Absence and Disability Management Practices for an Aging Workforce

The goal of Disability and Absence Management programming is to limit absence, control costs, and retain workers to maintain a productive workforce. This can include the development of supportive policies (e.g. flexible work options), manager and employee education, supportive benefit programs, return to work programs, among others. Increasingly, older workers have become a group of interest among Absence and Disability Management professionals, in part because many baby boomers are forgoing retirement and working longer. Projections suggest that by 2020 those 55 and over could account for 25% of workers. This shift is especially important given that disability prevalence increases with age – as the workforce ages, organizations will increasingly need to ensure their programming supports older workers. During the fall and winter of 2012-13, Cornell University’s Employment and Disability Institute and the Disability Management Employer Coalition (DMEC) collaborated on a survey and key informant interviews with DMEC members and conference attendees to learn more about what organizations are doing to respond to and prepare for an aging workforce

Extensive Conserved Synteny of Genes between the Karyotypes of Manduca sexta and Bombyx mori Revealed by BAC-FISH Mapping

BACKGROUND: Genome sequencing projects have been completed for several species representing four highly diverged holometabolous insect orders, Diptera, Hymenoptera, Coleoptera, and Lepidoptera. The striking evolutionary diversity of insects argues a need for efficient methods to apply genome information from such models to genetically uncharacterized species. Constructing conserved synteny maps plays a crucial role in this task. Here, we demonstrate the use of fluorescence in situ hybridization with bacterial artificial chromosome probes as a powerful tool for physical mapping of genes and comparative genome analysis in Lepidoptera, which have numerous and morphologically uniform holokinetic chromosomes. METHODOLOGY/PRINCIPAL FINDINGS: We isolated 214 clones containing 159 orthologs of well conserved single-copy genes of a sequenced lepidopteran model, the silkworm, Bombyx mori, from a BAC library of a sphingid with an unexplored genome, the tobacco hornworm, Manduca sexta. We then constructed a BAC-FISH karyotype identifying all 28 chromosomes of M. sexta by mapping 124 loci using the corresponding BAC clones. BAC probes from three M. sexta chromosomes also generated clear signals on the corresponding chromosomes of the convolvulus hawk moth, Agrius convolvuli, which belongs to the same subfamily, Sphinginae, as M. sexta. CONCLUSIONS/SIGNIFICANCE: Comparison of the M. sexta BAC physical map with the linkage map and genome sequence of B. mori pointed to extensive conserved synteny including conserved gene order in most chromosomes. Only a few rearrangements, including three inversions, three translocations, and two fission/fusion events were estimated to have occurred after the divergence of Bombycidae and Sphingidae. These results add to accumulating evidence for the stability of lepidopteran genomes. Generating signals on A. convolvuli chromosomes using heterologous M. sexta probes demonstrated that BAC-FISH with orthologous sequences can be used for karyotyping a wide range of related and genetically uncharacterized species, significantly extending the ability to develop synteny maps for comparative and functional genomics

The Effects of Apolipoprotein F Deficiency on High Density Lipoprotein Cholesterol Metabolism in Mice

Apolipoprotein F (apoF) is 29 kilodalton secreted sialoglycoprotein that resides on the HDL and LDL fractions of human plasma. Human ApoF is also known as Lipid Transfer Inhibitor protein (LTIP) based on its ability to inhibit cholesteryl ester transfer protein (CETP)-mediated transfer events between lipoproteins. In contrast to other apolipoproteins, ApoF is predicted to lack strong amphipathic alpha helices and its true physiological function remains unknown. We previously showed that overexpression of Apolipoprotein F in mice reduced HDL cholesterol levels by 20–25% by accelerating clearance from the circulation. In order to investigate the effect of physiological levels of ApoF expression on HDL cholesterol metabolism, we generated ApoF deficient mice. Unexpectedly, deletion of ApoF had no substantial impact on plasma lipid concentrations, HDL size, lipid or protein composition. Sex-specific differences were observed in hepatic cholesterol content as well as serum cholesterol efflux capacity. Female ApoF KO mice had increased liver cholesteryl ester content relative to wild type controls on a chow diet (KO: 3.4+/−0.9 mg/dl vs. WT: 1.2+/−0.3 mg/dl, p<0.05). No differences were observed in ABCG1-mediated cholesterol efflux capacity in either sex. Interestingly, ApoB-depleted serum from male KO mice was less effective at promoting ABCA1-mediated cholesterol efflux from J774 macrophages relative to WT controls

TRY plant trait database – enhanced coverage and open access

Plant traits - the morphological, anatomical, physiological, biochemical and phenological characteristics of plants - determine how plants respond to environmental factors, affect other trophic levels, and influence ecosystem properties and their benefits and detriments to people. Plant trait data thus represent the basis for a vast area of research spanning from evolutionary biology, community and functional ecology, to biodiversity conservation, ecosystem and landscape management, restoration, biogeography and earth system modelling. Since its foundation in 2007, the TRY database of plant traits has grown continuously. It now provides unprecedented data coverage under an open access data policy and is the main plant trait database used by the research community worldwide. Increasingly, the TRY database also supports new frontiers of trait‐based plant research, including the identification of data gaps and the subsequent mobilization or measurement of new data. To support this development, in this article we evaluate the extent of the trait data compiled in TRY and analyse emerging patterns of data coverage and representativeness. Best species coverage is achieved for categorical traits - almost complete coverage for ‘plant growth form’. However, most traits relevant for ecology and vegetation modelling are characterized by continuous intraspecific variation and trait–environmental relationships. These traits have to be measured on individual plants in their respective environment. Despite unprecedented data coverage, we observe a humbling lack of completeness and representativeness of these continuous traits in many aspects. We, therefore, conclude that reducing data gaps and biases in the TRY database remains a key challenge and requires a coordinated approach to data mobilization and trait measurements. This can only be achieved in collaboration with other initiatives

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