Exploring fungal diversity in deep-sea sediments from Okinawa Trough using high-throughput Illumina sequencing

Science.gov (United States)

Zhang, Xiao-Yong; Wang, Guang-Hua; Xu, Xin-Ya; Nong, Xu-Hua; Wang, Jie; Amin, Muhammad; Qi, Shu-Hua

2016-10-01

The present study investigated the fungal diversity in four different deep-sea sediments from Okinawa Trough using high-throughput Illumina sequencing of the nuclear ribosomal internal transcribed spacer-1 (ITS1). A total of 40,297 fungal ITS1 sequences clustered into 420 operational taxonomic units (OTUs) with 97% sequence similarity and 170 taxa were recovered from these sediments. Most ITS1 sequences (78%) belonged to the phylum Ascomycota, followed by Basidiomycota (17.3%), Zygomycota (1.5%) and Chytridiomycota (0.8%), and a small proportion (2.4%) belonged to unassigned fungal phyla. Compared with previous studies on fungal diversity of sediments from deep-sea environments by culture-dependent approach and clone library analysis, the present result suggested that Illumina sequencing had been dramatically accelerating the discovery of fungal community of deep-sea sediments. Furthermore, our results revealed that Sordariomycetes was the most diverse and abundant fungal class in this study, challenging the traditional view that the diversity of Sordariomycetes phylotypes was low in the deep-sea environments. In addition, more than 12 taxa accounted for 21.5% sequences were found to be rarely reported as deep-sea fungi, suggesting the deep-sea sediments from Okinawa Trough harbored a plethora of different fungal communities compared with other deep-sea environments. To our knowledge, this study is the first exploration of the fungal diversity in deep-sea sediments from Okinawa Trough using high-throughput Illumina sequencing.

miRBase: integrating microRNA annotation and deep-sequencing data.

Science.gov (United States)

Kozomara, Ana; Griffiths-Jones, Sam

2011-01-01

miRBase is the primary online repository for all microRNA sequences and annotation. The current release (miRBase 16) contains over 15,000 microRNA gene loci in over 140 species, and over 17,000 distinct mature microRNA sequences. Deep-sequencing technologies have delivered a sharp rise in the rate of novel microRNA discovery. We have mapped reads from short RNA deep-sequencing experiments to microRNAs in miRBase and developed web interfaces to view these mappings. The user can view all read data associated with a given microRNA annotation, filter reads by experiment and count, and search for microRNAs by tissue- and stage-specific expression. These data can be used as a proxy for relative expression levels of microRNA sequences, provide detailed evidence for microRNA annotations and alternative isoforms of mature microRNAs, and allow us to revisit previous annotations. miRBase is available online at: http://www.mirbase.org/.

Discovery radiomics via evolutionary deep radiomic sequencer discovery for pathologically proven lung cancer detection.

Science.gov (United States)

Shafiee, Mohammad Javad; Chung, Audrey G; Khalvati, Farzad; Haider, Masoom A; Wong, Alexander

2017-10-01

While lung cancer is the second most diagnosed form of cancer in men and women, a sufficiently early diagnosis can be pivotal in patient survival rates. Imaging-based, or radiomics-driven, detection methods have been developed to aid diagnosticians, but largely rely on hand-crafted features that may not fully encapsulate the differences between cancerous and healthy tissue. Recently, the concept of discovery radiomics was introduced, where custom abstract features are discovered from readily available imaging data. We propose an evolutionary deep radiomic sequencer discovery approach based on evolutionary deep intelligence. Motivated by patient privacy concerns and the idea of operational artificial intelligence, the evolutionary deep radiomic sequencer discovery approach organically evolves increasingly more efficient deep radiomic sequencers that produce significantly more compact yet similarly descriptive radiomic sequences over multiple generations. As a result, this framework improves operational efficiency and enables diagnosis to be run locally at the radiologist's computer while maintaining detection accuracy. We evaluated the evolved deep radiomic sequencer (EDRS) discovered via the proposed evolutionary deep radiomic sequencer discovery framework against state-of-the-art radiomics-driven and discovery radiomics methods using clinical lung CT data with pathologically proven diagnostic data from the LIDC-IDRI dataset. The EDRS shows improved sensitivity (93.42%), specificity (82.39%), and diagnostic accuracy (88.78%) relative to previous radiomics approaches.

Transcriptome sequences resolve deep relationships of the grape family.

Directory of Open Access Journals (Sweden)

Jun Wen

Full Text Available Previous phylogenetic studies of the grape family (Vitaceae yielded poorly resolved deep relationships, thus impeding our understanding of the evolution of the family. Next-generation sequencing now offers access to protein coding sequences very easily, quickly and cost-effectively. To improve upon earlier work, we extracted 417 orthologous single-copy nuclear genes from the transcriptomes of 15 species of the Vitaceae, covering its phylogenetic diversity. The resulting transcriptome phylogeny provides robust support for the deep relationships, showing the phylogenetic utility of transcriptome data for plants over a time scale at least since the mid-Cretaceous. The pros and cons of transcriptome data for phylogenetic inference in plants are also evaluated.

Deep ART Neural Model for Biologically Inspired Episodic Memory and Its Application to Task Performance of Robots.

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Park, Gyeong-Moon; Yoo, Yong-Ho; Kim, Deok-Hwa; Kim, Jong-Hwan

2017-06-26

Robots are expected to perform smart services and to undertake various troublesome or difficult tasks in the place of humans. Since these human-scale tasks consist of a temporal sequence of events, robots need episodic memory to store and retrieve the sequences to perform the tasks autonomously in similar situations. As episodic memory, in this paper we propose a novel Deep adaptive resonance theory (ART) neural model and apply it to the task performance of the humanoid robot, Mybot, developed in the Robot Intelligence Technology Laboratory at KAIST. Deep ART has a deep structure to learn events, episodes, and even more like daily episodes. Moreover, it can retrieve the correct episode from partial input cues robustly. To demonstrate the effectiveness and applicability of the proposed Deep ART, experiments are conducted with the humanoid robot, Mybot, for performing the three tasks of arranging toys, making cereal, and disposing of garbage.

Evaluation of Nine Somatic Variant Callers for Detection of Somatic Mutations in Exome and Targeted Deep Sequencing Data.

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Anne Bruun Krøigård

Full Text Available Next generation sequencing is extensively applied to catalogue somatic mutations in cancer, in research settings and increasingly in clinical settings for molecular diagnostics, guiding therapy decisions. Somatic variant callers perform paired comparisons of sequencing data from cancer tissue and matched normal tissue in order to detect somatic mutations. The advent of many new somatic variant callers creates a need for comparison and validation of the tools, as no de facto standard for detection of somatic mutations exists and only limited comparisons have been reported. We have performed a comprehensive evaluation using exome sequencing and targeted deep sequencing data of paired tumor-normal samples from five breast cancer patients to evaluate the performance of nine publicly available somatic variant callers: EBCall, Mutect, Seurat, Shimmer, Indelocator, Somatic Sniper, Strelka, VarScan 2 and Virmid for the detection of single nucleotide mutations and small deletions and insertions. We report a large variation in the number of calls from the nine somatic variant callers on the same sequencing data and highly variable agreement. Sequencing depth had markedly diverse impact on individual callers, as for some callers, increased sequencing depth highly improved sensitivity. For SNV calling, we report EBCall, Mutect, Virmid and Strelka to be the most reliable somatic variant callers for both exome sequencing and targeted deep sequencing. For indel calling, EBCall is superior due to high sensitivity and robustness to changes in sequencing depths.

DeepGO: predicting protein functions from sequence and interactions using a deep ontology-aware classifier

KAUST Repository

Kulmanov, Maxat

2017-09-27

Motivation A large number of protein sequences are becoming available through the application of novel high-throughput sequencing technologies. Experimental functional characterization of these proteins is time-consuming and expensive, and is often only done rigorously for few selected model organisms. Computational function prediction approaches have been suggested to fill this gap. The functions of proteins are classified using the Gene Ontology (GO), which contains over 40 000 classes. Additionally, proteins have multiple functions, making function prediction a large-scale, multi-class, multi-label problem. Results We have developed a novel method to predict protein function from sequence. We use deep learning to learn features from protein sequences as well as a cross-species protein–protein interaction network. Our approach specifically outputs information in the structure of the GO and utilizes the dependencies between GO classes as background information to construct a deep learning model. We evaluate our method using the standards established by the Computational Assessment of Function Annotation (CAFA) and demonstrate a significant improvement over baseline methods such as BLAST, in particular for predicting cellular locations.

DeepProbe: Information Directed Sequence Understanding and Chatbot Design via Recurrent Neural Networks

OpenAIRE

Yin, Zi; Chang, Keng-hao; Zhang, Ruofei

2017-01-01

Information extraction and user intention identification are central topics in modern query understanding and recommendation systems. In this paper, we propose DeepProbe, a generic information-directed interaction framework which is built around an attention-based sequence to sequence (seq2seq) recurrent neural network. DeepProbe can rephrase, evaluate, and even actively ask questions, leveraging the generative ability and likelihood estimation made possible by seq2seq models. DeepProbe makes...

DSAP: deep-sequencing small RNA analysis pipeline.

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Huang, Po-Jung; Liu, Yi-Chung; Lee, Chi-Ching; Lin, Wei-Chen; Gan, Richie Ruei-Chi; Lyu, Ping-Chiang; Tang, Petrus

2010-07-01

DSAP is an automated multiple-task web service designed to provide a total solution to analyzing deep-sequencing small RNA datasets generated by next-generation sequencing technology. DSAP uses a tab-delimited file as an input format, which holds the unique sequence reads (tags) and their corresponding number of copies generated by the Solexa sequencing platform. The input data will go through four analysis steps in DSAP: (i) cleanup: removal of adaptors and poly-A/T/C/G/N nucleotides; (ii) clustering: grouping of cleaned sequence tags into unique sequence clusters; (iii) non-coding RNA (ncRNA) matching: sequence homology mapping against a transcribed sequence library from the ncRNA database Rfam (http://rfam.sanger.ac.uk/); and (iv) known miRNA matching: detection of known miRNAs in miRBase (http://www.mirbase.org/) based on sequence homology. The expression levels corresponding to matched ncRNAs and miRNAs are summarized in multi-color clickable bar charts linked to external databases. DSAP is also capable of displaying miRNA expression levels from different jobs using a log(2)-scaled color matrix. Furthermore, a cross-species comparative function is also provided to show the distribution of identified miRNAs in different species as deposited in miRBase. DSAP is available at http://dsap.cgu.edu.tw.

LookSeq: A browser-based viewer for deep sequencing data

OpenAIRE

Manske, Heinrich Magnus; Kwiatkowski, Dominic P.

2009-01-01

Sequencing a genome to great depth can be highly informative about heterogeneity within an individual or a population. Here we address the problem of how to visualize the multiple layers of information contained in deep sequencing data. We propose an interactive AJAX-based web viewer for browsing large data sets of aligned sequence reads. By enabling seamless browsing and fast zooming, the LookSeq program assists the user to assimilate information at different levels of resolution, from an ov...

Deep amplicon sequencing reveals mixed phytoplasma infection within single grapevine plants

DEFF Research Database (Denmark)

Nicolaisen, Mogens; Contaldo, Nicoletta; Makarova, Olga

2011-01-01

The diversity of phytoplasmas within single plants has not yet been fully investigated. In this project, deep amplicon sequencing was used to generate 50,926 phytoplasma sequences from 11 phytoplasma-infected grapevine samples from a PCR amplicon in the 5' end of the 16S region. After clustering ...

A simple method for the parallel deep sequencing of full influenza A genomes

DEFF Research Database (Denmark)

Kampmann, Marie-Louise; Fordyce, Sarah Louise; Avila Arcos, Maria del Carmen

2011-01-01

Given the major threat of influenza A to human and animal health, and its ability to evolve rapidly through mutation and reassortment, tools that enable its timely characterization are necessary to help monitor its evolution and spread. For this purpose, deep sequencing can be a very valuable tool....... This study reports a comprehensive method that enables deep sequencing of the complete genomes of influenza A subtypes using the Illumina Genome Analyzer IIx (GAIIx). By using this method, the complete genomes of nine viruses were sequenced in parallel, representing the 2009 pandemic H1N1 virus, H5N1 virus...

Unified Deep Learning Architecture for Modeling Biology Sequence.

Science.gov (United States)

Wu, Hongjie; Cao, Chengyuan; Xia, Xiaoyan; Lu, Qiang

2017-10-09

Prediction of the spatial structure or function of biological macromolecules based on their sequence remains an important challenge in bioinformatics. When modeling biological sequences using traditional sequencing models, characteristics, such as long-range interactions between basic units, the complicated and variable output of labeled structures, and the variable length of biological sequences, usually lead to different solutions on a case-by-case basis. This study proposed the use of bidirectional recurrent neural networks based on long short-term memory or a gated recurrent unit to capture long-range interactions by designing the optional reshape operator to adapt to the diversity of the output labels and implementing a training algorithm to support the training of sequence models capable of processing variable-length sequences. Additionally, the merge and pooling operators enhanced the ability to capture short-range interactions between basic units of biological sequences. The proposed deep-learning model and its training algorithm might be capable of solving currently known biological sequence-modeling problems through the use of a unified framework. We validated our model on one of the most difficult biological sequence-modeling problems currently known, with our results indicating the ability of the model to obtain predictions of protein residue interactions that exceeded the accuracy of current popular approaches by 10% based on multiple benchmarks.

DeepGO: predicting protein functions from sequence and interactions using a deep ontology-aware classifier.

Science.gov (United States)

Kulmanov, Maxat; Khan, Mohammed Asif; Hoehndorf, Robert; Wren, Jonathan

2018-02-15

A large number of protein sequences are becoming available through the application of novel high-throughput sequencing technologies. Experimental functional characterization of these proteins is time-consuming and expensive, and is often only done rigorously for few selected model organisms. Computational function prediction approaches have been suggested to fill this gap. The functions of proteins are classified using the Gene Ontology (GO), which contains over 40 000 classes. Additionally, proteins have multiple functions, making function prediction a large-scale, multi-class, multi-label problem. We have developed a novel method to predict protein function from sequence. We use deep learning to learn features from protein sequences as well as a cross-species protein-protein interaction network. Our approach specifically outputs information in the structure of the GO and utilizes the dependencies between GO classes as background information to construct a deep learning model. We evaluate our method using the standards established by the Computational Assessment of Function Annotation (CAFA) and demonstrate a significant improvement over baseline methods such as BLAST, in particular for predicting cellular locations. Web server: http://deepgo.bio2vec.net, Source code: https://github.com/bio-ontology-research-group/deepgo. robert.hoehndorf@kaust.edu.sa. Supplementary data are available at Bioinformatics online. © The Author(s) 2017. Published by Oxford University Press.

Error Analysis of Deep Sequencing of Phage Libraries: Peptides Censored in Sequencing

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Wadim L. Matochko

2013-01-01

Full Text Available Next-generation sequencing techniques empower selection of ligands from phage-display libraries because they can detect low abundant clones and quantify changes in the copy numbers of clones without excessive selection rounds. Identification of errors in deep sequencing data is the most critical step in this process because these techniques have error rates >1%. Mechanisms that yield errors in Illumina and other techniques have been proposed, but no reports to date describe error analysis in phage libraries. Our paper focuses on error analysis of 7-mer peptide libraries sequenced by Illumina method. Low theoretical complexity of this phage library, as compared to complexity of long genetic reads and genomes, allowed us to describe this library using convenient linear vector and operator framework. We describe a phage library as N×1 frequency vector n=ni, where ni is the copy number of the ith sequence and N is the theoretical diversity, that is, the total number of all possible sequences. Any manipulation to the library is an operator acting on n. Selection, amplification, or sequencing could be described as a product of a N×N matrix and a stochastic sampling operator (Sa. The latter is a random diagonal matrix that describes sampling of a library. In this paper, we focus on the properties of Sa and use them to define the sequencing operator (Seq. Sequencing without any bias and errors is Seq=Sa IN, where IN is a N×N unity matrix. Any bias in sequencing changes IN to a nonunity matrix. We identified a diagonal censorship matrix (CEN, which describes elimination or statistically significant downsampling, of specific reads during the sequencing process.

Deep sequencing analysis of the developing mouse brain reveals a novel microRNA

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Piltz Sandra

2011-04-01

Full Text Available Abstract Background MicroRNAs (miRNAs are small non-coding RNAs that can exert multilevel inhibition/repression at a post-transcriptional or protein synthesis level during disease or development. Characterisation of miRNAs in adult mammalian brains by deep sequencing has been reported previously. However, to date, no small RNA profiling of the developing brain has been undertaken using this method. We have performed deep sequencing and small RNA analysis of a developing (E15.5 mouse brain. Results We identified the expression of 294 known miRNAs in the E15.5 developing mouse brain, which were mostly represented by let-7 family and other brain-specific miRNAs such as miR-9 and miR-124. We also discovered 4 putative 22-23 nt miRNAs: mm_br_e15_1181, mm_br_e15_279920, mm_br_e15_96719 and mm_br_e15_294354 each with a 70-76 nt predicted pre-miRNA. We validated the 4 putative miRNAs and further characterised one of them, mm_br_e15_1181, throughout embryogenesis. Mm_br_e15_1181 biogenesis was Dicer1-dependent and was expressed in E3.5 blastocysts and E7 whole embryos. Embryo-wide expression patterns were observed at E9.5 and E11.5 followed by a near complete loss of expression by E13.5, with expression restricted to a specialised layer of cells within the developing and early postnatal brain. Mm_br_e15_1181 was upregulated during neurodifferentiation of P19 teratocarcinoma cells. This novel miRNA has been identified as miR-3099. Conclusions We have generated and analysed the first deep sequencing dataset of small RNA sequences of the developing mouse brain. The analysis revealed a novel miRNA, miR-3099, with potential regulatory effects on early embryogenesis, and involvement in neuronal cell differentiation/function in the brain during late embryonic and early neonatal development.

An introduction to deep learning on biological sequence data: examples and solutions.

Science.gov (United States)

Jurtz, Vanessa Isabell; Johansen, Alexander Rosenberg; Nielsen, Morten; Almagro Armenteros, Jose Juan; Nielsen, Henrik; Sønderby, Casper Kaae; Winther, Ole; Sønderby, Søren Kaae

2017-11-15

Deep neural network architectures such as convolutional and long short-term memory networks have become increasingly popular as machine learning tools during the recent years. The availability of greater computational resources, more data, new algorithms for training deep models and easy to use libraries for implementation and training of neural networks are the drivers of this development. The use of deep learning has been especially successful in image recognition; and the development of tools, applications and code examples are in most cases centered within this field rather than within biology. Here, we aim to further the development of deep learning methods within biology by providing application examples and ready to apply and adapt code templates. Given such examples, we illustrate how architectures consisting of convolutional and long short-term memory neural networks can relatively easily be designed and trained to state-of-the-art performance on three biological sequence problems: prediction of subcellular localization, protein secondary structure and the binding of peptides to MHC Class II molecules. All implementations and datasets are available online to the scientific community at https://github.com/vanessajurtz/lasagne4bio. skaaesonderby@gmail.com. Supplementary data are available at Bioinformatics online. © The Author (2017). Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oup.com

Hybridization-based reconstruction of small non-coding RNA transcripts from deep sequencing data.

Science.gov (United States)

Ragan, Chikako; Mowry, Bryan J; Bauer, Denis C

2012-09-01

Recent advances in RNA sequencing technology (RNA-Seq) enables comprehensive profiling of RNAs by producing millions of short sequence reads from size-fractionated RNA libraries. Although conventional tools for detecting and distinguishing non-coding RNAs (ncRNAs) from reference-genome data can be applied to sequence data, ncRNA detection can be improved by harnessing the full information content provided by this new technology. Here we present NorahDesk, the first unbiased and universally applicable method for small ncRNAs detection from RNA-Seq data. NorahDesk utilizes the coverage-distribution of small RNA sequence data as well as thermodynamic assessments of secondary structure to reliably predict and annotate ncRNA classes. Using publicly available mouse sequence data from brain, skeletal muscle, testis and ovary, we evaluated our method with an emphasis on the performance for microRNAs (miRNAs) and piwi-interacting small RNA (piRNA). We compared our method with Dario and mirDeep2 and found that NorahDesk produces longer transcripts with higher read coverage. This feature makes it the first method particularly suitable for the prediction of both known and novel piRNAs.

Sequence-based prediction of protein protein interaction using a deep-learning algorithm.

Science.gov (United States)

Sun, Tanlin; Zhou, Bo; Lai, Luhua; Pei, Jianfeng

2017-05-25

Protein-protein interactions (PPIs) are critical for many biological processes. It is therefore important to develop accurate high-throughput methods for identifying PPI to better understand protein function, disease occurrence, and therapy design. Though various computational methods for predicting PPI have been developed, their robustness for prediction with external datasets is unknown. Deep-learning algorithms have achieved successful results in diverse areas, but their effectiveness for PPI prediction has not been tested. We used a stacked autoencoder, a type of deep-learning algorithm, to study the sequence-based PPI prediction. The best model achieved an average accuracy of 97.19% with 10-fold cross-validation. The prediction accuracies for various external datasets ranged from 87.99% to 99.21%, which are superior to those achieved with previous methods. To our knowledge, this research is the first to apply a deep-learning algorithm to sequence-based PPI prediction, and the results demonstrate its potential in this field.

Deep sequencing methods for protein engineering and design.

Science.gov (United States)

Wrenbeck, Emily E; Faber, Matthew S; Whitehead, Timothy A

2017-08-01

The advent of next-generation sequencing (NGS) has revolutionized protein science, and the development of complementary methods enabling NGS-driven protein engineering have followed. In general, these experiments address the functional consequences of thousands of protein variants in a massively parallel manner using genotype-phenotype linked high-throughput functional screens followed by DNA counting via deep sequencing. We highlight the use of information rich datasets to engineer protein molecular recognition. Examples include the creation of multiple dual-affinity Fabs targeting structurally dissimilar epitopes and engineering of a broad germline-targeted anti-HIV-1 immunogen. Additionally, we highlight the generation of enzyme fitness landscapes for conducting fundamental studies of protein behavior and evolution. We conclude with discussion of technological advances. Copyright © 2016 Elsevier Ltd. All rights reserved.

Continuous Distributed Representation of Biological Sequences for Deep Proteomics and Genomics.

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Ehsaneddin Asgari

Full Text Available We introduce a new representation and feature extraction method for biological sequences. Named bio-vectors (BioVec to refer to biological sequences in general with protein-vectors (ProtVec for proteins (amino-acid sequences and gene-vectors (GeneVec for gene sequences, this representation can be widely used in applications of deep learning in proteomics and genomics. In the present paper, we focus on protein-vectors that can be utilized in a wide array of bioinformatics investigations such as family classification, protein visualization, structure prediction, disordered protein identification, and protein-protein interaction prediction. In this method, we adopt artificial neural network approaches and represent a protein sequence with a single dense n-dimensional vector. To evaluate this method, we apply it in classification of 324,018 protein sequences obtained from Swiss-Prot belonging to 7,027 protein families, where an average family classification accuracy of 93%±0.06% is obtained, outperforming existing family classification methods. In addition, we use ProtVec representation to predict disordered proteins from structured proteins. Two databases of disordered sequences are used: the DisProt database as well as a database featuring the disordered regions of nucleoporins rich with phenylalanine-glycine repeats (FG-Nups. Using support vector machine classifiers, FG-Nup sequences are distinguished from structured protein sequences found in Protein Data Bank (PDB with a 99.8% accuracy, and unstructured DisProt sequences are differentiated from structured DisProt sequences with 100.0% accuracy. These results indicate that by only providing sequence data for various proteins into this model, accurate information about protein structure can be determined. Importantly, this model needs to be trained only once and can then be applied to extract a comprehensive set of information regarding proteins of interest. Moreover, this representation can be

miRBase: annotating high confidence microRNAs using deep sequencing data.

Science.gov (United States)

Kozomara, Ana; Griffiths-Jones, Sam

2014-01-01

We describe an update of the miRBase database (http://www.mirbase.org/), the primary microRNA sequence repository. The latest miRBase release (v20, June 2013) contains 24 521 microRNA loci from 206 species, processed to produce 30 424 mature microRNA products. The rate of deposition of novel microRNAs and the number of researchers involved in their discovery continue to increase, driven largely by small RNA deep sequencing experiments. In the face of these increases, and a range of microRNA annotation methods and criteria, maintaining the quality of the microRNA sequence data set is a significant challenge. Here, we describe recent developments of the miRBase database to address this issue. In particular, we describe the collation and use of deep sequencing data sets to assign levels of confidence to miRBase entries. We now provide a high confidence subset of miRBase entries, based on the pattern of mapped reads. The high confidence microRNA data set is available alongside the complete microRNA collection at http://www.mirbase.org/. We also describe embedding microRNA-specific Wikipedia pages on the miRBase website to encourage the microRNA community to contribute and share textual and functional information.

LookSeq: a browser-based viewer for deep sequencing data.

Science.gov (United States)

Manske, Heinrich Magnus; Kwiatkowski, Dominic P

2009-11-01

Sequencing a genome to great depth can be highly informative about heterogeneity within an individual or a population. Here we address the problem of how to visualize the multiple layers of information contained in deep sequencing data. We propose an interactive AJAX-based web viewer for browsing large data sets of aligned sequence reads. By enabling seamless browsing and fast zooming, the LookSeq program assists the user to assimilate information at different levels of resolution, from an overview of a genomic region to fine details such as heterogeneity within the sample. A specific problem, particularly if the sample is heterogeneous, is how to depict information about structural variation. LookSeq provides a simple graphical representation of paired sequence reads that is more revealing about potential insertions and deletions than are conventional methods.

CPSS: a computational platform for the analysis of small RNA deep sequencing data.

Science.gov (United States)

Zhang, Yuanwei; Xu, Bo; Yang, Yifan; Ban, Rongjun; Zhang, Huan; Jiang, Xiaohua; Cooke, Howard J; Xue, Yu; Shi, Qinghua

2012-07-15

Next generation sequencing (NGS) techniques have been widely used to document the small ribonucleic acids (RNAs) implicated in a variety of biological, physiological and pathological processes. An integrated computational tool is needed for handling and analysing the enormous datasets from small RNA deep sequencing approach. Herein, we present a novel web server, CPSS (a computational platform for the analysis of small RNA deep sequencing data), designed to completely annotate and functionally analyse microRNAs (miRNAs) from NGS data on one platform with a single data submission. Small RNA NGS data can be submitted to this server with analysis results being returned in two parts: (i) annotation analysis, which provides the most comprehensive analysis for small RNA transcriptome, including length distribution and genome mapping of sequencing reads, small RNA quantification, prediction of novel miRNAs, identification of differentially expressed miRNAs, piwi-interacting RNAs and other non-coding small RNAs between paired samples and detection of miRNA editing and modifications and (ii) functional analysis, including prediction of miRNA targeted genes by multiple tools, enrichment of gene ontology terms, signalling pathway involvement and protein-protein interaction analysis for the predicted genes. CPSS, a ready-to-use web server that integrates most functions of currently available bioinformatics tools, provides all the information wanted by the majority of users from small RNA deep sequencing datasets. CPSS is implemented in PHP/PERL+MySQL+R and can be freely accessed at http://mcg.ustc.edu.cn/db/cpss/index.html or http://mcg.ustc.edu.cn/sdap1/cpss/index.html.

Ultra-deep sequencing of intra-host rabies virus populations during cross-species transmission.

Directory of Open Access Journals (Sweden)

Monica K Borucki

2013-11-01

Full Text Available One of the hurdles to understanding the role of viral quasispecies in RNA virus cross-species transmission (CST events is the need to analyze a densely sampled outbreak using deep sequencing in order to measure the amount of mutation occurring on a small time scale. In 2009, the California Department of Public Health reported a dramatic increase (350 in the number of gray foxes infected with a rabies virus variant for which striped skunks serve as a reservoir host in Humboldt County. To better understand the evolution of rabies, deep-sequencing was applied to 40 unpassaged rabies virus samples from the Humboldt outbreak. For each sample, approximately 11 kb of the 12 kb genome was amplified and sequenced using the Illumina platform. Average coverage was 17,448 and this allowed characterization of the rabies virus population present in each sample at unprecedented depths. Phylogenetic analysis of the consensus sequence data demonstrated that samples clustered according to date (1995 vs. 2009 and geographic location (northern vs. southern. A single amino acid change in the G protein distinguished a subset of northern foxes from a haplotype present in both foxes and skunks, suggesting this mutation may have played a role in the observed increased transmission among foxes in this region. Deep-sequencing data indicated that many genetic changes associated with the CST event occurred prior to 2009 since several nonsynonymous mutations that were present in the consensus sequences of skunk and fox rabies samples obtained from 20032010 were present at the sub-consensus level (as rare variants in the viral population in skunk and fox samples from 1995. These results suggest that analysis of rare variants within a viral population may yield clues to ancestral genomes and identify rare variants that have the potential to be selected for if environment conditions change.

Determining mutant spectra of three RNA viral samples using ultra-deep sequencing

Energy Technology Data Exchange (ETDEWEB)

Chen, H

2012-06-06

RNA viruses have extremely high mutation rates that enable the virus to adapt to new host environments and even jump from one species to another. As part of a viral transmission study, three viral samples collected from naturally infected animals were sequenced using Illumina paired-end technology at ultra-deep coverage. In order to determine the mutant spectra within the viral quasispecies, it is critical to understand the sequencing error rates and control for false positive calls of viral variants (point mutantations). I will estimate the sequencing error rate from two control sequences and characterize the mutant spectra in the natural samples with this error rate.

Deep sequencing reveals double mutations in cis of MPL exon 10 in myeloproliferative neoplasms.

