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Biomedical devices such as islet‐encapsulating systems are used for treatment of type 1 diabetes (T1D). Despite recent strides in preventing biomaterial fibrosis, challenges remain for biomaterial scaffolds due to limitations on cells contained within. The study demonstrates that proliferation and function of insulinoma (INS‐1) cells as well as pancreatic rat islets may be improved in alginate hydrogels with optimized gel%, crosslinking, and stiffness. Quantitative polymerase chain reaction (qPCR)‐based graft phenotyping of encapsulated INS‐1 cells and pancreatic islets identified a hydrogel stiffness range between 600 and 1000 Pa that improved insulin Ins and Pdx1 gene expression as well as glucose‐sensitive insulin‐secretion. Barium chloride (BaCl₂) crosslinking time is also optimized due to toxicity of extended exposure. Despite possible benefits to cell viability, calcium chloride (CaCl₂)‐crosslinked hydrogels exhibited a sharp storage modulus loss in vitro. Despite improved stability, BaCl₂‐crosslinked hydrogels also exhibited stiffness losses over the same timeframe. It is believed that this is due to ion exchange with other species in culture media, as hydrogels incubated in dIH₂O exhibited significantly improved stability. To maintain cell viability and function while increasing 3D matrix stability, a range of useful media:dIH₂O dilution ratios for use are identified. Such findings have importance to carry out characterization and optimization of cell microphysiological systems with high fidelity in vitro.

 

In longitudinal genetic studies, investigators collect repeated measurements on a trait that changes with time along with genetic markers. For family-based longitudinal studies, since repeated measurements are nested within subjects and subjects are nested within families, both the subject level and the measurement level correlations must be taken into account in the statistical analysis to achieve more accurate estimation. In such studies, the primary interests include testing for a quantitative trait locus effect, and estimating the age-specific quantitative trait locus effect and residual polygenic heritability function. We propose flexible semiparametric models and their statistical estimation and hypothesis testing procedures for longitudinal genetic data. We employ penalized splines to estimate non-parametric functions in the model. We find that misspecifying the baseline function or the genetic effect function in a parametric analysis may lead to a substantially inflated or highly conservative type I error rate on testing and large mean-squared error on estimation. We apply the proposed approaches to examine age-specific effects of genetic variants reported in a recent genomewide association study of blood pressure collected in the Framingham Heart Study.

 

Abstract 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. 

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.