A General Framework for Comparing Embedding Visualizations Across Class-Label Hierarchies.

Projecting high-dimensional vectors into two dimensions for visualization, known as embedding visualization, facilitates perceptual reasoning and interpretation. Comparing multiple embedding visualizations drives decision-making in many domains, but traditional comparison methods are limited by a reliance on direct point correspondences. This requirement precludes comparisons without point correspondences, such as two different datasets of annotated images, and fails to capture meaningful higher-level relationships among point groups.

Extricating human tumour immune alterations from tissue inflammation.

Immunotherapies have achieved remarkable successes in the treatment of cancer, but major challenges remain. An inherent weakness of current treatment approaches is that therapeutically targeted pathways are not restricted to tumours, but are also found in other tissue microenvironments, complicating treatment. Despite great efforts to define inflammatory processes in the tumour microenvironment, the understanding of tumour-unique immune alterations is limited by a knowledge gap regarding the immune cell populations in inflamed human tissues.

New interpretable machine-learning method for single-cell data reveals correlates of clinical response to cancer immunotherapy.

We introduce a new method for single-cell cytometry studies, FAUST, which performs unbiased cell population discovery and annotation. FAUST processes experimental data on a per-sample basis and returns biologically interpretable cell phenotypes, making it well suited for the analysis of complex datasets. We provide simulation studies that compare FAUST with existing methodology, exemplifying its strength.

A regulatory T cell signature distinguishes the immune landscape of COVID-19 patients from those with other respiratory infections.

Despite recent studies of immunity to severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), little is known about how the immune response against SARS-CoV-2 differs from other respiratory infections. We compare the immune signature from hospitalized SARS-CoV-2–infected patients to patients hospitalized prepandemic with influenza or respiratory syncytial virus (RSV). Our in-depth profiling indicates that the immune landscape in SARS-CoV-2 patients is largely similar to flu or RSV patients.

genetic variants modify regulatory and conventional T cell phenotypes and functions.

We recently reported that the risk of sexually acquired HIV-1 infection is increased significantly by variants in the gene encoding CD101, a protein thought to modify inflammatory responses. Using blood samples from individuals with and without these variants, we demonstrate that variants modify the prevalence of circulating inflammatory cell types and show that variants are associated with increased proinflammatory cytokine production by circulating T cells. One category of variants is associated with a reduced capacity of regulatory T cells to suppress T cell cytokine production, resulting in a reduction in the baseline level of immune quiescence.

A differential regulatory T cell signature distinguishes the immune landscape of COVID-19 hospitalized patients from those hospitalized with other respiratory viral infections.

Unlabelled: SARS-CoV-2 infection has caused a lasting global pandemic costing millions of lives and untold additional costs. Understanding the immune response to SARS-CoV-2 has been one of the main challenges in the past year in order to decipher mechanisms of host responses and interpret disease pathogenesis. Comparatively little is known in regard to how the immune response against SARS-CoV-2 differs from other respiratory infections.

Exponential bounds for the hypergeometric distribution.

We establish exponential bounds for the hypergeometric distribution which include a finite sampling correction factor, but are otherwise analogous to bounds for the binomial distribution due to León and Perron ( (2003) 345-354) and Talagrand ( (1994) 28-76). We also extend a convex ordering of Kemperman's ( (1973) 149-164) for sampling without replacement from populations of real numbers between zero and one: a population of all zeros or ones (and hence yielding a hypergeometric distribution in the upper bound) gives the extreme case.

Finite sampling inequalities: an application to two-sample Kolmogorov-Smirnov statistics.

We review a finite-sampling exponential bound due to Serfling and discuss related exponential bounds for the hypergeometric distribution. We then discuss how such bounds motivate some new results for two-sample empirical processes. Our development complements recent results by Wei and Dudley (2012) concerning exponential bounds for two-sided Kolmogorov - Smirnov statistics by giving corresponding results for one-sided statistics with emphasis on "adjusted" inequalities of the type proved originally by Dvoretzky et al.

Virtual-tissue computer simulations define the roles of cell adhesion and proliferation in the onset of kidney cystic disease.

In autosomal dominant polycystic kidney disease (ADPKD), cysts accumulate and progressively impair renal function. Mutations in PKD1 and PKD2 genes are causally linked to ADPKD, but how these mutations drive cell behaviors that underlie ADPKD pathogenesis is unknown. Human ADPKD cysts frequently express cadherin-8 (cad8), and expression of cad8 ectopically in vitro suffices to initiate cystogenesis.