An integrated analysis of genes and functional pathways for aggression in human and rodent models

Human genome-wide association studies (GWAS), transcriptome analyses of animal models, and candidate gene studies have advanced our understanding of the genetic architecture of aggressive behaviors. However, each of these methods presents unique limitations. To generate a more confident and comprehensive view of the complex genetics underlying aggression, we undertook an integrated, cross-species approach. We focused on human and rodent models to derive eight gene lists from three main categories of genetic evidence: two sets of genes identified in GWAS studies, four sets implicated by transcriptome-wide studies of rodent models, and two sets of genes with causal evidence from online Mendelian inheritance in man (OMIM) and knockout (KO) mice reports. These gene sets were evaluated for overlap and pathway enrichment to extract their similarities and differences. We identified enriched common pathways such as the G-protein coupled receptor (GPCR) signaling pathway, axon guidance, reelin signaling in neurons, and ERK/MAPK signaling. Also, individual genes were ranked based on their cumulative weights to quantify their importance as risk factors for aggressive behavior, which resulted in 40 top-ranked and highly interconnected genes. The results of our cross-species and integrated approach provide insights into the genetic etiology of aggression

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To identify materials suitable as membrane supports for ion channel biosensors, six filter materials of varying hydrophobicity, tortuosity, and thickness were examined for their ability to support bilayer lipid membranes as determined by electrical impedance spectroscopy. Bilayers supported by hydrophobic materials (PTFE, polycarbonate, nylon, and silanised silver) had optimal resistance (14-19 GΩ) and capacitance (0.8-1.6 μF) values whereas those with low hydrophobicity did not form BLMs (PVDF) or were short-lived (unsilanised silver). The ability of ion channels to function in BLMs was assessed using a method recently reported to improve the efficiency of proteoliposome incorporation into PTFE-supported bilayers. Voltage-gated sodium channel activation by veratridine and inhibition by saxitoxin showed activity for PTFE, nylon, and silanised silver, but not polycarbonate. Bilayers on thicker, more tortuous, and hydrophobic materials produced higher current levels. Bilayers that self-assembled on PTFE filters were the longest lived and produced the most channel activity using this method

A diazirine-based photoaffinity probe for facile and efficient aptamer-protein covalent conjugation

National Basic Research Program of China [2010CB732402, 2013CB933703]; National Science Foundation of China [91313302, 21205100, 21275122, 21075104]; Fundamental Research Funds for the Central Universities [2012121025]; National Science Foundation for Distinguished Young Scholars of China [21325522]A photo-reactive functional labelling reagent, diazirine phosphoramidite, was designed and synthesized for easy and flexible sitespecific labelling of oligonucleotides with the diazirine moiety. The new reagent allows facile photo-crosslinking of oligonucleotide with its interacting partner for a variety of applications, including tertiary structure determination, molecular interaction study and biomarker discovery

Measurements of traffic-dominated pollutant emissions in a Chinese megacity

Direct measurements of NOx, CO and aromatic volatile organic compound (VOC) (benzene, toluene, C2-benzenes and C3-benzenes) flux were made for a central area of Beijing using the eddy-covariance technique. Measurements were made during two intensive field campaigns in central Beijing as part of the Air Pollution and Human Health (APHH) project, the first in November–December 2016 and the second during May–June 2017, to contrast wintertime and summertime emission rates. There was little difference in the magnitude of NOx flux between the two seasons (mean NOx flux was 4.41 mg m−2 h−1 in the winter compared to 3.55 mg m−2 h−1in the summer). CO showed greater seasonal variation, with mean CO flux in the winter campaign (34.7 mg m−2 h−1) being over twice that of the summer campaign (15.2 mg m−2 h−1). Larger emissions of aromatic VOCs in summer were attributed to increased evaporation due to higher temperatures. The largest fluxes in NOx and CO generally occurred during the morning and evening rush hour periods, indicating a major traffic source with high midday emissions of CO, indicating an additional influence from cooking fuel. Measured NOx and CO fluxes were then compared to the MEIC 2013 emissions inventory, which was found to significantly overestimate emissions for this region,providing evidence that proxy-based emissions inventories have positive biases in urban centres. This first set of pollutant fluxes measured in Beijing provides an important benchmark of emissions from the city which can help to inform and evaluate current emissions inventories

ENIGMA and global neuroscience: A decade of large-scale studies of the brain in health and disease across more than 40 countries.

This review summarizes the last decade of work by the ENIGMA (Enhancing NeuroImaging Genetics through Meta Analysis) Consortium, a global alliance of over 1400 scientists across 43 countries, studying the human brain in health and disease. Building on large-scale genetic studies that discovered the first robustly replicated genetic loci associated with brain metrics, ENIGMA has diversified into over 50 working groups (WGs), pooling worldwide data and expertise to answer fundamental questions in neuroscience, psychiatry, neurology, and genetics. Most ENIGMA WGs focus on specific psychiatric and neurological conditions, other WGs study normal variation due to sex and gender differences, or development and aging; still other WGs develop methodological pipelines and tools to facilitate harmonized analyses of "big data" (i.e., genetic and epigenetic data, multimodal MRI, and electroencephalography data). These international efforts have yielded the largest neuroimaging studies to date in schizophrenia, bipolar disorder, major depressive disorder, post-traumatic stress disorder, substance use disorders, obsessive-compulsive disorder, attention-deficit/hyperactivity disorder, autism spectrum disorders, epilepsy, and 22q11.2 deletion syndrome. More recent ENIGMA WGs have formed to study anxiety disorders, suicidal thoughts and behavior, sleep and insomnia, eating disorders, irritability, brain injury, antisocial personality and conduct disorder, and dissociative identity disorder. Here, we summarize the first decade of ENIGMA's activities and ongoing projects, and describe the successes and challenges encountered along the way. We highlight the advantages of collaborative large-scale coordinated data analyses for testing reproducibility and robustness of findings, offering the opportunity to identify brain systems involved in clinical syndromes across diverse samples and associated genetic, environmental, demographic, cognitive, and psychosocial factors

