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This inhibitor failed to reduce this cytokine levels in A549 cell cultures infected with chemotype II and their spontaneous variant yeasts, which also do not present α-glucan on their surface. The importance of SFKs and PKC δ in this event was also analyzed. Our results show that different isolates of H. capsulatum modulate distinct cell signaling pathways to promote cytokine secretion in host epithelial cells, emphasizing the existence of various mechanisms for Histoplasma pathogenicity. check details © The Author(s) 2020. Published by Oxford University Press on behalf of The International Society for Human and Animal Mycology.The adaptive immune system of cartilaginous fish (Elasmobranchii), comprising of classical hetero-tetrameric antibodies, is enhanced through the presence of a naturally occurring homodimeric antibody-like immunoglobulin-the new antigen receptor (IgNAR). The binding site of the IgNAR variable single-domain (VNAR) offers advantages of reduced size ( less then 1/10th of classical immunoglobulin) and extended binding topographies, making it an ideal candidate for accessing cryptic epitopes otherwise intractable to conventional antibodies. These attributes, coupled with high physicochemical stability and amenability to phage display, facilitate the selection of VNAR binders to challenging targets. Here, we explored the unique attributes of these single domains for potential application as bioprocessing reagents in the development of the SEED-Fc platform, designed to generate therapeutic bispecific antibodies. A panel of unique VNARs specific to the SEED homodimeric (monospecific) ‘by-products’ were isolated from a shark semi-synthetic VNAR library via phage display. The lead VNAR candidate exhibited low nanomolar affinity and superior selectivity to SEED homodimer, with functionality being retained upon exposure to extreme physicochemical conditions that mimic their applicability as purification agents. Ultimately, this work exemplifies the robustness of the semi-synthetic VNAR platform, the predisposition of the VNAR paratope to recognise novel epitopes and the potential for routine generation of tailor-made VNAR-based bioprocessing reagents. © The Author(s) 2020. Published by Oxford University Press. All rights reserved. For permissions, please e-mail journals.permissions@oup.com.We presented a case of a 30-week pregnant woman with COVID-19 delivering a healthy baby with no evidence of COVID-19. © The Author(s) 2020. Published by Oxford University Press for the Infectious Diseases Society of America. All rights reserved. For permissions, e-mail journals.permissions@oup.com.SUMMARY Single cell RNA-sequencing (scRNA-seq) technology enables studying gene expression programs from individual cells. However, these data are subject to diverse sources of variation, including “unwanted” variation that needs to be removed in downstream analyses (e.g., batch effects) and “wanted” or biological sources of variation (e.g., variation associated with a cell type) that needs to be precisely described. Surrogate variable analysis (SVA) based algorithms, are commonly used for batch correction and more recently for studying “wanted” variation in scRNA-seq data. However, interpreting whether these variables are biologically meaningful or stemming from technical reasons remains a challenge. To facilitate the interpretation of surrogate variables detected by algorithms including IA-SVA, SVA, or ZINB-WaVE, we developed an R Shiny application (Visual Surrogate Variable Analysis (V-SVA)) that provides a web-browser interface for the identification and annotation of hidden sources of variation in scRNA-seq data. This interactive framework includes tools for discovery of genes associated with detected sources of variation, gene annotation using publicly available databases and gene sets, and data visualization using dimension reduction methods. AVAILABILITY The V-SVA Shiny application is publicly hosted at https//vsva.jax.org/ and the source code is freely available at https//github.com/nlawlor/V-SVA. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online. © The Author(s) 2020. Published by Oxford University Press.Whether radiation therapy (RT) affects contralateral breast cancer (CBC) risk in women with pathogenic germline variants in moderate- to high-penetrance breast cancer-associated genes is unknown. In a population-based case-control study, we examined the association between RT, variants in ATM, BRCA1/2, or CHEK2*1100delC, and CBC risk. We analyzed 708 cases of women with CBC, and 1,399 controls with unilateral breast cancer, all diagnosed with first invasive breast cancer between 1985-2000, less then 55 years of age at diagnosis, and screened for variants in breast cancer-associated genes. Rate ratios and 95% confidence intervals were estimated using multivariable conditional logistic regression. RT did not modify the association between known pathogenic variants and CBC risk (e.g., BRCA1/2 pathogenic variant carriers without RT, RR 3.52, 95% CI 1.76-7.01; BRCA1/2 pathogenic variant carriers with RT, RR 4.46, 95% CI 2.96-6.71), suggesting that modifying RT plans for young women with breast cancer is unwarranted. Rare ATM missense variants, not currently identified as pathogenic, were associated with increased risk of RT-associated CBC (carriers of ATM rare missense variants of uncertain significance without RT, RR 0.38, 95% CI 0.09-1.55; carriers of ATM rare missense variants of uncertain significance with RT, RR 2.98, 95% CI 1.31-6.80). Further mechanistic studies will aid clinical decision-making related to RT. © The Author(s) 2020. Published by Oxford University Press. All rights reserved. For permissions, please email journals.permissions@oup.com.MOTIVATION Single cell RNA-sequencing (scRNA-seq) technology enables whole transcriptome profiling at single cell resolution and holds great promises in many biological and medical applications. Nevertheless, scRNA-seq often fails to capture expressed genes, leading to the prominent dropout problem. These dropouts cause many problems in down-stream analysis, such as significant increase of noises, power loss in differential expression analysis and obscuring of gene-to-gene or cell-to-cell relationship. Imputation of these dropout values can be beneficial in scRNA-seq data analysis. RESULTS In this paper, we model the dropout imputation problem as robust matrix decomposition. This model has minimal assumptions and allows us to develop a computational efficient imputation method called scRMD. Extensive data analysis shows that scRMD can accurately recover the dropout values and help to improve downstream analysis such as differential expression analysis and clustering analysis. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.