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We seek to completely revise current models of airborne transmission of respiratory viruses by providing never-before-seen atomic-level views of the SARS-CoV-2 virus within a respiratory aerosol. Our work dramatically extends the capabilities of multiscale computational microscopy to address the significant gaps that exist in current experimental methods, which are limited in their ability to interrogate aerosols at the atomic/molecular level and thus obscure our understanding of airborne transmission. We demonstrate how our integrated data-driven platform provides a new way of exploring the composition, structure, and dynamics of aerosols and aerosolized viruses, while driving simulation method development along several important axes. We present a series of initial scientific discoveries for the SARS-CoV-2 Delta variant, noting that the full scientific impact of this work has yet to be realized.

We develop a generalizable AI-driven workflow that leverages heterogeneous HPC resources to explore the time-dependent dynamics of molecular systems. We use this workflow to investigate the mechanisms of infectivity of the SARS-CoV-2 spike protein, the main viral infection machinery. Our workflow enables more efficient investigation of spike dynamics in a variety of complex environments, including within a complete SARS-CoV-2 viral envelope simulation, which contains 305 million atoms and shows strong scaling on ORNL Summit using NAMD. We present several novel scientific discoveries, including the elucidation of the spike’s full glycan shield, the role of spike glycans in modulating the infectivity of the virus, and the characterization of the flexible interactions between the spike and the human ACE2 receptor. We also demonstrate how AI can accelerate conformational sampling across different systems and pave the way for the future application of such methods to additional studies in SARS-CoV-2 and other molecular systems.

BackgroundFamily screening of a 48-year-old male with recently diagnosed IgG4-related disease (IgG4-RD) revealed unanticipated elevations in plasma IgG4 in his two healthy teenaged sons.MethodsWe performed gene sequencing, immune cell studies, HLA typing, and analyses of circulating cytotoxic CD4+ T lymphocytes and plasmablasts to seek clues to pathogenesis. DNA from a separate cohort of 99 patients with known IgG4-RD was also sequenced for the presence of genetic variants in a specific gene, FGFBP2.ResultsThe three share a previously unreported heterozygous single base deletion in fibroblast growth factor binding protein type 2 (FGFBP2), which causes a frameshift in the coding sequence. The FGFBP2 protein is secreted by cytotoxic T-lymphocytes and binds fibroblast growth factor. The variant sequence in the FGFBP2 protein is predicted to form a disordered random coil rather than a helical-turn-helix structure, unable to adopt a stable conformation. The proband and the two sons had 5-10-fold higher numbers of circulating cytotoxic CD4 + T cells and plasmablasts compared to matched controls. The three members also share a homozygous missense common variant in FGFBP2 found in heterozygous form in ~40% of the population. This common variant was found in 73% of an independent, well characterized IgG4-RD cohort, showing enrichment in idiopathic IgG4-RD.ConclusionsThe presence of a shared deleterious variant and homozygous common variant in FGFBP2 in the proband and sons strongly implicates this cytotoxic T cell product in the pathophysiology of IgG4-RD. The high prevalence of a common FGFBP2 variant in sporadic IgG4-RD supports the likelihood of participation in disease.

We describe the development and implementation of an online graduate bioethics program that weaves a theme of health justice throughout the curriculum. Our account relies on a constructionist model of curriculum development and adult teaching and learning theory. Our curriculum draws upon core values of Jesuit higher education, including content with particular attention to justice for marginalized and vulnerable members of society and pedagogical strategies that cultivate students' capacities for critical thinking and engagement with ethics and justice issues in the context of healthcare. We propose four major contributions from the health justice literature as key content areas for inclusion in bioethics programs interested in focusing on health justice. We identify gaps in the literature and suggest how they might be addressed. Finally, we give examples of content, pedagogy, and preliminary findings from specific courses in our program, all in hopes of stimulating more conversation about how students learn about health justice.

Systemic sclerosis (SSc) is an autoimmune fibrotic disease whose pathogenesis is poorly understood and lacks effective therapies. We undertook quantitative analyses of T cell infiltrates in the skin of 35 untreated patients with early diffuse SSc and here show that [CD4.sup.+] cytotoxic T cells and [CD8.sup.+] T cells contribute prominently to these infiltrates. We also observed an accumulation of apoptotic cells in SSc tissues, suggesting that recurring cell death may contribute to tissue damage and remodeling in this fibrotic disease. HLA-DR-expressing endothelial cells were frequent targets of apoptosis in SSc, consistent with the prominent vasculopathy seen in patients with this disease. A circulating effector population of cytotoxic [CD4.sup.+] T cells, which exhibited signatures of enhanced metabolic activity, was clonally expanded in patients with systemic sclerosis. These data suggest that cytotoxic T cells may induce the apoptotic death of endothelial and other cells in systemic sclerosis. Cell loss driven by immune cells may be followed by overly exuberant tissue repair processes that lead to fibrosis and tissue dysfunction., Introduction Systemic sclerosis (SSc) is a systemic autoimmune inflammatory disorder of unknown etiology characterized by prominent fibrosis that may affect many organs including the skin, lungs, kidneys, and gastrointestinal tract [...]

ObjectiveTo examine the association of previously identified autoimmune disease susceptibility loci with granulomatosis with polyangiitis (Wegener's) (GPA), and to determine whether the genetic susceptibility profiles of other autoimmune diseases are associated with those of GPA.MethodsGenetic data from 2 cohorts were meta-analyzed. Genotypes for 168 previously identified single-nucleotide polymorphisms (SNPs) associated with susceptibility to different autoimmune diseases were ascertained in a total of 880 patients with GPA and 1,969 control subjects of European descent. Single-marker associations were identified using additive logistic regression models. Associations of multiple SNPs with GPA were assessed using genetic risk scores based on susceptibility loci for Crohn's disease, type 1 diabetes, systemic lupus erythematosus, rheumatoid arthritis (RA), celiac disease, and ulcerative colitis. Adjustment for population substructure was performed in all analyses, using ancestry-informative markers and principal components analysis.ResultsGenetic polymorphisms in CTLA4 were significantly associated with GPA in the single-marker meta-analysis (odds ratio [OR] 0.79, 95% confidence interval [95% CI] 0.70-0.89, P = 9.8 × 10(-5) ). The genetic risk score for RA susceptibility markers was significantly associated with GPA (OR 1.05 per 1-unit increase in genetic risk score, 95% CI 1.02-1.08, P = 5.1 × 10(-5) ).ConclusionRA and GPA may arise from a similar genetic predisposition. Aside from CTLA4, other loci previously found to be associated with common autoimmune diseases were not statistically significantly associated with GPA in this study.

The graphics processing unit (GPU) has become an integral part oftoday's mainstream computing systems. Over the past six years, therehas been a marked increase in the performance and capabilities ofGPUs. The modern GPU is not only a powerful graphics engine but also ahighly-parallel programmable processor featuring peak arithmetic andmemory bandwidth that substantially outpaces its CPU counterpart. TheGPU's rapid increase in both programmability and capability hasspawned a research community that has successfully mapped a broadrange of computationally demanding, complex problems to the GPU. Thiseffort in general-purpose computing on the GPU (GPGPU), alsoknown as GPU computing, has positioned the GPU as a compellingalternative to traditional microprocessors in high-performancecomputer systems of the future. We describe the background, hardware,and programming model for GPU computing, summarize the state of theart in tools and techniques, and present four GPU computing successesin game physics and computational biophysics that deliverorder-of-magnitude performance gains over optimized CPU applications.