Improving Minuteman III Maintenance Concepts

Since the end of the Cold War, the Air Force has sought out efficiencies across multiple processes to transform into a cost-effective force. However, processes applicable to the Minuteman III (MM III) weapon system have only recently seen efforts to increase effectiveness. The purpose of this research is to investigate whether the use of third generation maintenance concepts could benefit the sustainment of the MM III through its planned retirement around 2030. Primary and secondary sources outlining the history of the strategic missile force and its current state were collected. Themes from each era were analyzed using Prospect Theory as a means to understand the past and interpret the current state. The resulting interpretation led to propositions on how third generation maintenance concepts could be applied to the sustainment of the MM III as well as benefit its planned replacement, the Ground Based Strategic Deterrent (GBDS) program

Improved Streaming Algorithms for Weighted Matching, via Unweighted Matching

We present a (4 + epsilon) approximation algorithm for weighted graph matching which applies in the semistreaming, sliding window, and MapReduce models; this single algorithm improves the previous best algorithm in each model. The algorithm operates by reducing the maximum-weight matching problem to a polylog number of copies of the maximum-cardinality matching problem. The algorithm also extends to provide approximation guarantees for the more general problem of finding weighted independent sets in p-systems (which include intersections of p matroids and p-bounded hypergraph matching)

Heterogeneity of Rural Consumer Perceptions of Health Service Access Across Four Regions of Victoria

Access to a range of services, including health care, ranks among the key determinants of health and wellbeing. It varies with both health system supply factors and consumer demand characteristics. For rural populations, access to health services can be restricted for a variety of reasons, contributing to poorer health outcomes compared with metropolitan populations. Access to health care differs between communities, despite commonly being seen as homogenous in terms of lack of service and poor access. This article seeks to examine consumer perceptions of access to health service in four shires in rural Victoria and explore differences between rural areas. These insights may assist health services to reorient their modes of service provision to be more accessible to rural health consumers. A confidential self-administered questionnaire was mailed to randomly selected households in the four shires. A total of 1,271 questionnaires were returned (35 percent response rate) with 75 percent of respondents reporting good access to health care overall. Many factors contributed significantly to the perception of health access; however, these factors were unique to each rural community. The implication of this heterogeneity is that rural health care services must be tailored to promote equitable, quality health care outcomes with attention to local community needs at the core of efforts. Only locally-targeted actions will achieve optimal health service planning and delivery

Heterogeneity of rural consumer perceptions of health service access across four regions of Victoria

Access to a range of services, including healthcare, ranks among the key determinants of health and wellbeing. It varies with both health system supply factors and consumer demand characteristics. For rural populations, access to health services can be..

