Projected U.S. drought extremes through the twenty-first century with vapor pressure deficit.

Global warming is expected to enhance drought extremes in the United States throughout the twenty-first century. Projecting these changes can be complex in regions with large variability in atmospheric and soil moisture on small spatial scales. Vapor Pressure Deficit (VPD) is a valuable measure of evaporative demand as moisture moves from the surface into the atmosphere and a dynamic measure of drought. Here, VPD is used to identify short-term drought with the Standardized VPD Drought Index (SVDI); and used to characterize future extreme droughts using grid dependent stationary and non-stationary generalized extreme value (GEV) models, and a random sampling technique is developed to quantify multimodel uncertainties. The GEV analysis was performed with projections using the Weather Research and Forecasting model, downscaled from three Global Climate Models based on the Representative Concentration Pathway 8.5 for present, mid-century and late-century. Results show the VPD based index (SVDI) accurately identifies the timing and magnitude short-term droughts, and extreme VPD is increasing across the United States and by the end of the twenty-first century. The number of days VPD is above 9 kPa increases by 10 days along California's coastline, 30-40 days in the northwest and Midwest, and 100 days in California's Central Valley.

Uncertainty-Informed Deep Transfer Learning of Perfluoroalkyl and Polyfluoroalkyl Substance Toxicity.

Perfluoroalkyl and polyfluoroalkyl substances (PFAS) pose a significant hazard because of their widespread industrial uses, environmental persistence, and bioaccumulation. A growing, increasingly diverse inventory of PFAS, including 8163 chemicals, has recently been updated by the U.S. Environmental Protection Agency. However, with the exception of a handful of well-studied examples, little is known about their human toxicity potential because of the substantial resources required for in vivo toxicity experiments. We tackle the problem of expensive in vivo experiments by evaluating multiple machine learning (ML) methods, including random forests, deep neural networks (DNN), graph convolutional networks, and Gaussian processes, for predicting acute toxicity (e.g., median lethal dose, or LD50) of PFAS compounds. To address the scarcity of toxicity information for PFAS, publicly available datasets of oral rat LD50 for all organic compounds are aggregated and used to develop state-of-the-art ML source models for transfer learning. A total of 519 fluorinated compounds containing two or more C-F bonds with known toxicity are used for knowledge transfer to ensembles of the best-performing source model, DNN, to generate the target models for the PFAS domain with access to uncertainty. This study predicts toxicity for PFAS with a defined chemical structure. To further inform prediction confidence, the transfer-learned model is embedded within a SelectiveNet architecture, where the model is allowed to identify regions of prediction with greater confidence and abstain from those with high uncertainty using a calibrated cutoff rate.

Three-dimensional sperm surface reconstruction: a novel approach to assessing sperm morphology.

OBJECTIVE: To create a rapid, inexpensive, efficient, and reproducible real-time three-dimensional (3-D) analysis of viable spermatozoa. Previous studies have demonstrated that abnormal semen profiles are associated with a modest increase in the frequency of sperm chromosomal abnormalities, and that sperm with aberrations in the shape and contours of the head may be carriers of chromatinic defects. Although high-power magnification and enhanced video-generated magnification have been suggested, these techniques are inherently limited by the clarity of the image, the time required for the analysis, and the risk of variable head-positioning during imaging. DESIGN: In vitro experiment. SETTING: University-affiliated infertility research laboratory. PATIENT(S): Anonymous sperm donors. INTERVENTION(S): Individual motile sperm were identified, analyzed at ×600 magnification, and a 10-second digital video was obtained. MAIN OUTCOME MEASURE(S): Image-tracking software captured serial photographs of sperm from recorded videos. Images were automatically extracted from each video frame using enhanced correlation coefficient maximization; the general shape of the sperm was extracted via space-carving. The reconstructed image was rotated to permit viewing from any direction, and the final image was rendered through interpolation. RESULT(S): This technique yielded images that enable noninvasive, 3-D, real-time, in vitro assessment of sperm surface morphology. CONCLUSION(S): This proof-of-principle demonstrates that by keeping spermatozoa in a fluid environment, a 3-D sperm-surface reconstruction can be created. This technique can be automated, requires minimal computing power, and utilizes equipment already available in most embryology laboratories.