548 research outputs found

    Assimilation of Wind Profiles from Multiple Doppler Radar Wind Profilers for Space Launch Vehicle Applications

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    Space launch vehicles utilize atmospheric winds in design of the vehicle and during day-of-launch (DOL) operations to assess affects of wind loading on the vehicle and to optimize vehicle performance during ascent. The launch ranges at NASA's Kennedy Space Center co-located with the United States Air Force's (USAF) Eastern Range (ER) at Cape Canaveral Air Force Station and USAF's Western Range (WR) at Vandenberg Air Force Base have extensive networks of in-situ and remote sensing instrumentation to measure atmospheric winds. Each instrument's technique to measure winds has advantages and disadvantages in regards to use for vehicle engineering assessments. Balloons measure wind at all altitudes necessary for vehicle assessments, but two primary disadvantages exist when applying balloon output on DOL. First, balloons need approximately one hour to reach required altitude. For vehicle assessments this occurs at 60 kft (18.3 km). Second, balloons are steered by atmospheric winds down range of the launch site that could significantly differ from those winds along the vehicle ascent trajectory. Figure 1 illustrates the spatial separation of balloon measurements from the surface up to approximately 55 kft (16.8 km) during the Space Shuttle launch on 10 December 2006. The balloon issues are mitigated by use of vertically pointing Doppler Radar Wind Profilers (DRWPs). However, multiple DRWP instruments are required to provide wind data up to 60 kft (18.3 km) for vehicle trajectory assessments. The various DRWP systems have different operating configurations resulting in different temporal and spatial sampling intervals. Therefore, software was developed to combine data from both DRWP-generated profiles into a single profile for use in vehicle trajectory analyses. Details on how data from various wind measurement systems are combined and sample output will be presented in the following sections

    Automated prediction of mastitis infection patterns in dairy herds using machine learning

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    © 2020, The Author(s). Mastitis in dairy cattle is extremely costly both in economic and welfare terms and is one of the most significant drivers of antimicrobial usage in dairy cattle. A critical step in the prevention of mastitis is the diagnosis of the predominant route of transmission of pathogens into either contagious (CONT) or environmental (ENV), with environmental being further subdivided as transmission during either the nonlactating “dry” period (EDP) or lactating period (EL). Using data from 1000 farms, random forest algorithms were able to replicate the complex herd level diagnoses made by specialist veterinary clinicians with a high degree of accuracy. An accuracy of 98%, positive predictive value (PPV) of 86% and negative predictive value (NPV) of 99% was achieved for the diagnosis of CONT vs ENV (with CONT as a “positive” diagnosis), and an accuracy of 78%, PPV of 76% and NPV of 81% for the diagnosis of EDP vs EL (with EDP as a “positive” diagnosis). An accurate, automated mastitis diagnosis tool has great potential to aid non-specialist veterinary clinicians to make a rapid herd level diagnosis and promptly implement appropriate control measures for an extremely damaging disease in terms of animal health, productivity, welfare and antimicrobial use

    Rice Galaxy: An open resource for plant science

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    Background: Rice molecular genetics, breeding, genetic diversity, and allied research (such as rice-pathogen interaction) have adopted sequencing technologies and high-density genotyping platforms for genome variation analysis and gene discovery. Germplasm collections representing rice diversity, improved varieties, and elite breeding materials are accessible through rice gene banks for use in research and breeding, with many having genome sequences and high-density genotype data available. Combining phenotypic and genotypic information on these accessions enables genome-wide association analysis, which is driving quantitative trait loci discovery and molecular marker development. Comparative sequence analyses across quantitative trait loci regions facilitate the discovery of novel alleles. Analyses involving DNA sequences and large genotyping matrices for thousands of samples, however, pose a challenge to non−computer savvy rice researchers. Findings: The Rice Galaxy resource has shared datasets that include high-density genotypes from the 3,000 Rice Genomes project and sequences with corresponding annotations from 9 published rice genomes. The Rice Galaxy web server and deployment installer includes tools for designing single-nucleotide polymorphism assays, analyzing genome-wide association studies, population diversity, rice−bacterial pathogen diagnostics, and a suite of published genomic prediction methods. A prototype Rice Galaxy compliant to Open Access, Open Data, and Findable, Accessible, Interoperable, and Reproducible principles is also presented. Conclusions: Rice Galaxy is a freely available resource that empowers the plant research community to perform state-of-the-art analyses and utilize publicly available big datasets for both fundamental and applied science

    Hsf1 and Hsp90 orchestrate temperature-dependent global transcriptional remodelling and chromatin architecture in Candida albicans

