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A key question in SARS-CoV-2 infection is why viral loads and patient outcomes vary dramatically across individuals. Because spatial-temporal dynamics of viral spread and immune response are challenging to study in vivo, we developed Spatial Immune Model of Coronavirus (SIMCoV), a scalable computational model that simulates hundreds of millions of lung cells, including respiratory epithelial cells and T cells. SIMCoV replicates viral growth dynamics observed in patients and shows how spatially dispersed infections can lead to increased viral loads. The model also shows how the timing and strength of the T cell response can affect viral persistence, oscillations, and control. By incorporating spatial interactions, SIMCoV provides a parsimonious explanation for the dramatically different viral load trajectories among patients by varying only the number of initial sites of infection and the magnitude and timing of the T cell immune response. When the branching airway structure of the lung is explicitly represented, we find that virus spreads faster than in a 2D layer of epithelial cells, but much more slowly than in an undifferentiated 3D grid or in a well-mixed differential equation model. These results illustrate how realistic, spatially explicit computational models can improve understanding of within-host dynamics of SARS-CoV-2 infection.

GPUs are a key enabler of the revolution in machine learning and high-performance computing, functioning as de facto co-processors to accelerate large-scale computation. As the programming stack and tool support have matured, GPUs have also become accessible to programmers, who may lack detailed knowledge of the underlying architecture and fail to fully leverage the GPU’s computation power. GEVO (Gpu optimization using EVOlutionary computation) is a tool for automatically discovering optimization opportunities and tuning the performance of GPU kernels in the LLVM representation. GEVO uses population-based search to find edits to GPU code compiled to LLVM-IR and improves performance on desired criteria while retaining required functionality. We demonstrate that GEVO improves the execution time of general-purpose GPU programs and machine learning (ML) models on NVIDIA Tesla P100. For the Rodinia benchmarks, GEVO improves GPU kernel runtime performance by an average of 49.48% and by as much as 412% over the fully compiler-optimized baseline. If kernel output accuracy is relaxed to tolerate up to 1% error, GEVO can find kernel variants that outperform the baseline by an average of 51.08%. For the ML workloads, GEVO achieves kernel performance improvement for SVM on the MNIST handwriting recognition (3.24×) and the a9a income predictionmore »(2.93×) datasets with no loss of model accuracy. GEVO achieves 1.79× kernel performance improvement on image classification using ResNet18/CIFAR-10, with less than 1% model accuracy reduction.« less

Analysis of municipal wastewater, or sewage for public health applications is a rapidly expanding field aimed at understanding emerging epidemiological trends, including human and disease migration. The newly gained ability to extract and analyze genetic material from wastewater poses important societal and ethical questions, including: How to safeguard data? Who owns genetic data recovered from wastewater? What are the ethical and legal issues surrounding its use? In the U.S., both corporate and legal policies regarding privacy have been historically reactive instead of proactive. In wastewater-based epidemiology (WBE), the pace of innovation has outpaced the ability of social and legal mechanisms to keep up. To address this discrepancy, early and robust discussions of the research, policies, and ethics surrounding WBE analysis and genetics is needed. This paper contributes to this discussion by examining ownership issues for human genetic data recovered from wastewater and the uses to which it may be put. We focus particularly on the risks associated with personally identifiable data, highlighting potential risks, relevant privacy-enhancing technologies, and appropriate ethics. The paper proposes an approach for people conducting WBE studies to help them systematically consider the ethical and privacy implications of their work.