Surface-atmosphere fluxes of volatile organic compounds in Beijing

Mixing ratios of volatile organic compounds (VOCs) were recorded in two field campaigns in central Beijing as part of the Air Pollution and Human Health in a Chinese Megacity (APHH) project. These data were used to calculate, for the first time in Beijing, the surface-atmosphere fluxes of VOCs using eddy covariance, giving a top-down estimation of VOC emissions from a central area of the city. The results were then used to evaluate the accuracy of the Multi-resolution Emission Inventory for China (MEIC). The APHH winter and summer campaigns took place in November and December 2016 and May and June 2017, respectively. The largest VOC fluxes observed were of small oxygenated compounds such as methanol, ethanol + formic acid and acetaldehyde, with average emission rates of 8.31±8.5, 3.97±3.9 and 1.83±2.0nmolm-2s-1, respectively, in the summer. A large flux of isoprene was observed in the summer, with an average emission rate of 5.31±7.7nmolm-2s-1. While oxygenated VOCs made up 60% of the molar VOC flux measured, when fluxes were scaled by ozone formation potential and peroxyacyl nitrate (PAN) formation potential the high reactivity of isoprene and monoterpenes meant that these species represented 30% and 28% of the flux contribution to ozone and PAN formation potential, respectively. Comparison of measured fluxes with the emission inventory showed that the inventory failed to capture the magnitude of VOC emissions at the local scale

Database resources of the National Center for Biotechnology Information

In addition to maintaining the GenBank® nucleic acid sequence database, the National Center for Biotechnology Information (NCBI) provides analysis and retrieval resources for the data in GenBank and other biological data made available through the NCBI web site. NCBI resources include Entrez, the Entrez Programming Utilities, MyNCBI, PubMed, PubMed Central, Entrez Gene, the NCBI Taxonomy Browser, BLAST, BLAST Link (BLink), Electronic PCR, OrfFinder, Spidey, Splign, Reference Sequence, UniGene, HomoloGene, ProtEST, dbMHC, dbSNP, Cancer Chromosomes, Entrez Genomes and related tools, the Map Viewer, Model Maker, Evidence Viewer, Trace Archive, Sequence Read Archive, Retroviral Genotyping Tools, HIV-1/Human Protein Interaction Database, Gene Expression Omnibus, Entrez Probe, GENSAT, Online Mendelian Inheritance in Man, Online Mendelian Inheritance in Animals, the Molecular Modeling Database, the Conserved Domain Database, the Conserved Domain Architecture Retrieval Tool, Biosystems, Peptidome, Protein Clusters and the PubChem suite of small molecule databases. Augmenting many of the web applications are custom implementations of the BLAST program optimized to search specialized data sets. All these resources can be accessed through the NCBI home page at www.ncbi.nlm.nih.gov

Consortium neuroscience of attention deficit/hyperactivity disorder and autism spectrum disorder:The ENIGMA adventure

International audienc

Evaluation of individual and ensemble probabilistic forecasts of COVID-19 mortality in the United States

Short-term probabilistic forecasts of the trajectory of the COVID-19 pandemic in the United States have served as a visible and important communication channel between the scientific modeling community and both the general public and decision-makers. Forecasting models provide specific, quantitative, and evaluable predictions that inform short-term decisions such as healthcare staffing needs, school closures, and allocation of medical supplies. Starting in April 2020, the US COVID-19 Forecast Hub (https://covid19forecasthub.org/) collected, disseminated, and synthesized tens of millions of specific predictions from more than 90 different academic, industry, and independent research groups. A multimodel ensemble forecast that combined predictions from dozens of groups every week provided the most consistently accurate probabilistic forecasts of incident deaths due to COVID-19 at the state and national level from April 2020 through October 2021. The performance of 27 individual models that submitted complete forecasts of COVID-19 deaths consistently throughout this year showed high variability in forecast skill across time, geospatial units, and forecast horizons. Two-thirds of the models evaluated showed better accuracy than a naïve baseline model. Forecast accuracy degraded as models made predictions further into the future, with probabilistic error at a 20-wk horizon three to five times larger than when predicting at a 1-wk horizon. This project underscores the role that collaboration and active coordination between governmental public-health agencies, academic modeling teams, and industry partners can play in developing modern modeling capabilities to support local, state, and federal response to outbreaks

The United States COVID-19 Forecast Hub dataset

Academic researchers, government agencies, industry groups, and individuals have produced forecasts at an unprecedented scale during the COVID-19 pandemic. To leverage these forecasts, the United States Centers for Disease Control and Prevention (CDC) partnered with an academic research lab at the University of Massachusetts Amherst to create the US COVID-19 Forecast Hub. Launched in April 2020, the Forecast Hub is a dataset with point and probabilistic forecasts of incident cases, incident hospitalizations, incident deaths, and cumulative deaths due to COVID-19 at county, state, and national, levels in the United States. Included forecasts represent a variety of modeling approaches, data sources, and assumptions regarding the spread of COVID-19. The goal of this dataset is to establish a standardized and comparable set of short-term forecasts from modeling teams. These data can be used to develop ensemble models, communicate forecasts to the public, create visualizations, compare models, and inform policies regarding COVID-19 mitigation. These open-source data are available via download from GitHub, through an online API, and through R packages