Dengue virus (DENV) is an increasingly important human pathogen, with already half of the globe's population living in environments with transmission potential. Since many cases are missed by direct detection methods (RT-PCR or antigen tests), serological assays play an important role in the diagnostic process. However, individual assays can suffer from low sensitivity and specificity and interpreting results from multiple assays remains challenging, particularly because interpretations from multiple assays may differ, creating uncertainty over how to generate finalized interpretations. We develop a Bayesian mixture model that can jointly model data from multiple paired serological assays, to infer infection events. We first test the performance of our model using simulated data. We then apply our model to 677 pairs of acute and convalescent serum collected as a part of illness and household investigations across two longitudinal cohort studies in Kamphaeng Phet, Thailand, including data from 232 RT-PCR confirmed infections (gold standard). We compare the classification of the new model to prior standard interpretations that independently utilize information from either the hemagglutination inhibition assay (HAI) or the enzyme-linked immunosorbent assay (EIA). We find that additional serological assays improve accuracy of infection detection for both simulated and real world data. Models incorporating paired IgG and IgM data as well as those incorporating IgG, IgM, and HAI data consistently have higher accuracy when using PCR confirmed infections as a gold standard (87-90% F1 scores, a combined metric of sensitivity and specificity) than currently implemented cut-point approaches (82-84% F1 scores). Our results provide a probabilistic framework through which multiple serological assays across different platforms can be leveraged across sequential serum samples to provide insight into whether individuals have recently experienced a DENV infection. These methods are applicable to other pathogen systems where multiple serological assays can be leveraged to quantify infection history.

The U.S. Department of Energy has identified exascale-class wind farm simulation as critical to wind energy scientific discovery. A primary objective of the ExaWind project is to build high-performance, predictive computational fluid dynamics (CFD) tools that satisfy these modeling needs. GPU accelerators will serve as the computational thoroughbreds of next-generation, exascale-class supercomputers. Here, we report on our efforts in preparing the ExaWind unstructured mesh solver, Nalu-Wind, for exascale-class machines. For computing at this scale, a simple port of the incompressible-flow algorithms to GPUs is insufficient. To achieve high performance, one needs novel algorithms that are application aware, memory efficient, and optimized for the latest-generation GPU devices. The result of our efforts are unstructured-mesh simulations of wind turbines that can effectively leverage thousands of GPUs. In particular, we demonstrate a first-of-its-kind, incompressible-flow simulation using Algebraic Multigrid solvers that strong scales to more than 4000 GPUs on the Summit supercomputer.

Climate modeling is a grand challenge problem where scientific progress is measured not in terms of the largest problem that can be solved but by the highest achievable integration rate. These models have been notably absent in previous Gordon Bell competitions due to their inability to scale to large processor counts. A scalable and efficient spectral element atmospheric model is presented. A new semi-implicit time stepping scheme accelerates the integration rate relative to an explicit model by a factor of two, achieving 130 years per day at T63L30 equivalent resolution. Execution rates are reported for the standard shallow water and Held-Suarez climate benchmarks on IBM SP clusters. The explicit T170 equivalent multi-layer shallow water model sustains 343 Gflops at NERSC, 206 Gflops at NPACI (SDSC) and 127 Gflops at NCAR. An explicit Held-Suarez integration sustains 369 Gflops on 128 16-way IBM nodes at NERSC.

The EDMC (electrostatically driven Monte Carlo) method has proven to be an effective computational tool for searching the potential energy hypersurface of polypeptide molecules consisting of up to 20 amino acid residues. Such a Monte Carlo search combined with gradient-based energy minimization of molecular conformations results in the need for 100 gigaflop or higher performance levels. The parallel EDMC algorithm has been designed to exploit currently available supercomputing technology. The implementation on the iPSC/2 described appears to represent an improvement over the original version for the IBM 3090. A performance analysis indicates that the attainable parallelism is limited by the underlying acceptance rate of search. It is demonstrated that a coarse-grained approach is suitable for architectures such as the CRAY-XMP, particularly if vectorization techniques can be exploited. Tests on the Intel iPSC/2-VX computer have shown, however, that even the easily vectorized parts of the computation may not overcome a large vector pipeline latency.>