Risk of severe disease and death due to COVID-19 is increased in certain patient demographic groups, including those of advanced age, male sex, and obese body mass index. Investigations of the biological variations that contribute to this risk have been hampered by heterogeneous severity, with immunologic features of critical disease potentially obscuring differences between risk groups. To examine immune heterogeneity related to demographic risk factors, we enrolled 38 patients hospitalized with clinically homogeneous COVID-19 pneumonia - defined as oxygen saturation less than 94% on room air without respiratory failure, septic shock, or multiple organ dysfunction - and performed single-cell RNA-Seq of leukocytes collected at admission. Examination of individual risk factors identified strong shifts within neutrophil and monocyte/dendritic cell (Mo/DC) compartments, revealing altered immune cell type-specific responses in higher risk COVID-19 patient subgroups. Specifically, we found transcriptional evidence of altered neutrophil maturation in aged versus young patients and enhanced cytokine responses in Mo/DCs of male versus female patients. Such innate immune cell alterations may contribute to outcome differences linked to these risk factors. They also highlight the importance of diverse patient cohorts in studies of therapies targeting the immune response in COVID-19.
Test selection techniques are used to reduce the human effort involved in software testing. Most research focusses on selecting efficient sets of test cases according to various coverage criteria for directed testing. We introduce a new technique to select efficient sets of sub domains from which new test cases can be sampled at random to achieve a high mutation score. We first present a technique for evolving multiple sub domains, each of which target a different group of mutants. The evolved sub domains are shown to achieve an average 160% improvement in mutation score compared to random testing with six real world Java programs. We then present a technique for selecting sets of the evolved sub domains to reduce the human effort involved in evaluating sampled test cases without reducing their fault finding effectiveness. This technique significantly reduces the number of sub domains for four of the six programs with a negligible difference in mutation score.
ABSTRACT Atopic dermatitis (AD) is a highly heritable and common inflammatory skin condition affecting children and adults worldwide. Multi-ancestry approaches to AD genetic association studies are poised to boost power to detect genetic signal and identify ancestry-specific loci contributing to AD risk. Here, we present a multi-ancestry GWAS meta-analysis of twelve AD cohorts from five ancestral populations totaling 56,146 cases and 602,280 controls. We report 101 genomic loci associated with AD, including 15 loci that have not been previously associated with AD or eczema. Fine-mapping, QTL colocalization, and cell-type enrichment analyses identified genes and cell types implicated in AD pathophysiology. Functional analyses in keratinocytes provide evidence for genes that could play a role in AD through epidermal barrier function. Our study provides new insights into the etiology of AD by harnessing multiple genetic and functional approaches to unveil the mechanisms by which AD-associated variants impact genes and cell types. Disclosure Statement BRG, MO, CH, KMS are employees of AbbVie. FT was an employee of AbbVie at the time of the study. JEG (University of Michigan) has received research support from AbbVie, Janssen, Almirall, Prometheus Biosciences/Merck, BMS/Celgene, Boehringer Ingelheim, Galderma, Eli Lilly, and advisor to Sanofi, Eli Lilly, Galderma, BMS, Boehringer Ingelheim. MKS, RU, MTP, QL, RW, JMK, LCT are employees of University of Michigan and have no funding to disclose. MEM, AHS, FDM, DW, JTG, HH are employees of the Children’s Hospital of Philadelphia and no funding to disclose. The design, study conduct, and financial support for this research were provided by AbbVie. AbbVie participated in the interpretation of data, review, and approval of the publication.
Abstract The temporal evolution of microstructural features in metals and ceramics has been the subject of intense investigation over many years because deviations from normal grain growth behavior are ubiquitous and strongly dictate observed mechanical and magnetic properties. To distinguish among different grain growth scenarios, we examine the time evolution of the information content of both synthetic and experimental coarsening microstructures as quantified by both a computable information density (CID) and a spectral entropy along with selected metrics and measures of shared information and interaction strength. In these approaches, microstructural evolution is described in terms of two time series representations, namely: (1) strings and their compressed counterparts that reflect the information contained in the configuration of a system over time, and (2) the spectra of graph Laplacians that embody the information contained in a coarsening grain network. These approaches permit one to characterize dynamically evolving microstructures and to identify correlation times associated with different coarsening scenarios. Moreover, as the information content of a system is a proxy for the entropy, a thermodynamic description of grain growth is also described.
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