Science.gov (United States)

Pietra, Daniela; Brisci, Angela; Rumi, Elisa; Boggi, Sabrina; Elena, Chiara; Pietrelli, Alessandro; Bordoni, Roberta; Ferrari, Maurizio; Passamonti, Francesco; De Bellis, Gianluca; Cremonesi, Laura; Cazzola, Mario

2011-04-01

Somatic mutations of MPL exon 10, mainly involving a W515 substitution, have been described in JAK2 (V617F)-negative patients with essential thrombocythemia and primary myelofibrosis. We used direct sequencing and high-resolution melt analysis to identify mutations of MPL exon 10 in 570 patients with myeloproliferative neoplasms, and allele specific PCR and deep sequencing to further characterize a subset of mutated patients. Somatic mutations were detected in 33 of 221 patients (15%) with JAK2 (V617F)-negative essential thrombocythemia or primary myelofibrosis. Only one patient with essential thrombocythemia carried both JAK2 (V617F) and MPL (W515L). High-resolution melt analysis identified abnormal patterns in all the MPL mutated cases, while direct sequencing did not detect the mutant MPL in one fifth of them. In 3 cases carrying double MPL mutations, deep sequencing analysis showed identical load and location in cis of the paired lesions, indicating their simultaneous occurrence on the same chromosome.

Chiron: translating nanopore raw signal directly into nucleotide sequence using deep learning

KAUST Repository

Teng, Haotian; Cao, Minh Duc; Hall, Michael B; Duarte, Tania; Wang, Sheng; Coin, Lachlan J M

2018-01-01

Sequencing by translocating DNA fragments through an array of nanopores is a rapidly maturing technology that offers faster and cheaper sequencing than other approaches. However, accurately deciphering the DNA sequence from the noisy and complex electrical signal is challenging. Here, we report Chiron, the first deep learning model to achieve end-to-end basecalling and directly translate the raw signal to DNA sequence without the error-prone segmentation step. Trained with only a small set of 4,000 reads, we show that our model provides state-of-the-art basecalling accuracy, even on previously unseen species. Chiron achieves basecalling speeds of more than 2,000 bases per second using desktop computer graphics processing units.

Chiron: translating nanopore raw signal directly into nucleotide sequence using deep learning

KAUST Repository

Teng, Haotian

2018-04-10

Sequencing by translocating DNA fragments through an array of nanopores is a rapidly maturing technology that offers faster and cheaper sequencing than other approaches. However, accurately deciphering the DNA sequence from the noisy and complex electrical signal is challenging. Here, we report Chiron, the first deep learning model to achieve end-to-end basecalling and directly translate the raw signal to DNA sequence without the error-prone segmentation step. Trained with only a small set of 4,000 reads, we show that our model provides state-of-the-art basecalling accuracy, even on previously unseen species. Chiron achieves basecalling speeds of more than 2,000 bases per second using desktop computer graphics processing units.

Deep RNA sequencing of the skeletal muscle transcriptome in swimming fish.

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Arjan P Palstra

Full Text Available Deep RNA sequencing (RNA-seq was performed to provide an in-depth view of the transcriptome of red and white skeletal muscle of exercised and non-exercised rainbow trout (Oncorhynchus mykiss with the specific objective to identify expressed genes and quantify the transcriptomic effects of swimming-induced exercise. Pubertal autumn-spawning seawater-raised female rainbow trout were rested (n = 10 or swum (n = 10 for 1176 km at 0.75 body-lengths per second in a 6,000-L swim-flume under reproductive conditions for 40 days. Red and white muscle RNA of exercised and non-exercised fish (4 lanes was sequenced and resulted in 15-17 million reads per lane that, after de novo assembly, yielded 149,159 red and 118,572 white muscle contigs. Most contigs were annotated using an iterative homology search strategy against salmonid ESTs, the zebrafish Danio rerio genome and general Metazoan genes. When selecting for large contigs (>500 nucleotides, a number of novel rainbow trout gene sequences were identified in this study: 1,085 and 1,228 novel gene sequences for red and white muscle, respectively, which included a number of important molecules for skeletal muscle function. Transcriptomic analysis revealed that sustained swimming increased transcriptional activity in skeletal muscle and specifically an up-regulation of genes involved in muscle growth and developmental processes in white muscle. The unique collection of transcripts will contribute to our understanding of red and white muscle physiology, specifically during the long-term reproductive migration of salmonids.

A Bioinformatic Pipeline for Monitoring of the Mutational Stability of Viral Drug Targets with Deep-Sequencing Technology.

Science.gov (United States)

Kravatsky, Yuri; Chechetkin, Vladimir; Fedoseeva, Daria; Gorbacheva, Maria; Kravatskaya, Galina; Kretova, Olga; Tchurikov, Nickolai

2017-11-23

The efficient development of antiviral drugs, including efficient antiviral small interfering RNAs (siRNAs), requires continuous monitoring of the strict correspondence between a drug and the related highly variable viral DNA/RNA target(s). Deep sequencing is able to provide an assessment of both the general target conservation and the frequency of particular mutations in the different target sites. The aim of this study was to develop a reliable bioinformatic pipeline for the analysis of millions of short, deep sequencing reads corresponding to selected highly variable viral sequences that are drug target(s). The suggested bioinformatic pipeline combines the available programs and the ad hoc scripts based on an original algorithm of the search for the conserved targets in the deep sequencing data. We also present the statistical criteria for the threshold of reliable mutation detection and for the assessment of variations between corresponding data sets. These criteria are robust against the possible sequencing errors in the reads. As an example, the bioinformatic pipeline is applied to the study of the conservation of RNA interference (RNAi) targets in human immunodeficiency virus 1 (HIV-1) subtype A. The developed pipeline is freely available to download at the website http://virmut.eimb.ru/. Brief comments and comparisons between VirMut and other pipelines are also presented.

Deep sequencing of cardiac microRNA-mRNA interactomes in clinical and experimental cardiomyopathy.

Science.gov (United States)

Matkovich, Scot J; Dorn, Gerald W

2015-01-01

MicroRNAs are a family of short (~21 nucleotide) noncoding RNAs that serve key roles in cellular growth and differentiation and the response of the heart to stress stimuli. As the sequence-specific recognition element of RNA-induced silencing complexes (RISCs), microRNAs bind mRNAs and prevent their translation via mechanisms that may include transcript degradation and/or prevention of ribosome binding. Short microRNA sequences and the ability of microRNAs to bind to mRNA sites having only partial/imperfect sequence complementarity complicate purely computational analyses of microRNA-mRNA interactomes. Furthermore, computational microRNA target prediction programs typically ignore biological context, and therefore the principal determinants of microRNA-mRNA binding: the presence and quantity of each. To address these deficiencies we describe an empirical method, developed via studies of stressed and failing hearts, to determine disease-induced changes in microRNAs, mRNAs, and the mRNAs targeted to the RISC, without cross-linking mRNAs to RISC proteins. Deep sequencing methods are used to determine RNA abundances, delivering unbiased, quantitative RNA data limited only by their annotation in the genome of interest. We describe the laboratory bench steps required to perform these experiments, experimental design strategies to achieve an appropriate number of sequencing reads per biological replicate, and computer-based processing tools and procedures to convert large raw sequencing data files into gene expression measures useful for differential expression analyses.

Deep learning—Accelerating Next Generation Performance Analysis Systems?

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Heike Brock

2018-02-01

Full Text Available Deep neural network architectures show superior performance in recognition and prediction tasks of the image, speech and natural language domains. The success of such multi-layered networks encourages their implementation in further application scenarios as the retrieval of relevant motion information for performance enhancement in sports. However, to date deep learning is only seldom applied to activity recognition problems of the human motion domain. Therefore, its use for sports data analysis might remain abstract to many practitioners. This paper provides a survey on recent works in the field of high-performance motion data and examines relevant technologies for subsequent deployment in real training systems. In particular, it discusses aspects of data acquisition, processing and network modeling. Analysis suggests the advantage of deep neural networks under difficult and noisy data conditions. However, further research is necessary to confirm the benefit of deep learning for next generation performance analysis systems.

Foundations of Sequence-to-Sequence Modeling for Time Series

OpenAIRE

Kuznetsov, Vitaly; Mariet, Zelda

2018-01-01

The availability of large amounts of time series data, paired with the performance of deep-learning algorithms on a broad class of problems, has recently led to significant interest in the use of sequence-to-sequence models for time series forecasting. We provide the first theoretical analysis of this time series forecasting framework. We include a comparison of sequence-to-sequence modeling to classical time series models, and as such our theory can serve as a quantitative guide for practiti...

Deep learning methods for protein torsion angle prediction.

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Li, Haiou; Hou, Jie; Adhikari, Badri; Lyu, Qiang; Cheng, Jianlin

2017-09-18

Deep learning is one of the most powerful machine learning methods that has achieved the state-of-the-art performance in many domains. Since deep learning was introduced to the field of bioinformatics in 2012, it has achieved success in a number of areas such as protein residue-residue contact prediction, secondary structure prediction, and fold recognition. In this work, we developed deep learning methods to improve the prediction of torsion (dihedral) angles of proteins. We design four different deep learning architectures to predict protein torsion angles. The architectures including deep neural network (DNN) and deep restricted Boltzmann machine (DRBN), deep recurrent neural network (DRNN) and deep recurrent restricted Boltzmann machine (DReRBM) since the protein torsion angle prediction is a sequence related problem. In addition to existing protein features, two new features (predicted residue contact number and the error distribution of torsion angles extracted from sequence fragments) are used as input to each of the four deep learning architectures to predict phi and psi angles of protein backbone. The mean absolute error (MAE) of phi and psi angles predicted by DRNN, DReRBM, DRBM and DNN is about 20-21° and 29-30° on an independent dataset. The MAE of phi angle is comparable to the existing methods, but the MAE of psi angle is 29°, 2° lower than the existing methods. On the latest CASP12 targets, our methods also achieved the performance better than or comparable to a state-of-the art method. Our experiment demonstrates that deep learning is a valuable method for predicting protein torsion angles. The deep recurrent network architecture performs slightly better than deep feed-forward architecture, and the predicted residue contact number and the error distribution of torsion angles extracted from sequence fragments are useful features for improving prediction accuracy.

Prognostic value of deep sequencing method for minimal residual disease detection in multiple myeloma

Science.gov (United States)

Lahuerta, Juan J.; Pepin, François; González, Marcos; Barrio, Santiago; Ayala, Rosa; Puig, Noemí; Montalban, María A.; Paiva, Bruno; Weng, Li; Jiménez, Cristina; Sopena, María; Moorhead, Martin; Cedena, Teresa; Rapado, Immaculada; Mateos, María Victoria; Rosiñol, Laura; Oriol, Albert; Blanchard, María J.; Martínez, Rafael; Bladé, Joan; San Miguel, Jesús; Faham, Malek; García-Sanz, Ramón

2014-01-01

We assessed the prognostic value of minimal residual disease (MRD) detection in multiple myeloma (MM) patients using a sequencing-based platform in bone marrow samples from 133 MM patients in at least very good partial response (VGPR) after front-line therapy. Deep sequencing was carried out in patients in whom a high-frequency myeloma clone was identified and MRD was assessed using the IGH-VDJH, IGH-DJH, and IGK assays. The results were contrasted with those of multiparametric flow cytometry (MFC) and allele-specific oligonucleotide polymerase chain reaction (ASO-PCR). The applicability of deep sequencing was 91%. Concordance between sequencing and MFC and ASO-PCR was 83% and 85%, respectively. Patients who were MRD– by sequencing had a significantly longer time to tumor progression (TTP) (median 80 vs 31 months; P < .0001) and overall survival (median not reached vs 81 months; P = .02), compared with patients who were MRD+. When stratifying patients by different levels of MRD, the respective TTP medians were: MRD ≥10−3 27 months, MRD 10−3 to 10−5 48 months, and MRD <10−5 80 months (P = .003 to .0001). Ninety-two percent of VGPR patients were MRD+. In complete response patients, the TTP remained significantly longer for MRD– compared with MRD+ patients (131 vs 35 months; P = .0009). PMID:24646471

Performance of deep geothermal energy systems

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Manikonda, Nikhil

Geothermal energy is an important source of clean and renewable energy. This project deals with the study of deep geothermal power plants for the generation of electricity. The design involves the extraction of heat from the Earth and its conversion into electricity. This is performed by allowing fluid deep into the Earth where it gets heated due to the surrounding rock. The fluid gets vaporized and returns to the surface in a heat pipe. Finally, the energy of the fluid is converted into electricity using turbine or organic rankine cycle (ORC). The main feature of the system is the employment of side channels to increase the amount of thermal energy extracted. A finite difference computer model is developed to solve the heat transport equation. The numerical model was employed to evaluate the performance of the design. The major goal was to optimize the output power as a function of parameters such as thermal diffusivity of the rock, depth of the main well, number and length of lateral channels. The sustainable lifetime of the system for a target output power of 2 MW has been calculated for deep geothermal systems with drilling depths of 8000 and 10000 meters, and a financial analysis has been performed to evaluate the economic feasibility of the system for a practical range of geothermal parameters. Results show promising an outlook for deep geothermal systems for practical applications.

A Bioinformatic Pipeline for Monitoring of the Mutational Stability of Viral Drug Targets with Deep-Sequencing Technology

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Yuri Kravatsky

2017-11-01

Full Text Available The efficient development of antiviral drugs, including efficient antiviral small interfering RNAs (siRNAs, requires continuous monitoring of the strict correspondence between a drug and the related highly variable viral DNA/RNA target(s. Deep sequencing is able to provide an assessment of both the general target conservation and the frequency of particular mutations in the different target sites. The aim of this study was to develop a reliable bioinformatic pipeline for the analysis of millions of short, deep sequencing reads corresponding to selected highly variable viral sequences that are drug target(s. The suggested bioinformatic pipeline combines the available programs and the ad hoc scripts based on an original algorithm of the search for the conserved targets in the deep sequencing data. We also present the statistical criteria for the threshold of reliable mutation detection and for the assessment of variations between corresponding data sets. These criteria are robust against the possible sequencing errors in the reads. As an example, the bioinformatic pipeline is applied to the study of the conservation of RNA interference (RNAi targets in human immunodeficiency virus 1 (HIV-1 subtype A. The developed pipeline is freely available to download at the website http://virmut.eimb.ru/. Brief comments and comparisons between VirMut and other pipelines are also presented.

DeepARG: a deep learning approach for predicting antibiotic resistance genes from metagenomic data.

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Arango-Argoty, Gustavo; Garner, Emily; Pruden, Amy; Heath, Lenwood S; Vikesland, Peter; Zhang, Liqing

2018-02-01

Growing concerns about increasing rates of antibiotic resistance call for expanded and comprehensive global monitoring. Advancing methods for monitoring of environmental media (e.g., wastewater, agricultural waste, food, and water) is especially needed for identifying potential resources of novel antibiotic resistance genes (ARGs), hot spots for gene exchange, and as pathways for the spread of ARGs and human exposure. Next-generation sequencing now enables direct access and profiling of the total metagenomic DNA pool, where ARGs are typically identified or predicted based on the "best hits" of sequence searches against existing databases. Unfortunately, this approach produces a high rate of false negatives. To address such limitations, we propose here a deep learning approach, taking into account a dissimilarity matrix created using all known categories of ARGs. Two deep learning models, DeepARG-SS and DeepARG-LS, were constructed for short read sequences and full gene length sequences, respectively. Evaluation of the deep learning models over 30 antibiotic resistance categories demonstrates that the DeepARG models can predict ARGs with both high precision (> 0.97) and recall (> 0.90). The models displayed an advantage over the typical best hit approach, yielding consistently lower false negative rates and thus higher overall recall (> 0.9). As more data become available for under-represented ARG categories, the DeepARG models' performance can be expected to be further enhanced due to the nature of the underlying neural networks. Our newly developed ARG database, DeepARG-DB, encompasses ARGs predicted with a high degree of confidence and extensive manual inspection, greatly expanding current ARG repositories. The deep learning models developed here offer more accurate antimicrobial resistance annotation relative to current bioinformatics practice. DeepARG does not require strict cutoffs, which enables identification of a much broader diversity of ARGs. The

Genomic region operation kit for flexible processing of deep sequencing data.

Science.gov (United States)

Ovaska, Kristian; Lyly, Lauri; Sahu, Biswajyoti; Jänne, Olli A; Hautaniemi, Sampsa

2013-01-01

Computational analysis of data produced in deep sequencing (DS) experiments is challenging due to large data volumes and requirements for flexible analysis approaches. Here, we present a mathematical formalism based on set algebra for frequently performed operations in DS data analysis to facilitate translation of biomedical research questions to language amenable for computational analysis. With the help of this formalism, we implemented the Genomic Region Operation Kit (GROK), which supports various DS-related operations such as preprocessing, filtering, file conversion, and sample comparison. GROK provides high-level interfaces for R, Python, Lua, and command line, as well as an extension C++ API. It supports major genomic file formats and allows storing custom genomic regions in efficient data structures such as red-black trees and SQL databases. To demonstrate the utility of GROK, we have characterized the roles of two major transcription factors (TFs) in prostate cancer using data from 10 DS experiments. GROK is freely available with a user guide from >http://csbi.ltdk.helsinki.fi/grok/.

Analysis of Plasmodium falciparum diversity in natural infections by deep sequencing

OpenAIRE

Manske, Magnus; Miotto, Olivo; Campino, Susana; Auburn, Sarah; Almagro-Garcia, Jacob; Maslen, Gareth; O?Brien, Jack; Djimde, Abdoulaye; Doumbo, Ogobara; Zongo, Issaka; Ouedraogo, Jean-Bosco; Michon, Pascal; Mueller, Ivo; Siba, Peter; Nzila, Alexis

2012-01-01

: Malaria elimination strategies require surveillance of the parasite population for genetic changes that demand a public health response, such as new forms of drug resistance. Here we describe methods for the large-scale analysis of genetic variation in Plasmodium falciparum by deep sequencing of parasite DNA obtained from the blood of patients with malaria, either directly or after short-term culture. Analysis of 86,158 exonic single nucleotide polymorphisms that passed genotyping quality c...

Exploring the Mechanisms of Gastrointestinal Cancer Development Using Deep Sequencing Analysis

International Nuclear Information System (INIS)

Matsumoto, Tomonori; Shimizu, Takahiro; Takai, Atsushi; Marusawa, Hiroyuki

2015-01-01

Next-generation sequencing (NGS) technologies have revolutionized cancer genomics due to their high throughput sequencing capacity. Reports of the gene mutation profiles of various cancers by many researchers, including international cancer genome research consortia, have increased over recent years. In addition to detecting somatic mutations in tumor cells, NGS technologies enable us to approach the subject of carcinogenic mechanisms from new perspectives. Deep sequencing, a method of optimizing the high throughput capacity of NGS technologies, allows for the detection of genetic aberrations in small subsets of premalignant and/or tumor cells in noncancerous chronically inflamed tissues. Genome-wide NGS data also make it possible to clarify the mutational signatures of each cancer tissue by identifying the precise pattern of nucleotide alterations in the cancer genome, providing new information regarding the mechanisms of tumorigenesis. In this review, we highlight these new methods taking advantage of NGS technologies, and discuss our current understanding of carcinogenic mechanisms elucidated from such approaches

Exploring the Mechanisms of Gastrointestinal Cancer Development Using Deep Sequencing Analysis

Energy Technology Data Exchange (ETDEWEB)

Matsumoto, Tomonori; Shimizu, Takahiro; Takai, Atsushi; Marusawa, Hiroyuki, E-mail: maru@kuhp.kyoto-u.ac.jp [Department of Gastroenterology and Hepatology, Graduate School of Medicine, Kyoto University, 54 Shogoin-Kawahara-cho, Sakyo-ku, Kyoto 606-8507 (Japan)

2015-06-15

Next-generation sequencing (NGS) technologies have revolutionized cancer genomics due to their high throughput sequencing capacity. Reports of the gene mutation profiles of various cancers by many researchers, including international cancer genome research consortia, have increased over recent years. In addition to detecting somatic mutations in tumor cells, NGS technologies enable us to approach the subject of carcinogenic mechanisms from new perspectives. Deep sequencing, a method of optimizing the high throughput capacity of NGS technologies, allows for the detection of genetic aberrations in small subsets of premalignant and/or tumor cells in noncancerous chronically inflamed tissues. Genome-wide NGS data also make it possible to clarify the mutational signatures of each cancer tissue by identifying the precise pattern of nucleotide alterations in the cancer genome, providing new information regarding the mechanisms of tumorigenesis. In this review, we highlight these new methods taking advantage of NGS technologies, and discuss our current understanding of carcinogenic mechanisms elucidated from such approaches.

Deep sequencing discovery of novel and conserved microRNAs in trifoliate orange (Citrus trifoliata

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Yu Huaping

2010-07-01

Full Text Available Abstract Background MicroRNAs (miRNAs play a critical role in post-transcriptional gene regulation and have been shown to control many genes involved in various biological and metabolic processes. There have been extensive studies to discover miRNAs and analyze their functions in model plant species, such as Arabidopsis and rice. Deep sequencing technologies have facilitated identification of species-specific or lowly expressed as well as conserved or highly expressed miRNAs in plants. Results In this research, we used Solexa sequencing to discover new microRNAs in trifoliate orange (Citrus trifoliata which is an important rootstock of citrus. A total of 13,106,753 reads representing 4,876,395 distinct sequences were obtained from a short RNA library generated from small RNA extracted from C. trifoliata flower and fruit tissues. Based on sequence similarity and hairpin structure prediction, we found that 156,639 reads representing 63 sequences from 42 highly conserved miRNA families, have perfect matches to known miRNAs. We also identified 10 novel miRNA candidates whose precursors were all potentially generated from citrus ESTs. In addition, five miRNA* sequences were also sequenced. These sequences had not been earlier described in other plant species and accumulation of the 10 novel miRNAs were confirmed by qRT-PCR analysis. Potential target genes were predicted for most conserved and novel miRNAs. Moreover, four target genes including one encoding IRX12 copper ion binding/oxidoreductase and three genes encoding NB-LRR disease resistance protein have been experimentally verified by detection of the miRNA-mediated mRNA cleavage in C. trifoliata. Conclusion Deep sequencing of short RNAs from C. trifoliata flowers and fruits identified 10 new potential miRNAs and 42 highly conserved miRNA families, indicating that specific miRNAs exist in C. trifoliata. These results show that regulatory miRNAs exist in agronomically important trifoliate orange

Transcriptional Slippage and RNA Editing Increase the Diversity of Transcripts in Chloroplasts: Insight from Deep Sequencing of Vigna radiata Genome and Transcriptome.

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Ching-Ping Lin

Full Text Available We performed deep sequencing of the nuclear and organellar genomes of three mungbean genotypes: Vigna radiata ssp. sublobata TC1966, V. radiata var. radiata NM92 and the recombinant inbred line RIL59 derived from a cross between TC1966 and NM92. Moreover, we performed deep sequencing of the RIL59 transcriptome to investigate transcript variability. The mungbean chloroplast genome has a quadripartite structure including a pair of inverted repeats separated by two single copy regions. A total of 213 simple sequence repeats were identified in the chloroplast genomes of NM92 and RIL59; 78 single nucleotide variants and nine indels were discovered in comparing the chloroplast genomes of TC1966 and NM92. Analysis of the mungbean chloroplast transcriptome revealed mRNAs that were affected by transcriptional slippage and RNA editing. Transcriptional slippage frequency was positively correlated with the length of simple sequence repeats of the mungbean chloroplast genome (R2=0.9911. In total, 41 C-to-U editing sites were found in 23 chloroplast genes and in one intergenic spacer. No editing site that swapped U to C was found. A combination of bioinformatics and experimental methods revealed that the plastid-encoded RNA polymerase-transcribed genes psbF and ndhA are affected by transcriptional slippage in mungbean and in main lineages of land plants, including three dicots (Glycine max, Brassica rapa, and Nicotiana tabacum, two monocots (Oryza sativa and Zea mays, two gymnosperms (Pinus taeda and Ginkgo biloba and one moss (Physcomitrella patens. Transcript analysis of the rps2 gene showed that transcriptional slippage could affect transcripts at single sequence repeat regions with poly-A runs. It showed that transcriptional slippage together with incomplete RNA editing may cause sequence diversity of transcripts in chloroplasts of land plants.

Deep Recurrent Neural Networks for Human Activity Recognition

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Abdulmajid Murad

2017-11-01

Full Text Available Adopting deep learning methods for human activity recognition has been effective in extracting discriminative features from raw input sequences acquired from body-worn sensors. Although human movements are encoded in a sequence of successive samples in time, typical machine learning methods perform recognition tasks without exploiting the temporal correlations between input data samples. Convolutional neural networks (CNNs address this issue by using convolutions across a one-dimensional temporal sequence to capture dependencies among input data. However, the size of convolutional kernels restricts the captured range of dependencies between data samples. As a result, typical models are unadaptable to a wide range of activity-recognition configurations and require fixed-length input windows. In this paper, we propose the use of deep recurrent neural networks (DRNNs for building recognition models that are capable of capturing long-range dependencies in variable-length input sequences. We present unidirectional, bidirectional, and cascaded architectures based on long short-term memory (LSTM DRNNs and evaluate their effectiveness on miscellaneous benchmark datasets. Experimental results show that our proposed models outperform methods employing conventional machine learning, such as support vector machine (SVM and k-nearest neighbors (KNN. Additionally, the proposed models yield better performance than other deep learning techniques, such as deep believe networks (DBNs and CNNs.

Deep Recurrent Neural Networks for Human Activity Recognition.

Science.gov (United States)

Murad, Abdulmajid; Pyun, Jae-Young

2017-11-06

Adopting deep learning methods for human activity recognition has been effective in extracting discriminative features from raw input sequences acquired from body-worn sensors. Although human movements are encoded in a sequence of successive samples in time, typical machine learning methods perform recognition tasks without exploiting the temporal correlations between input data samples. Convolutional neural networks (CNNs) address this issue by using convolutions across a one-dimensional temporal sequence to capture dependencies among input data. However, the size of convolutional kernels restricts the captured range of dependencies between data samples. As a result, typical models are unadaptable to a wide range of activity-recognition configurations and require fixed-length input windows. In this paper, we propose the use of deep recurrent neural networks (DRNNs) for building recognition models that are capable of capturing long-range dependencies in variable-length input sequences. We present unidirectional, bidirectional, and cascaded architectures based on long short-term memory (LSTM) DRNNs and evaluate their effectiveness on miscellaneous benchmark datasets. Experimental results show that our proposed models outperform methods employing conventional machine learning, such as support vector machine (SVM) and k-nearest neighbors (KNN). Additionally, the proposed models yield better performance than other deep learning techniques, such as deep believe networks (DBNs) and CNNs.

High-throughput sequencing and analysis of the gill tissue transcriptome from the deep-sea hydrothermal vent mussel Bathymodiolus azoricus

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Gomes Paula

2010-10-01

Full Text Available Abstract Background Bathymodiolus azoricus is a deep-sea hydrothermal vent mussel found in association with large faunal communities living in chemosynthetic environments at the bottom of the sea floor near the Azores Islands. Investigation of the exceptional physiological reactions that vent mussels have adopted in their habitat, including responses to environmental microbes, remains a difficult challenge for deep-sea biologists. In an attempt to reveal genes potentially involved in the deep-sea mussel innate immunity we carried out a high-throughput sequence analysis of freshly collected B. azoricus transcriptome using gills tissues as the primary source of immune transcripts given its strategic role in filtering the surrounding waterborne potentially infectious microorganisms. Additionally, a substantial EST data set was produced and from which a comprehensive collection of genes coding for putative proteins was organized in a dedicated database, "DeepSeaVent" the first deep-sea vent animal transcriptome database based on the 454 pyrosequencing technology. Results A normalized cDNA library from gills tissue was sequenced in a full 454 GS-FLX run, producing 778,996 sequencing reads. Assembly of the high quality reads resulted in 75,407 contigs of which 3,071 were singletons. A total of 39,425 transcripts were conceptually translated into amino-sequences of which 22,023 matched known proteins in the NCBI non-redundant protein database, 15,839 revealed conserved protein domains through InterPro functional classification and 9,584 were assigned with Gene Ontology terms. Queries conducted within the database enabled the identification of genes putatively involved in immune and inflammatory reactions which had not been previously evidenced in the vent mussel. Their physical counterpart was confirmed by semi-quantitative quantitative Reverse-Transcription-Polymerase Chain Reactions (RT-PCR and their RNA transcription level by quantitative PCR (q

Development of genic-SSR markers by deep transcriptome sequencing in pigeonpea [Cajanus cajan (L.) Millspaugh

Science.gov (United States)

2011-01-01

Background Pigeonpea [Cajanus cajan (L.) Millspaugh], one of the most important food legumes of semi-arid tropical and subtropical regions, has limited genomic resources, particularly expressed sequence based (genic) markers. We report a comprehensive set of validated genic simple sequence repeat (SSR) markers using deep transcriptome sequencing, and its application in genetic diversity analysis and mapping. Results In this study, 43,324 transcriptome shotgun assembly unigene contigs were assembled from 1.696 million 454 GS-FLX sequence reads of separate pooled cDNA libraries prepared from leaf, root, stem and immature seed of two pigeonpea varieties, Asha and UPAS 120. A total of 3,771 genic-SSR loci, excluding homopolymeric and compound repeats, were identified; of which 2,877 PCR primer pairs were designed for marker development. Dinucleotide was the most common repeat motif with a frequency of 60.41%, followed by tri- (34.52%), hexa- (2.62%), tetra- (1.67%) and pentanucleotide (0.76%) repeat motifs. Primers were synthesized and tested for 772 of these loci with repeat lengths of ≥18 bp. Of these, 550 markers were validated for consistent amplification in eight diverse pigeonpea varieties; 71 were found to be polymorphic on agarose gel electrophoresis. Genetic diversity analysis was done on 22 pigeonpea varieties and eight wild species using 20 highly polymorphic genic-SSR markers. The number of alleles at these loci ranged from 4-10 and the polymorphism information content values ranged from 0.46 to 0.72. Neighbor-joining dendrogram showed distinct separation of the different groups of pigeonpea cultivars and wild species. Deep transcriptome sequencing of the two parental lines helped in silico identification of polymorphic genic-SSR loci to facilitate the rapid development of an intra-species reference genetic map, a subset of which was validated for expected allelic segregation in the reference mapping population. Conclusion We developed 550 validated genic

Deep Ion Torrent sequencing identifies soil fungal community shifts after frequent prescribed fires in a southeastern US forest ecosystem.