Genetic regulation of RNA splicing in human pancreatic islets

Background Non-coding genetic variants that influence gene transcription in pancreatic islets play a major role in the susceptibility to type 2 diabetes (T2D), and likely also contribute to type 1 diabetes (T1D) risk. For many loci, however, the mechanisms through which non-coding variants influence diabetes susceptibility are unknown. Results We examine splicing QTLs (sQTLs) in pancreatic islets from 399 human donors and observe that common genetic variation has a widespread influence on the splicing of genes with established roles in islet biology and diabetes. In parallel, we profile expression QTLs (eQTLs) and use transcriptome-wide association as well as genetic co-localization studies to assign islet sQTLs or eQTLs to T2D and T1D susceptibility signals, many of which lack candidate effector genes. This analysis reveals biologically plausible mechanisms, including the association of T2D with an sQTL that creates a nonsense isoform in ERO1B, a regulator of ER-stress and proinsulin biosynthesis. The expanded list of T2D risk effector genes reveals overrepresented pathways, including regulators of G-protein-mediated cAMP production. The analysis of sQTLs also reveals candidate effector genes for T1D susceptibility such as DCLRE1B, a senescence regulator, and lncRNA MEG3. Conclusions These data expose widespread effects of common genetic variants on RNA splicing in pancreatic islets. The results support a role for splicing variation in diabetes susceptibility, and offer a new set of genetic targets with potential therapeutic benefit.This research was supported by Ministerio de Ciencia e Innovación (BFU2014-54284-R, RTI2018-095666-B-I00), Medical Research Council (MR/L02036X/1), a Wellcome Trust Senior Investigator Award (WT101033), European Research Council Advanced Grant (789055), EU Horizon 2020 TDSystems (667191), ESPACE (874710), and Marie Sklodowska-Curie (643062, ZENCODE). S.B.G was supported by a Juan de la Cierva postdoctoral fellowship (MINECO; FJCI-2017-32090). M.C.A was supported by a Boehringer Ingelheim Fonds PhD fellowship. Work in CRG was supported by the CERCA Programme, Generalitat de Catalunya, Centro de Excelencia Severo Ochoa (CEX2020-001049), and support of the Spanish Ministry of Science and Innovation to the EMBL partnership. Work in Imperial College was supported by NIHR Imperial Biomedical Research Centre. M.I. was supported by a European Research Council consolidator award (101002275). D.J.M.C. and J.A.T. were supported by JDRF grants 9-2011-253, 5-SRA-2015-130-A-N, 4- SRA-2017-473-A-N, and Wellcome grants 091157/Z/10/Z and 107212/Z/15/Z, to the Diabetes and Inflammation Laboratory, Oxford, as well as the Oxford Biomedical Research Computing (BMRC) facility, a joint development between the Wellcome Centre for Human Genetics and the Big Data Institute supported by Health Data Research UK and NIHR Oxford Biomedical Research Centre, and Wellcome Trust Core Award grant 203141/Z/16/Z. D.M.J.C analysis with the UK Biobank Resource was conducted under Application 31295. A.L.G. is a Wellcome Senior Fellow in Basic Biomedical Science and was supported by the Wellcome Trust (095101, 200837, 106130, 203141), the NIDDK (U01DK105535 and UM1 DK126185), and the Oxford NIHR Biomedical Research Centre.Peer Reviewed"Article signat per 20 autors/es: Goutham Atla, Silvia Bonàs-Guarch, Mirabai Cuenca-Ardura, Anthony Beucher, Daniel J. M. Crouch, Javier Garcia-Hurtado, Ignasi Moran, the T2DSystems Consortium, Manuel Irimia, Rashmi B. Prasad, Anna L. Gloyn, Lorella Marselli, Mara Suleiman, Thierry Berney, Eelco J. P. de Koning, Julie Kerr-Conte, Francois Pattou, John A. Todd, Lorenzo Piemonti & Jorge Ferrer"Postprint (published version

Application of the LymphGen classification tool to 928 clinically and genetically-characterised cases of diffuse large B cell lymphoma (DLBCL).

We recently published results of targeted sequencing applied to 928 unselected cases of DLBCL registered in the Haematological Malignancy Research Network (HMRN) registry (1). Clustering allowed us to resolve five genomic subtypes. These subtypes shared considerable overlap with those proposed in two independent genomic studies(2, 3), suggesting the potential to use genetics to stratify patients by both risk and biology. In the original studies, clustering techniques were applied to sample cohorts to reveal molecular substructure, but left open the challenge of how to classify an individual patient. This was addressed by the LymphGen classification tool (4). LymphGen assigns an individual case to one of six molecular subtypes. The tool accommodates data from exome or targeted sequencing, either with or without copy number variant (CNV) data. Separate gene expression data allows classification of a seventh, MYC-driven subtype defined by a double hit (DHL) or molecular high-grade (MHG) gene expression signature(5-7).HR was funded by a studentship from the Medical Research Council. DH was supported by a Clinician Scientist Fellowship from the Medical Research Council (MR/M008584/1). The Hodson laboratory receives core funding from Wellcome and MRC to the Wellcome-MRC Cambridge Stem Cell Institute and core funding from the CRUK Cambridge Cancer Centre. HMRN is supported by BCUK 15037 and CRUK 18362

Improved eukaryotic detection compatible with large-scale automated analysis of metagenomes

Background: Eukaryotes such as fungi and protists frequently accompany bacteria and archaea in microbial communities. Unfortunately, their presence is difficult to study with “shotgun” metagenomic sequencing since prokaryotic signals dominate in most environments. Recent methods for eukaryotic detection use eukaryote-specific marker genes, but they do not incorporate strategies to handle the presence of eukaryotes that are not represented in the reference marker gene set, and they are not compatible with web-based tools for downstream analysis. Results: Here, we present CORRAL (for Clustering Of Related Reference ALignments), a tool for the identification of eukaryotes in shotgun metagenomic data based on alignments to eukaryote-specific marker genes and Markov clustering. Using a combination of simulated datasets, mock community standards, and large publicly available human microbiome studies, we demonstrate that our method is not only sensitive and accurate but is also capable of inferring the presence of eukaryotes not included in the marker gene reference, such as novel strains. Finally, we deploy CORRAL on our MicrobiomeDB.org resource, producing an atlas of eukaryotes present in various environments of the human body and linking their presence to study covariates. Conclusions: CORRAL allows eukaryotic detection to be automated and carried out at scale. Implementation of CORRAL in MicrobiomeDB.org creates a running atlas of microbial eukaryotes in metagenomic studies. Since our approach is independent of the reference used, it may be applicable to other contexts where shotgun metagenomic reads are matched against redundant but non-exhaustive databases, such as the identification of bacterial virulence genes or taxonomic classification of viral reads

