01), whereas the risk for viral infection was lower (IRR, 1.4; P = .07). Among potentially vaccine-preventable organisms, the IRR was 3.0 (95% CI, 2.1-4.3) vs the non-HCT group. Although the incidences of all infections decreased with time, the relative risk in almost all categories remained significantly increased in ≥5-year HCT survivors vs other groups. Risk factors for late infection included history of relapse and for some infections, history of chronic graft-versus-host disease. Providers caring for HCT survivors should maintain vigilance for infections and ensure adherence to antimicrobial prophylaxis and vaccination guidelines. © 2020 by The American Society of Hematology.We report on 59 Hodgkin lymphoma patients undergoing haploidentical stem cell transplantation (SCT; haplo-SCT) with posttransplant cyclophosphamide (PTCy) as graft-versus-host disease (GVHD) prophylaxis, comparing outcomes based on pretransplant exposure to checkpoint inhibitors (CPIs). Considering pretransplant characteristics, the 2 cohorts (CPI = 29 patients vs no-CPI = 30 patients) were similar, except for the number of prior lines of therapy (6 vs 4; P less then .001). With a median follow-up of 26 months (range, 7.5-55 months), by univariate analysis, the 100-day cumulative incidence of grade 2-4 acute GVHD was 41% in the CPI group vs 33% in the no-CPI group (P = .456), whereas the 1-year cumulative incidence of moderate to severe chronic GVHD was 7% vs 8%, respectively (P = .673). In the CPI cohort, the 2-year cumulative incidence of relapse appeared lower compared with the no-CPI cohort (0 vs 20%; P = .054). No differences were observed in terms of overall survival (OS), progression-free survival (PFS), and nonrelapse mortality (NRM) (at 2 years, 77% vs 71% [P = .599], 78% vs 53% [P = .066], and 15% vs 21% [P = .578], respectively). By multivariable analysis, CPI before SCT was an independent protective factor for PFS (hazard ratio [HR], 0.32; P = .037). Stable disease (SD)/progressive disease (PD) was an independent negative prognostic factor for both OS and PFS (HR, 14.3; P less then .001 and HR, 14.1; P less then .001, respectively) . In conclusion, CPI as a bridge to haplo-SCT seems to improve PFS, with no impact on toxicity profile. © 2020 by The American Society of Hematology.Actomyosin-undercoated adherens junctions are critical for epithelial cell integrity and remodeling. Actomyosin associates with adherens junctions through αE-catenin complexed with β-catenin and E-cadherin in vivo; however, in vitro biochemical studies in solution showed that αE-catenin complexed with β-catenin binds to F-actin less efficiently than αE-catenin that is not complexed with β-catenin. Although a "catch-bond model" partly explains this inconsistency, the mechanism for this inconsistency between the in vivo and in vitro results remains elusive. We herein demonstrate that afadin binds to αE-catenin complexed with β-catenin and enhances its F-actin-binding activity in a novel mechanism, eventually inducing the proper actomyosin organization through αE-catenin complexed with β-catenin and E-cadherin at adherens junctions. © 2020 Sakakibara et al.MOTIVATION The submission of annotated sequence data to public sequence databases constitutes a central pillar in biological research. The surge of novel DNA sequences awaiting database submission due to the application of next-generation sequencing has increased the need for software tools that facilitate bulk submissions. This need has yet to be met with the concurrent development of tools to automate the preparatory work preceding such submissions. RESULTS I introduce annonex2embl, a Python package that automates the preparation of complete sequence flatfiles for large-scale sequence submissions to the European Nucleotide Archive.  https://www.selleckchem.com/products/wnt-c59-c59.html  The tool enables the conversion of DNA sequence alignments that are co-supplied with sequence annotations and metadata to submission-ready flatfiles. Among other features, the software automatically accounts for length differences among the input sequences while maintaining correct annotations, automatically interlaces metadata to each record, and displays a design suitable for easy integration into bioinformatic workflows. As proof of its utility, annonex2embl is employed in preparing a dataset of more than 1,500 fungal DNA sequences for database submission. AVAILABILITY annonex2embl is freely available via the Python package index at http//pypi.python.org/pypi/annonex2embl. 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.MOTIVATION Protein domains are subunits that can fold and function independently. Correct domain boundary assignment is thus a critical step towards accurate protein structure and function analyses. There is, however, no efficient algorithm available for accurate domain prediction from sequence. The problem is particularly challenging for proteins with discontinuous domains, which consist of domain segments that are separated along the sequence. RESULTS We developed a new algorithm, FUpred, which predicts protein domain boundaries utilizing contact maps created by deep residual neural networks coupled with co-evolutionary precision matrices. The core idea of the algorithm is to retrieve domain boundary locations by maximizing the number of intra-domain contacts, while minimizing the number of inter-domain contacts from the contact maps. FUpred was tested on a large-scale dataset consisting of 2,549 proteins and generated correct single- and multi-domain classifications with an MCC of 0.799, which was 19.1% (or 5.3%) higher than the best machine learning (or threading) based method. For proteins with discontinuous domains, the DBD (domain boundary detection) and NDO (normalized domain overlapping) scores of FUpred were 0.788 and 0.521, respectively, which were 17.3% and 23.8% higher than the best control method. The results demonstrate a new avenue to accurately detect domain composition from sequence alone, especially for discontinuous, multi-domain proteins. AVAILABILITY https//zhanglab.ccmb.med.umich.edu/FUpred. 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.