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Brown, Shawn P; Callaham, Mac A; Oliver, Alena K; Jumpponen, Ari

2013-12-01

Prescribed burning is a common management tool to control fuel loads, ground vegetation, and facilitate desirable game species. We evaluated soil fungal community responses to long-term prescribed fire treatments in a loblolly pine forest on the Piedmont of Georgia and utilized deep Internal Transcribed Spacer Region 1 (ITS1) amplicon sequencing afforded by the recent Ion Torrent Personal Genome Machine (PGM). These deep sequence data (19,000 + reads per sample after subsampling) indicate that frequent fires (3-year fire interval) shift soil fungus communities, whereas infrequent fires (6-year fire interval) permit system resetting to a state similar to that without prescribed fire. Furthermore, in nonmetric multidimensional scaling analyses, primarily ectomycorrhizal taxa were correlated with axes associated with long fire intervals, whereas soil saprobes tended to be correlated with the frequent fire recurrence. We conclude that (1) multiplexed Ion Torrent PGM analyses allow deep cost effective sequencing of fungal communities but may suffer from short read lengths and inconsistent sequence quality adjacent to the sequencing adaptor; (2) frequent prescribed fires elicit a shift in soil fungal communities; and (3) such shifts do not occur when fire intervals are longer. Our results emphasize the general responsiveness of these forests to management, and the importance of fire return intervals in meeting management objectives. © 2013 Federation of European Microbiological Societies. Published by John Wiley & Sons Ltd. All rights reserved.

High-Quality Draft Single-Cell Genome Sequence Belonging to the Archaeal Candidate Division SA1, Isolated from Nereus Deep in the Red Sea

KAUST Repository

Ngugi, David; Stingl, Ulrich

2018-01-01

Candidate division SA1 encompasses a phylogenetically coherent archaeal group ubiquitous in deep hypersaline anoxic brines around the globe. Recently, the genome sequences of two cultivated representatives from hypersaline soda lake sediments were published. Here, we present a single-cell genome sequence from Nereus Deep in the Red Sea that represents a putatively novel family within SA1.

High-Quality Draft Single-Cell Genome Sequence Belonging to the Archaeal Candidate Division SA1, Isolated from Nereus Deep in the Red Sea

KAUST Repository

Ngugi, David

2018-05-09

Candidate division SA1 encompasses a phylogenetically coherent archaeal group ubiquitous in deep hypersaline anoxic brines around the globe. Recently, the genome sequences of two cultivated representatives from hypersaline soda lake sediments were published. Here, we present a single-cell genome sequence from Nereus Deep in the Red Sea that represents a putatively novel family within SA1.

Poly(A)-tag deep sequencing data processing to extract poly(A) sites.

Science.gov (United States)

Wu, Xiaohui; Ji, Guoli; Li, Qingshun Quinn

2015-01-01

Polyadenylation [poly(A)] is an essential posttranscriptional processing step in the maturation of eukaryotic mRNA. The advent of next-generation sequencing (NGS) technology has offered feasible means to generate large-scale data and new opportunities for intensive study of polyadenylation, particularly deep sequencing of the transcriptome targeting the junction of 3'-UTR and the poly(A) tail of the transcript. To take advantage of this unprecedented amount of data, we present an automated workflow to identify polyadenylation sites by integrating NGS data cleaning, processing, mapping, normalizing, and clustering. In this pipeline, a series of Perl scripts are seamlessly integrated to iteratively map the single- or paired-end sequences to the reference genome. After mapping, the poly(A) tags (PATs) at the same genome coordinate are grouped into one cleavage site, and the internal priming artifacts removed. Then the ambiguous region is introduced to parse the genome annotation for cleavage site clustering. Finally, cleavage sites within a close range of 24 nucleotides and from different samples can be clustered into poly(A) clusters. This procedure could be used to identify thousands of reliable poly(A) clusters from millions of NGS sequences in different tissues or treatments.

Identification and Removal of Contaminant Sequences From Ribosomal Gene Databases: Lessons From the Census of Deep Life.

Science.gov (United States)

Sheik, Cody S; Reese, Brandi Kiel; Twing, Katrina I; Sylvan, Jason B; Grim, Sharon L; Schrenk, Matthew O; Sogin, Mitchell L; Colwell, Frederick S

2018-01-01

Earth's subsurface environment is one of the largest, yet least studied, biomes on Earth, and many questions remain regarding what microorganisms are indigenous to the subsurface. Through the activity of the Census of Deep Life (CoDL) and the Deep Carbon Observatory, an open access 16S ribosomal RNA gene sequence database from diverse subsurface environments has been compiled. However, due to low quantities of biomass in the deep subsurface, the potential for incorporation of contaminants from reagents used during sample collection, processing, and/or sequencing is high. Thus, to understand the ecology of subsurface microorganisms (i.e., the distribution, richness, or survival), it is necessary to minimize, identify, and remove contaminant sequences that will skew the relative abundances of all taxa in the sample. In this meta-analysis, we identify putative contaminants associated with the CoDL dataset, recommend best practices for removing contaminants from samples, and propose a series of best practices for subsurface microbiology sampling. The most abundant putative contaminant genera observed, independent of evenness across samples, were Propionibacterium , Aquabacterium , Ralstonia , and Acinetobacter . While the top five most frequently observed genera were Pseudomonas , Propionibacterium , Acinetobacter , Ralstonia , and Sphingomonas . The majority of the most frequently observed genera (high evenness) were associated with reagent or potential human contamination. Additionally, in DNA extraction blanks, we observed potential archaeal contaminants, including methanogens, which have not been discussed in previous contamination studies. Such contaminants would directly affect the interpretation of subsurface molecular studies, as methanogenesis is an important subsurface biogeochemical process. Utilizing previously identified contaminant genera, we found that ∼27% of the total dataset were identified as contaminant sequences that likely originate from DNA

Protein model discrimination using mutational sensitivity derived from deep sequencing.

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Adkar, Bharat V; Tripathi, Arti; Sahoo, Anusmita; Bajaj, Kanika; Goswami, Devrishi; Chakrabarti, Purbani; Swarnkar, Mohit K; Gokhale, Rajesh S; Varadarajan, Raghavan

2012-02-08

A major bottleneck in protein structure prediction is the selection of correct models from a pool of decoys. Relative activities of ∼1,200 individual single-site mutants in a saturation library of the bacterial toxin CcdB were estimated by determining their relative populations using deep sequencing. This phenotypic information was used to define an empirical score for each residue (RankScore), which correlated with the residue depth, and identify active-site residues. Using these correlations, ∼98% of correct models of CcdB (RMSD ≤ 4Å) were identified from a large set of decoys. The model-discrimination methodology was further validated on eleven different monomeric proteins using simulated RankScore values. The methodology is also a rapid, accurate way to obtain relative activities of each mutant in a large pool and derive sequence-structure-function relationships without protein isolation or characterization. It can be applied to any system in which mutational effects can be monitored by a phenotypic readout. Copyright © 2012 Elsevier Ltd. All rights reserved.

Deep-Sea, Deep-Sequencing: Metabarcoding Extracellular DNA from Sediments of Marine Canyons.

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Magdalena Guardiola

Full Text Available Marine sediments are home to one of the richest species pools on Earth, but logistics and a dearth of taxonomic work-force hinders the knowledge of their biodiversity. We characterized α- and β-diversity of deep-sea assemblages from submarine canyons in the western Mediterranean using an environmental DNA metabarcoding. We used a new primer set targeting a short eukaryotic 18S sequence (ca. 110 bp. We applied a protocol designed to obtain extractions enriched in extracellular DNA from replicated sediment corers. With this strategy we captured information from DNA (local or deposited from the water column that persists adsorbed to inorganic particles and buffered short-term spatial and temporal heterogeneity. We analysed replicated samples from 20 localities including 2 deep-sea canyons, 1 shallower canal, and two open slopes (depth range 100-2,250 m. We identified 1,629 MOTUs, among which the dominant groups were Metazoa (with representatives of 19 phyla, Alveolata, Stramenopiles, and Rhizaria. There was a marked small-scale heterogeneity as shown by differences in replicates within corers and within localities. The spatial variability between canyons was significant, as was the depth component in one of the canyons where it was tested. Likewise, the composition of the first layer (1 cm of sediment was significantly different from deeper layers. We found that qualitative (presence-absence and quantitative (relative number of reads data showed consistent trends of differentiation between samples and geographic areas. The subset of exclusively benthic MOTUs showed similar patterns of β-diversity and community structure as the whole dataset. Separate analyses of the main metazoan phyla (in number of MOTUs showed some differences in distribution attributable to different lifestyles. Our results highlight the differentiation that can be found even between geographically close assemblages, and sets the ground for future monitoring and conservation

Draft Genome Sequence of Deep-Sea Alteromonas sp. Strain V450 Isolated from the Marine Sponge Leiodermatium sp.

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Wang, Guojun; Barrett, Nolan H; McCarthy, Peter J

2017-02-02

The proteobacterium Alteromonas sp. strain V450 was isolated from the Atlantic deep-sea sponge Leiodermatium sp. Here, we report the draft genome sequence of this strain, with a genome size of approx. 4.39 Mb and a G+C content of 44.01%. The results will aid deep-sea microbial ecology, evolution, and sponge-microbe association studies. Copyright © 2017 Wang et al.

DeepPVP: phenotype-based prioritization of causative variants using deep learning

KAUST Repository

Boudellioua, Imene

2018-05-02

Background: Prioritization of variants in personal genomic data is a major challenge. Recently, computational methods that rely on comparing phenotype similarity have shown to be useful to identify causative variants. In these methods, pathogenicity prediction is combined with a semantic similarity measure to prioritize not only variants that are likely to be dysfunctional but those that are likely involved in the pathogenesis of a patient\\'s phenotype. Results: We have developed DeepPVP, a variant prioritization method that combined automated inference with deep neural networks to identify the likely causative variants in whole exome or whole genome sequence data. We demonstrate that DeepPVP performs significantly better than existing methods, including phenotype-based methods that use similar features. DeepPVP is freely available at https://github.com/bio-ontology-research-group/phenomenet-vp Conclusions: DeepPVP further improves on existing variant prioritization methods both in terms of speed as well as accuracy.

Deep sequence characterisation of a divergent HPIV-4a from an adult with prolonged influenza-like illness

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Katherine E. Arden

2015-12-01

Deep sequencing allowed identification and genomic characterisation of a possible pathogen from an ILI as well as being an important tool to aid future understanding of the linkages between viral genetic variation, transmission and disease prognosis.

Enhanced arbovirus surveillance with deep sequencing: Identification of novel rhabdoviruses and bunyaviruses in Australian mosquitoes.

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Coffey, Lark L; Page, Brady L; Greninger, Alexander L; Herring, Belinda L; Russell, Richard C; Doggett, Stephen L; Haniotis, John; Wang, Chunlin; Deng, Xutao; Delwart, Eric L

2014-01-05

Viral metagenomics characterizes known and identifies unknown viruses based on sequence similarities to any previously sequenced viral genomes. A metagenomics approach was used to identify virus sequences in Australian mosquitoes causing cytopathic effects in inoculated mammalian cell cultures. Sequence comparisons revealed strains of Liao Ning virus (Reovirus, Seadornavirus), previously detected only in China, livestock-infecting Stretch Lagoon virus (Reovirus, Orbivirus), two novel dimarhabdoviruses, named Beaumont and North Creek viruses, and two novel orthobunyaviruses, named Murrumbidgee and Salt Ash viruses. The novel virus proteomes diverged by ≥ 50% relative to their closest previously genetically characterized viral relatives. Deep sequencing also generated genomes of Warrego and Wallal viruses, orbiviruses linked to kangaroo blindness, whose genomes had not been fully characterized. This study highlights viral metagenomics in concert with traditional arbovirus surveillance to characterize known and new arboviruses in field-collected mosquitoes. Follow-up epidemiological studies are required to determine whether the novel viruses infect humans. © 2013 Elsevier Inc. All rights reserved.

3' terminal diversity of MRP RNA and other human noncoding RNAs revealed by deep sequencing.

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Goldfarb, Katherine C; Cech, Thomas R

2013-09-21

Post-transcriptional 3' end processing is a key component of RNA regulation. The abundant and essential RNA subunit of RNase MRP has been proposed to function in three distinct cellular compartments and therefore may utilize this mode of regulation. Here we employ 3' RACE coupled with high-throughput sequencing to characterize the 3' terminal sequences of human MRP RNA and other noncoding RNAs that form RNP complexes. The 3' terminal sequence of MRP RNA from HEK293T cells has a distinctive distribution of genomically encoded termini (including an assortment of U residues) with a portion of these selectively tagged by oligo(A) tails. This profile contrasts with the relatively homogenous 3' terminus of an in vitro transcribed MRP RNA control and the differing 3' terminal profiles of U3 snoRNA, RNase P RNA, and telomerase RNA (hTR). 3' RACE coupled with deep sequencing provides a valuable framework for the functional characterization of 3' terminal sequences of noncoding RNAs.

A deep learning method for lincRNA detection using auto-encoder algorithm.

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Yu, Ning; Yu, Zeng; Pan, Yi

2017-12-06

RNA sequencing technique (RNA-seq) enables scientists to develop novel data-driven methods for discovering more unidentified lincRNAs. Meantime, knowledge-based technologies are experiencing a potential revolution ignited by the new deep learning methods. By scanning the newly found data set from RNA-seq, scientists have found that: (1) the expression of lincRNAs appears to be regulated, that is, the relevance exists along the DNA sequences; (2) lincRNAs contain some conversed patterns/motifs tethered together by non-conserved regions. The two evidences give the reasoning for adopting knowledge-based deep learning methods in lincRNA detection. Similar to coding region transcription, non-coding regions are split at transcriptional sites. However, regulatory RNAs rather than message RNAs are generated. That is, the transcribed RNAs participate the biological process as regulatory units instead of generating proteins. Identifying these transcriptional regions from non-coding regions is the first step towards lincRNA recognition. The auto-encoder method achieves 100% and 92.4% prediction accuracy on transcription sites over the putative data sets. The experimental results also show the excellent performance of predictive deep neural network on the lincRNA data sets compared with support vector machine and traditional neural network. In addition, it is validated through the newly discovered lincRNA data set and one unreported transcription site is found by feeding the whole annotated sequences through the deep learning machine, which indicates that deep learning method has the extensive ability for lincRNA prediction. The transcriptional sequences of lincRNAs are collected from the annotated human DNA genome data. Subsequently, a two-layer deep neural network is developed for the lincRNA detection, which adopts the auto-encoder algorithm and utilizes different encoding schemes to obtain the best performance over intergenic DNA sequence data. Driven by those newly

Deep sequencing analysis of HBV genotype shift and correlation with antiviral efficiency during adefovir dipivoxil therapy.

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Yuwei Wang

Full Text Available Viral genotype shift in chronic hepatitis B (CHB patients during antiviral therapy has been reported, but the underlying mechanism remains elusive.38 CHB patients treated with ADV for one year were selected for studying genotype shift by both deep sequencing and Sanger sequencing method.Sanger sequencing method found that 7.9% patients showed mixed genotype before ADV therapy. In contrast, all 38 patients showed mixed genotype before ADV treatment by deep sequencing. 95.5% mixed genotype rate was also obtained from additional 200 treatment-naïve CHB patients. Of the 13 patients with genotype shift, the fraction of the minor genotype in 5 patients (38% increased gradually during the course of ADV treatment. Furthermore, responses to ADV and HBeAg seroconversion were associated with the high rate of genotype shift, suggesting drug and immune pressure may be key factors to induce genotype shift. Interestingly, patients with genotype C had a significantly higher rate of genotype shift than genotype B. In genotype shift group, ADV treatment induced a marked enhancement of genotype B ratio accompanied by a reduction of genotype C ratio, suggesting genotype C may be more sensitive to ADV than genotype B. Moreover, patients with dominant genotype C may have a better therapeutic effect. Finally, genotype shifts was correlated with clinical improvement in terms of ALT.Our findings provided a rational explanation for genotype shift among ADV-treated CHB patients. The genotype and genotype shift might be associated with antiviral efficiency.

Evolution of simeprevir-resistant variants over time by ultra-deep sequencing in HCV genotype 1b.

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Akuta, Norio; Suzuki, Fumitaka; Sezaki, Hitomi; Suzuki, Yoshiyuki; Hosaka, Tetsuya; Kobayashi, Masahiro; Kobayashi, Mariko; Saitoh, Satoshi; Ikeda, Kenji; Kumada, Hiromitsu

2014-08-01

Using ultra-deep sequencing technology, the present study was designed to investigate the evolution of simeprevir-resistant variants (amino acid substitutions of aa80, aa155, aa156, and aa168 positions in HCV NS3 region) over time. In Toranomon Hospital, 18 Japanese patients infected with HCV genotype 1b, received triple therapy of simeprevir/PEG-IFN/ribavirin (DRAGON or CONCERT study). Sustained virological response rate was 67%, and that was significantly higher in patients with IL28B rs8099917 TT than in those with non-TT. Six patients, who did not achieve sustained virological response, were tested for resistant variants by ultra-deep sequencing, at the baseline, at the time of re-elevation of viral loads, and at 96 weeks after the completion of treatment. Twelve of 18 resistant variants, detected at re-elevation of viral load, were de novo resistant variants. Ten of 12 de novo resistant variants become undetectable over time, and that five of seven resistant variants, detected at baseline, persisted over time. In one patient, variants of Q80R at baseline (0.3%) increased at 96-week after the cessation of treatment (10.2%), and de novo resistant variants of D168E (0.3%) also increased at 96-week after the cessation of treatment (9.7%). In conclusion, the present study indicates that the emergence of simeprevir-resistant variants after the start of treatment could not be predicted at baseline, and the majority of de novo resistant variants become undetectable over time. Further large-scale prospective studies should be performed to investigate the clinical utility in detecting simeprevir-resistant variants. © 2014 Wiley Periodicals, Inc.

The subclonal structure and genomic evolution of oral squamous cell carcinoma revealed by ultra-deep sequencing

DEFF Research Database (Denmark)

Tabatabaeifar, Siavosh; Thomassen, Mads; Larsen, Martin J

2017-01-01

Recent studies suggest that head and neck squamous cell carcinomas are very heterogeneous between patients; however the subclonal structure remains unexplored mainly due to studies using only a single biopsy per patient. To deconvolutethe clonal structure and describe the genomic cancer evolution......, we applied whole-exome sequencing combined with ultra-deep targeted sequencing on oral squamous cell carcinomas (OSCC). From each patient, a set of biopsies was sampled from distinct geographical sites in primary tumor and lymph node metastasis.We demonstrate that the included OSCCs show a high...

Improved detection of CXCR4-using HIV by V3 genotyping: application of population-based and "deep" sequencing to plasma RNA and proviral DNA.

Science.gov (United States)

Swenson, Luke C; Moores, Andrew; Low, Andrew J; Thielen, Alexander; Dong, Winnie; Woods, Conan; Jensen, Mark A; Wynhoven, Brian; Chan, Dennison; Glascock, Christopher; Harrigan, P Richard

2010-08-01

Tropism testing should rule out CXCR4-using HIV before treatment with CCR5 antagonists. Currently, the recombinant phenotypic Trofile assay (Monogram) is most widely utilized; however, genotypic tests may represent alternative methods. Independent triplicate amplifications of the HIV gp120 V3 region were made from either plasma HIV RNA or proviral DNA. These underwent standard, population-based sequencing with an ABI3730 (RNA n = 63; DNA n = 40), or "deep" sequencing with a Roche/454 Genome Sequencer-FLX (RNA n = 12; DNA n = 12). Position-specific scoring matrices (PSSMX4/R5) (-6.96 cutoff) and geno2pheno[coreceptor] (5% false-positive rate) inferred tropism from V3 sequence. These methods were then independently validated with a separate, blinded dataset (n = 278) of screening samples from the maraviroc MOTIVATE trials. Standard sequencing of HIV RNA with PSSM yielded 69% sensitivity and 91% specificity, relative to Trofile. The validation dataset gave 75% sensitivity and 83% specificity. Proviral DNA plus PSSM gave 77% sensitivity and 71% specificity. "Deep" sequencing of HIV RNA detected >2% inferred-CXCR4-using virus in 8/8 samples called non-R5 by Trofile, and <2% in 4/4 samples called R5. Triplicate analyses of V3 standard sequence data detect greater proportions of CXCR4-using samples than previously achieved. Sequencing proviral DNA and "deep" V3 sequencing may also be useful tools for assessing tropism.

Ultra-deep sequencing reveals high prevalence and broad structural diversity of hepatitis B surface antigen mutations in a global population.

Science.gov (United States)

Gencay, Mikael; Hübner, Kirsten; Gohl, Peter; Seffner, Anja; Weizenegger, Michael; Neofytos, Dionysios; Batrla, Richard; Woeste, Andreas; Kim, Hyon-Suk; Westergaard, Gaston; Reinsch, Christine; Brill, Eva; Thu Thuy, Pham Thi; Hoang, Bui Huu; Sonderup, Mark; Spearman, C Wendy; Pabinger, Stephan; Gautier, Jérémie; Brancaccio, Giuseppina; Fasano, Massimo; Santantonio, Teresa; Gaeta, Giovanni B; Nauck, Markus; Kaminski, Wolfgang E

2017-01-01

The diversity of the hepatitis B surface antigen (HBsAg) has a significant impact on the performance of diagnostic screening tests and the clinical outcome of hepatitis B infection. Neutralizing or diagnostic antibodies against the HBsAg are directed towards its highly conserved major hydrophilic region (MHR), in particular towards its "a" determinant subdomain. Here, we explored, on a global scale, the genetic diversity of the HBsAg MHR in a large, multi-ethnic cohort of randomly selected subjects with HBV infection from four continents. A total of 1553 HBsAg positive blood samples of subjects originating from 20 different countries across Africa, America, Asia and central Europe were characterized for amino acid variation in the MHR. Using highly sensitive ultra-deep sequencing, we found 72.8% of the successfully sequenced subjects (n = 1391) demonstrated amino acid sequence variation in the HBsAg MHR. This indicates that the global variation frequency in the HBsAg MHR is threefold higher than previously reported. The majority of the amino acid mutations were found in the HBV genotypes B (28.9%) and C (25.4%). Collectively, we identified 345 distinct amino acid mutations in the MHR. Among these, we report 62 previously unknown mutations, which extends the worldwide pool of currently known HBsAg MHR mutations by 22%. Importantly, topological analysis identified the "a" determinant upstream flanking region as the structurally most diverse subdomain of the HBsAg MHR. The highest prevalence of "a" determinant region mutations was observed in subjects from Asia, followed by the African, American and European cohorts, respectively. Finally, we found that more than half (59.3%) of all HBV subjects investigated carried multiple MHR mutations. Together, this worldwide ultra-deep sequencing based genotyping study reveals that the global prevalence and structural complexity of variation in the hepatitis B surface antigen have, to date, been significantly underappreciated.

Identification of miRNAs and their target genes in developing soybean seeds by deep sequencing

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Chen Shou-Yi

2011-01-01

Full Text Available Abstract Background MicroRNAs (miRNAs regulate gene expression by mediating gene silencing at transcriptional and post-transcriptional levels in higher plants. miRNAs and related target genes have been widely studied in model plants such as Arabidopsis and rice; however, the number of identified miRNAs in soybean (Glycine max is limited, and global identification of the related miRNA targets has not been reported in previous research. Results In our study, a small RNA library and a degradome library were constructed from developing soybean seeds for deep sequencing. We identified 26 new miRNAs in soybean by bioinformatic analysis and further confirmed their expression by stem-loop RT-PCR. The miRNA star sequences of 38 known miRNAs and 8 new miRNAs were also discovered, providing additional evidence for the existence of miRNAs. Through degradome sequencing, 145 and 25 genes were identified as targets of annotated miRNAs and new miRNAs, respectively. GO analysis indicated that many of the identified miRNA targets may function in soybean seed development. Additionally, a soybean homolog of Arabidopsis SUPPRESSOR OF GENE SLIENCING 3 (AtSGS3 was detected as a target of the newly identified miRNA Soy_25, suggesting the presence of feedback control of miRNA biogenesis. Conclusions We have identified large numbers of miRNAs and their related target genes through deep sequencing of a small RNA library and a degradome library. Our study provides more information about the regulatory network of miRNAs in soybean and advances our understanding of miRNA functions during seed development.

microRNA expression profiling in fetal single ventricle malformation identified by deep sequencing.

Science.gov (United States)

Yu, Zhang-Bin; Han, Shu-Ping; Bai, Yun-Fei; Zhu, Chun; Pan, Ya; Guo, Xi-Rong

2012-01-01

microRNAs (miRNAs) have emerged as key regulators in many biological processes, particularly cardiac growth and development, although the specific miRNA expression profile associated with this process remains to be elucidated. This study aimed to characterize the cellular microRNA profile involved in the development of congenital heart malformation, through the investigation of single ventricle (SV) defects. Comprehensive miRNA profiling in human fetal SV cardiac tissue was performed by deep sequencing. Differential expression of 48 miRNAs was revealed by sequencing by oligonucleotide ligation and detection (SOLiD) analysis. Of these, 38 were down-regulated and 10 were up-regulated in differentiated SV cardiac tissue, compared to control cardiac tissue. This was confirmed by real-time quantitative reverse transcription-polymerase chain reaction (qRT-PCR) analysis. Predicted target genes of the 48 differentially expressed miRNAs were analyzed by gene ontology and categorized according to cellular process, regulation of biological process and metabolic process. Pathway-Express analysis identified the WNT and mTOR signaling pathways as the most significant processes putatively affected by the differential expression of these miRNAs. The candidate genes involved in cardiac development were identified as potential targets for these differentially expressed microRNAs and the collaborative network of microRNAs and cardiac development related-mRNAs was constructed. These data provide the basis for future investigation of the mechanism of the occurrence and development of fetal SV malformations.

Identification and characterization of novel serum microRNA candidates from deep sequencing in cervical cancer patients.

Science.gov (United States)

Juan, Li; Tong, Hong-li; Zhang, Pengjun; Guo, Guanghong; Wang, Zi; Wen, Xinyu; Dong, Zhennan; Tian, Ya-ping

2014-09-03

Small non-coding microRNAs (miRNAs) are involved in cancer development and progression, and serum profiles of cervical cancer patients may be useful for identifying novel miRNAs. We performed deep sequencing on serum pools of cervical cancer patients and healthy controls with 3 replicates and constructed a small RNA library. We used MIREAP to predict novel miRNAs and identified 2 putative novel miRNAs between serum pools of cervical cancer patients and healthy controls after filtering out pseudo-pre-miRNAs using Triplet-SVM analysis. The 2 putative novel miRNAs were validated by real time PCR and were significantly decreased in cervical cancer patients compared with healthy controls. One novel miRNA had an area under curve (AUC) of 0.921 (95% CI: 0.883, 0.959) with a sensitivity of 85.7% and a specificity of 88.2% when discriminating between cervical cancer patients and healthy controls. Our results suggest that characterizing serum profiles of cervical cancers by Solexa sequencing may be a good method for identifying novel miRNAs and that the validated novel miRNAs described here may be cervical cancer-associated biomarkers.

Deep sequencing as a method of typing bluetongue virus isolates.

Science.gov (United States)

Rao, Pavuluri Panduranga; Reddy, Yella Narasimha; Ganesh, Kapila; Nair, Shreeja G; Niranjan, Vidya; Hegde, Nagendra R

2013-11-01

Bluetongue (BT) is an economically important endemic disease of livestock in tropics and subtropics. In addition, its recent spread to temperate regions like North America and Northern Europe is of serious concern. Rapid serotyping and characterization of BT virus (BTV) is an essential step in the identification of origin of the virus and for controlling the disease. Serotyping of BTV is typically performed by serum neutralization, and of late by nucleotide sequencing. This report describes the near complete genome sequencing and typing of two isolates of BTV using Illumina next generation sequencing platform. Two of the BTV RNAs were multiplexed with ten other unknown samples. Viral RNA was isolated and fragmented, reverse transcribed, the cDNA ends were repaired and ligated with a multiplex oligo. The genome library was amplified using primers complementary to the ligated oligo and subjected to single and paired end sequencing. The raw reads were assembled using a de novo method and reference-based assembly was performed based on the contig data. Near complete sequences of all segments of BTV were obtained with more than 20× coverage, and single read sequencing method was sufficient to identify the genotype and serotype of the virus. The two viruses used in this study were typed as BTV-1 and BTV-9E. Copyright © 2013 Elsevier B.V. All rights reserved.

Deep sequencing-based analysis of the anaerobic stimulon in Neisseria gonorrhoeae

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Clark Virginia L

2011-01-01

Full Text Available Abstract Background Maintenance of an anaerobic denitrification system in the obligate human pathogen, Neisseria gonorrhoeae, suggests that an anaerobic lifestyle may be important during the course of infection. Furthermore, mounting evidence suggests that reduction of host-produced nitric oxide has several immunomodulary effects on the host. However, at this point there have been no studies analyzing the complete gonococcal transcriptome response to anaerobiosis. Here we performed deep sequencing to compare the gonococcal transcriptomes of aerobically and anaerobically grown cells. Using the information derived from this sequencing, we discuss the implications of the robust transcriptional response to anaerobic growth. Results We determined that 198 chromosomal genes were differentially expressed (~10% of the genome in response to anaerobic conditions. We also observed a large induction of genes encoded within the cryptic plasmid, pJD1. Validation of RNA-seq data using translational-lacZ fusions or RT-PCR demonstrated the RNA-seq results to be very reproducible. Surprisingly, many genes of prophage origin were induced anaerobically, as well as several transcriptional regulators previously unknown to be involved in anaerobic growth. We also confirmed expression and regulation of a small RNA, likely a functional equivalent of fnrS in the Enterobacteriaceae family. We also determined that many genes found to be responsive to anaerobiosis have also been shown to be responsive to iron and/or oxidative stress. Conclusions Gonococci will be subject to many forms of environmental stress, including oxygen-limitation, during the course of infection. Here we determined that the anaerobic stimulon in gonococci was larger than previous studies would suggest. Many new targets for future research have been uncovered, and the results derived from this study may have helped to elucidate factors or mechanisms of virulence that may have otherwise been overlooked.