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    We thank Karim Gharbi and Urmi Trivedi for their assistance with RNA sequencing, carried out in the GenePool genomics facility (University of Edinburgh). We also thank Susan Fairley and Eduardo De Paiva Alves (Centre for Genome Enabled Biology and Medicine, University of Aberdeen) for help with the initial bioinformatics analysis. We thank Aaron Mitchell for kindly providing the ALS3 mutant, Julian Naglik for the gift of TR146 cells, and Jon Richardson for technical assistance. We thank the Genomics and Bioinformatics core of the Faculty of Health Sciences for Next Generation Sequencing and Bioinformatics support, the Information and Communication Technology Office at the University of Macau for providing access to a High Performance Computer and Jacky Chan and William Pang for their expert support on the High Performance Computer. Finally, we thank Amanda Veri for generating CaLC2928. M.D.L. is supported by a Sir Henry Wellcome Postdoctoral Fellowship (Wellcome Trust 096072), R.A.F. by a Wellcome Trust-Massachusetts Institute of Technology (MIT) Postdoctoral Fellowship, L.E.C. by a Canada Research Chair in Microbial Genomics and Infectious Disease and by Canadian Institutes of Health Research Grants MOP-119520 and MOP-86452, A.J. P.B. was supported by the UK Biotechnology and Biological Sciences Research Council (BB/F00513X/1) and by the European Research Council (ERC-2009-AdG-249793-STRIFE), KHW is supported by the Science and Technology Development Fund of Macau S.A.R (FDCT) (085/2014/A2) and the Research and Development Administrative Office of the University of Macau (SRG2014-00003-FHS) and R.T.W. by the Burroughs Wellcome fund and NIH R15AO094406. Data availability RNA-sequencing data sets are available at ArrayExpress (www.ebi.ac.uk) under accession code E-MTAB-4075. ChIP-seq data sets are available at the NCBI SRA database (http://www.ncbi.nlm.nih.gov) under accession code SRP071687. The authors declare that all other data supporting the findings of this study are available within the article and its supplementary information files, or from the corresponding author upon request.Peer reviewedPublisher PD

    Acquisition and Evolution of Plant Pathogenesis–Associated Gene Clusters and Candidate Determinants of Tissue-Specificity in Xanthomonas

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    Xanthomonas is a large genus of plant-associated and plant-pathogenic bacteria. Collectively, members cause diseases on over 392 plant species. Individually, they exhibit marked host- and tissue-specificity. The determinants of this specificity are unknown.To assess potential contributions to host- and tissue-specificity, pathogenesis-associated gene clusters were compared across genomes of eight Xanthomonas strains representing vascular or non-vascular pathogens of rice, brassicas, pepper and tomato, and citrus. The gum cluster for extracellular polysaccharide is conserved except for gumN and sequences downstream. The xcs and xps clusters for type II secretion are conserved, except in the rice pathogens, in which xcs is missing. In the otherwise conserved hrp cluster, sequences flanking the core genes for type III secretion vary with respect to insertion sequence element and putative effector gene content. Variation at the rpf (regulation of pathogenicity factors) cluster is more pronounced, though genes with established functional relevance are conserved. A cluster for synthesis of lipopolysaccharide varies highly, suggesting multiple horizontal gene transfers and reassortments, but this variation does not correlate with host- or tissue-specificity. Phylogenetic trees based on amino acid alignments of gum, xps, xcs, hrp, and rpf cluster products generally reflect strain phylogeny. However, amino acid residues at four positions correlate with tissue specificity, revealing hpaA and xpsD as candidate determinants. Examination of genome sequences of xanthomonads Xylella fastidiosa and Stenotrophomonas maltophilia revealed that the hrp, gum, and xcs clusters are recent acquisitions in the Xanthomonas lineage.Our results provide insight into the ancestral Xanthomonas genome and indicate that differentiation with respect to host- and tissue-specificity involved not major modifications or wholesale exchange of clusters, but subtle changes in a small number of genes or in non-coding sequences, and/or differences outside the clusters, potentially among regulatory targets or secretory substrates

    A Multizone Technique for Billet Inspection

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    An ultrasonic inspection system has been developed in response to FAA recommendations for improved inspection of titanium billet [1]. This prototype system — called Multizone — has been transitioned to the factory floor and has inspected 1,000,000+ pounds of Ti billet in 1993–94. It is a real-time, PC based platform that employs custom built analog electronics using up to 8 parallel (non-multiplexed) channels, each with a remote pulser/receiver matched to the ultrasonic transducer. Scanned helically, the billet is divided into concentric zones with a focused transducer used to acquire peak detected C-Scan image data for each zone. The depth of each zone is established by the depth of focus of that transducer. C-Scan image data from all channels are displayed simultaneously on a 1024×1280 CRT and scroll as the inspection advances along the billet length. The data are written to optical storage upon completion of the inspection. The analog electronics are fully synchronous and could provide a baseline system for the acquisition of full waveforms. Custom post scan analysis software has been developed to detect flaws using signal to noise based algorithms. This software provides more reproducible results than conventional systems and greatly reduces operator fatigue and the chance for error. This paper will discuss the system architecture and operation. A companion paper in this volume discusses inspection results. [2

    Evaluation of machine-learning methods for ligand-based virtual screening

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    Machine-learning methods can be used for virtual screening by analysing the structural characteristics of molecules of known (in)activity, and we here discuss the use of kernel discrimination and naive Bayesian classifier (NBC) methods for this purpose. We report a kernel method that allows the processing of molecules represented by binary, integer and real-valued descriptors, and show that it is little different in screening performance from a previously described kernel that had been developed specifically for the analysis of binary fingerprint representations of molecular structure. We then evaluate the performance of an NBC when the training-set contains only a very few active molecules. In such cases, a simpler approach based on group fusion would appear to provide superior screening performance, especially when structurally heterogeneous datasets are to be processed
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