Introduction to the Minitrack on Digitalization of Work

Non peer reviewe

Environment and Co-occurring Native Mussel Species, but Not Host Genetics, Impact the Microbiome of a Freshwater Invasive Species (Corbicula fluminea)

The Asian clam Corbicula fluminea (Family: Cyneridae) has aggressively invaded freshwater habitats worldwide, resulting in dramatic ecological changes and declines of native bivalves such as freshwater mussels (Family: Unionidae), one of the most imperiled faunal groups. Despite increases in our knowledge of invasive C. fluminea biology, little is known of how intrinsic and extrinsic factors, including co-occurring native species, influence its microbiome. We investigated the gut bacterial microbiome across genetically differentiated populations of C. fluminea in the Tennessee and Mobile River Basins in the Southeastern United States and compared them to those of six co-occurring species of native freshwater mussels. The gut microbiome of C. fluminea was diverse, differed with environmental conditions and varied spatially among rivers, but was unrelated to host genetic variation. Microbial source tracking suggested that the gut microbiome of C. fluminea may be influenced by the presence of co-occurring native mussels. Inferred functions from 16S rRNA gene data using PICRUST2 predicted a high prevalence and diversity of degradation functions in the C. fluminea microbiome, especially the degradation of carbohydrates and aromatic compounds. Such modularity and functional diversity of the microbiome of C. fluminea may be an asset, allowing to acclimate to an extensive range of nutritional sources in invaded habitats, which could play a vital role in its invasive success

Management flight simulators to support climate negotiations

a b s t r a c t Under the United Nations Framework Convention on Climate Change (UNFCCC) the nations of the world have pledged to limit warming to no more than 2 C above preindustrial levels. However, negotiators and policymakers lack the capability to assess the impact of greenhouse gas (GHG) emissions reduction proposals offered by the parties on warming and the climate. The climate is a complex dynamical system driven by multiple feedback processes, accumulations, time delays and nonlinearities, but research shows poor understanding of these processes is widespread, even among highly educated people with strong technical backgrounds. Existing climate models are opaque to policymakers and too slow to be effective either in the fast-paced context of policy making or as learning environments to help improve people's understanding of climate dynamics. Here we describe C-ROADS (Climate Rapid Overview And Decision Support), a transparent, intuitive policy simulation model that provides policymakers, negotiators, educators, businesses, the media, and the public with the ability to explore, for themselves, the likely consequences of GHG emissions policies. The model runs on an ordinary laptop in seconds, offers an intuitive interface and has been carefully grounded in the best available science. We describe the need for such tools, the structure of the model, and calibration to climate data and state of the art general circulation models. We also describe how C-ROADS is being used by officials and policymakers in key UNFCCC parties, including the United States, China and the United Nations

Twenty questions about design behavior for sustainability, report of the International Expert Panel on behavioral science for design

How behavioral scientists, engineers, and architects can work together to advance how we all understand and practice design—in order to enhance sustainability in the built environment, and beyond.https://www.nature.com/documents/design_behavior_for_sustainability.pdfPublished versio

Prospective evaluation of echocardiographic parameters and cardiac biomarkers in healthy dogs eating four custom-formulated diets

IntroductionDiet-associated dilated cardiomyopathy (DCM) has been suspected in breeds that have not been previously noted to have a predisposition to the DCM phenotype. This study hypothesized that over 210 days, dogs fed diets with varying amounts of animal-sourced protein and carbohydrate sources would not be negatively impacted in terms of their cardiac parameters and function.MethodsThirty-two purebred beagles and 33 mixed-breed hounds were randomized into four diet groups and studied for 210 days. The diet groups were as follows: the high-animal-protein grain-free (HAGF) group, the low-animal-protein grain-free (LAGF) group, the high-animal-protein grain-inclusive (HAGI), and the low-animal-protein grain-inclusive (LAGI) group. Cardiac-specific biomarkers, endomyocardial biopsies, and linear and volumetric echocardiographic parameters were evaluated.ResultsThere was a treatment-by-day-by-breed effect observed for the normalized left ventricular internal diameter at end-diastole (p = 0.0387) and for the normalized left ventricular internal diameter at end-systole (p = 0.0178). On day 210, mixed-breed hounds fed the LAGI diet had a smaller normalized left ventricular internal diameter at end-diastole than on day 90. On day 210, beagles fed the LAGF diet had a larger normalized left ventricular internal diameter at end-systole than those fed the LAGI diet. Fractional shortening for beagles in the LAGF group was significantly lower (p = 0.007) than for those in the HAGI and LAGI groups. Cardiac-specific biomarkers and endomyocardial biopsies were not significantly different between breeds, diets, and various time points.DiscussionThis study did not detect the development of cardiac dysfunction throughout the study period through the echocardiographic parameters measured, select cardiac biomarkers, or endomyocardial biopsies. There were noted interactions of treatment, breed, and time; therefore, isolating a diet association was not possible. Future research should further investigate the other factors that may help to identify the variable(s) and possible mechanisms underlying suspected diet-associated DCM in dogs