Microbial Dark Matter: Unusual intervening sequences in 16S rRNA genes of candidate phyla from the deep subsurface

Energy Technology Data Exchange (ETDEWEB)

Jarett, Jessica; Stepanauskas, Ramunas; Kieft, Thomas; Onstott, Tullis; Woyke, Tanja

2014-03-17

The Microbial Dark Matter project has sequenced genomes from over 200 single cells from candidate phyla, greatly expanding our knowledge of the ecology, inferred metabolism, and evolution of these widely distributed, yet poorly understood lineages. The second phase of this project aims to sequence an additional 800 single cells from known as well as potentially novel candidate phyla derived from a variety of environments. In order to identify whole genome amplified single cells, screening based on phylogenetic placement of 16S rRNA gene sequences is being conducted. Briefly, derived 16S rRNA gene sequences are aligned to a custom version of the Greengenes reference database and added to a reference tree in ARB using parsimony. In multiple samples from deep subsurface habitats but not from other habitats, a large number of sequences proved difficult to align and therefore to place in the tree. Based on comparisons to reference sequences and structural alignments using SSU-ALIGN, many of these ?difficult? sequences appear to originate from candidate phyla, and contain intervening sequences (IVSs) within the 16S rRNA genes. These IVSs are short (39 - 79 nt) and do not appear to be self-splicing or to contain open reading frames. IVSs were found in the loop regions of stem-loop structures in several different taxonomic groups. Phylogenetic placement of sequences is strongly affected by IVSs; two out of three groups investigated were classified as different phyla after their removal. Based on data from samples screened in this project, IVSs appear to be more common in microbes occurring in deep subsurface habitats, although the reasons for this remain elusive.

Research on performance of upstream pumping mechanical seal with different deep spiral groove

International Nuclear Information System (INIS)

Wang, Q; Chen, H L; Liu, T; Liu, Y H; Liu, Z B; Liu, D H

2012-01-01

As one new type of mechanical seal, Upstream Pumping Mechanical Seal has been widely used in fluid machinery. In this paper, structure of spiral groove is innovatively optimized to improve performance of Upstream Pumping Mechanical Seal with Spiral Groove: keeping the dam zone and the weir zone not changed, changing the bottom shape of spiral groove only, substituting different deep spiral groove for equal deep spiral groove. The simulation on Upstream Pumping Mechanical Seal with different deep spiral grooves is done using FVM method. According to calculation, the performances of opening force and pressure distribution on seals face are obtained. Five types of spiral grooves are analyzed, namely equal deep spiral groove, circumferential convergent ladder-like different deep spiral groove, circumferential divergent ladder-like different deep spiral groove, radial convergent ladder-like different deep spiral groove and radial divergent ladder-like different deep spiral groove. This paper works on twenty-five working conditions. The results indicate the performances of circumferential divergent 2-ladder different deep spiral groove are better than the others, with more opening force and better stabilization, while with the same leakage. The outcome provides theoretical support for application of Upstream Pumping Mechanical Seal with circumferential convergent ladder-like different deep spiral groove.

Research on performance of upstream pumping mechanical seal with different deep spiral groove

Science.gov (United States)

Wang, Q.; Chen, H. L.; Liu, T.; Liu, Y. H.; Liu, Z. B.; Liu, D. H.

2012-11-01

As one new type of mechanical seal, Upstream Pumping Mechanical Seal has been widely used in fluid machinery. In this paper, structure of spiral groove is innovatively optimized to improve performance of Upstream Pumping Mechanical Seal with Spiral Groove: keeping the dam zone and the weir zone not changed, changing the bottom shape of spiral groove only, substituting different deep spiral groove for equal deep spiral groove. The simulation on Upstream Pumping Mechanical Seal with different deep spiral grooves is done using FVM method. According to calculation, the performances of opening force and pressure distribution on seals face are obtained. Five types of spiral grooves are analyzed, namely equal deep spiral groove, circumferential convergent ladder-like different deep spiral groove, circumferential divergent ladder-like different deep spiral groove, radial convergent ladder-like different deep spiral groove and radial divergent ladder-like different deep spiral groove. This paper works on twenty-five working conditions. The results indicate the performances of circumferential divergent 2-ladder different deep spiral groove are better than the others, with more opening force and better stabilization, while with the same leakage. The outcome provides theoretical support for application of Upstream Pumping Mechanical Seal with circumferential convergent ladder-like different deep spiral groove.

Deep sequencing reveals persistence of cell-associated mumps vaccine virus in chronic encephalitis.

Science.gov (United States)

Morfopoulou, Sofia; Mee, Edward T; Connaughton, Sarah M; Brown, Julianne R; Gilmour, Kimberly; Chong, W K 'Kling'; Duprex, W Paul; Ferguson, Deborah; Hubank, Mike; Hutchinson, Ciaran; Kaliakatsos, Marios; McQuaid, Stephen; Paine, Simon; Plagnol, Vincent; Ruis, Christopher; Virasami, Alex; Zhan, Hong; Jacques, Thomas S; Schepelmann, Silke; Qasim, Waseem; Breuer, Judith

2017-01-01

Routine childhood vaccination against measles, mumps and rubella has virtually abolished virus-related morbidity and mortality. Notwithstanding this, we describe here devastating neurological complications associated with the detection of live-attenuated mumps virus Jeryl Lynn (MuV JL5 ) in the brain of a child who had undergone successful allogeneic transplantation for severe combined immunodeficiency (SCID). This is the first confirmed report of MuV JL5 associated with chronic encephalitis and highlights the need to exclude immunodeficient individuals from immunisation with live-attenuated vaccines. The diagnosis was only possible by deep sequencing of the brain biopsy. Sequence comparison of the vaccine batch to the MuV JL5 isolated from brain identified biased hypermutation, particularly in the matrix gene, similar to those found in measles from cases of SSPE. The findings provide unique insights into the pathogenesis of paramyxovirus brain infections.

Sequencing Infrastructure Investments under Deep Uncertainty Using Real Options Analysis

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Nishtha Manocha

2018-02-01

Full Text Available The adaptation tipping point and adaptation pathway approach developed to make decisions under deep uncertainty do not shed light on which among the multiple available pathways should be chosen as the preferred pathway. This creates the need to extend these approaches by means of suitable tools that can help sequence actions and subsequently enable the outlining of relevant policies. This paper presents two sequencing approaches, namely, the “Build to Target” and “Build Up” approach, to aid in sub-selecting a set of preferred pathways. Both approaches differ in the levels of flexibility they offer. They are exemplified by means of two case studies wherein the Net Present Valuation and the Real Options Analysis are employed as selection criterions. The results demonstrate the benefit of these two approaches when used in conjunction with the adaptation pathways and show how the pathways selected by means of a Build to Target approach generally have a value greater than, or at least the same as, the pathways selected by the Build Up approach. Further, this paper also demonstrates the capacity of Real Options to quantify and capture the economic value of flexibility, which cannot be done by traditional valuation approaches such as Net Present Valuation.

Identification of microRNAs from Amur grape (Vitis amurensis Rupr.) by deep sequencing and analysis of microRNA variations with bioinformatics.

Science.gov (United States)

Wang, Chen; Han, Jian; Liu, Chonghuai; Kibet, Korir Nicholas; Kayesh, Emrul; Shangguan, Lingfei; Li, Xiaoying; Fang, Jinggui

2012-03-29

MicroRNA (miRNA) is a class of functional non-coding small RNA with 19-25 nucleotides in length while Amur grape (Vitis amurensis Rupr.) is an important wild fruit crop with the strongest cold resistance among the Vitis species, is used as an excellent breeding parent for grapevine, and has elicited growing interest in wine production. To date, there is a relatively large number of grapevine miRNAs (vv-miRNAs) from cultivated grapevine varieties such as Vitis vinifera L. and hybrids of V. vinifera and V. labrusca, but there is no report on miRNAs from Vitis amurensis Rupr, a wild grapevine species. A small RNA library from Amur grape was constructed and Solexa technology used to perform deep sequencing of the library followed by subsequent bioinformatics analysis to identify new miRNAs. In total, 126 conserved miRNAs belonging to 27 miRNA families were identified, and 34 known but non-conserved miRNAs were also found. Significantly, 72 new potential Amur grape-specific miRNAs were discovered. The sequences of these new potential va-miRNAs were further validated through miR-RACE, and accumulation of 18 new va-miRNAs in seven tissues of grapevines confirmed by real time RT-PCR (qRT-PCR) analysis. The expression levels of va-miRNAs in flowers and berries were found to be basically consistent in identity to those from deep sequenced sRNAs libraries of combined corresponding tissues. We also describe the conservation and variation of va-miRNAs using miR-SNPs and miR-LDs during plant evolution based on comparison of orthologous sequences, and further reveal that the number and sites of miR-SNP in diverse miRNA families exhibit distinct divergence. Finally, 346 target genes for the new miRNAs were predicted and they include a number of Amur grape stress tolerance genes and many genes regulating anthocyanin synthesis and sugar metabolism. Deep sequencing of short RNAs from Amur grape flowers and berries identified 72 new potential miRNAs and 34 known but non-conserved mi

Identification of microRNAs from Amur grape (vitis amurensis Rupr. by deep sequencing and analysis of microRNA variations with bioinformatics

Directory of Open Access Journals (Sweden)

Wang Chen

2012-03-01

Full Text Available Abstract Background MicroRNA (miRNA is a class of functional non-coding small RNA with 19-25 nucleotides in length while Amur grape (Vitis amurensis Rupr. is an important wild fruit crop with the strongest cold resistance among the Vitis species, is used as an excellent breeding parent for grapevine, and has elicited growing interest in wine production. To date, there is a relatively large number of grapevine miRNAs (vv-miRNAs from cultivated grapevine varieties such as Vitis vinifera L. and hybrids of V. vinifera and V. labrusca, but there is no report on miRNAs from Vitis amurensis Rupr, a wild grapevine species. Results A small RNA library from Amur grape was constructed and Solexa technology used to perform deep sequencing of the library followed by subsequent bioinformatics analysis to identify new miRNAs. In total, 126 conserved miRNAs belonging to 27 miRNA families were identified, and 34 known but non-conserved miRNAs were also found. Significantly, 72 new potential Amur grape-specific miRNAs were discovered. The sequences of these new potential va-miRNAs were further validated through miR-RACE, and accumulation of 18 new va-miRNAs in seven tissues of grapevines confirmed by real time RT-PCR (qRT-PCR analysis. The expression levels of va-miRNAs in flowers and berries were found to be basically consistent in identity to those from deep sequenced sRNAs libraries of combined corresponding tissues. We also describe the conservation and variation of va-miRNAs using miR-SNPs and miR-LDs during plant evolution based on comparison of orthologous sequences, and further reveal that the number and sites of miR-SNP in diverse miRNA families exhibit distinct divergence. Finally, 346 target genes for the new miRNAs were predicted and they include a number of Amur grape stress tolerance genes and many genes regulating anthocyanin synthesis and sugar metabolism. Conclusions Deep sequencing of short RNAs from Amur grape flowers and berries identified 72

Influence of deep RIE tolerances on comb-drive actuator performance

International Nuclear Information System (INIS)

Chen, Bangtao; Miao, Jianmin

2007-01-01

This paper analyses the various etching tolerances and profiles of comb-drive microstructures by using deep reactive ion etching (RIE) and studies their influence on the actuator's performance. The comb-drive actuators studied in this paper are fabricated with the silicon-on-glass (SOG) wafer process using deep RIE and wafer bonding, which present very high-aspect-ratio and high-strength microstructures. However, the deep RIE process generates some tolerances and varies the dimension and profile of comb fingers and flexures due to the process limitations. We have analysed the different etching tolerances and studied their influence on the actuator's performance, in terms of the electrostatic force, flexure stiffness, actuator's displacement, air damping and quality factor of the actuator. The analysis shows that the comb fingers with a positive slope profile generated a larger electrostatic force, and the flexures with a negative profile induced the loss of the actuator's stiffness. The combination of these two profiles leads to a great increase in the actuator's displacement and decrease in the quality factor. The measured results of the SOG fabricated actuators have demonstrated the influence of deep RIE tolerance on the actuator's performance

Deep Sequencing Reveals the Complete Genome and Evidence for Transcriptional Activity of the First Virus-Like Sequences Identified in Aristotelia chilensis (Maqui Berry

Directory of Open Access Journals (Sweden)

Javier Villacreses

2015-04-01

Full Text Available Here, we report the genome sequence and evidence for transcriptional activity of a virus-like element in the native Chilean berry tree Aristotelia chilensis. We propose to name the endogenous sequence as Aristotelia chilensis Virus 1 (AcV1. High-throughput sequencing of the genome of this tree uncovered an endogenous viral element, with a size of 7122 bp, corresponding to the complete genome of AcV1. Its sequence contains three open reading frames (ORFs: ORFs 1 and 2 shares 66%–73% amino acid similarity with members of the Caulimoviridae virus family, especially the Petunia vein clearing virus (PVCV, Petuvirus genus. ORF1 encodes a movement protein (MP; ORF2 a Reverse Transcriptase (RT and a Ribonuclease H (RNase H domain; and ORF3 showed no amino acid sequence similarity with any other known virus proteins. Analogous to other known endogenous pararetrovirus sequences (EPRVs, AcV1 is integrated in the genome of Maqui Berry and showed low viral transcriptional activity, which was detected by deep sequencing technology (DNA and RNA-seq. Phylogenetic analysis of AcV1 and other pararetroviruses revealed a closer resemblance with Petuvirus. Overall, our data suggests that AcV1 could be a new member of Caulimoviridae family, genus Petuvirus, and the first evidence of this kind of virus in a fruit plant.

Genomic variation in macrophage-cultured European porcine reproductive and respiratory syndrome virus Olot/91 revealed using ultra-deep next generation sequencing.

Science.gov (United States)

Lu, Zen H; Brown, Alexander; Wilson, Alison D; Calvert, Jay G; Balasch, Monica; Fuentes-Utrilla, Pablo; Loecherbach, Julia; Turner, Frances; Talbot, Richard; Archibald, Alan L; Ait-Ali, Tahar

2014-03-04

Porcine Reproductive and Respiratory Syndrome (PRRS) is a disease of major economic impact worldwide. The etiologic agent of this disease is the PRRS virus (PRRSV). Increasing evidence suggest that microevolution within a coexisting quasispecies population can give rise to high sequence heterogeneity in PRRSV. We developed a pipeline based on the ultra-deep next generation sequencing approach to first construct the complete genome of a European PRRSV, strain Olot/9, cultured on macrophages and then capture the rare variants representative of the mixed quasispecies population. Olot/91 differs from the reference Lelystad strain by about 5% and a total of 88 variants, with frequencies as low as 1%, were detected in the mixed population. These variants included 16 non-synonymous variants concentrated in the genes encoding structural and nonstructural proteins; including Glycoprotein 2a and 5. Using an ultra-deep sequencing methodology, the complete genome of Olot/91 was constructed without any prior knowledge of the sequence. Rare variants that constitute minor fractions of the heterogeneous PRRSV population could successfully be detected to allow further exploration of microevolutionary events.

Magnetic resonance imaging (MRI) of articular cartilage of the knee using ultrashort echo time (uTE) sequences with spiral acquisition

International Nuclear Information System (INIS)

Goto, Hajimu; Fujii, Masahiko; Iwama, Yuki; Aoyama, Nobukazu; Ohno, Yoshiharu; Sugimura, Kazuro

2012-01-01

The objective of this study was to evaluate the sensitivity of ultrashort echo time (uTE) sequence for visualisation of calcified deep layers of articular cartilage. MRI with a uTE sequence was performed on five healthy volunteers. Signals from the calcified deep layers of the articular knee cartilage were evaluated on uTE subtraction images and computed tomography images. The calcified deep layers of the articular cartilage changed from having a low to a high signal when imaged with a uTE sequence. The reported uTE sequence was effective in imaging the deep layers of the knee cartilage.

3′ terminal diversity of MRP RNA and other human noncoding RNAs revealed by deep sequencing

Science.gov (United States)

2013-01-01

Background Post-transcriptional 3′ end processing is a key component of RNA regulation. The abundant and essential RNA subunit of RNase MRP has been proposed to function in three distinct cellular compartments and therefore may utilize this mode of regulation. Here we employ 3′ RACE coupled with high-throughput sequencing to characterize the 3′ terminal sequences of human MRP RNA and other noncoding RNAs that form RNP complexes. Results The 3′ terminal sequence of MRP RNA from HEK293T cells has a distinctive distribution of genomically encoded termini (including an assortment of U residues) with a portion of these selectively tagged by oligo(A) tails. This profile contrasts with the relatively homogenous 3′ terminus of an in vitro transcribed MRP RNA control and the differing 3′ terminal profiles of U3 snoRNA, RNase P RNA, and telomerase RNA (hTR). Conclusions 3′ RACE coupled with deep sequencing provides a valuable framework for the functional characterization of 3′ terminal sequences of noncoding RNAs. PMID:24053768

SHARAKU: an algorithm for aligning and clustering read mapping profiles of deep sequencing in non-coding RNA processing.

Science.gov (United States)

Tsuchiya, Mariko; Amano, Kojiro; Abe, Masaya; Seki, Misato; Hase, Sumitaka; Sato, Kengo; Sakakibara, Yasubumi

2016-06-15

Deep sequencing of the transcripts of regulatory non-coding RNA generates footprints of post-transcriptional processes. After obtaining sequence reads, the short reads are mapped to a reference genome, and specific mapping patterns can be detected called read mapping profiles, which are distinct from random non-functional degradation patterns. These patterns reflect the maturation processes that lead to the production of shorter RNA sequences. Recent next-generation sequencing studies have revealed not only the typical maturation process of miRNAs but also the various processing mechanisms of small RNAs derived from tRNAs and snoRNAs. We developed an algorithm termed SHARAKU to align two read mapping profiles of next-generation sequencing outputs for non-coding RNAs. In contrast with previous work, SHARAKU incorporates the primary and secondary sequence structures into an alignment of read mapping profiles to allow for the detection of common processing patterns. Using a benchmark simulated dataset, SHARAKU exhibited superior performance to previous methods for correctly clustering the read mapping profiles with respect to 5'-end processing and 3'-end processing from degradation patterns and in detecting similar processing patterns in deriving the shorter RNAs. Further, using experimental data of small RNA sequencing for the common marmoset brain, SHARAKU succeeded in identifying the significant clusters of read mapping profiles for similar processing patterns of small derived RNA families expressed in the brain. The source code of our program SHARAKU is available at http://www.dna.bio.keio.ac.jp/sharaku/, and the simulated dataset used in this work is available at the same link. Accession code: The sequence data from the whole RNA transcripts in the hippocampus of the left brain used in this work is available from the DNA DataBank of Japan (DDBJ) Sequence Read Archive (DRA) under the accession number DRA004502. yasu@bio.keio.ac.jp Supplementary data are available

Deep Packet/Flow Analysis using GPUs

Energy Technology Data Exchange (ETDEWEB)

Gong, Qian [Fermi National Accelerator Lab. (FNAL), Batavia, IL (United States); Wu, Wenji [Fermi National Accelerator Lab. (FNAL), Batavia, IL (United States); DeMar, Phil [Fermi National Accelerator Lab. (FNAL), Batavia, IL (United States)

2017-11-12

Deep packet inspection (DPI) faces severe performance challenges in high-speed networks (40/100 GE) as it requires a large amount of raw computing power and high I/O throughputs. Recently, researchers have tentatively used GPUs to address the above issues and boost the performance of DPI. Typically, DPI applications involve highly complex operations in both per-packet and per-flow data level, often in real-time. The parallel architecture of GPUs fits exceptionally well for per-packet network traffic processing. However, for stateful network protocols such as TCP, their data stream need to be reconstructed in a per-flow level to deliver a consistent content analysis. Since the flow-centric operations are naturally antiparallel and often require large memory space for buffering out-of-sequence packets, they can be problematic for GPUs, whose memory is normally limited to several gigabytes. In this work, we present a highly efficient GPU-based deep packet/flow analysis framework. The proposed design includes a purely GPU-implemented flow tracking and TCP stream reassembly. Instead of buffering and waiting for TCP packets to become in sequence, our framework process the packets in batch and uses a deterministic finite automaton (DFA) with prefix-/suffix- tree method to detect patterns across out-of-sequence packets that happen to be located in different batches. In conclusion, evaluation shows that our code can reassemble and forward tens of millions of packets per second and conduct a stateful signature-based deep packet inspection at 55 Gbit/s using an NVIDIA K40 GPU.

Identification of ribonucleotide reductase mutation causing temperature-sensitivity of herpes simplex virus isolates from whitlow by deep sequencing.

Science.gov (United States)

Daikoku, Tohru; Oyama, Yukari; Yajima, Misako; Sekizuka, Tsuyoshi; Kuroda, Makoto; Shimada, Yuka; Takehara, Kazuhiko; Miwa, Naoko; Okuda, Tomoko; Sata, Tetsutaro; Shiraki, Kimiyasu

2015-06-01

Herpes simplex virus 2 caused a genital ulcer, and a secondary herpetic whitlow appeared during acyclovir therapy. The secondary and recurrent whitlow isolates were acyclovir-resistant and temperature-sensitive in contrast to a genital isolate. We identified the ribonucleotide reductase mutation responsible for temperature-sensitivity by deep-sequencing analysis.

Rapid and Deep Proteomes by Faster Sequencing on a Benchtop Quadrupole Ultra-High-Field Orbitrap Mass Spectrometer

DEFF Research Database (Denmark)

Kelstrup, Christian D; Jersie-Christensen, Rosa R; Batth, Tanveer Singh

2014-01-01

per second or up to 600 new peptides sequenced per gradient minute. We identify 4400 proteins from one microgram of HeLa digest using a one hour gradient, which is an approximately 30% improvement compared to previous instrumentation. In addition, we show very deep proteome coverage can be achieved...... in less than 24 hours of analysis time by offline high pH reversed-phase peptide fractionation from which we identify more than 140,000 unique peptide sequences. This is comparable to state-of-the-art multi-day, multi-enzyme efforts. Finally the acquisition methods are evaluated for single...

DeepRT: deep learning for peptide retention time prediction in proteomics

OpenAIRE

Ma, Chunwei; Zhu, Zhiyong; Ye, Jun; Yang, Jiarui; Pei, Jianguo; Xu, Shaohang; Zhou, Ruo; Yu, Chang; Mo, Fan; Wen, Bo; Liu, Siqi

2017-01-01

Accurate predictions of peptide retention times (RT) in liquid chromatography have many applications in mass spectrometry-based proteomics. Herein, we present DeepRT, a deep learning based software for peptide retention time prediction. DeepRT automatically learns features directly from the peptide sequences using the deep convolutional Neural Network (CNN) and Recurrent Neural Network (RNN) model, which eliminates the need to use hand-crafted features or rules. After the feature learning, pr...

Ultra Deep Sequencing of a Baculovirus Population Reveals Widespread Genomic Variations

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Aurélien Chateigner

2015-07-01

Full Text Available Viruses rely on widespread genetic variation and large population size for adaptation. Large DNA virus populations are thought to harbor little variation though natural populations may be polymorphic. To measure the genetic variation present in a dsDNA virus population, we deep sequenced a natural strain of the baculovirus Autographa californica multiple nucleopolyhedrovirus. With 124,221X average genome coverage of our 133,926 bp long consensus, we could detect low frequency mutations (0.025%. K-means clustering was used to classify the mutations in four categories according to their frequency in the population. We found 60 high frequency non-synonymous mutations under balancing selection distributed in all functional classes. These mutants could alter viral adaptation dynamics, either through competitive or synergistic processes. Lastly, we developed a technique for the delimitation of large deletions in next generation sequencing data. We found that large deletions occur along the entire viral genome, with hotspots located in homologous repeat regions (hrs. Present in 25.4% of the genomes, these deletion mutants presumably require functional complementation to complete their infection cycle. They might thus have a large impact on the fitness of the baculovirus population. Altogether, we found a wide breadth of genomic variation in the baculovirus population, suggesting it has high adaptive potential.

Deep-sequencing to resolve complex diversity of apicomplexan parasites in platypuses and echidnas: Proof of principle for wildlife disease investigation.

Science.gov (United States)

Šlapeta, Jan; Saverimuttu, Stefan; Vogelnest, Larry; Sangster, Cheryl; Hulst, Frances; Rose, Karrie; Thompson, Paul; Whittington, Richard

2017-11-01

The short-beaked echidna (Tachyglossus aculeatus) and the platypus (Ornithorhynchus anatinus) are iconic egg-laying monotremes (Mammalia: Monotremata) from Australasia. The aim of this study was to demonstrate the utility of diversity profiles in disease investigations of monotremes. Using small subunit (18S) rDNA amplicon deep-sequencing we demonstrated the presence of apicomplexan parasites and confirmed by direct and cloned amplicon gene sequencing Theileria ornithorhynchi, Theileria tachyglossi, Eimeria echidnae and Cryptosporidium fayeri. Using a combination of samples from healthy and diseased animals, we show a close evolutionary relationship between species of coccidia (Eimeria) and piroplasms (Theileria) from the echidna and platypus. The presence of E. echidnae was demonstrated in faeces and tissues affected by disseminated coccidiosis. Moreover, the presence of E. echidnae DNA in the blood of echidnas was associated with atoxoplasma-like stages in white blood cells, suggesting Hepatozoon tachyglossi blood stages are disseminated E. echidnae stages. These next-generation DNA sequencing technologies are suited to material and organisms that have not been previously characterised and for which the material is scarce. The deep sequencing approach supports traditional diagnostic methods, including microscopy, clinical pathology and histopathology, to better define the status quo. This approach is particularly suitable for wildlife disease investigation. Copyright © 2017 Elsevier B.V. All rights reserved.

Deep-sequencing protocols influence the results obtained in small-RNA sequencing.

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Joern Toedling

Full Text Available Second-generation sequencing is a powerful method for identifying and quantifying small-RNA components of cells. However, little attention has been paid to the effects of the choice of sequencing platform and library preparation protocol on the results obtained. We present a thorough comparison of small-RNA sequencing libraries generated from the same embryonic stem cell lines, using different sequencing platforms, which represent the three major second-generation sequencing technologies, and protocols. We have analysed and compared the expression of microRNAs, as well as populations of small RNAs derived from repetitive elements. Despite the fact that different libraries display a good correlation between sequencing platforms, qualitative and quantitative variations in the results were found, depending on the protocol used. Thus, when comparing libraries from different biological samples, it is strongly recommended to use the same sequencing platform and protocol in order to ensure the biological relevance of the comparisons.

Deep learning improves prediction of CRISPR-Cpf1 guide RNA activity.

Science.gov (United States)

Kim, Hui Kwon; Min, Seonwoo; Song, Myungjae; Jung, Soobin; Choi, Jae Woo; Kim, Younggwang; Lee, Sangeun; Yoon, Sungroh; Kim, Hyongbum Henry

2018-03-01

We present two algorithms to predict the activity of AsCpf1 guide RNAs. Indel frequencies for 15,000 target sequences were used in a deep-learning framework based on a convolutional neural network to train Seq-deepCpf1. We then incorporated chromatin accessibility information to create the better-performing DeepCpf1 algorithm for cell lines for which such information is available and show that both algorithms outperform previous machine learning algorithms on our own and published data sets.

Performance Analysis of High-Speed Deep/Shallow Recessed Hybrid Bearing

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Lei Wang

2013-01-01

Full Text Available The present paper proposes a theoretical analysis of the performance of deep/shallow recessed hybrid bearing. It is intended that, on the basis of the numerical results drawn from this study, appropriate shallow recess depth and width can be determined for use in the bearing design process. By adopting bulk flow theory, the turbulent Reynolds equation and energy equation are modified and solved numerically including concentrated inertia effects at the recess edge with different depth and width of shallow recess. The results indicate that the load capacity, drag torque increases as the depth of shallow recess is shallower and the width ratio (half angle of deep recess versus half angle of shallow recess is smaller. In contrast, the flow rate decreases as the depth of shallow recess is shallower and the width ratio is smaller. Nevertheless, the appropriate design of the depth and width of shallow recess might well induce the performance of high-speed deep/shallow recessed hybrid bearing.

Inspecting Targeted Deep Sequencing of Whole Genome Amplified DNA Versus Fresh DNA for Somatic Mutation Detection: A Genetic Study in Myelodysplastic Syndrome Patients.

Science.gov (United States)

Palomo, Laura; Fuster-Tormo, Francisco; Alvira, Daniel; Ademà, Vera; Armengol, María Pilar; Gómez-Marzo, Paula; de Haro, Nuri; Mallo, Mar; Xicoy, Blanca; Zamora, Lurdes; Solé, Francesc

2017-08-01

Whole genome amplification (WGA) has become an invaluable method for preserving limited samples of precious stock material and has been used during the past years as an alternative tool to increase the amount of DNA before library preparation for next-generation sequencing. Myelodysplastic syndromes (MDS) are a group of clonal hematopoietic stem cell disorders characterized by presenting somatic mutations in several myeloid-related genes. In this work, targeted deep sequencing has been performed on four paired fresh DNA and WGA DNA samples from bone marrow of MDS patients, to assess the feasibility of using WGA DNA for detecting somatic mutations. The results of this study highlighted that, in general, the sequencing and alignment statistics of fresh DNA and WGA DNA samples were similar. However, after variant calling and when considering variants detected at all frequencies, there was a high level of discordance between fresh DNA and WGA DNA (overall, a higher number of variants was detected in WGA DNA). After proper filtering, a total of three somatic mutations were detected in the cohort. All somatic mutations detected in fresh DNA were also identified in WGA DNA and validated by whole exome sequencing.

Mapping vaccinia virus DNA replication origins at nucleotide level by deep sequencing.