Bringing bioinformatics to schools with the 4273pi project

The work was supported by the Science and Technology Facilities Council (STFC) under Grants STFC ST/R000328/1 (including salary to S.A.B., D.B., H.P., T.R.M. and non-salary costs) and STFC ST/T000872/1 (including salary to S.A.B., D.B., K.C., T.R.M. and non-salary costs), the Darwin Trust of Edinburgh (https://darwintrust.bio.ed.ac.uk; including salary to S.A.B. and H.P. and non-salary costs), the Wellcome Trust-University of Edinburgh Institutional Strategic Support Fund under Wellcome Trust Grant number 204804/Z/16/Z (salary to H.P.), a Public Engagement with Genetics Tier 2 Grant from the Genetics Society (https://genetics.org.uk; non-salary costs), the Natural Environment Research Council (NERC) under Grant NE/P000592/1 (including salary to N.C. and M.G.R. and non-salary costs), the Biotechnology and Biological Sciences Research Council (BBSRC) under Grant BB/S018506/1 (including salary to F.A. and non-salary costs), the School of Biological Sciences at the University of Edinburgh (https://www.ed.ac.uk/biology; including salary to S.A.B. and H.P. and non-salary costs) and its Institute of Evolutionary Biology (https://www.ed.ac.uk/biology/evolutionary-biology; non-salary costs), the Access for Rural Communities project (ARC) at University of St Andrews (https://www.st-andrews.ac.uk/study/access/projects/arc; non-salary costs) and the Engineering and Physical Sciences Research Council (EPSRC) under Grant EP/V52038X/1 (including salary to S.A.B. and non-salary costs). E.W.J.W. is supported by a Sir Henry Dale Fellowship jointly funded by the Wellcome Trust and the Royal Society [208779/Z/17/Z] (including salary to E.W.J.W.).Over the last few decades, the nature of life sciences research has changed enormously, generating a need for a workforce with a variety of computational skills such as those required to store, manage, and analyse the large biological datasets produced by next-generation sequencing. Those with such expertise are increasingly in demand for employment in both research and industry. Despite this, bioinformatics education has failed to keep pace with advances in research. At secondary school level, computing is often taught in isolation from other sciences, and its importance in biological research is not fully realised, leaving pupils unprepared for the computational component of Higher Education and, subsequently, research in the life sciences. The 4273pi Bioinformatics at School project (https://4273pi.org) aims to address this issue by designing and delivering curriculum-linked, hands-on bioinformatics workshops for secondary school biology pupils, with an emphasis on equitable access. So far, we have reached over 180 schools across Scotland through visits or teacher events, and our open education resources are used internationally. Here, we describe our project, our aims and motivations, and the practical lessons we have learned from implementing a successful bioinformatics education project over the last 5 years.Publisher PDFPeer reviewe

Genome analysis of the ubiquitous boxwood pathogen Pseudonectria foliicola

Boxwood (Buxus spp.) are broad-leaved, evergreen landscape plants valued for their longevity and ornamental qualities. Volutella leaf and stem blight, caused by the ascomycete fungi Pseudonectria foliicola and P. buxi, is one of the major diseases affecting the health and ornamental qualities of boxwood. Although this disease is less severe than boxwood blight caused by Calonectria pseudonaviculata and C. henricotiae, its widespread occurrence and disfiguring symptoms have caused substantial economic losses to the ornamental industry. In this study, we sequenced the genome of P. foliicola isolate ATCC13545 using Illumina technology and compared it to other publicly available fungal pathogen genomes to better understand the biology of this organism. A de novo assembly estimated the genome size of P. foliicola at 28.7 Mb (425 contigs; N50 = 184,987 bp; avg. coverage 188×), with just 9,272 protein-coding genes. To our knowledge, P. foliicola has the smallest known genome within the Nectriaceae. Consistent with the small size of the genome, the secretome, CAzyme and secondary metabolite profiles of this fungus are reduced relative to two other surveyed Nectriaceae fungal genomes: Dactylonectria macrodidyma JAC15-245 and Fusarium graminearum Ph-1. Interestingly, a large cohort of genes associated with reduced virulence and loss of pathogenicity was identified from the P. foliicola dataset. These data are consistent with the latest observations by plant pathologists that P. buxi and most likely P. foliicola, are opportunistic, latent pathogens that prey upon weak and stressed boxwood plants