Selection of Clostridium spp. in biological sand filters neutralizing synthetic acid mine drainage

In this study, three biological sand filter (BSF) were contaminated with a synthetic iron- [1500 mg L-1 Fe(II), 500 mg L-1 Fe(III)] and sulphate-rich (6000 mg L-1 SO2/4-) acid mine drainage (AMD) (pH = 2), for 24 days, to assess the remediation capacity and the evolution of autochthonous bacterial communities (monitored by T-RFLP and 16S rRNA gene clone libraries). To stimulate BSF bioremediation involving sulphate-reducing bacteria, a readily degradable carbon source (glucose, 8000 mg L-1) was incorporated into the influent AMD. Complete neutralization and average removal efficiencies of 81.5 (±5.6)%, 95.8 (±1.2)% and 32.8 (±14.0)% for Fe(II), Fe(III) and sulphate were observed, respectively. Our results suggest that microbial iron reduction and sulphate reduction associated with iron precipitation were the main processes contributing to AMD neutralization. The effect of AMD on BSF sediment bacterial communities was highly reproducible. There was a decrease in diversity, and notably a single dominant operational taxonomic unit (OTU), closely related to Clostridium beijerinckii, which represented up to 65% of the total community at the end of the study period.Web of Scienc

Genome-wide evolutionary dynamics of influenza B viruses on a global scale

The global-scale epidemiology and genome-wide evolutionary dynamics of influenza B remain poorly understood compared with influenza A viruses. We compiled a spatio-temporally comprehensive dataset of influenza B viruses, comprising over 2,500 genomes sampled worldwide between 1987 and 2015, including 382 newly-sequenced genomes that fill substantial gaps in previous molecular surveillance studies. Our contributed data increase the number of available influenza B virus genomes in Europe, Africa and Central Asia, improving the global context to study influenza B viruses. We reveal Yamagata-lineage diversity results from co-circulation of two antigenically-distinct groups that also segregate genetically across the entire genome, without evidence of intra-lineage reassortment. In contrast, Victoria-lineage diversity stems from geographic segregation of different genetic clades, with variability in the degree of geographic spread among clades. Differences between the lineages are reflected in their antigenic dynamics, as Yamagata-lineage viruses show alternating dominance between antigenic groups, while Victoria-lineage viruses show antigenic drift of a single lineage. Structural mapping of amino acid substitutions on trunk branches of influenza B gene phylogenies further supports these antigenic differences and highlights two potential mechanisms of adaptation for polymerase activity. Our study provides new insights into the epidemiological and molecular processes shaping influenza B virus evolution globally

2021 Taxonomic update of phylum Negarnaviricota (Riboviria: Orthornavirae), including the large orders Bunyavirales and Mononegavirales.

Correction to: 2021 Taxonomic update of phylum Negarnaviricota (Riboviria: Orthornavirae), including the large orders Bunyavirales and Mononegavirales. Archives of Virology (2021) 166:3567–3579. https://doi.org/10.1007/s00705-021-05266-wIn March 2021, following the annual International Committee on Taxonomy of Viruses (ICTV) ratification vote on newly proposed taxa, the phylum Negarnaviricota was amended and emended. The phylum was expanded by four families (Aliusviridae, Crepuscuviridae, Myriaviridae, and Natareviridae), three subfamilies (Alpharhabdovirinae, Betarhabdovirinae, and Gammarhabdovirinae), 42 genera, and 200 species. Thirty-nine species were renamed and/or moved and seven species were abolished. This article presents the updated taxonomy of Negarnaviricota as now accepted by the ICTV.This work was supported in part through Laulima Government Solutions, LLC prime contract with the US National Institute of Allergy and Infectious Diseases (NIAID) under Contract No. HHSN272201800013C. J.H.K. performed this work as an employee of Tunnell Government Services (TGS), a subcontractor of Laulima Government Solutions, LLC under Contract No. HHSN272201800013C. This work was also supported in part with federal funds from the National Cancer Institute (NCI), National Institutes of Health (NIH), under Contract No. 75N91019D00024, Task Order No. 75N91019F00130 to I.C., who was supported by the Clinical Monitoring Research Program Directorate, Frederick National Lab for Cancer Research. This work was also funded in part by Contract No. HSHQDC-15-C-00064 awarded by DHS S&T for the management and operation of The National Biodefense Analysis and Countermeasures Center, a federally funded research and development center operated by the Battelle National Biodefense Institute (V.W.); and NIH contract HHSN272201000040I/HHSN27200004/D04 and grant R24AI120942 (N.V., R.B.T.). S.S. acknowledges partial support from the Special Research Initiative of Mississippi Agricultural and Forestry Experiment Station (MAFES), Mississippi State University, and the National Institute of Food and Agriculture, US Department of Agriculture, Hatch Project 1021494. Part of this work was supported by the Francis Crick Institute which receives its core funding from Cancer Research UK (FC001030), the UK Medical Research Council (FC001030), and the Wellcome Trust (FC001030).S

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