Science.gov (United States)

Senkevich, Tatiana G; Bruno, Daniel; Martens, Craig; Porcella, Stephen F; Wolf, Yuri I; Moss, Bernard

2015-09-01

Poxviruses reproduce in the host cytoplasm and encode most or all of the enzymes and factors needed for expression and synthesis of their double-stranded DNA genomes. Nevertheless, the mode of poxvirus DNA replication and the nature and location of the replication origins remain unknown. A current but unsubstantiated model posits only leading strand synthesis starting at a nick near one covalently closed end of the genome and continuing around the other end to generate a concatemer that is subsequently resolved into unit genomes. The existence of specific origins has been questioned because any plasmid can replicate in cells infected by vaccinia virus (VACV), the prototype poxvirus. We applied directional deep sequencing of short single-stranded DNA fragments enriched for RNA-primed nascent strands isolated from the cytoplasm of VACV-infected cells to pinpoint replication origins. The origins were identified as the switching points of the fragment directions, which correspond to the transition from continuous to discontinuous DNA synthesis. Origins containing a prominent initiation point mapped to a sequence within the hairpin loop at one end of the VACV genome and to the same sequence within the concatemeric junction of replication intermediates. These findings support a model for poxvirus genome replication that involves leading and lagging strand synthesis and is consistent with the requirements for primase and ligase activities as well as earlier electron microscopic and biochemical studies implicating a replication origin at the end of the VACV genome.

InFusion: Advancing Discovery of Fusion Genes and Chimeric Transcripts from Deep RNA-Sequencing Data.

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Konstantin Okonechnikov

Full Text Available Analysis of fusion transcripts has become increasingly important due to their link with cancer development. Since high-throughput sequencing approaches survey fusion events exhaustively, several computational methods for the detection of gene fusions from RNA-seq data have been developed. This kind of analysis, however, is complicated by native trans-splicing events, the splicing-induced complexity of the transcriptome and biases and artefacts introduced in experiments and data analysis. There are a number of tools available for the detection of fusions from RNA-seq data; however, certain differences in specificity and sensitivity between commonly used approaches have been found. The ability to detect gene fusions of different types, including isoform fusions and fusions involving non-coding regions, has not been thoroughly studied yet. Here, we propose a novel computational toolkit called InFusion for fusion gene detection from RNA-seq data. InFusion introduces several unique features, such as discovery of fusions involving intergenic regions, and detection of anti-sense transcription in chimeric RNAs based on strand-specificity. Our approach demonstrates superior detection accuracy on simulated data and several public RNA-seq datasets. This improved performance was also evident when evaluating data from RNA deep-sequencing of two well-established prostate cancer cell lines. InFusion identified 26 novel fusion events that were validated in vitro, including alternatively spliced gene fusion isoforms and chimeric transcripts that include intergenic regions. The toolkit is freely available to download from http:/bitbucket.org/kokonech/infusion.

Draft Genome Sequences of TwoThiomicrospiraStrains Isolated from the Brine-Seawater Interface of Kebrit Deep in the Red Sea

KAUST Repository

Zhang, Guishan

2016-03-11

Two Thiomicrospira strains, WB1 and XS5, were isolated from the Kebrit Deep brine-seawater interface in the Red Sea, Saudi Arabia. Here, we present the draft genome sequences of these gammaproteobacteria, which both produce sulfuric acid from thiosulfate in culture.

Draft Genome Sequences of TwoThiomicrospiraStrains Isolated from the Brine-Seawater Interface of Kebrit Deep in the Red Sea

KAUST Repository

Zhang, Guishan; Haroon, Mohamed; Zhang, Ruifu; Hikmawan, Tyas I.; Stingl, Ulrich

2016-01-01

Two Thiomicrospira strains, WB1 and XS5, were isolated from the Kebrit Deep brine-seawater interface in the Red Sea, Saudi Arabia. Here, we present the draft genome sequences of these gammaproteobacteria, which both produce sulfuric acid from thiosulfate in culture.

Identification of microRNAs Involved in the Host Response to Enterovirus 71 Infection by a Deep Sequencing Approach

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Lunbiao Cui

2010-01-01

Full Text Available Role of microRNA (miRNA has been highlighted in pathogen-host interactions recently. To identify cellular miRNAs involved in the host response to enterovirus 71 (EV71 infection, we performed a comprehensive miRNA profiling in EV71-infected Hep2 cells through deep sequencing. 64 miRNAs were found whose expression levels changed for more than 2-fold in response to EV71 infection. Gene ontology analysis revealed that many of these mRNAs play roles in neurological process, immune response, and cell death pathways, which are known to be associated with the extreme virulence of EV71. To our knowledge, this is the first study on host miRNAs expression alteration response to EV71 infection. Our findings supported the hypothesis that certain miRNAs might be essential in the host-pathogen interactions.

DRREP: deep ridge regressed epitope predictor.

Science.gov (United States)

Sher, Gene; Zhi, Degui; Zhang, Shaojie

2017-10-03

The ability to predict epitopes plays an enormous role in vaccine development in terms of our ability to zero in on where to do a more thorough in-vivo analysis of the protein in question. Though for the past decade there have been numerous advancements and improvements in epitope prediction, on average the best benchmark prediction accuracies are still only around 60%. New machine learning algorithms have arisen within the domain of deep learning, text mining, and convolutional networks. This paper presents a novel analytically trained and string kernel using deep neural network, which is tailored for continuous epitope prediction, called: Deep Ridge Regressed Epitope Predictor (DRREP). DRREP was tested on long protein sequences from the following datasets: SARS, Pellequer, HIV, AntiJen, and SEQ194. DRREP was compared to numerous state of the art epitope predictors, including the most recently published predictors called LBtope and DMNLBE. Using area under ROC curve (AUC), DRREP achieved a performance improvement over the best performing predictors on SARS (13.7%), HIV (8.9%), Pellequer (1.5%), and SEQ194 (3.1%), with its performance being matched only on the AntiJen dataset, by the LBtope predictor, where both DRREP and LBtope achieved an AUC of 0.702. DRREP is an analytically trained deep neural network, thus capable of learning in a single step through regression. By combining the features of deep learning, string kernels, and convolutional networks, the system is able to perform residue-by-residue prediction of continues epitopes with higher accuracy than the current state of the art predictors.

High-resolution deep sequencing reveals biodiversity, population structure, and persistence of HIV-1 quasispecies within host ecosystems

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Yin Li

2012-12-01

Full Text Available Abstract Background Deep sequencing provides the basis for analysis of biodiversity of taxonomically similar organisms in an environment. While extensively applied to microbiome studies, population genetics studies of viruses are limited. To define the scope of HIV-1 population biodiversity within infected individuals, a suite of phylogenetic and population genetic algorithms was applied to HIV-1 envelope hypervariable domain 3 (Env V3 within peripheral blood mononuclear cells from a group of perinatally HIV-1 subtype B infected, therapy-naïve children. Results Biodiversity of HIV-1 Env V3 quasispecies ranged from about 70 to 270 unique sequence clusters across individuals. Viral population structure was organized into a limited number of clusters that included the dominant variants combined with multiple clusters of low frequency variants. Next generation viral quasispecies evolved from low frequency variants at earlier time points through multiple non-synonymous changes in lineages within the evolutionary landscape. Minor V3 variants detected as long as four years after infection co-localized in phylogenetic reconstructions with early transmitting viruses or with subsequent plasma virus circulating two years later. Conclusions Deep sequencing defines HIV-1 population complexity and structure, reveals the ebb and flow of dominant and rare viral variants in the host ecosystem, and identifies an evolutionary record of low-frequency cell-associated viral V3 variants that persist for years. Bioinformatics pipeline developed for HIV-1 can be applied for biodiversity studies of virome populations in human, animal, or plant ecosystems.

Recurrent chimeric RNAs enriched in human prostate cancer identified by deep sequencing

Science.gov (United States)

Kannan, Kalpana; Wang, Liguo; Wang, Jianghua; Ittmann, Michael M.; Li, Wei; Yen, Laising

2011-01-01

Transcription-induced chimeric RNAs, possessing sequences from different genes, are expected to increase the proteomic diversity through chimeric proteins or altered regulation. Despite their importance, few studies have focused on chimeric RNAs especially regarding their presence/roles in human cancers. By deep sequencing the transcriptome of 20 human prostate cancer and 10 matched benign prostate tissues, we obtained 1.3 billion sequence reads, which led to the identification of 2,369 chimeric RNA candidates. Chimeric RNAs occurred in significantly higher frequency in cancer than in matched benign samples. Experimental investigation of a selected 46 set led to the confirmation of 32 chimeric RNAs, of which 27 were highly recurrent and previously undescribed in prostate cancer. Importantly, a subset of these chimeras was present in prostate cancer cell lines, but not detectable in primary human prostate epithelium cells, implying their associations with cancer. These chimeras contain discernable 5′ and 3′ splice sites at the RNA junction, indicating that their formation is mediated by splicing. Their presence is also largely independent of the expression of parental genes, suggesting that other factors are involved in their production and regulation. One chimera, TMEM79-SMG5, is highly differentially expressed in human cancer samples and therefore a potential biomarker. The prevalence of chimeric RNAs may allow the limited number of human genes to encode a substantially larger number of RNAs and proteins, forming an additional layer of cellular complexity. Together, our results suggest that chimeric RNAs are widespread, and increased chimeric RNA events could represent a unique class of molecular alteration in cancer. PMID:21571633

Ultra-deep sequencing of mouse mitochondrial DNA: mutational patterns and their origins.

Directory of Open Access Journals (Sweden)

Adam Ameur

2011-03-01

Full Text Available Somatic mutations of mtDNA are implicated in the aging process, but there is no universally accepted method for their accurate quantification. We have used ultra-deep sequencing to study genome-wide mtDNA mutation load in the liver of normally- and prematurely-aging mice. Mice that are homozygous for an allele expressing a proof-reading-deficient mtDNA polymerase (mtDNA mutator mice have 10-times-higher point mutation loads than their wildtype siblings. In addition, the mtDNA mutator mice have increased levels of a truncated linear mtDNA molecule, resulting in decreased sequence coverage in the deleted region. In contrast, circular mtDNA molecules with large deletions occur at extremely low frequencies in mtDNA mutator mice and can therefore not drive the premature aging phenotype. Sequence analysis shows that the main proportion of the mutation load in heterozygous mtDNA mutator mice and their wildtype siblings is inherited from their heterozygous mothers consistent with germline transmission. We found no increase in levels of point mutations or deletions in wildtype C57Bl/6N mice with increasing age, thus questioning the causative role of these changes in aging. In addition, there was no increased frequency of transversion mutations with time in any of the studied genotypes, arguing against oxidative damage as a major cause of mtDNA mutations. Our results from studies of mice thus indicate that most somatic mtDNA mutations occur as replication errors during development and do not result from damage accumulation in adult life.

Deep geologic disposal. Lessons learnt from recent performance assessment studies

International Nuclear Information System (INIS)

Pescatore, C.; Andersson, J.

1998-01-01

Performance assessment (PA) studies are part of the decision basis for the siting, operation, and closure of deep repositories of long-lived nuclear wastes. In 1995 the NEA set up the Working Group on Integrated Performance Assessments of Deep Repositories (IPAG) with the goals to analyse existing PA studies, learn about what has been produced to date, and shed light on what could be done in future studies. Ten organisations submitted their most recent PA study for analysis and discussion, including written answers to over 70 questions. Waste management programmes, disposal concepts, geologies, and different types and amounts of waste offered a unique opportunity for exchanging information, assessing progress in PA since 1990, and identifying recent trends. A report was completed whose main lessons are overviewed. (author)

Analysis of Plasmodium falciparum diversity in natural infections by deep sequencing

Science.gov (United States)

Manske, Magnus; Miotto, Olivo; Campino, Susana; Auburn, Sarah; Almagro-Garcia, Jacob; Maslen, Gareth; O’Brien, Jack; Djimde, Abdoulaye; Doumbo, Ogobara; Zongo, Issaka; Ouedraogo, Jean-Bosco; Michon, Pascal; Mueller, Ivo; Siba, Peter; Nzila, Alexis; Borrmann, Steffen; Kiara, Steven M.; Marsh, Kevin; Jiang, Hongying; Su, Xin-Zhuan; Amaratunga, Chanaki; Fairhurst, Rick; Socheat, Duong; Nosten, Francois; Imwong, Mallika; White, Nicholas J.; Sanders, Mandy; Anastasi, Elisa; Alcock, Dan; Drury, Eleanor; Oyola, Samuel; Quail, Michael A.; Turner, Daniel J.; Rubio, Valentin Ruano; Jyothi, Dushyanth; Amenga-Etego, Lucas; Hubbart, Christina; Jeffreys, Anna; Rowlands, Kate; Sutherland, Colin; Roper, Cally; Mangano, Valentina; Modiano, David; Tan, John C.; Ferdig, Michael T.; Amambua-Ngwa, Alfred; Conway, David J.; Takala-Harrison, Shannon; Plowe, Christopher V.; Rayner, Julian C.; Rockett, Kirk A.; Clark, Taane G.; Newbold, Chris I.; Berriman, Matthew; MacInnis, Bronwyn; Kwiatkowski, Dominic P.

2013-01-01

Malaria elimination strategies require surveillance of the parasite population for genetic changes that demand a public health response, such as new forms of drug resistance. 1,2 Here we describe methods for large-scale analysis of genetic variation in Plasmodium falciparum by deep sequencing of parasite DNA obtained from the blood of patients with malaria, either directly or after short term culture. Analysis of 86,158 exonic SNPs that passed genotyping quality control in 227 samples from Africa, Asia and Oceania provides genome-wide estimates of allele frequency distribution, population structure and linkage disequilibrium. By comparing the genetic diversity of individual infections with that of the local parasite population, we derive a metric of within-host diversity that is related to the level of inbreeding in the population. An open-access web application has been established for exploration of regional differences in allele frequency and of highly differentiated loci in the P. falciparum genome. PMID:22722859

Geochemical features and effects on deep-seated fluids during the May-June 2012 southern Po Valley seismic sequence

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Francesco Italiano

2012-10-01

Full Text Available A periodic sampling of the groundwaters and dissolved and free gases in selected deep wells located in the area affected by the May-June 2012 southern Po Valley seismic sequence has provided insight into seismogenic-induced changes of the local aquifer systems. The results obtained show progressive changes in the fluid geochemistry, allowing it to be established that deep-seated fluids were mobilized during the seismic sequence and reached surface layers along faults and fractures, which generated significant geochemical anomalies. The May-June 2012 seismic swarm (mainshock on May 29, 2012, M 5.8; 7 shocks M >5, about 200 events 3 > M > 5 induced several modifications in the circulating fluids. This study reports the preliminary results obtained for the geochemical features of the waters and gases collected over the epicentral area from boreholes drilled at different depths, thus intercepting water and gases with different origins and circulation. The aim of the investigations was to improve our knowledge of the fluids circulating over the seismic area (e.g. origin, provenance, interactions, mixing of different components, temporal changes. This was achieved by collecting samples from both shallow and deep-drilled boreholes, and then, after the selection of the relevant sites, we looked for temporal changes with mid-to-long-term monitoring activity following a constant sampling rate. This allowed us to gain better insight into the relationships between the fluid circulation and the faulting activity. The sampling sites are listed in Table 1, along with the analytical results of the gas phase. […

Deep learning

CERN Document Server

Goodfellow, Ian; Courville, Aaron

2016-01-01

Deep learning is a form of machine learning that enables computers to learn from experience and understand the world in terms of a hierarchy of concepts. Because the computer gathers knowledge from experience, there is no need for a human computer operator to formally specify all the knowledge that the computer needs. The hierarchy of concepts allows the computer to learn complicated concepts by building them out of simpler ones; a graph of these hierarchies would be many layers deep. This book introduces a broad range of topics in deep learning. The text offers mathematical and conceptual background, covering relevant concepts in linear algebra, probability theory and information theory, numerical computation, and machine learning. It describes deep learning techniques used by practitioners in industry, including deep feedforward networks, regularization, optimization algorithms, convolutional networks, sequence modeling, and practical methodology; and it surveys such applications as natural language proces...

Deep sequencing-based transcriptome analysis of Plutella xylostella larvae parasitized by Diadegma semiclausum

Science.gov (United States)

2011-01-01

Background Parasitoid insects manipulate their hosts' physiology by injecting various factors into their host upon parasitization. Transcriptomic approaches provide a powerful approach to study insect host-parasitoid interactions at the molecular level. In order to investigate the effects of parasitization by an ichneumonid wasp (Diadegma semiclausum) on the host (Plutella xylostella), the larval transcriptome profile was analyzed using a short-read deep sequencing method (Illumina). Symbiotic polydnaviruses (PDVs) associated with ichneumonid parasitoids, known as ichnoviruses, play significant roles in host immune suppression and developmental regulation. In the current study, D. semiclausum ichnovirus (DsIV) genes expressed in P. xylostella were identified and their sequences compared with other reported PDVs. Five of these genes encode proteins of unknown identity, that have not previously been reported. Results De novo assembly of cDNA sequence data generated 172,660 contigs between 100 and 10000 bp in length; with 35% of > 200 bp in length. Parasitization had significant impacts on expression levels of 928 identified insect host transcripts. Gene ontology data illustrated that the majority of the differentially expressed genes are involved in binding, catalytic activity, and metabolic and cellular processes. In addition, the results show that transcription levels of antimicrobial peptides, such as gloverin, cecropin E and lysozyme, were up-regulated after parasitism. Expression of ichnovirus genes were detected in parasitized larvae with 19 unique sequences identified from five PDV gene families including vankyrin, viral innexin, repeat elements, a cysteine-rich motif, and polar residue rich protein. Vankyrin 1 and repeat element 1 genes showed the highest transcription levels among the DsIV genes. Conclusion This study provides detailed information on differential expression of P. xylostella larval genes following parasitization, DsIV genes expressed in the

Congruent Deep Relationships in the Grape Family (Vitaceae) Based on Sequences of Chloroplast Genomes and Mitochondrial Genes via Genome Skimming.

Science.gov (United States)

Zhang, Ning; Wen, Jun; Zimmer, Elizabeth A

2015-01-01

Vitaceae is well-known for having one of the most economically important fruits, i.e., the grape (Vitis vinifera). The deep phylogeny of the grape family was not resolved until a recent phylogenomic analysis of 417 nuclear genes from transcriptome data. However, it has been reported extensively that topologies based on nuclear and organellar genes may be incongruent due to differences in their evolutionary histories. Therefore, it is important to reconstruct a backbone phylogeny of the grape family using plastomes and mitochondrial genes. In this study,next-generation sequencing data sets of 27 species were obtained using genome skimming with total DNAs from silica-gel preserved tissue samples on an Illumina NextSeq 500 instrument [corrected]. Plastomes were assembled using the combination of de novo and reference genome (of V. vinifera) methods. Sixteen mitochondrial genes were also obtained via genome skimming using the reference genome of V. vinifera. Extensive phylogenetic analyses were performed using maximum likelihood and Bayesian methods. The topology based on either plastome data or mitochondrial genes is congruent with the one using hundreds of nuclear genes, indicating that the grape family did not exhibit significant reticulation at the deep level. The results showcase the power of genome skimming in capturing extensive phylogenetic data: especially from chloroplast and mitochondrial DNAs.

Congruent Deep Relationships in the Grape Family (Vitaceae Based on Sequences of Chloroplast Genomes and Mitochondrial Genes via Genome Skimming.

Directory of Open Access Journals (Sweden)

Ning Zhang

Full Text Available Vitaceae is well-known for having one of the most economically important fruits, i.e., the grape (Vitis vinifera. The deep phylogeny of the grape family was not resolved until a recent phylogenomic analysis of 417 nuclear genes from transcriptome data. However, it has been reported extensively that topologies based on nuclear and organellar genes may be incongruent due to differences in their evolutionary histories. Therefore, it is important to reconstruct a backbone phylogeny of the grape family using plastomes and mitochondrial genes. In this study,next-generation sequencing data sets of 27 species were obtained using genome skimming with total DNAs from silica-gel preserved tissue samples on an Illumina NextSeq 500 instrument [corrected]. Plastomes were assembled using the combination of de novo and reference genome (of V. vinifera methods. Sixteen mitochondrial genes were also obtained via genome skimming using the reference genome of V. vinifera. Extensive phylogenetic analyses were performed using maximum likelihood and Bayesian methods. The topology based on either plastome data or mitochondrial genes is congruent with the one using hundreds of nuclear genes, indicating that the grape family did not exhibit significant reticulation at the deep level. The results showcase the power of genome skimming in capturing extensive phylogenetic data: especially from chloroplast and mitochondrial DNAs.

Deep Learning and Its Applications in Biomedicine.

Science.gov (United States)

Cao, Chensi; Liu, Feng; Tan, Hai; Song, Deshou; Shu, Wenjie; Li, Weizhong; Zhou, Yiming; Bo, Xiaochen; Xie, Zhi

2018-02-01

Advances in biological and medical technologies have been providing us explosive volumes of biological and physiological data, such as medical images, electroencephalography, genomic and protein sequences. Learning from these data facilitates the understanding of human health and disease. Developed from artificial neural networks, deep learning-based algorithms show great promise in extracting features and learning patterns from complex data. The aim of this paper is to provide an overview of deep learning techniques and some of the state-of-the-art applications in the biomedical field. We first introduce the development of artificial neural network and deep learning. We then describe two main components of deep learning, i.e., deep learning architectures and model optimization. Subsequently, some examples are demonstrated for deep learning applications, including medical image classification, genomic sequence analysis, as well as protein structure classification and prediction. Finally, we offer our perspectives for the future directions in the field of deep learning. Copyright © 2018. Production and hosting by Elsevier B.V.

Sequence Selection and Performance in DS/CDMA Systems

Directory of Open Access Journals (Sweden)

Jefferson Santos Ambrosio

2016-03-01

Full Text Available In this work key concepts on coding division multiple access (CDMA communication systems have been discussed. The sequence selection impact on the performance and capacity of direct sequence CDMA (DS/CDMA systems under AWGN and increasing system loading, as well as under multiple antennas channels was investigated.

Phylogenetic and genome-wide deep-sequencing analyses of canine parvovirus reveal co-infection with field variants and emergence of a recent recombinant strain.

Directory of Open Access Journals (Sweden)

Ruben Pérez

Full Text Available Canine parvovirus (CPV, a fast-evolving single-stranded DNA virus, comprises three antigenic variants (2a, 2b, and 2c with different frequencies and genetic variability among countries. The contribution of co-infection and recombination to the genetic variability of CPV is far from being fully elucidated. Here we took advantage of a natural CPV population, recently formed by the convergence of divergent CPV-2c and CPV-2a strains, to study co-infection and recombination. Complete sequences of the viral coding region of CPV-2a and CPV-2c strains from 40 samples were generated and analyzed using phylogenetic tools. Two samples showed co-infection and were further analyzed by deep sequencing. The sequence profile of one of the samples revealed the presence of CPV-2c and CPV-2a strains that differed at 29 nucleotides. The other sample included a minor CPV-2a strain (13.3% of the viral population and a major recombinant strain (86.7%. The recombinant strain arose from inter-genotypic recombination between CPV-2c and CPV-2a strains within the VP1/VP2 gene boundary. Our findings highlight the importance of deep-sequencing analysis to provide a better understanding of CPV molecular diversity.

Phylogenetic and Genome-Wide Deep-Sequencing Analyses of Canine Parvovirus Reveal Co-Infection with Field Variants and Emergence of a Recent Recombinant Strain

Science.gov (United States)

Pérez, Ruben; Calleros, Lucía; Marandino, Ana; Sarute, Nicolás; Iraola, Gregorio; Grecco, Sofia; Blanc, Hervé; Vignuzzi, Marco; Isakov, Ofer; Shomron, Noam; Carrau, Lucía; Hernández, Martín; Francia, Lourdes; Sosa, Katia; Tomás, Gonzalo; Panzera, Yanina

2014-01-01

Canine parvovirus (CPV), a fast-evolving single-stranded DNA virus, comprises three antigenic variants (2a, 2b, and 2c) with different frequencies and genetic variability among countries. The contribution of co-infection and recombination to the genetic variability of CPV is far from being fully elucidated. Here we took advantage of a natural CPV population, recently formed by the convergence of divergent CPV-2c and CPV-2a strains, to study co-infection and recombination. Complete sequences of the viral coding region of CPV-2a and CPV-2c strains from 40 samples were generated and analyzed using phylogenetic tools. Two samples showed co-infection and were further analyzed by deep sequencing. The sequence profile of one of the samples revealed the presence of CPV-2c and CPV-2a strains that differed at 29 nucleotides. The other sample included a minor CPV-2a strain (13.3% of the viral population) and a major recombinant strain (86.7%). The recombinant strain arose from inter-genotypic recombination between CPV-2c and CPV-2a strains within the VP1/VP2 gene boundary. Our findings highlight the importance of deep-sequencing analysis to provide a better understanding of CPV molecular diversity. PMID:25365348

Profile of microbial communities on carbonate stones of the medieval church of San Leonardo di Siponto (Italy) by Illumina-based deep sequencing.

Science.gov (United States)

Chimienti, Guglielmina; Piredda, Roberta; Pepe, Gabriella; van der Werf, Inez Dorothé; Sabbatini, Luigia; Crecchio, Carmine; Ricciuti, Patrizia; D'Erchia, Anna Maria; Manzari, Caterina; Pesole, Graziano

2016-10-01

Comprehensive studies of the biodiversity of the microbial epilithic community on monuments may provide critical insights for clarifying factors involved in the colonization processes. We carried out a high-throughput investigation of the communities colonizing the medieval church of San Leonardo di Siponto (Italy) by Illumina-based deep sequencing. The metagenomic analysis of sequences revealed the presence of Archaea, Bacteria, and Eukarya. Bacteria were Actinobacteria, Proteobacteria, Bacteroidetes, Cyanobacteria, Chloroflexi, Firmicutes and Candidatus Saccharibacteria. The predominant phylum was Actinobacteria, with the orders Actynomycetales and Rubrobacteriales, represented by the genera Pseudokineococcus, Sporichthya, Blastococcus, Arthrobacter, Geodermatophilus, Friedmanniella, Modestobacter, and Rubrobacter, respectively. Cyanobacteria sequences showing strong similarity with an uncultured bacterium sequence were identified. The presence of the green algae Oocystaceae and Trebuxiaceae was revealed. The microbial diversity was explored at qualitative and quantitative levels, evaluating the richness (the number of operational taxonomic units (OTUs)) and the abundance of reads associated with each OTU. The rarefaction curves approached saturation, suggesting that the majority of OTUs were recovered. The results highlighted a structured community, showing low diversity, made up of extremophile organisms adapted to desiccation and UV radiation. Notably, the microbiome appeared to be composed not only of microorganisms possibly involved in biodeterioration but also of carbonatogenic bacteria, such as those belonging to the genus Arthrobacter, which could be useful in bioconservation. Our investigation demonstrated that molecular tools, and in particular the easy-to-run next-generation sequencing, are powerful to perform a microbiological diagnosis in order to plan restoration and protection strategies.

Draft Genome Sequence of Pseudoalteromonas sp. Strain XI10 Isolated from the Brine-Seawater Interface of Erba Deep in the Red Sea

KAUST Repository

Zhang, Guishan; Haroon, Mohamed; Zhang, Ruifu; Hikmawan, Tyas I.; Stingl, Ulrich

2016-01-01

Pseudoalteromonas sp. strain XI10 was isolated from the brine-seawater interface of Erba Deep in the Red Sea, Saudi Arabia. Here, we present the draft genome sequence of strain XI10, a gammaproteobacterium that synthesizes polysaccharides for biofilm formation when grown in liquid culture.

Draft Genome Sequence of Pseudoalteromonas sp. Strain XI10 Isolated from the Brine-Seawater Interface of Erba Deep in the Red Sea

KAUST Repository

Zhang, Guishan

2016-03-10

Pseudoalteromonas sp. strain XI10 was isolated from the brine-seawater interface of Erba Deep in the Red Sea, Saudi Arabia. Here, we present the draft genome sequence of strain XI10, a gammaproteobacterium that synthesizes polysaccharides for biofilm formation when grown in liquid culture.

Deep Sequencing of 71 Candidate Genes to Characterize Variation Associated with Alcohol Dependence.

Science.gov (United States)

Clark, Shaunna L; McClay, Joseph L; Adkins, Daniel E; Kumar, Gaurav; Aberg, Karolina A; Nerella, Srilaxmi; Xie, Linying; Collins, Ann L; Crowley, James J; Quackenbush, Corey R; Hilliard, Christopher E; Shabalin, Andrey A; Vrieze, Scott I; Peterson, Roseann E; Copeland, William E; Silberg, Judy L; McGue, Matt; Maes, Hermine; Iacono, William G; Sullivan, Patrick F; Costello, Elizabeth J; van den Oord, Edwin J

2017-04-01

Previous genomewide association studies (GWASs) have identified a number of putative risk loci for alcohol dependence (AD). However, only a few loci have replicated and these replicated variants only explain a small proportion of AD risk. Using an innovative approach, the goal of this study was to generate hypotheses about potentially causal variants for AD that can be explored further through functional studies. We employed targeted capture of 71 candidate loci and flanking regions followed by next-generation deep sequencing (mean coverage 78X) in 806 European Americans. Regions included in our targeted capture library were genes identified through published GWAS of alcohol, all human alcohol and aldehyde dehydrogenases, reward system genes including dopaminergic and opioid receptors, prioritized candidate genes based on previous associations, and genes involved in the absorption, distribution, metabolism, and excretion of drugs. We performed single-locus tests to determine if any single variant was associated with AD symptom count. Sets of variants that overlapped with biologically meaningful annotations were tested for association in aggregate. No single, common variant was significantly associated with AD in our study. We did, however, find evidence for association with several variant sets. Two variant sets were significant at the q-value <0.10 level: a genic enhancer for ADHFE1 (p = 1.47 × 10 -5 ; q = 0.019), an alcohol dehydrogenase, and ADORA1 (p = 5.29 × 10 -5 ; q = 0.035), an adenosine receptor that belongs to a G-protein-coupled receptor gene family. To our knowledge, this is the first sequencing study of AD to examine variants in entire genes, including flanking and regulatory regions. We found that in addition to protein coding variant sets, regulatory variant sets may play a role in AD. From these findings, we have generated initial functional hypotheses about how these sets may influence AD. Copyright © 2017 by the Research Society on

Comparison of illumina and 454 deep sequencing in participants failing raltegravir-based antiretroviral therapy.

