Hence, a back panel with 0.125-mm lead equivalent is sufficient to protect the staff from tertiary radiation created within the room environment. © The Author(s) 2020. Published by Oxford University Press. All rights reserved. For Permissions, please email journals.permissions@oup.com.MOTIVATION TNT (a widely used program for phylogenetic analysis) includes an interpreter for a scripting language, but that implementation is non-standard and uses several conventions of its own. This paper describes the implementation and basic usage of a C-interpreter (with all the ISO essentials) now included in TNT. A phylogenetic library includes functions that can be used for manipulating trees and data, as well as other phylogeny-specific tasks. This greatly extends the capabilities of TNT. AVAILABILITY versions of TNT including the C interpreter for scripts can be downloaded from http//www.lillo.org.ar/phylogeny/tnt/. © The Author(s) (2020). Published by Oxford University Press. All rights reserved. For Permissions, please email journals.permissions@oup.com.SUMMARY QuartataWeb is a user-friendly server developed for polypharmacological and chemogenomics analyses. Users can easily obtain information on experimentally verified (known) and computationally predicted (new) interactions between 5,494 drugs and 2,807 human proteins in DrugBank, and between 315,514 chemicals and 9,457 human proteins in the STITCH database. In addition, QuartataWeb links targets to KEGG pathways and GO annotations, completing the bridge from drugs/chemicals to function via protein targets and cellular pathways. It allows users to query a series of chemicals, drug combinations, or multiple targets, to enable multi-drug, multi-target, multi-pathway analyses, toward facilitating the design of polypharmacological treatments for complex diseases. AVAILABILITY AND IMPLEMENTATION QuartataWeb is freely accessible at http//quartata.csb.pitt.edu. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online. © The Author(s) 2020. Published by Oxford University Press.SUMMARY We have developed a software tool to improve the image quality in FIB-SEM stacks PolishEM. Based on a Gaussian-blur model, it automatically estimates and compensates for the blur affecting each individual image. It also includes correction for artefacts commonly arising in FIB-SEM (e.g. curtaining). PolishEM has been optimized for an efficient processing of huge FIB-SEM stacks on standard computers. AVAILABILITY AND IMPLEMENTATION polishEM has been developed in C. GPL source code and binaries for Linux, OSX and Windows are available at http//www.cnb.csic.es/%7ejjfernandez/polishem. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online. © The Author(s) (2020). Published by Oxford University Press. All rights reserved. For Permissions, please email journals.permissions@oup.com.INTRODUCTION ageing is accompanied by impairments in immune responses due to remodelling of the immune system (immunesenescence).  https://www.selleckchem.com/products/zanubrutini-bgb-3111.html  Additionally, a decline in habitual physical activity has been reported in older adults. We have recently published that specific features of immunesenescence, such as thymic involution and naïve/memory T-cell ratio, are prevented by maintenance of a high level of physical activity. This study compares immune ageing between sedentary and physically active older adults. METHODS a cross-sectional study recruited 211 healthy older adults (60-79 years) and assessed their physical activity levels using an actigraph. We compared T- and B-cell immune parameters between relatively sedentary (n = 25) taking 2,000-4,500 steps/day and more physically active older adults (n = 25) taking 10,500-15,000 steps/day. RESULTS we found a higher frequency of naïve CD4 (P = 0.01) and CD8 (P = 0.02) and a lower frequency of memory CD4 cells (P = 0.01) and CD8 (P = 0.04) T cells in the physically active group compared with the sedentary group. Elevated serum IL7 (P = 0.03) and IL15 (P = 0.003), cytokines that play an essential role in T-cell survival, were seen in the physically active group. Interestingly, a positive association was observed between IL15 levels and peripheral CD4 naïve T-cell frequency (P = 0.023). DISCUSSION we conclude that a moderate level of physical activity may be required to give a very broad suppression of immune ageing, though 10,500-15,000 steps/day has a beneficial effect on the naïve T-cell pool. © The Author(s) 2020. Published by Oxford University Press on behalf of the British Geriatrics Society. All rights reserved. For permissions, please email journals.permissions@oup.com.MOTIVATION Synthetic lethality (SL) is a promising form of gene interaction for cancer therapy, as it is able to identify specific genes to target at cancer cells without disrupting normal cells. As high-throughput wet-lab settings are often costly and face various challenges, computational approaches have become a practical complement. In particular, predicting SLs can be formulated as a link prediction task on a graph of interacting genes. Although matrix factorization techniques have been widely adopted in link prediction, they focus on mapping genes to latent representations in isolation, without aggregating information from neighboring genes. Graph convolutional networks (GCN) can capture such neighborhood dependency in a graph. However, it is still challenging to apply GCN for SL prediction as SL interactions are extremely sparse, which is more likely to cause overfitting. RESULTS In this paper, we propose a novel Dual-Dropout GCN (DDGCN) for learning more robust gene representations for SL prediction. We employ both coarse-grained node dropout and fine-grained edge dropout to address the issue that standard dropout in vanilla GCN is often inadequate in reducing overfitting on sparse graphs. In particular, coarse-grained node dropout can efficiently and systematically enforce dropout at the node (gene) level, while fine-grained edge dropout can further fine-tune the dropout at the interaction (edge) level. We further present a theoretical framework to justify our model architecture. Finally, we conduct extensive experiments on human SL datasets and the results demonstrate the superior performance of our model in comparison with state-of-the-art methods. AVAILABILITY DDGCN is implemented in python 3.7, open-source and freely available at https//github.com/CXX1113/Dual-DropoutGCN. © The Author(s) (2020). Published by Oxford University Press. 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