CFM-ID 4.0 also features a neutral loss function that allows users to identify similar or substituent compounds where no match can be found using CFM-ID’s regular MS/MS-to-compound identification utility. Finally, the CFM-ID 4.0 web server now offers a much more refined user interface that is easier to use, supports molecular formula identification (from MS/MS data), provides more interactively viewable data (including proposed fragment ion structures) and displays MS mirror plots for comparing predicted with observed MS/MS spectra. These improvements should make CFM-ID 4.0 much more useful to the community and should make small molecule identification much easier, faster, and more accurate.

PHASTEST (PHAge Search Tool with Enhanced Sequence Translation) is the successor to the PHAST and PHASTER prophage finding web servers. PHASTEST is designed to support the rapid identification, annotation and visualization of prophage sequences within bacterial genomes and plasmids. PHASTEST also supports rapid annotation and interactive visualization of all other genes (protein coding regions, tRNA/tmRNA/rRNA sequences) in bacterial genomes. Given that bacterial genome sequencing has become so routine, the need for fast tools to comprehensively annotate bacterial genomes has become progressively more important. PHASTEST not only offers faster and more accurate prophage annotations than its predecessors, it also provides more complete whole genome annotations and much improved genome visualization capabilities. In standardized tests, we found that PHASTEST is 31% faster and 2–3% more accurate in prophage identification than PHASTER. Specifically, PHASTEST can process a typical bacterial genome in 3.2 min (raw sequence) or in 1.3 min when given a pre-annotated GenBank file. Improvements in PHASTEST’s ability to annotate bacterial genomes now make it a particularly powerful tool for whole genome annotation. In addition, PHASTEST now offers a much more modern and responsive visualization interface that allows users to generate, edit, annotate and interactively visualize (via zooming, rotating, dragging, panning, resetting), colourful, publication quality genome maps. PHASTEST continues to offer popular options such as an API for programmatic queries, a Docker image for local installations, support for multiple (metagenomic) queries and the ability to perform automated look-ups against thousands of previously PHAST-annotated bacterial genomes. PHASTEST is available online at https://phastest.ca.

PlasMapper 3.0 is a web server that allows users to generate, edit, annotate and interactively visualize publication quality plasmid maps. Plasmid maps are used to plan, design, share and publish critical information about gene cloning experiments. PlasMapper 3.0 is the successor to PlasMapper 2.0 and offers many features found only in commercial plasmid mapping/editing packages. PlasMapper 3.0 allows users to paste or upload plasmid sequences as input or to upload existing plasmid maps from its large database of >2000 pre-annotated plasmids (PlasMapDB). This database can be searched by plasmid names, sequence features, restriction sites, preferred host organisms, and sequence length. PlasMapper 3.0 also supports the annotation of new or never-before-seen plasmids using its own feature database that contains common promoters, terminators, regulatory sequences, replication origins, selectable markers and other features found in most cloning plasmids. PlasMapper 3.0 has several interactive sequence editors/viewers that allow users to select and view plasmid regions, insert genes, modify restriction sites or perform codon optimization. The graphics for PlasMapper 3.0 have also been substantially upgraded. It now offers an interactive, full-color plasmid viewer/editor that allows users to zoom, rotate, re-color, linearize, circularize, edit annotated features and modify plasmid images or labels to improve the esthetic qualities of their plasmid map and textual displays. All the plasmid images and textual displays are downloadable in multiple formats. PlasMapper 3.0 is available online at https://plasmapper.ca.

The Natural Products Magnetic Resonance Database (NP-MRD) is a comprehensive, freely available electronic resource for the deposition, distribution, searching and retrieval of nuclear magnetic resonance (NMR) data on natural products, metabolites and other biologically derived chemicals. NMR spectroscopy has long been viewed as the ‘gold standard’ for the structure determination of novel natural products and novel metabolites. NMR is also widely used in natural product dereplication and the characterization of biofluid mixtures (metabolomics). All of these NMR applications require large collections of high quality, well-annotated, referential NMR spectra of pure compounds. Unfortunately, referential NMR spectral collections for natural products are quite limited. It is because of the critical need for dedicated, open access natural product NMR resources that the NP-MRD was funded by the National Institute of Health (NIH). Since its launch in 2020, the NP-MRD has grown quickly to become the world's largest repository for NMR data on natural products and other biological substances. It currently contains both structural and NMR data for nearly 41,000 natural product compounds from >7400 different living species. All structural, spectroscopic and descriptive data in the NP-MRD is interactively viewable, searchable and fully downloadable in multiple formats. Extensive hyperlinks to other databases of relevance are also provided. The NP-MRD also supports community deposition of NMR assignments and NMR spectra (1D and 2D) of natural products and related meta-data. The deposition system performs extensive data enrichment, automated data format conversion and spectral/assignment evaluation. Details of these database features, how they are implemented and plans for future upgrades are also provided. The NP-MRD is available at https://np-mrd.org.