Directory of Open Access Journals (Sweden)

Jonathan Z Li

Full Text Available The impact of raltegravir-resistant HIV-1 minority variants (MVs on raltegravir treatment failure is unknown. Illumina sequencing offers greater throughput than 454, but sequence analysis tools for viral sequencing are needed. We evaluated Illumina and 454 for the detection of HIV-1 raltegravir-resistant MVs.A5262 was a single-arm study of raltegravir and darunavir/ritonavir in treatment-naïve patients. Pre-treatment plasma was obtained from 5 participants with raltegravir resistance at the time of virologic failure. A control library was created by pooling integrase clones at predefined proportions. Multiplexed sequencing was performed with Illumina and 454 platforms at comparable costs. Illumina sequence analysis was performed with the novel snp-assess tool and 454 sequencing was analyzed with V-Phaser.Illumina sequencing resulted in significantly higher sequence coverage and a 0.095% limit of detection. Illumina accurately detected all MVs in the control library at ≥0.5% and 7/10 MVs expected at 0.1%. 454 sequencing failed to detect any MVs at 0.1% with 5 false positive calls. For MVs detected in the patient samples by both 454 and Illumina, the correlation in the detected variant frequencies was high (R2 = 0.92, P<0.001. Illumina sequencing detected 2.4-fold greater nucleotide MVs and 2.9-fold greater amino acid MVs compared to 454. The only raltegravir-resistant MV detected was an E138K mutation in one participant by Illumina sequencing, but not by 454.In participants of A5262 with raltegravir resistance at virologic failure, baseline raltegravir-resistant MVs were rarely detected. At comparable costs to 454 sequencing, Illumina demonstrated greater depth of coverage, increased sensitivity for detecting HIV MVs, and fewer false positive variant calls.

Effects of Sequences of Cognitions on Group Performance Over Time.

Science.gov (United States)

Molenaar, Inge; Chiu, Ming Ming

2017-04-01

Extending past research showing that sequences of low cognitions (low-level processing of information) and high cognitions (high-level processing of information through questions and elaborations) influence the likelihoods of subsequent high and low cognitions, this study examines whether sequences of cognitions are related to group performance over time; 54 primary school students (18 triads) discussed and wrote an essay about living in another country (32,375 turns of talk). Content analysis and statistical discourse analysis showed that within each lesson, groups with more low cognitions or more sequences of low cognition followed by high cognition added more essay words. Groups with more high cognitions, sequences of low cognition followed by low cognition, or sequences of high cognition followed by an action followed by low cognition, showed different words and sequences, suggestive of new ideas. The links between cognition sequences and group performance over time can inform facilitation and assessment of student discussions.

Numerical prediction and performance experiment in a deep-well centrifugal pump with different impeller outlet width

Science.gov (United States)

Shi, Weidong; Zhou, Ling; Lu, Weigang; Pei, Bing; Lang, Tao

2013-01-01

The existing research of the deep-well centrifugal pump mainly focuses on reduce the manufacturing cost and improve the pump performance, and how to combine above two aspects together is the most difficult and important topic. In this study, the performances of the deep-well centrifugal pump with four different impeller outlet widths are studied by the numerical, theoretical and experimental methods in this paper. Two stages deep-well centrifugal pump equipped with different impellers are simulated employing the commercial CFD software to solve the Navier-Stokes equations for three-dimensional incompressible steady flow. The sensitivity analyses of the grid size and turbulence model have been performed to improve numerical accuracy. The flow field distributions are acquired and compared under the design operating conditions, including the static pressure, turbulence kinetic energy and velocity. The prototype is manufactured and tested to certify the numerical predicted performance. The numerical results of pump performance are higher than the test results, but their change trends have an acceptable agreement with each other. The performance results indicted that the oversize impeller outlet width leads to poor pump performances and increasing shaft power. Changing the performance of deep-well centrifugal pump by alter impeller outlet width is practicable and convenient, which is worth popularizing in the engineering application. The proposed research enhances the theoretical basis of pump design to improve the performance and reduce the manufacturing cost of deep-well centrifugal pump.

Sequence of structures in fine-grained turbidites: Comparison of recent deep-sea and ancient flysch sediments

Science.gov (United States)

Stow, Dorrik A. V.; Shanmugam, Ganapathy

1980-01-01

A comparative study of the sequence of sedimentary structures in ancient and modern fine-grained turbidites is made in three contrasting areas. They are (1) Holocene and Pleistocene deep-sea muds of the Nova Scotian Slope and Rise, (2) Middle Ordovician Sevier Shale of the Valley and Ridge Province of the Southern Appalachians, and (3) Cambro-Ordovician Halifax Slate of the Meguma Group in Nova Scotia. A standard sequence of structures is proposed for fine-grained turbidites. The complete sequence has nine sub-divisions that are here termed T 0 to T 8. "The lower subdivision (T 0) comprises a silt lamina which has a sharp, scoured and load-cast base, internal parallel-lamination and cross-lamination, and a sharp current-lineated or wavy surface with 'fading-ripples' (= Type C etc. …)." (= Type C ripple-drift cross-lamination, Jopling and Walker, 1968). The overlying sequence shows textural and compositional grading through alternating silt and mud laminae. A convolute-laminated sub-division (T 1) is overlain by low-amplitude climbing ripples (T 2), thin regular laminae (T 3), thin indistinct laminae (T 4), and thin wipsy or convolute laminae (T 5). The topmost three divisions, graded mud (T 6), ungraded mud (T 7) and bioturbated mud (T 8), do not have silt laminae but rare patchy silt lenses and silt pseudonodules and a thin zone of micro-burrowing near the upper surface. The proposed sequence is analogous to the Bouma (1962) structural scheme for sandy turbidites and is approximately equivalent to Bouma's (C)DE divisions. The repetition of partial sequences characterizes different parts of the slope/base-of-slope/basin plain environment, and represents deposition from different stages of evolution of a large, muddy, turbidity flow. Microstructural detail and sequence are well preserved in ancient and even slightly metamorphosed sediments. Their recognition is important for determining depositional processes and for palaeoenvironmental interpretation.

DEEP: a general computational framework for predicting enhancers

KAUST Repository

Kleftogiannis, Dimitrios A.

2014-11-05

Transcription regulation in multicellular eukaryotes is orchestrated by a number of DNA functional elements located at gene regulatory regions. Some regulatory regions (e.g. enhancers) are located far away from the gene they affect. Identification of distal regulatory elements is a challenge for the bioinformatics research. Although existing methodologies increased the number of computationally predicted enhancers, performance inconsistency of computational models across different cell-lines, class imbalance within the learning sets and ad hoc rules for selecting enhancer candidates for supervised learning, are some key questions that require further examination. In this study we developed DEEP, a novel ensemble prediction framework. DEEP integrates three components with diverse characteristics that streamline the analysis of enhancer\\'s properties in a great variety of cellular conditions. In our method we train many individual classification models that we combine to classify DNA regions as enhancers or non-enhancers. DEEP uses features derived from histone modification marks or attributes coming from sequence characteristics. Experimental results indicate that DEEP performs better than four state-of-the-art methods on the ENCODE data. We report the first computational enhancer prediction results on FANTOM5 data where DEEP achieves 90.2% accuracy and 90% geometric mean (GM) of specificity and sensitivity across 36 different tissues. We further present results derived using in vivo-derived enhancer data from VISTA database. DEEP-VISTA, when tested on an independent test set, achieved GM of 80.1% and accuracy of 89.64%. DEEP framework is publicly available at http://cbrc.kaust.edu.sa/deep/.

DEEP: a general computational framework for predicting enhancers

KAUST Repository

Kleftogiannis, Dimitrios A.; Kalnis, Panos; Bajic, Vladimir B.

2014-01-01

Transcription regulation in multicellular eukaryotes is orchestrated by a number of DNA functional elements located at gene regulatory regions. Some regulatory regions (e.g. enhancers) are located far away from the gene they affect. Identification of distal regulatory elements is a challenge for the bioinformatics research. Although existing methodologies increased the number of computationally predicted enhancers, performance inconsistency of computational models across different cell-lines, class imbalance within the learning sets and ad hoc rules for selecting enhancer candidates for supervised learning, are some key questions that require further examination. In this study we developed DEEP, a novel ensemble prediction framework. DEEP integrates three components with diverse characteristics that streamline the analysis of enhancer's properties in a great variety of cellular conditions. In our method we train many individual classification models that we combine to classify DNA regions as enhancers or non-enhancers. DEEP uses features derived from histone modification marks or attributes coming from sequence characteristics. Experimental results indicate that DEEP performs better than four state-of-the-art methods on the ENCODE data. We report the first computational enhancer prediction results on FANTOM5 data where DEEP achieves 90.2% accuracy and 90% geometric mean (GM) of specificity and sensitivity across 36 different tissues. We further present results derived using in vivo-derived enhancer data from VISTA database. DEEP-VISTA, when tested on an independent test set, achieved GM of 80.1% and accuracy of 89.64%. DEEP framework is publicly available at http://cbrc.kaust.edu.sa/deep/.

Fine-Grained Energy and Performance Profiling framework for Deep Convolutional Neural Networks

OpenAIRE

Rodrigues, Crefeda Faviola; Riley, Graham; Lujan, Mikel

2018-01-01

There is a huge demand for on-device execution of deep learning algorithms on mobile and embedded platforms. These devices present constraints on the application due to limited resources and power. Hence, developing energy-efficient solutions to address this issue will require innovation in algorithmic design, software and hardware. Such innovation requires benchmarking and characterization of Deep Neural Networks based on performance and energy-consumption alongside accuracy. However, curren...

DeepLoc: prediction of protein subcellular localization using deep learning

DEFF Research Database (Denmark)

Almagro Armenteros, Jose Juan; Sønderby, Casper Kaae; Sønderby, Søren Kaae

2017-01-01

The prediction of eukaryotic protein subcellular localization is a well-studied topic in bioinformatics due to its relevance in proteomics research. Many machine learning methods have been successfully applied in this task, but in most of them, predictions rely on annotation of homologues from...... knowledge databases. For novel proteins where no annotated homologues exist, and for predicting the effects of sequence variants, it is desirable to have methods for predicting protein properties from sequence information only. Here, we present a prediction algorithm using deep neural networks to predict...... current state-of-the-art algorithms, including those relying on homology information. The method is available as a web server at http://www.cbs.dtu.dk/services/DeepLoc . Example code is available at https://github.com/JJAlmagro/subcellular_localization . The dataset is available at http...

Deep sequencing of foot-and-mouth disease virus reveals RNA sequences involved in genome packaging.

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Logan, Grace; Newman, Joseph; Wright, Caroline F; Lasecka-Dykes, Lidia; Haydon, Daniel T; Cottam, Eleanor M; Tuthill, Tobias J

2017-10-18

Non-enveloped viruses protect their genomes by packaging them into an outer shell or capsid of virus-encoded proteins. Packaging and capsid assembly in RNA viruses can involve interactions between capsid proteins and secondary structures in the viral genome as exemplified by the RNA bacteriophage MS2 and as proposed for other RNA viruses of plants, animals and human. In the picornavirus family of non-enveloped RNA viruses, the requirements for genome packaging remain poorly understood. Here we show a novel and simple approach to identify predicted RNA secondary structures involved in genome packaging in the picornavirus foot-and-mouth disease virus (FMDV). By interrogating deep sequencing data generated from both packaged and unpackaged populations of RNA we have determined multiple regions of the genome with constrained variation in the packaged population. Predicted secondary structures of these regions revealed stem loops with conservation of structure and a common motif at the loop. Disruption of these features resulted in attenuation of virus growth in cell culture due to a reduction in assembly of mature virions. This study provides evidence for the involvement of predicted RNA structures in picornavirus packaging and offers a readily transferable methodology for identifying packaging requirements in many other viruses. Importance In order to transmit their genetic material to a new host, non-enveloped viruses must protect their genomes by packaging them into an outer shell or capsid of virus-encoded proteins. For many non-enveloped RNA viruses the requirements for this critical part of the viral life cycle remain poorly understood. We have identified RNA sequences involved in genome packaging of the picornavirus foot-and-mouth disease virus. This virus causes an economically devastating disease of livestock affecting both the developed and developing world. The experimental methods developed to carry out this work are novel, simple and transferable to the

A visual tracking method based on deep learning without online model updating

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Tang, Cong; Wang, Yicheng; Feng, Yunsong; Zheng, Chao; Jin, Wei

2018-02-01

The paper proposes a visual tracking method based on deep learning without online model updating. In consideration of the advantages of deep learning in feature representation, deep model SSD (Single Shot Multibox Detector) is used as the object extractor in the tracking model. Simultaneously, the color histogram feature and HOG (Histogram of Oriented Gradient) feature are combined to select the tracking object. In the process of tracking, multi-scale object searching map is built to improve the detection performance of deep detection model and the tracking efficiency. In the experiment of eight respective tracking video sequences in the baseline dataset, compared with six state-of-the-art methods, the method in the paper has better robustness in the tracking challenging factors, such as deformation, scale variation, rotation variation, illumination variation, and background clutters, moreover, its general performance is better than other six tracking methods.

Sequence specific motor performance gains after memory consolidation in children and adolescents.

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Shoshi Dorfberger

Full Text Available Memory consolidation for a trained sequence of finger opposition movements, in 9- and 12-year-old children, was recently found to be significantly less susceptible to interference by a subsequent training experience, compared to that of 17-year-olds. It was suggested that, in children, the experience of training on any sequence of finger movements may affect the performance of the sequence elements, component movements, rather than the sequence as a unit; the latter has been implicated in the learning of the task by adults. This hypothesis implied a possible childhood advantage in the ability to transfer the gains from a trained to the reversed, untrained, sequence of movements. Here we report the results of transfer tests undertaken to test this proposal in 9-, 12-, and 17-year-olds after training in the finger-to-thumb opposition sequence (FOS learning task. Our results show that the performance gains in the trained sequence partially transferred from the left, trained hand, to the untrained hand at 48-hours after a single training session in the three age-groups tested. However, there was very little transfer of the gains from the trained to the untrained, reversed, sequence performed by either hand. The results indicate sequence specific post-training gains in FOS performance, as opposed to a general improvement in performance of the individual, component, movements that comprised both the trained and untrained sequences. These results do not support the proposal that the reduced susceptibility to interference, in children before adolescence, reflects a difference in movement syntax representation after training.

Sequence specific motor performance gains after memory consolidation in children and adolescents.

Science.gov (United States)

Dorfberger, Shoshi; Adi-Japha, Esther; Karni, Avi

2012-01-01

Memory consolidation for a trained sequence of finger opposition movements, in 9- and 12-year-old children, was recently found to be significantly less susceptible to interference by a subsequent training experience, compared to that of 17-year-olds. It was suggested that, in children, the experience of training on any sequence of finger movements may affect the performance of the sequence elements, component movements, rather than the sequence as a unit; the latter has been implicated in the learning of the task by adults. This hypothesis implied a possible childhood advantage in the ability to transfer the gains from a trained to the reversed, untrained, sequence of movements. Here we report the results of transfer tests undertaken to test this proposal in 9-, 12-, and 17-year-olds after training in the finger-to-thumb opposition sequence (FOS) learning task. Our results show that the performance gains in the trained sequence partially transferred from the left, trained hand, to the untrained hand at 48-hours after a single training session in the three age-groups tested. However, there was very little transfer of the gains from the trained to the untrained, reversed, sequence performed by either hand. The results indicate sequence specific post-training gains in FOS performance, as opposed to a general improvement in performance of the individual, component, movements that comprised both the trained and untrained sequences. These results do not support the proposal that the reduced susceptibility to interference, in children before adolescence, reflects a difference in movement syntax representation after training.

Genome-wide analyses of long noncoding RNA expression profiles correlated with radioresistance in nasopharyngeal carcinoma via next-generation deep sequencing.

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Li, Guo; Liu, Yong; Liu, Chao; Su, Zhongwu; Ren, Shuling; Wang, Yunyun; Deng, Tengbo; Huang, Donghai; Tian, Yongquan; Qiu, Yuanzheng

2016-09-06

Radioresistance is one of the major factors limiting the therapeutic efficacy and prognosis of patients with nasopharyngeal carcinoma (NPC). Accumulating evidence has suggested that aberrant expression of long noncoding RNAs (lncRNAs) contributes to cancer progression. Therefore, here we identified lncRNAs associated with radioresistance in NPC. The differential expression profiles of lncRNAs associated with NPC radioresistance were constructed by next-generation deep sequencing by comparing radioresistant NPC cells with their parental cells. LncRNA-related mRNAs were predicted and analyzed using bioinformatics algorithms compared with the mRNA profiles related to radioresistance obtained in our previous study. Several lncRNAs and associated mRNAs were validated in established NPC radioresistant cell models and NPC tissues. By comparison between radioresistant CNE-2-Rs and parental CNE-2 cells by next-generation deep sequencing, a total of 781 known lncRNAs and 2054 novel lncRNAs were annotated. The top five upregulated and downregulated known/novel lncRNAs were detected using quantitative real-time reverse transcription-polymerase chain reaction, and 7/10 known lncRNAs and 3/10 novel lncRNAs were demonstrated to have significant differential expression trends that were the same as those predicted by deep sequencing. From the prediction process, 13 pairs of lncRNAs and their associated genes were acquired, and the prediction trends of three pairs were validated in both radioresistant CNE-2-Rs and 6-10B-Rs cell lines, including lncRNA n373932 and SLITRK5, n409627 and PRSS12, and n386034 and RIMKLB. LncRNA n373932 and its related SLITRK5 showed dramatic expression changes in post-irradiation radioresistant cells and a negative expression correlation in NPC tissues (R = -0.595, p < 0.05). Our study provides an overview of the expression profiles of radioresistant lncRNAs and potentially related mRNAs, which will facilitate future investigations into the

Protein Solvent-Accessibility Prediction by a Stacked Deep Bidirectional Recurrent Neural Network

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Buzhong Zhang

2018-05-01

Full Text Available Residue solvent accessibility is closely related to the spatial arrangement and packing of residues. Predicting the solvent accessibility of a protein is an important step to understand its structure and function. In this work, we present a deep learning method to predict residue solvent accessibility, which is based on a stacked deep bidirectional recurrent neural network applied to sequence profiles. To capture more long-range sequence information, a merging operator was proposed when bidirectional information from hidden nodes was merged for outputs. Three types of merging operators were used in our improved model, with a long short-term memory network performing as a hidden computing node. The trained database was constructed from 7361 proteins extracted from the PISCES server using a cut-off of 25% sequence identity. Sequence-derived features including position-specific scoring matrix, physical properties, physicochemical characteristics, conservation score and protein coding were used to represent a residue. Using this method, predictive values of continuous relative solvent-accessible area were obtained, and then, these values were transformed into binary states with predefined thresholds. Our experimental results showed that our deep learning method improved prediction quality relative to current methods, with mean absolute error and Pearson’s correlation coefficient values of 8.8% and 74.8%, respectively, on the CB502 dataset and 8.2% and 78%, respectively, on the Manesh215 dataset.

Protein Solvent-Accessibility Prediction by a Stacked Deep Bidirectional Recurrent Neural Network.

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Zhang, Buzhong; Li, Linqing; Lü, Qiang

2018-05-25

Residue solvent accessibility is closely related to the spatial arrangement and packing of residues. Predicting the solvent accessibility of a protein is an important step to understand its structure and function. In this work, we present a deep learning method to predict residue solvent accessibility, which is based on a stacked deep bidirectional recurrent neural network applied to sequence profiles. To capture more long-range sequence information, a merging operator was proposed when bidirectional information from hidden nodes was merged for outputs. Three types of merging operators were used in our improved model, with a long short-term memory network performing as a hidden computing node. The trained database was constructed from 7361 proteins extracted from the PISCES server using a cut-off of 25% sequence identity. Sequence-derived features including position-specific scoring matrix, physical properties, physicochemical characteristics, conservation score and protein coding were used to represent a residue. Using this method, predictive values of continuous relative solvent-accessible area were obtained, and then, these values were transformed into binary states with predefined thresholds. Our experimental results showed that our deep learning method improved prediction quality relative to current methods, with mean absolute error and Pearson's correlation coefficient values of 8.8% and 74.8%, respectively, on the CB502 dataset and 8.2% and 78%, respectively, on the Manesh215 dataset.

Deep Space Spaceflight: The Challenge of Crew Performance in Autonomous Operations

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Thaxton, S. S.; Williams, T. J.; Norsk, P.; Zwart, S.; Crucian, B.; Antonsen, E. L.

2018-02-01

Distance from Earth and limited communications in future missions will increase the demands for crew autonomy and dependence on automation, and Deep Space Gateway presents an opportunity to study the impacts of these increased demands on human performance.

Cough event classification by pretrained deep neural network.

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Liu, Jia-Ming; You, Mingyu; Wang, Zheng; Li, Guo-Zheng; Xu, Xianghuai; Qiu, Zhongmin

2015-01-01

Cough is an essential symptom in respiratory diseases. In the measurement of cough severity, an accurate and objective cough monitor is expected by respiratory disease society. This paper aims to introduce a better performed algorithm, pretrained deep neural network (DNN), to the cough classification problem, which is a key step in the cough monitor. The deep neural network models are built from two steps, pretrain and fine-tuning, followed by a Hidden Markov Model (HMM) decoder to capture tamporal information of the audio signals. By unsupervised pretraining a deep belief network, a good initialization for a deep neural network is learned. Then the fine-tuning step is a back propogation tuning the neural network so that it can predict the observation probability associated with each HMM states, where the HMM states are originally achieved by force-alignment with a Gaussian Mixture Model Hidden Markov Model (GMM-HMM) on the training samples. Three cough HMMs and one noncough HMM are employed to model coughs and noncoughs respectively. The final decision is made based on viterbi decoding algorihtm that generates the most likely HMM sequence for each sample. A sample is labeled as cough if a cough HMM is found in the sequence. The experiments were conducted on a dataset that was collected from 22 patients with respiratory diseases. Patient dependent (PD) and patient independent (PI) experimental settings were used to evaluate the models. Five criteria, sensitivity, specificity, F1, macro average and micro average are shown to depict different aspects of the models. From overall evaluation criteria, the DNN based methods are superior to traditional GMM-HMM based method on F1 and micro average with maximal 14% and 11% error reduction in PD and 7% and 10% in PI, meanwhile keep similar performances on macro average. They also surpass GMM-HMM model on specificity with maximal 14% error reduction on both PD and PI. In this paper, we tried pretrained deep neural network in

Advancing Eucalyptus genomics: identification and sequencing of lignin biosynthesis genes from deep-coverage BAC libraries

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Kudrna David

2011-03-01

Full Text Available Abstract Background Eucalyptus species are among the most planted hardwoods in the world because of their rapid growth, adaptability and valuable wood properties. The development and integration of genomic resources into breeding practice will be increasingly important in the decades to come. Bacterial artificial chromosome (BAC libraries are key genomic tools that enable positional cloning of important traits, synteny evaluation, and the development of genome framework physical maps for genetic linkage and genome sequencing. Results We describe the construction and characterization of two deep-coverage BAC libraries EG_Ba and EG_Bb obtained from nuclear DNA fragments of E. grandis (clone BRASUZ1 digested with HindIII and BstYI, respectively. Genome coverages of 17 and 15 haploid genome equivalents were estimated for EG_Ba and EG_Bb, respectively. Both libraries contained large inserts, with average sizes ranging from 135 Kb (Eg_Bb to 157 Kb (Eg_Ba, very low extra-nuclear genome contamination providing a probability of finding a single copy gene ≥ 99.99%. Libraries were screened for the presence of several genes of interest via hybridizations to high-density BAC filters followed by PCR validation. Five selected BAC clones were sequenced and assembled using the Roche GS FLX technology providing the whole sequence of the E. grandis chloroplast genome, and complete genomic sequences of important lignin biosynthesis genes. Conclusions The two E. grandis BAC libraries described in this study represent an important milestone for the advancement of Eucalyptus genomics and forest tree research. These BAC resources have a highly redundant genome coverage (> 15×, contain large average inserts and have a very low percentage of clones with organellar DNA or empty vectors. These publicly available BAC libraries are thus suitable for a broad range of applications in genetic and genomic research in Eucalyptus and possibly in related species of Myrtaceae

DEEPre: sequence-based enzyme EC number prediction by deep learning

KAUST Repository

Li, Yu

2017-10-20

Annotation of enzyme function has a broad range of applications, such as metagenomics, industrial biotechnology, and diagnosis of enzyme deficiency-caused diseases. However, the time and resource required make it prohibitively expensive to experimentally determine the function of every enzyme. Therefore, computational enzyme function prediction has become increasingly important. In this paper, we develop such an approach, determining the enzyme function by predicting the Enzyme Commission number.We propose an end-to-end feature selection and classification model training approach, as well as an automatic and robust feature dimensionality uniformization method, DEEPre, in the field of enzyme function prediction. Instead of extracting manuallycrafted features from enzyme sequences, our model takes the raw sequence encoding as inputs, extracting convolutional and sequential features from the raw encoding based on the classification result to directly improve the prediction performance. The thorough cross-fold validation experiments conducted on two large-scale datasets show that DEEPre improves the prediction performance over the previous state-of-the-art methods. In addition, our server outperforms five other servers in determining the main class of enzymes on a separate low-homology dataset. Two case studies demonstrate DEEPre\\'s ability to capture the functional difference of enzyme isoforms.The server could be accessed freely at http://www.cbrc.kaust.edu.sa/DEEPre.

DEEPre: sequence-based enzyme EC number prediction by deep learning

KAUST Repository

Li, Yu; Wang, Sheng; Umarov, Ramzan; Xie, Bingqing; Fan, Ming; Li, Lihua; Gao, Xin

2017-01-01

Annotation of enzyme function has a broad range of applications, such as metagenomics, industrial biotechnology, and diagnosis of enzyme deficiency-caused diseases. However, the time and resource required make it prohibitively expensive to experimentally determine the function of every enzyme. Therefore, computational enzyme function prediction has become increasingly important. In this paper, we develop such an approach, determining the enzyme function by predicting the Enzyme Commission number.We propose an end-to-end feature selection and classification model training approach, as well as an automatic and robust feature dimensionality uniformization method, DEEPre, in the field of enzyme function prediction. Instead of extracting manuallycrafted features from enzyme sequences, our model takes the raw sequence encoding as inputs, extracting convolutional and sequential features from the raw encoding based on the classification result to directly improve the prediction performance. The thorough cross-fold validation experiments conducted on two large-scale datasets show that DEEPre improves the prediction performance over the previous state-of-the-art methods. In addition, our server outperforms five other servers in determining the main class of enzymes on a separate low-homology dataset. Two case studies demonstrate DEEPre's ability to capture the functional difference of enzyme isoforms.The server could be accessed freely at http://www.cbrc.kaust.edu.sa/DEEPre.

Simultaneous identification of DNA and RNA viruses present in pig faeces using process-controlled deep sequencing.

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Jana Sachsenröder

Full Text Available BACKGROUND: Animal faeces comprise a community of many different microorganisms including bacteria and viruses. Only scarce information is available about the diversity of viruses present in the faeces of pigs. Here we describe a protocol, which was optimized for the purification of the total fraction of viral particles from pig faeces. The genomes of the purified DNA and RNA viruses were simultaneously amplified by PCR and subjected to deep sequencing followed by bioinformatic analyses. The efficiency of the method was monitored using a process control consisting of three bacteriophages (T4, M13 and MS2 with different morphology and genome types. Defined amounts of the bacteriophages were added to the sample and their abundance was assessed by quantitative PCR during the preparation procedure. RESULTS: The procedure was applied to a pooled faecal sample of five pigs. From this sample, 69,613 sequence reads were generated. All of the added bacteriophages were identified by sequence analysis of the reads. In total, 7.7% of the reads showed significant sequence identities with published viral sequences. They mainly originated from bacteriophages (73.9% and mammalian viruses (23.9%; 0.8% of the sequences showed identities to plant viruses. The most abundant detected porcine viruses were kobuvirus, rotavirus C, astrovirus, enterovirus B, sapovirus and picobirnavirus. In addition, sequences with identities to the chimpanzee stool-associated circular ssDNA virus were identified. Whole genome analysis indicates that this virus, tentatively designated as pig stool-associated circular ssDNA virus (PigSCV, represents a novel pig virus. CONCLUSION: The established protocol enables the simultaneous detection of DNA and RNA viruses in pig faeces including the identification of so far unknown viruses. It may be applied in studies investigating aetiology, epidemiology and ecology of diseases. The implemented process control serves as quality control, ensures

Genetic diversity of archaea in deep-sea hydrothermal vent environments.

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Takai, K; Horikoshi, K

1999-08-01

Molecular phylogenetic analysis of naturally occurring archaeal communities in deep-sea hydrothermal vent environments was carried out by PCR-mediated small subunit rRNA gene (SSU rDNA) sequencing. As determined through partial sequencing of rDNA clones amplified with archaea-specific primers, the archaeal populations in deep-sea hydrothermal vent environments showed a great genetic diversity, and most members of these populations appeared to be uncultivated and unidentified organisms. In the phylogenetic analysis, a number of rDNA sequences obtained from deep-sea hydrothermal vents were placed in deep lineages of the crenarchaeotic phylum prior to the divergence of cultivated thermophilic members of the crenarchaeota or between thermophilic members of the euryarchaeota and members of the methanogen-halophile clade. Whole cell in situ hybridization analysis suggested that some microorganisms of novel phylotypes predicted by molecular phylogenetic analysis were likely present in deep-sea hydrothermal vent environments. These findings expand our view of the genetic diversity of archaea in deep-sea hydrothermal vent environments and of the phylogenetic organization of archaea.

Transcriptome analysis of the model protozoan, Tetrahymena thermophila, using Deep RNA sequencing.

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Jie Xiong

Full Text Available BACKGROUND: The ciliated protozoan Tetrahymena thermophila is a well-studied single-celled eukaryote model organism for cellular and molecular biology. However, the lack of extensive T. thermophila cDNA libraries or a large expressed sequence tag (EST database limited the quality of the original genome annotation. METHODOLOGY/PRINCIPAL FINDINGS: This RNA-seq study describes the first deep sequencing analysis of the T. thermophila transcriptome during the three major stages of the life cycle: growth, starvation and conjugation. Uniquely mapped reads covered more than 96% of the 24,725 predicted gene models in the somatic genome. More than 1,000 new transcribed regions were identified. The great dynamic range of RNA-seq allowed detection of a nearly six order-of-magnitude range of measurable gene expression orchestrated by this cell. RNA-seq also allowed the first prediction of transcript untranslated regions (UTRs and an updated (larger size estimate of the T. thermophila transcriptome: 57 Mb, or about 55% of the somatic genome. Our study identified nearly 1,500 alternative splicing (AS events distributed over 5.2% of T. thermophila genes. This percentage represents a two order-of-magnitude increase over previous EST-based estimates in Tetrahymena. Evidence of stage-specific regulation of alternative splicing was also obtained. Finally, our study allowed us to completely confirm about 26.8% of the genes originally predicted by the gene finder, to correct coding sequence boundaries and intron-exon junctions for about a third, and to reassign microarray probes and correct earlier microarray data. CONCLUSIONS/SIGNIFICANCE: RNA-seq data significantly improve the genome annotation and provide a fully comprehensive view of the global transcriptome of T. thermophila. To our knowledge, 5.2% of T. thermophila genes with AS is the highest percentage of genes showing AS reported in a unicellular eukaryote. Tetrahymena thus becomes an excellent unicellular

Intelligent fault diagnosis of rolling bearings using an improved deep recurrent neural network

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Jiang, Hongkai; Li, Xingqiu; Shao, Haidong; Zhao, Ke

2018-06-01

Traditional intelligent fault diagnosis methods for rolling bearings heavily depend on manual feature extraction and feature selection. For this purpose, an intelligent deep learning method, named the improved deep recurrent neural network (DRNN), is proposed in this paper. Firstly, frequency spectrum sequences are used as inputs to reduce the input size and ensure good robustness. Secondly, DRNN is constructed by the stacks of the recurrent hidden layer to automatically extract the features from the input spectrum sequences. Thirdly, an adaptive learning rate is adopted to improve the training performance of the constructed DRNN. The proposed method is verified with experimental rolling bearing data, and the results confirm that the proposed method is more effective than traditional intelligent fault diagnosis methods.

Genome-wide detection and analysis of hippocampus core promoters using DeepCAGE

DEFF Research Database (Denmark)

Valen, Eivind; Pascarella, Giovanni; Chalk, Alistair

2009-01-01

in a given tissue. Here, we present a new method for high-throughput sequencing of 5' cDNA tags-DeepCAGE: merging the Cap Analysis of Gene Expression method with ultra-high-throughput sequence technology. We apply DeepCAGE to characterize 1.4 million sequenced TSS from mouse hippocampus and reveal a wealth...

Training Sequences and their Effects on Task Performance and User Outcomes

DEFF Research Database (Denmark)

Sanford, Clive Carlton

2007-01-01

This article introduces the concept of information technology (IT) training sequencesand examines how sequencing of conceptual and procedural training impact IT task performance, user satisfaction and users' self-efficacy. Using assimilation theory, we develop four hypotheses related to training...... sequences. These hypotheses were then tested in a database design context using a quasi-experimental study involving student subjects. Empirical results demonstrate improved far-transfer andnear-transfer task performance and higher self-efficacy for subjects trained in the conceptual-procedural sequence vs...

Personalized mapping of the deep brain with a white matter attenuated inversion recovery (WAIR) sequence at 1.5-tesla: Experience based on a series of 156 patients.

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Zerroug, A; Gabrillargues, J; Coll, G; Vassal, F; Jean, B; Chabert, E; Claise, B; Khalil, T; Sakka, L; Feschet, F; Durif, F; Boyer, L; Coste, J; Lemaire, J-J

2016-08-01

Deep brain mapping has been proposed for direct targeting in stereotactic functional surgery, aiming to personalize electrode implantation according to individual MRI anatomy without atlas or statistical template. We report our clinical experience of direct targeting in a series of 156 patients operated on using a dedicated Inversion Recovery Turbo Spin Echo sequence at 1.5-tesla, called White Matter Attenuated Inversion Recovery (WAIR). After manual contouring of all pertinent structures and 3D planning of trajectories, 312 DBS electrodes were implanted. Detailed anatomy of close neighbouring structures, whether gray nuclei or white matter regions, was identified during each planning procedure. We gathered the experience of these 312 deep brain mappings and elaborated consistent procedures of anatomical MRI mapping for pallidal, subthalamic and ventral thalamic regions. We studied the number of times the central track anatomically optimized was selected for implantation of definitive electrodes. WAIR sequence provided high-quality images of most common functional targets, successfully used for pure direct stereotactic targeting: the central track corresponding to the optimized primary anatomical trajectory was chosen for implantation of definitive electrodes in 90.38%. WAIR sequence is anatomically reliable, enabling precise deep brain mapping and direct stereotactic targeting under routine clinical conditions. Copyright © 2016 Elsevier Masson SAS. All rights reserved.

AMID: Accurate Magnetic Indoor Localization Using Deep Learning

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Namkyoung Lee

2018-05-01

Full Text Available Geomagnetic-based indoor positioning has drawn a great attention from academia and industry due to its advantage of being operable without infrastructure support and its reliable signal characteristics. However, it must overcome the problems of ambiguity that originate with the nature of geomagnetic data. Most studies manage this problem by incorporating particle filters along with inertial sensors. However, they cannot yield reliable positioning results because the inertial sensors in smartphones cannot precisely predict the movement of users. There have been attempts to recognize the magnetic sequence pattern, but these attempts are proven only in a one-dimensional space, because magnetic intensity fluctuates severely with even a slight change of locations. This paper proposes accurate magnetic indoor localization using deep learning (AMID, an indoor positioning system that recognizes magnetic sequence patterns using a deep neural network. Features are extracted from magnetic sequences, and then the deep neural network is used for classifying the sequences by patterns that are generated by nearby magnetic landmarks. Locations are estimated by detecting the landmarks. AMID manifested the proposed features and deep learning as an outstanding classifier, revealing the potential of accurate magnetic positioning with smartphone sensors alone. The landmark detection accuracy was over 80% in a two-dimensional environment.

Protein Secondary Structure Prediction Using Deep Convolutional Neural Fields.

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Wang, Sheng; Peng, Jian; Ma, Jianzhu; Xu, Jinbo

2016-01-11

Protein secondary structure (SS) prediction is important for studying protein structure and function. When only the sequence (profile) information is used as input feature, currently the best predictors can obtain ~80% Q3 accuracy, which has not been improved in the past decade. Here we present DeepCNF (Deep Convolutional Neural Fields) for protein SS prediction. DeepCNF is a Deep Learning extension of Conditional Neural Fields (CNF), which is an integration of Conditional Random Fields (CRF) and shallow neural networks. DeepCNF can model not only complex sequence-structure relationship by a deep hierarchical architecture, but also interdependency between adjacent SS labels, so it is much more powerful than CNF. Experimental results show that DeepCNF can obtain ~84% Q3 accuracy, ~85% SOV score, and ~72% Q8 accuracy, respectively, on the CASP and CAMEO test proteins, greatly outperforming currently popular predictors. As a general framework, DeepCNF can be used to predict other protein structure properties such as contact number, disorder regions, and solvent accessibility.

Equivalent drawbead performance in deep drawing simulations

NARCIS (Netherlands)

Meinders, Vincent T.; Geijselaers, Hubertus J.M.; Huetink, Han

1999-01-01

Drawbeads are applied in the deep drawing process to improve the control of the material flow during the forming operation. In simulations of the deep drawing process these drawbeads can be replaced by an equivalent drawbead model. In this paper the usage of an equivalent drawbead model in the

A computational approach to distinguish somatic vs. germline origin of genomic alterations from deep sequencing of cancer specimens without a matched normal.

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James X Sun

2018-02-01

Full Text Available A key constraint in genomic testing in oncology is that matched normal specimens are not commonly obtained in clinical practice. Thus, while well-characterized genomic alterations do not require normal tissue for interpretation, a significant number of alterations will be unknown in whether they are germline or somatic, in the absence of a matched normal control. We introduce SGZ (somatic-germline-zygosity, a computational method for predicting somatic vs. germline origin and homozygous vs. heterozygous or sub-clonal state of variants identified from deep massively parallel sequencing (MPS of cancer specimens. The method does not require a patient matched normal control, enabling broad application in clinical research. SGZ predicts the somatic vs. germline status of each alteration identified by modeling the alteration's allele frequency (AF, taking into account the tumor content, tumor ploidy, and the local copy number. Accuracy of the prediction depends on the depth of sequencing and copy number model fit, which are achieved in our clinical assay by sequencing to high depth (>500x using MPS, covering 394 cancer-related genes and over 3,500 genome-wide single nucleotide polymorphisms (SNPs. Calls are made using a statistic based on read depth and local variability of SNP AF. To validate the method, we first evaluated performance on samples from 30 lung and colon cancer patients, where we sequenced tumors and matched normal tissue. We examined predictions for 17 somatic hotspot mutations and 20 common germline SNPs in 20,182 clinical cancer specimens. To assess the impact of stromal admixture, we examined three cell lines, which were titrated with their matched normal to six levels (10-75%. Overall, predictions were made in 85% of cases, with 95-99% of variants predicted correctly, a significantly superior performance compared to a basic approach based on AF alone. We then applied the SGZ method to the COSMIC database of known somatic variants

Using Deep Learning Model for Meteorological Satellite Cloud Image Prediction

Science.gov (United States)

Su, X.

2017-12-01

A satellite cloud image contains much weather information such as precipitation information. Short-time cloud movement forecast is important for precipitation forecast and is the primary means for typhoon monitoring. The traditional methods are mostly using the cloud feature matching and linear extrapolation to predict the cloud movement, which makes that the nonstationary process such as inversion and deformation during the movement of the cloud is basically not considered. It is still a hard task to predict cloud movement timely and correctly. As deep learning model could perform well in learning spatiotemporal features, to meet this challenge, we could regard cloud image prediction as a spatiotemporal sequence forecasting problem and introduce deep learning model to solve this problem. In this research, we use a variant of Gated-Recurrent-Unit(GRU) that has convolutional structures to deal with spatiotemporal features and build an end-to-end model to solve this forecast problem. In this model, both the input and output are spatiotemporal sequences. Compared to Convolutional LSTM(ConvLSTM) model, this model has lower amount of parameters. We imply this model on GOES satellite data and the model perform well.

Discrepancy between Hepatitis C Virus Genotypes and NS4-Based Serotypes: Association with Their Subgenomic Sequences

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Nan Nwe Win

2017-01-01

Full Text Available Determination of hepatitis C virus (HCV genotypes plays an important role in the direct-acting agent era. Discrepancies between HCV genotyping and serotyping assays are occasionally observed. Eighteen samples with discrepant results between genotyping and serotyping methods were analyzed. HCV serotyping and genotyping were based on the HCV nonstructural 4 (NS4 region and 5′-untranslated region (5′-UTR, respectively. HCV core and NS4 regions were chosen to be sequenced and were compared with the genotyping and serotyping results. Deep sequencing was also performed for the corresponding HCV NS4 regions. Seventeen out of 18 discrepant samples could be sequenced by the Sanger method. Both HCV core and NS4 sequences were concordant with that of genotyping in the 5′-UTR in all 17 samples. In cloning analysis of the HCV NS4 region, there were several amino acid variations, but each sequence was much closer to the peptide with the same genotype. Deep sequencing revealed that minor clones with different subgenotypes existed in two of the 17 samples. Genotyping by genome amplification showed high consistency, while several false reactions were detected by serotyping. The deep sequencing method also provides accurate genotyping results and may be useful for analyzing discrepant cases. HCV genotyping should be correctly determined before antiviral treatment.

Deep-learning: investigating deep neural networks hyper-parameters and comparison of performance to shallow methods for modeling bioactivity data.

Science.gov (United States)

Koutsoukas, Alexios; Monaghan, Keith J; Li, Xiaoli; Huan, Jun

2017-06-28

In recent years, research in artificial neural networks has resurged, now under the deep-learning umbrella, and grown extremely popular. Recently reported success of DL techniques in crowd-sourced QSAR and predictive toxicology competitions has showcased these methods as powerful tools in drug-discovery and toxicology research. The aim of this work was dual, first large number of hyper-parameter configurations were explored to investigate how they affect the performance of DNNs and could act as starting points when tuning DNNs and second their performance was compared to popular methods widely employed in the field of cheminformatics namely Naïve Bayes, k-nearest neighbor, random forest and support vector machines. Moreover, robustness of machine learning methods to different levels of artificially introduced noise was assessed. The open-source Caffe deep-learning framework and modern NVidia GPU units were utilized to carry out this study, allowing large number of DNN configurations to be explored. We show that feed-forward deep neural networks are capable of achieving strong classification performance and outperform shallow methods across diverse activity classes when optimized. Hyper-parameters that were found to play critical role are the activation function, dropout regularization, number hidden layers and number of neurons. When compared to the rest methods, tuned DNNs were found to statistically outperform, with p value <0.01 based on Wilcoxon statistical test. DNN achieved on average MCC units of 0.149 higher than NB, 0.092 than kNN, 0.052 than SVM with linear kernel, 0.021 than RF and finally 0.009 higher than SVM with radial basis function kernel. When exploring robustness to noise, non-linear methods were found to perform well when dealing with low levels of noise, lower than or equal to 20%, however when dealing with higher levels of noise, higher than 30%, the Naïve Bayes method was found to perform well and even outperform at the highest level of

Characterization and Development of EST-SSRs by Deep Transcriptome Sequencing in Chinese Cabbage (Brassica rapa L. ssp. pekinensis

Directory of Open Access Journals (Sweden)

Qian Ding

2015-01-01

Full Text Available Simple sequence repeats (SSRs are among the most important markers for population analysis and have been widely used in plant genetic mapping and molecular breeding. Expressed sequence tag-SSR (EST-SSR markers, located in the coding regions, are potentially more efficient for QTL mapping, gene targeting, and marker-assisted breeding. In this study, we investigated 51,694 nonredundant unigenes, assembled from clean reads from deep transcriptome sequencing with a Solexa/Illumina platform, for identification and development of EST-SSRs in Chinese cabbage. In total, 10,420 EST-SSRs with over 12 bp were identified and characterized, among which 2744 EST-SSRs are new and 2317 are known ones showing polymorphism with previously reported SSRs. A total of 7877 PCR primer pairs for 1561 EST-SSR loci were designed, and primer pairs for twenty-four EST-SSRs were selected for primer evaluation. In nineteen EST-SSR loci (79.2%, amplicons were successfully generated with high quality. Seventeen (89.5% showed polymorphism in twenty-four cultivars of Chinese cabbage. The polymorphic alleles of each polymorphic locus were sequenced, and the results showed that most polymorphisms were due to variations of SSR repeat motifs. The EST-SSRs identified and characterized in this study have important implications for developing new tools for genetics and molecular breeding in Chinese cabbage.

Key roles for freshwater Actinobacteria revealed by deep metagenomic sequencing.

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Ghai, Rohit; Mizuno, Carolina Megumi; Picazo, Antonio; Camacho, Antonio; Rodriguez-Valera, Francisco

2014-12-01

Freshwater ecosystems are critical but fragile environments directly affecting society and its welfare. However, our understanding of genuinely freshwater microbial communities, constrained by our capacity to manipulate its prokaryotic participants in axenic cultures, remains very rudimentary. Even the most abundant components, freshwater Actinobacteria, remain largely unknown. Here, applying deep metagenomic sequencing to the microbial community of a freshwater reservoir, we were able to circumvent this traditional bottleneck and reconstruct de novo seven distinct streamlined actinobacterial genomes. These genomes represent three new groups of photoheterotrophic, planktonic Actinobacteria. We describe for the first time genomes of two novel clades, acMicro (Micrococcineae, related to Luna2,) and acAMD (Actinomycetales, related to acTH1). Besides, an aggregate of contigs belonged to a new branch of the Acidimicrobiales. All are estimated to have small genomes (approximately 1.2 Mb), and their GC content varied from 40 to 61%. One of the Micrococcineae genomes encodes a proteorhodopsin, a rhodopsin type reported for the first time in Actinobacteria. The remarkable potential capacity of some of these genomes to transform recalcitrant plant detrital material, particularly lignin-derived compounds, suggests close linkages between the terrestrial and aquatic realms. Moreover, abundances of Actinobacteria correlate inversely to those of Cyanobacteria that are responsible for prolonged and frequently irretrievable damage to freshwater ecosystems. This suggests that they might serve as sentinels of impending ecological catastrophes. © 2014 John Wiley & Sons Ltd.

Performance evaluation of 2D and 3D deep learning approaches for automatic segmentation of multiple organs on CT images

Science.gov (United States)

Zhou, Xiangrong; Yamada, Kazuma; Kojima, Takuya; Takayama, Ryosuke; Wang, Song; Zhou, Xinxin; Hara, Takeshi; Fujita, Hiroshi

2018-02-01

The purpose of this study is to evaluate and compare the performance of modern deep learning techniques for automatically recognizing and segmenting multiple organ regions on 3D CT images. CT image segmentation is one of the important task in medical image analysis and is still very challenging. Deep learning approaches have demonstrated the capability of scene recognition and semantic segmentation on nature images and have been used to address segmentation problems of medical images. Although several works showed promising results of CT image segmentation by using deep learning approaches, there is no comprehensive evaluation of segmentation performance of the deep learning on segmenting multiple organs on different portions of CT scans. In this paper, we evaluated and compared the segmentation performance of two different deep learning approaches that used 2D- and 3D deep convolutional neural networks (CNN) without- and with a pre-processing step. A conventional approach that presents the state-of-the-art performance of CT image segmentation without deep learning was also used for comparison. A dataset that includes 240 CT images scanned on different portions of human bodies was used for performance evaluation. The maximum number of 17 types of organ regions in each CT scan were segmented automatically and compared to the human annotations by using ratio of intersection over union (IU) as the criterion. The experimental results demonstrated the IUs of the segmentation results had a mean value of 79% and 67% by averaging 17 types of organs that segmented by a 3D- and 2D deep CNN, respectively. All the results of the deep learning approaches showed a better accuracy and robustness than the conventional segmentation method that used probabilistic atlas and graph-cut methods. The effectiveness and the usefulness of deep learning approaches were demonstrated for solving multiple organs segmentation problem on 3D CT images.

Insights into the genetic structure and diversity of 38 South Asian Indians from deep whole-genome sequencing.

Science.gov (United States)

Wong, Lai-Ping; Lai, Jason Kuan-Han; Saw, Woei-Yuh; Ong, Rick Twee-Hee; Cheng, Anthony Youzhi; Pillai, Nisha Esakimuthu; Liu, Xuanyao; Xu, Wenting; Chen, Peng; Foo, Jia-Nee; Tan, Linda Wei-Lin; Koo, Seok-Hwee; Soong, Richie; Wenk, Markus Rene; Lim, Wei-Yen; Khor, Chiea-Chuen; Little, Peter; Chia, Kee-Seng; Teo, Yik-Ying

2014-05-01

South Asia possesses a significant amount of genetic diversity due to considerable intergroup differences in culture and language. There have been numerous reports on the genetic structure of Asian Indians, although these have mostly relied on genotyping microarrays or targeted sequencing of the mitochondria and Y chromosomes. Asian Indians in Singapore are primarily descendants of immigrants from Dravidian-language-speaking states in south India, and 38 individuals from the general population underwent deep whole-genome sequencing with a target coverage of 30X as part of the Singapore Sequencing Indian Project (SSIP). The genetic structure and diversity of these samples were compared against samples from the Singapore Sequencing Malay Project and populations in Phase 1 of the 1,000 Genomes Project (1 KGP). SSIP samples exhibited greater intra-population genetic diversity and possessed higher heterozygous-to-homozygous genotype ratio than other Asian populations. When compared against a panel of well-defined Asian Indians, the genetic makeup of the SSIP samples was closely related to South Indians. However, even though the SSIP samples clustered distinctly from the Europeans in the global population structure analysis with autosomal SNPs, eight samples were assigned to mitochondrial haplogroups that were predominantly present in Europeans and possessed higher European admixture than the remaining samples. An analysis of the relative relatedness between SSIP with two archaic hominins (Denisovan, Neanderthal) identified higher ancient admixture in East Asian populations than in SSIP. The data resource for these samples is publicly available and is expected to serve as a valuable complement to the South Asian samples in Phase 3 of 1 KGP.

Population-genomic variation within RNA viruses of the Western honey bee, Apis mellifera, inferred from deep sequencing.

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Cornman, Robert Scott; Boncristiani, Humberto; Dainat, Benjamin; Chen, Yanping; vanEngelsdorp, Dennis; Weaver, Daniel; Evans, Jay D

2013-03-07

Deep sequencing of viruses isolated from infected hosts is an efficient way to measure population-genetic variation and can reveal patterns of dispersal and natural selection. In this study, we mined existing Illumina sequence reads to investigate single-nucleotide polymorphisms (SNPs) within two RNA viruses of the Western honey bee (Apis mellifera), deformed wing virus (DWV) and Israel acute paralysis virus (IAPV). All viral RNA was extracted from North American samples of honey bees or, in one case, the ectoparasitic mite Varroa destructor. Coverage depth was generally lower for IAPV than DWV, and marked gaps in coverage occurred in several narrow regions (selection. The Kakugo strain of DWV fell outside of all other DWV sequences at 100% bootstrap support. IAPV consensus sequences supported the existence of multiple clades as had been previously reported, and Fu and Li's D was closer to neutral expectation overall, although a sliding-window analysis identified a significantly positive D within the protease region, suggesting selection maintains diversity in that region. Within-sample mean diversity was comparable between the two viruses on average, although for both viruses there was substantial variation among samples in mean diversity at third codon positions and in the number of high-diversity sites. FST values were bimodal for DWV, likely reflecting neutral divergence in two low-diversity populations, whereas IAPV had several sites that were strong outliers with very low FST. This initial survey of genetic variation within honey bee RNA viruses suggests future directions for studies examining the underlying causes of population-genetic structure in these economically important pathogens.

Complete genome sequence of the aerobic, heterotroph Marinithermus hydrothermalis type strain (T1T) from a deep-sea hydrothermal vent chimney

Energy Technology Data Exchange (ETDEWEB)

Copeland, A [U.S. Department of Energy, Joint Genome Institute; Gu, Wei [U.S. Department of Energy, Joint Genome Institute; Yasawong, Montri [HZI - Helmholtz Centre for Infection Research, Braunschweig, Germany; Lapidus, Alla L. [U.S. Department of Energy, Joint Genome Institute; Lucas, Susan [U.S. Department of Energy, Joint Genome Institute; Deshpande, Shweta [U.S. Department of Energy, Joint Genome Institute; Pagani, Ioanna [U.S. Department of Energy, Joint Genome Institute; Tapia, Roxanne [Los Alamos National Laboratory (LANL); Cheng, Jan-Fang [U.S. Department of Energy, Joint Genome Institute; Goodwin, Lynne A. [Los Alamos National Laboratory (LANL); Pitluck, Sam [U.S. Department of Energy, Joint Genome Institute; Liolios, Konstantinos [U.S. Department of Energy, Joint Genome Institute; Ivanova, N [U.S. Department of Energy, Joint Genome Institute; Mavromatis, K [U.S. Department of Energy, Joint Genome Institute; Mikhailova, Natalia [U.S. Department of Energy, Joint Genome Institute; Pati, Amrita [U.S. Department of Energy, Joint Genome Institute; Chen, Amy [U.S. Department of Energy, Joint Genome Institute; Palaniappan, Krishna [U.S. Department of Energy, Joint Genome Institute; Land, Miriam L [ORNL; Pan, Chongle [ORNL; Brambilla, Evelyne-Marie [DSMZ - German Collection of Microorganisms and Cell Cultures GmbH, Braunschweig, Germany; Rohde, Manfred [HZI - Helmholtz Centre for Infection Research, Braunschweig, Germany; Tindall, Brian [DSMZ - German Collection of Microorganisms and Cell Cultures GmbH, Braunschweig, Germany; Sikorski, Johannes [DSMZ - German Collection of Microorganisms and Cell Cultures GmbH, Braunschweig, Germany; Goker, Markus [DSMZ - German Collection of Microorganisms and Cell Cultures GmbH, Braunschweig, Germany; Detter, J. Chris [U.S. Department of Energy, Joint Genome Institute; Bristow, James [U.S. Department of Energy, Joint Genome Institute; Eisen, Jonathan [U.S. Department of Energy, Joint Genome Institute; Markowitz, Victor [U.S. Department of Energy, Joint Genome Institute; Hugenholtz, Philip [U.S. Department of Energy, Joint Genome Institute; Kyrpides, Nikos C [U.S. Department of Energy, Joint Genome Institute; Klenk, Hans-Peter [DSMZ - German Collection of Microorganisms and Cell Cultures GmbH, Braunschweig, Germany; Woyke, Tanja [U.S. Department of Energy, Joint Genome Institute

2012-01-01

Marinithermus hydrothermalis Sako et al. 2003 is the type species of the monotypic genus Marinithermus. M. hydrothermalis T1 T was the first isolate within the phylum ThermusDeinococcus to exhibit optimal growth under a salinity equivalent to that of sea water and to have an absolute requirement for NaCl for growth. M. hydrothermalis T1 T is of interest because it may provide a new insight into the ecological significance of the aerobic, thermophilic decomposers in the circulation of organic compounds in deep-sea hydrothermal vent ecosystems. This is the first completed genome sequence of a member of the genus Marinithermus and the seventh sequence from the family Thermaceae. Here we describe the features of this organism, together with the complete genome sequence and annotation. The 2,269,167 bp long genome with its 2,251 protein-coding and 59 RNA genes is a part of the Genomic Encyclopedia of Bacteria and Archaea project.

Complete genome sequence of the aerobic, heterotroph Marinithermus hydrothermalis type strain (T1(T)) from a deep-sea hydrothermal vent chimney.

Science.gov (United States)

Copeland, Alex; Gu, Wei; Yasawong, Montri; Lapidus, Alla; Lucas, Susan; Deshpande, Shweta; Pagani, Ioanna; Tapia, Roxanne; Cheng, Jan-Fang; Goodwin, Lynne A; Pitluck, Sam; Liolios, Konstantinos; Ivanova, Natalia; Mavromatis, Konstantinos; Mikhailova, Natalia; Pati, Amrita; Chen, Amy; Palaniappan, Krishna; Land, Miriam; Pan, Chongle; Brambilla, Evelyne-Marie; Rohde, Manfred; Tindall, Brian J; Sikorski, Johannes; Göker, Markus; Detter, John C; Bristow, James; Eisen, Jonathan A; Markowitz, Victor; Hugenholtz, Philip; Kyrpides, Nikos C; Klenk, Hans-Peter; Woyke, Tanja

2012-03-19

Marinithermus hydrothermalis Sako et al. 2003 is the type species of the monotypic genus Marinithermus. M. hydrothermalis T1(T) was the first isolate within the phylum "Thermus-Deinococcus" to exhibit optimal growth under a salinity equivalent to that of sea water and to have an absolute requirement for NaCl for growth. M. hydrothermalis T1(T) is of interest because it may provide a new insight into the ecological significance of the aerobic, thermophilic decomposers in the circulation of organic compounds in deep-sea hydrothermal vent ecosystems. This is the first completed genome sequence of a member of the genus Marinithermus and the seventh sequence from the family Thermaceae. Here we describe the features of this organism, together with the complete genome sequence and annotation. The 2,269,167 bp long genome with its 2,251 protein-coding and 59 RNA genes is a part of the Genomic Encyclopedia of Bacteria and Archaea project.

Exome Sequencing and the Management of Neurometabolic Disorders.

Science.gov (United States)

Tarailo-Graovac, Maja; Shyr, Casper; Ross, Colin J; Horvath, Gabriella A; Salvarinova, Ramona; Ye, Xin C; Zhang, Lin-Hua; Bhavsar, Amit P; Lee, Jessica J Y; Drögemöller, Britt I; Abdelsayed, Mena; Alfadhel, Majid; Armstrong, Linlea; Baumgartner, Matthias R; Burda, Patricie; Connolly, Mary B; Cameron, Jessie; Demos, Michelle; Dewan, Tammie; Dionne, Janis; Evans, A Mark; Friedman, Jan M; Garber, Ian; Lewis, Suzanne; Ling, Jiqiang; Mandal, Rupasri; Mattman, Andre; McKinnon, Margaret; Michoulas, Aspasia; Metzger, Daniel; Ogunbayo, Oluseye A; Rakic, Bojana; Rozmus, Jacob; Ruben, Peter; Sayson, Bryan; Santra, Saikat; Schultz, Kirk R; Selby, Kathryn; Shekel, Paul; Sirrs, Sandra; Skrypnyk, Cristina; Superti-Furga, Andrea; Turvey, Stuart E; Van Allen, Margot I; Wishart, David; Wu, Jiang; Wu, John; Zafeiriou, Dimitrios; Kluijtmans, Leo; Wevers, Ron A; Eydoux, Patrice; Lehman, Anna M; Vallance, Hilary; Stockler-Ipsiroglu, Sylvia; Sinclair, Graham; Wasserman, Wyeth W; van Karnebeek, Clara D

2016-06-09

Whole-exome sequencing has transformed gene discovery and diagnosis in rare diseases. Translation into disease-modifying treatments is challenging, particularly for intellectual developmental disorder. However, the exception is inborn errors of metabolism, since many of these disorders are responsive to therapy that targets pathophysiological features at the molecular or cellular level. To uncover the genetic basis of potentially treatable inborn errors of metabolism, we combined deep clinical phenotyping (the comprehensive characterization of the discrete components of a patient's clinical and biochemical phenotype) with whole-exome sequencing analysis through a semiautomated bioinformatics pipeline in consecutively enrolled patients with intellectual developmental disorder and unexplained metabolic phenotypes. We performed whole-exome sequencing on samples obtained from 47 probands. Of these patients, 6 were excluded, including 1 who withdrew from the study. The remaining 41 probands had been born to predominantly nonconsanguineous parents of European descent. In 37 probands, we identified variants in 2 genes newly implicated in disease, 9 candidate genes, 22 known genes with newly identified phenotypes, and 9 genes with expected phenotypes; in most of the genes, the variants were classified as either pathogenic or probably pathogenic. Complex phenotypes of patients in five families were explained by coexisting monogenic conditions. We obtained a diagnosis in 28 of 41 probands (68%) who were evaluated. A test of a targeted intervention was performed in 18 patients (44%). Deep phenotyping and whole-exome sequencing in 41 probands with intellectual developmental disorder and unexplained metabolic abnormalities led to a diagnosis in 68%, the identification of 11 candidate genes newly implicated in neurometabolic disease, and a change in treatment beyond genetic counseling in 44%. (Funded by BC Children's Hospital Foundation and others.).