Anxious temperament, characterized by heightened behavioral and physiological reactivity to potential threat, is an early childhood risk factor for the later development of stress-related psychopathology. Using a well-validated nonhuman primate model, we tested the hypothesis that the prefrontal cortex (PFC) is critical in regulating the expression of primate anxiety-like behavior, as well as the function of subcortical components of the anxiety-related neural circuit. We performed aspiration lesions of a narrow ‘strip’ of the posterior orbitofrontal cortex (OFC) intended to disrupt both cortex and axons entering, exiting and coursing through the pOFC, particularly those of the uncinate fasciculus (UF), a white matter tract that courses adjacent to and through this region. The OFC is of particular interest as a potential regulatory region because of its extensive reciprocal connections with amygdala, other subcortical structures and other frontal lobe regions. We validated this lesion method by demonstrating marked lesion-induced decreases in the microstructural integrity of the UF, which contains most of the fibers that connect the ventral PFC with temporal lobe structures as well as with other frontal regions. While the lesions resulted in modest decreases in threat-related behavior, they substantially decreased metabolism in components of the circuit underlying threat processing. These findings provide evidence for the importance of structural connectivity between the PFC and key subcortical structures in regulating the functions of brain regions known to be involved in the adaptive and maladaptive expression of anxiety.

Automatic phenotype concept recognition from unstructured text remains a challenging task in biomedical text mining research. Previous works that address the task typically use dictionary-based matching methods, which can achieve high precision but suffer from lower recall. Recently, machine learning-based methods have been proposed to identify biomedical concepts, which can recognize more unseen concept synonyms by automatic feature learning. However, most methods require large corpora of manually annotated data for model training, which is difficult to obtain due to the high cost of human annotation. In this paper, we propose PhenoTagger, a hybrid method that combines both dictionary and machine learning-based methods to recognize Human Phenotype Ontology (HPO) concepts in unstructured biomedical text. We first use all concepts and synonyms in HPO to construct a dictionary, which is then used to automatically build a distantly supervised training dataset for machine learning. Next, a cutting-edge deep learning model is trained to classify each candidate phrase (n-gram from input sentence) into a corresponding concept label. Finally, the dictionary and machine learning-based prediction results are combined for improved performance. Our method is validated with two HPO corpora, and the results show that PhenoTagger compares favorably to previous methods. In addition, to demonstrate the generalizability of our method, we retrained PhenoTagger using the disease ontology MEDIC for disease concept recognition to investigate the effect of training on different ontologies. Experimental results on the NCBI disease corpus show that PhenoTagger without requiring manually annotated training data achieves competitive performance as compared with state-of-the-art supervised methods., Accepted by Bioinformatics

In this study, we compared the effects of using neutral face masks vs non-face pattern masks on amygdala activity to masked fearful faces. Twenty-seven subjects viewed 18 s blocks of either fearful or happy faces masked with either neutral faces or patterns, while their brain activity was measured using functional magnetic resonance imaging. Results replicated increased amygdala activation to face-masked fearful vs happy faces. In the pattern mask condition, the amygdala discriminated between masked fearful and happy faces, but this effect manifested as a decrease in activation to fearful faces compared to happy faces. This interactive effect between facial expression and mask stimulus shows that amygdala responses to masked fearful faces are influenced by the fearful stimuli per se as well as their interaction with the mask stimulus.

how the presence of a child with sickle cell anemia affects the child's family. However, clinical reports on such families and empirical data available on how the presence of children with other chronic ill nesses affects family dynamics both suggest that the presence of a child with sickle-cell anemia indeed may influence the ways in which family members interact with each other and with others beyond the family. Hence, the goal of this research was to ex plore how a child with sickle-cell anemia shapes family dynamics. In clinical work with families, several authors have reported that parents who have

Mortality rates for all malignant neoplasms (combined) and for 34 site-specific cancer categories for selected high and low altitude populations are compared using two related techniques: confidence interval overlap for strictly descriptive purposes and analysis of standardized mortality ratios using lower and upper 95% statistical significance factors for the ratio of an observed value of a poisson variable to its expectation. Techniques are employed to minimize confounding due to industrialization, urbanization or selected cultural characteristics. Cancer mortality data for U.S. counties averaged over the 20-year period, 1950--1969, were used. For most comparisons a deficit in cancer mortality in high altitude counties was observed. The largest differences between the low and high altitude groups were found for cancers of the tongue and mouth, esophagus, larynx, lung and melanoma. Some limitations of ecologic studies are discussed.