Discovery of Bovine Digital Dermatitis-Associated Treponema spp. in the Dairy Herd Environment by a Targeted Deep-Sequencing Approach

DEFF Research Database (Denmark)

Schou, Kirstine Klitgaard; Weiss Nielsen, Martin; Ingerslev, Hans-Christian

2014-01-01

The bacteria associated with the infectious claw disease bovine digital dermatitis (DD) are spirochetes of the genus Treponema; however, their environmental reservoir remains unknown. To our knowledge, the current study is the first report of the discovery and phylogenetic characterization of r...... of this disease among cows within a herd as well as between herds. To address the issue of DD infection reservoirs, we searched for evidence of DD-associated treponemes in fresh feces, in slurry, and in hoof lesions by deep sequencing of the V3 and V4 hypervariable regions of the 16S rRNA gene coupled...... with identification at the operational-taxonomic-unit level. Using treponeme-specific primers in this high-throughput approach, we identified small amounts of DNA (on average 0.6% of the total amount of sequence reads) from DD-associated treponemes in 43 of 64 samples from slurry and cow feces collected from six...

An introduction to Deep learning on biological sequence data - Examples and solutions

DEFF Research Database (Denmark)

Jurtz, Vanessa Isabell; Johansen, Alexander Rosenberg; Nielsen, Morten

2017-01-01

Deep neural network architectures such as convolutional and long short-term memory networks have become increasingly popular as machine learning tools during the recent years. The availability of greater computational resources, more data, new algorithms for training deep models and easy to use....... Here, we aim to further the development of deep learning methods within biology by providing application examples and ready to apply and adapt code templates. Given such examples, we illustrate how architectures consisting of convolutional and long short-term memory neural networks can relatively...

Deep breathing exercises performed 2 months following cardiac surgery: a randomized controlled trial.

Science.gov (United States)

Westerdahl, Elisabeth; Urell, Charlotte; Jonsson, Marcus; Bryngelsson, Ing-Liss; Hedenström, Hans; Emtner, Margareta

2014-01-01

Postoperative breathing exercises are recommended to cardiac surgery patients. Instructions concerning how long patients should continue exercises after discharge vary, and the significance of treatment needs to be determined. Our aim was to assess the effects of home-based deep breathing exercises performed with a positive expiratory pressure device for 2 months following cardiac surgery. The study design was a prospective, single-blinded, parallel-group, randomized trial. Patients performing breathing exercises 2 months after cardiac surgery (n = 159) were compared with a control group (n = 154) performing no breathing exercises after discharge. The intervention consisted of 30 slow deep breaths performed with a positive expiratory pressure device (10-15 cm H2O), 5 times a day, during the first 2 months after surgery. The outcomes were lung function measurements, oxygen saturation, thoracic excursion mobility, subjective perception of breathing and pain, patient-perceived quality of recovery (40-Item Quality of Recovery score), health-related quality of life (36-Item Short Form Health Survey), and self-reported respiratory tract infection/pneumonia and antibiotic treatment. Two months postoperatively, the patients had significantly reduced lung function, with a mean decrease in forced expiratory volume in 1 second to 93 ± 12% (P< .001) of preoperative values. Oxygenation had returned to preoperative values, and 5 of 8 aspects in the 36-Item Short Form Health Survey were improved compared with preoperative values (P< .01). There were no significant differences between the groups in any of the measured outcomes. No significant differences in lung function, subjective perceptions, or quality of life were found between patients performing home-based deep breathing exercises and control patients 2 months after cardiac surgery.

VL1 Digs A Deep Hole On Mars

Science.gov (United States)

1977-01-01

VIKING LANDER DIGS A DEEP HOLE ON MARS -- This six-inch-deep, 12- inch-wide, 29-inch-long hole was dug Feb. 12 and 14 by Viking Lander 1 as the first sequence in an attempt to reach a foot beneath the surface of the red planet. The activity is in the same area where Lander 1 acquired its first soil samples last July. The trench was dug by repeatedly backhoeing in a left-right-center pattern. The backhoe teeth produced the small parallel ridges at the far end of the trench (upper left). The larger ridges running the length of the trench are material left behind during the backhoe operation. What appears to be small rocks along the ridges and in the soil at the near end of the trench are really small dirt clods. The clods and the steepness of the trench walls indicate the material is cohesive and behaves something like ordinary flour. After a later sequence, to be performed March 1 and 2, a soil sample will be taken from the bottom of the trench for inorganic soil analysis and later for biology analysis. Information about the soil taken from the bottom of the trench may help explain the weathering process on Mars and may help resolve the dilemma created by Viking findings that first suggest but then cast doubt on the possibility of life in the Martian soil. The trench shown here is a result of one of the most complex command sequences yet performed by the lander. Viking l has been operating at Chryse Planitia on Mars since it landed July 20, 1976.

Deep sequencing and flow cytometric characterization of expanded effector memory CD8+CD57+ T cells frequently reveals T-cell receptor Vβ oligoclonality and CDR3 homology in acquired aplastic anemia.

Science.gov (United States)

Giudice, Valentina; Feng, Xingmin; Lin, Zenghua; Hu, Wei; Zhang, Fanmao; Qiao, Wangmin; Ibanez, Maria Del Pilar Fernandez; Rios, Olga; Young, Neal S

2018-05-01

Oligoclonal expansion of CD8 + CD28 - lymphocytes has been considered indirect evidence for a pathogenic immune response in acquired aplastic anemia. A subset of CD8 + CD28 - cells with CD57 expression, termed effector memory cells, is expanded in several immune-mediated diseases and may have a role in immune surveillance. We hypothesized that effector memory CD8 + CD28 - CD57 + cells may drive aberrant oligoclonal expansion in aplastic anemia. We found CD8 + CD57 + cells frequently expanded in the blood of aplastic anemia patients, with oligoclonal characteristics by flow cytometric Vβ usage analysis: skewing in 1-5 Vβ families and frequencies of immunodominant clones ranging from 1.98% to 66.5%. Oligoclonal characteristics were also observed in total CD8 + cells from aplastic anemia patients with CD8 + CD57 + cell expansion by T-cell receptor deep sequencing, as well as the presence of 1-3 immunodominant clones. Oligoclonality was confirmed by T-cell receptor repertoire deep sequencing of enriched CD8 + CD57 + cells, which also showed decreased diversity compared to total CD4 + and CD8 + cell pools. From analysis of complementarity-determining region 3 sequences in the CD8 + cell pool, a total of 29 sequences were shared between patients and controls, but these sequences were highly expressed in aplastic anemia subjects and also present in their immunodominant clones. In summary, expansion of effector memory CD8 + T cells is frequent in aplastic anemia and mirrors Vβ oligoclonal expansion. Flow cytometric Vβ usage analysis combined with deep sequencing technologies allows high resolution characterization of the T-cell receptor repertoire, and might represent a useful tool in the diagnosis and periodic evaluation of aplastic anemia patients. (Registered at clinicaltrials.gov identifiers: 00001620, 01623167, 00001397, 00071045, 00081523, 00961064 ). Copyright © 2018 Ferrata Storti Foundation.

Assessment of Student Music Performances Using Deep Neural Networks

Directory of Open Access Journals (Sweden)

Kumar Ashis Pati

2018-03-01

Full Text Available Music performance assessment is a highly subjective task often relying on experts to gauge both the technical and aesthetic aspects of the performance from the audio signal. This article explores the task of building computational models for music performance assessment, i.e., analyzing an audio recording of a performance and rating it along several criteria such as musicality, note accuracy, etc. Much of the earlier work in this area has been centered around using hand-crafted features intended to capture relevant aspects of a performance. However, such features are based on our limited understanding of music perception and may not be optimal. In this article, we propose using Deep Neural Networks (DNNs for the task and compare their performance against a baseline model using standard and hand-crafted features. We show that, using input representations at different levels of abstraction, DNNs can outperform the baseline models across all assessment criteria. In addition, we use model analysis techniques to further explain the model predictions in an attempt to gain useful insights into the assessment process. The results demonstrate the potential of using supervised feature learning techniques to better characterize music performances.

Performance Analysis for Cooperative Communication System with QC-LDPC Codes Constructed with Integer Sequences

Directory of Open Access Journals (Sweden)

Yan Zhang

2015-01-01

Full Text Available This paper presents four different integer sequences to construct quasi-cyclic low-density parity-check (QC-LDPC codes with mathematical theory. The paper introduces the procedure of the coding principle and coding. Four different integer sequences constructing QC-LDPC code are compared with LDPC codes by using PEG algorithm, array codes, and the Mackey codes, respectively. Then, the integer sequence QC-LDPC codes are used in coded cooperative communication. Simulation results show that the integer sequence constructed QC-LDPC codes are effective, and overall performance is better than that of other types of LDPC codes in the coded cooperative communication. The performance of Dayan integer sequence constructed QC-LDPC is the most excellent performance.

CAPES: Unsupervised Storage Performance Tuning Using Neural Network-Based Deep Reinforcement Learning

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CERN. Geneva

2017-01-01

Parameter tuning is an important task of storage performance optimization. Current practice usually involves numerous tweak-benchmark cycles that are slow and costly. To address this issue, we developed CAPES, a model-less deep reinforcement learning-based unsupervised parameter tuning system driven by a deep neural network (DNN). It is designed to nd the optimal values of tunable parameters in computer systems, from a simple client-server system to a large data center, where human tuning can be costly and often cannot achieve optimal performance. CAPES takes periodic measurements of a target computer system’s state, and trains a DNN which uses Q-learning to suggest changes to the system’s current parameter values. CAPES is minimally intrusive, and can be deployed into a production system to collect training data and suggest tuning actions during the system’s daily operation. Evaluation of a prototype on a Lustre system demonstrates an increase in I/O throughput up to 45% at saturation point. About the...

A deep learning pipeline for Indian dance style classification

Science.gov (United States)

Dewan, Swati; Agarwal, Shubham; Singh, Navjyoti

2018-04-01

In this paper, we address the problem of dance style classification to classify Indian dance or any dance in general. We propose a 3-step deep learning pipeline. First, we extract 14 essential joint locations of the dancer from each video frame, this helps us to derive any body region location within the frame, we use this in the second step which forms the main part of our pipeline. Here, we divide the dancer into regions of important motion in each video frame. We then extract patches centered at these regions. Main discriminative motion is captured in these patches. We stack the features from all such patches of a frame into a single vector and form our hierarchical dance pose descriptor. Finally, in the third step, we build a high level representation of the dance video using the hierarchical descriptors and train it using a Recurrent Neural Network (RNN) for classification. Our novelty also lies in the way we use multiple representations for a single video. This helps us to: (1) Overcome the RNN limitation of learning small sequences over big sequences such as dance; (2) Extract more data from the available dataset for effective deep learning by training multiple representations. Our contributions in this paper are three-folds: (1) We provide a deep learning pipeline for classification of any form of dance; (2) We prove that a segmented representation of a dance video works well with sequence learning techniques for recognition purposes; (3) We extend and refine the ICD dataset and provide a new dataset for evaluation of dance. Our model performs comparable or better in some cases than the state-of-the-art on action recognition benchmarks.

Differential working memory correlates for implicit sequence performance in young and older adults.

Science.gov (United States)

Bo, Jin; Jennett, S; Seidler, R D

2012-09-01

Our recent work has revealed that visuospatial working memory (VSWM) relates to the rate of explicit motor sequence learning (Bo and Seidler in J Neurophysiol 101:3116-3125, 2009) and implicit sequence performance (Bo et al. in Exp Brain Res 214:73-81, 2011a) in young adults (YA). Although aging has a detrimental impact on many cognitive functions, including working memory, older adults (OA) still rely on their declining working memory resources in an effort to optimize explicit motor sequence learning. Here, we evaluated whether age-related differences in VSWM and/or verbal working memory (VWM) performance relates to implicit performance change in the serial reaction time (SRT) sequence task in OA. Participants performed two computerized working memory tasks adapted from change detection working memory assessments (Luck and Vogel in Nature 390:279-281, 1997), an implicit SRT task and several neuropsychological tests. We found that, although OA exhibited an overall reduction in both VSWM and VWM, both OA and YA showed similar performance in the implicit SRT task. Interestingly, while VSWM and VWM were significantly correlated with each other in YA, there was no correlation between these two working memory scores in OA. In YA, the rate of SRT performance change (exponential fit to the performance curve) was significantly correlated with both VSWM and VWM, while in contrast, OA's performance was only correlated with VWM, and not VSWM. These results demonstrate differential reliance on VSWM and VWM for SRT performance between YA and OA. OA may utilize VWM to maintain optimized performance of second-order conditional sequences.

Transcriptome sequencing of the Microarray Quality Control (MAQC RNA reference samples using next generation sequencing

Directory of Open Access Journals (Sweden)

Thierry-Mieg Danielle

2009-06-01

Full Text Available Abstract Background Transcriptome sequencing using next-generation sequencing platforms will soon be competing with DNA microarray technologies for global gene expression analysis. As a preliminary evaluation of these promising technologies, we performed deep sequencing of cDNA synthesized from the Microarray Quality Control (MAQC reference RNA samples using Roche's 454 Genome Sequencer FLX. Results We generated more that 3.6 million sequence reads of average length 250 bp for the MAQC A and B samples and introduced a data analysis pipeline for translating cDNA read counts into gene expression levels. Using BLAST, 90% of the reads mapped to the human genome and 64% of the reads mapped to the RefSeq database of well annotated genes with e-values ≤ 10-20. We measured gene expression levels in the A and B samples by counting the numbers of reads that mapped to individual RefSeq genes in multiple sequencing runs to evaluate the MAQC quality metrics for reproducibility, sensitivity, specificity, and accuracy and compared the results with DNA microarrays and Quantitative RT-PCR (QRTPCR from the MAQC studies. In addition, 88% of the reads were successfully aligned directly to the human genome using the AceView alignment programs with an average 90% sequence similarity to identify 137,899 unique exon junctions, including 22,193 new exon junctions not yet contained in the RefSeq database. Conclusion Using the MAQC metrics for evaluating the performance of gene expression platforms, the ExpressSeq results for gene expression levels showed excellent reproducibility, sensitivity, and specificity that improved systematically with increasing shotgun sequencing depth, and quantitative accuracy that was comparable to DNA microarrays and QRTPCR. In addition, a careful mapping of the reads to the genome using the AceView alignment programs shed new light on the complexity of the human transcriptome including the discovery of thousands of new splice variants.

Interference Suppression Performance of Automotive UWB Radars Using Pseudo Random Sequences

Directory of Open Access Journals (Sweden)

I. Pasya

2015-12-01

Full Text Available Ultra wideband (UWB automotive radars have attracted attention from the viewpoint of reducing traffic accidents. The performance of automotive radars may be degraded by interference from nearby radars using the same frequency. In this study, a scenario where two cars pass each other on a road was considered. Considering the utilization of cross-polarization, the desired-to-undesired signal power ratio (DUR was found to vary approximately from -10 to 30 dB. Different pseudo random sequences were employed for spectrum spreading the different radar signals to mitigate the interference effects. This paper evaluates the interference suppression provided by maximum length sequence (MLS and Gold sequence (GS through numerical simulations of the radar’s performance in terms of probability of false alarm and probability of detection. It was found that MLS and GS yielded nearly the same performance when the DUR is -10 dB (worst case; for example when fixing the probability of false alarm to 0.0001, the probabilities of detection were 0.964 and 0.946 respectively. The GS are more advantageous than MLS due to larger number of different sequences having the same length in GS than in MLS.

DeepGO: predicting protein functions from sequence and interactions using a deep ontology-aware classifier

KAUST Repository

Kulmanov, Maxat; Khan, Mohammed Asif; Hoehndorf, Robert

2017-01-01

A large number of protein sequences are becoming available through the application of novel high-throughput sequencing technologies. Experimental functional characterization of these proteins is time-consuming and expensive, and is often

Invited talk: Deep Learning Meets Physics

CERN Multimedia

CERN. Geneva

2018-01-01

Deep Learning has emerged as one of the most successful fields of machine learning and artificial intelligence with overwhelming success in industrial speech, text and vision benchmarks. Consequently it evolved into the central field of research for IT giants like Google, facebook, Microsoft, Baidu, and Amazon. Deep Learning is founded on novel neural network techniques, the recent availability of very fast computers, and massive data sets. In its core, Deep Learning discovers multiple levels of abstract representations of the input. The main obstacle to learning deep neural networks is the vanishing gradient problem. The vanishing gradient impedes credit assignment to the first layers of a deep network or to early elements of a sequence, therefore limits model selection. Major advances in Deep Learning can be related to avoiding the vanishing gradient like stacking, ReLUs, residual networks, highway networks, and LSTM. For Deep Learning, we suggested self-normalizing neural networks (SNNs) which automatica...

Insights into the genetic structure and diversity of 38 South Asian Indians from deep whole-genome sequencing.

Directory of Open Access Journals (Sweden)

Lai-Ping Wong

2014-05-01

Full Text Available South Asia possesses a significant amount of genetic diversity due to considerable intergroup differences in culture and language. There have been numerous reports on the genetic structure of Asian Indians, although these have mostly relied on genotyping microarrays or targeted sequencing of the mitochondria and Y chromosomes. Asian Indians in Singapore are primarily descendants of immigrants from Dravidian-language-speaking states in south India, and 38 individuals from the general population underwent deep whole-genome sequencing with a target coverage of 30X as part of the Singapore Sequencing Indian Project (SSIP. The genetic structure and diversity of these samples were compared against samples from the Singapore Sequencing Malay Project and populations in Phase 1 of the 1,000 Genomes Project (1 KGP. SSIP samples exhibited greater intra-population genetic diversity and possessed higher heterozygous-to-homozygous genotype ratio than other Asian populations. When compared against a panel of well-defined Asian Indians, the genetic makeup of the SSIP samples was closely related to South Indians. However, even though the SSIP samples clustered distinctly from the Europeans in the global population structure analysis with autosomal SNPs, eight samples were assigned to mitochondrial haplogroups that were predominantly present in Europeans and possessed higher European admixture than the remaining samples. An analysis of the relative relatedness between SSIP with two archaic hominins (Denisovan, Neanderthal identified higher ancient admixture in East Asian populations than in SSIP. The data resource for these samples is publicly available and is expected to serve as a valuable complement to the South Asian samples in Phase 3 of 1 KGP.

Genetic diversity of archaea in deep-sea hydrothermal vent environments.

OpenAIRE

Takai, K; Horikoshi, K

1999-01-01

Molecular phylogenetic analysis of naturally occurring archaeal communities in deep-sea hydrothermal vent environments was carried out by PCR-mediated small subunit rRNA gene (SSU rDNA) sequencing. As determined through partial sequencing of rDNA clones amplified with archaea-specific primers, the archaeal populations in deep-sea hydrothermal vent environments showed a great genetic diversity, and most members of these populations appeared to be uncultivated and unidentified organisms. In the...

Whole-Genome Sequencing Coupled to Imputation Discovers Genetic Signals for Anthropometric Traits

NARCIS (Netherlands)

I. Tachmazidou (Ioanna); Süveges, D. (Dániel); J. Min (Josine); G.R.S. Ritchie (Graham R.S.); Steinberg, J. (Julia); K. Walter (Klaudia); V. Iotchkova (Valentina); J.A. Schwartzentruber (Jeremy); J. Huang (Jian); Y. Memari (Yasin); McCarthy, S. (Shane); Crawford, A.A. (Andrew A.); C. Bombieri (Cristina); M. Cocca (Massimiliano); A.-E. Farmaki (Aliki-Eleni); T.R. Gaunt (Tom); P. Jousilahti (Pekka); M.N. Kooijman (Marjolein ); Lehne, B. (Benjamin); G. Malerba (Giovanni); S. Männistö (Satu); A. Matchan (Angela); M.C. Medina-Gomez (Carolina); S. Metrustry (Sarah); A. Nag (Abhishek); I. Ntalla (Ioanna); L. Paternoster (Lavinia); N.W. Rayner (Nigel William); C. Sala (Cinzia); W.R. Scott (William R.); H.A. Shihab (Hashem A.); L. Southam (Lorraine); B. St Pourcain (Beate); M. Traglia (Michela); K. Trajanoska (Katerina); Zaza, G. (Gialuigi); W. Zhang (Weihua); M.S. Artigas; Bansal, N. (Narinder); M. Benn (Marianne); Chen, Z. (Zhongsheng); P. Danecek (Petr); Lin, W.-Y. (Wei-Yu); A. Locke (Adam); J. Luan (Jian'An); A.K. Manning (Alisa); Mulas, A. (Antonella); C. Sidore (Carlo); A. Tybjaerg-Hansen; A. Varbo (Anette); M. Zoledziewska (Magdalena); C. Finan (Chris); Hatzikotoulas, K. (Konstantinos); A.E. Hendricks (Audrey E.); J.P. Kemp (John); A. Moayyeri (Alireza); Panoutsopoulou, K. (Kalliope); Szpak, M. (Michal); S.G. Wilson (Scott); M. Boehnke (Michael); F. Cucca (Francesco); Di Angelantonio, E. (Emanuele); C. Langenberg (Claudia); C.M. Lindgren (Cecilia M.); McCarthy, M.I. (Mark I.); A.P. Morris (Andrew); B.G. Nordestgaard (Børge); R.A. Scott (Robert); M.D. Tobin (Martin); N.J. Wareham (Nick); P.R. Burton (Paul); J.C. Chambers (John); Smith, G.D. (George Davey); G.V. Dedoussis (George); J.F. Felix (Janine); O.H. Franco (Oscar); Gambaro, G. (Giovanni); P. Gasparini (Paolo); C.J. Hammond (Christopher J.); A. Hofman (Albert); V.W.V. Jaddoe (Vincent); M.E. Kleber (Marcus); J.S. Kooner (Jaspal S.); M. Perola (Markus); C.L. Relton (Caroline); S.M. Ring (Susan); F. Rivadeneira Ramirez (Fernando); V. Salomaa (Veikko); T.D. Spector (Timothy); O. Stegle (Oliver); D. Toniolo (Daniela); A.G. Uitterlinden (André); I.E. Barroso (Inês); C.M.T. Greenwood (Celia); Perry, J.R.B. (John R.B.); Walker, B.R. (Brian R.); A.S. Butterworth (Adam); Y. Xue (Yali); R. Durbin (Richard); K.S. Small (Kerrin); N. Soranzo (Nicole); N.J. Timpson (Nicholas); E. Zeggini (Eleftheria)

2016-01-01

textabstractDeep sequence-based imputation can enhance the discovery power of genome-wide association studies by assessing previously unexplored variation across the common- and low-frequency spectra. We applied a hybrid whole-genome sequencing (WGS) and deep imputation approach to examine the

Whole-Genome Sequencing Coupled to Imputation Discovers Genetic Signals for Anthropometric Traits

DEFF Research Database (Denmark)

Tachmazidou, Ioanna; Süveges, Dániel; Min, Josine L

2017-01-01

Deep sequence-based imputation can enhance the discovery power of genome-wide association studies by assessing previously unexplored variation across the common- and low-frequency spectra. We applied a hybrid whole-genome sequencing (WGS) and deep imputation approach to examine the broader alleli...

Performance Analysis of High-Speed Deep/Shallow Recessed Hybrid Bearing

OpenAIRE

Lei Wang; Shuyun Jiang

2013-01-01

The present paper proposes a theoretical analysis of the performance of deep/shallow recessed hybrid bearing. It is intended that, on the basis of the numerical results drawn from this study, appropriate shallow recess depth and width can be determined for use in the bearing design process. By adopting bulk flow theory, the turbulent Reynolds equation and energy equation are modified and solved numerically including concentrated inertia effects at the recess edge with different depth and widt...

Production-Level Facial Performance Capture Using Deep Convolutional Neural Networks

OpenAIRE

Laine, Samuli; Karras, Tero; Aila, Timo; Herva, Antti; Saito, Shunsuke; Yu, Ronald; Li, Hao; Lehtinen, Jaakko

2016-01-01

We present a real-time deep learning framework for video-based facial performance capture -- the dense 3D tracking of an actor's face given a monocular video. Our pipeline begins with accurately capturing a subject using a high-end production facial capture pipeline based on multi-view stereo tracking and artist-enhanced animations. With 5-10 minutes of captured footage, we train a convolutional neural network to produce high-quality output, including self-occluded regions, from a monocular v...

SKI SITE-94, deep repository performance assessment project, summary

International Nuclear Information System (INIS)

1999-01-01

SITE-94 is a comprehensive performance assessment exercise for a hypothetical repository for spent nuclear fuel at a real site in Sweden. SITE-94 was carried out to develop the capability and tools to enable Swedish Nuclear Power Inspectorate (SKI) to review fully the proposals for a deep repository which are expected to be made by the Swedish Nuclear Fuel and Waste Management Company, SKB (the implementor). Sweden is one of the leading countries in the research and development of geological disposal of radioactive waste. The developed methodology for performance assessment has attracted interests from other countries. The Summary of the main report of the SITE-94 project is translated here into Japanese to allow to make the information on the methodology and the related issues available among Japanese concerned. (author)

Performance Evaluation of Deep Learning Tools in Docker Containers

OpenAIRE

Xu, Pengfei; Shi, Shaohuai; Chu, Xiaowen

2017-01-01

With the success of deep learning techniques in a broad range of application domains, many deep learning software frameworks have been developed and are being updated frequently to adapt to new hardware features and software libraries, which bring a big challenge for end users and system administrators. To address this problem, container techniques are widely used to simplify the deployment and management of deep learning software. However, it remains unknown whether container techniques brin...

Structural Correlates of Skilled Performance on a Motor Sequence Task

Directory of Open Access Journals (Sweden)

Christopher J Steele

2012-10-01

Full Text Available The brain regions functionally engaged in motor sequence performance are well established, but the structural characteristics of these regions and the fibre pathways involved have been less well studied. In addition, relatively few studies have combined multiple magnetic resonance imaging (MRI and behavioural performance measures in the same sample. Therefore, the current study used diffusion tensor imaging, probabilistic tractography, and voxel-based morphometry to determine the structural correlates of skilled motor performance. Further, we compared these findings with fMRI results in the same sample. We correlated final performance and rate of improvement measures on a temporal motor sequence task with skeletonised fractional anisotropy (FA and whole brain grey matter (GM volume. Final synchronisation performance was negatively correlated with FA in white matter underlying bilateral sensorimotor cortex – an effect that was mediated by a positive correlation with radial diffusivity. Multi-fibre tractography indicated that this region contained crossing fibres from the corticospinal tract and superior longitudinal fasciculus (SLF. The identified SLF pathway linked parietal and auditory cortical regions that have been shown to be functionally engaged in this task. Thus, we hypothesise that enhanced synchronisation performance on this task may be related to greater fibre integrity of the SLF. Rate of improvement on synchronisation was positively correlated with GM volume in cerebellar lobules HVI and V – regions that showed training-related decreases in activity in the same sample. Taken together, our results link individual differences in brain structure and function to motor sequence performance on the same task. Further, our study illustrates the utility of using multiple MR measures and analysis techniques to specify the interpretation of structural findings.

OPTSDNA: Performance evaluation of an efficient distributed bioinformatics system for DNA sequence analysis.

Science.gov (United States)

Khan, Mohammad Ibrahim; Sheel, Chotan

2013-01-01

Storage of sequence data is a big concern as the amount of data generated is exponential in nature at several locations. Therefore, there is a need to develop techniques to store data using compression algorithm. Here we describe optimal storage algorithm (OPTSDNA) for storing large amount of DNA sequences of varying length. This paper provides performance analysis of optimal storage algorithm (OPTSDNA) of a distributed bioinformatics computing system for analysis of DNA sequences. OPTSDNA algorithm is used for storing various sizes of DNA sequences into database. DNA sequences of different lengths were stored by using this algorithm. These input DNA sequences are varied in size from very small to very large. Storage size is calculated by this algorithm. Response time is also calculated in this work. The efficiency and performance of the algorithm is high (in size calculation with percentage) when compared with other known with sequential approach.

EFL LEARNERS REPAIR SEQUENCE TYPES ANALYSIS AS PEER- ASSESSMENT IN ORAL PERFORMANCE

Directory of Open Access Journals (Sweden)

Novia Trisanti

2017-04-01

Full Text Available There are certain concerns that EFL teacher needs to observe in assessing students oral performance, such as the amount of words which the learners utter, the grammatical errors that they make, the hesitation and certain expression that they produce. This paper attempts to give overview of research results using qualitative method which show the impacts of repair sequence types analysis on those elements needed to be observed as students peer and self-assessment to enhance their speaking ability. The subject was tertiary level learners of English Department, State University of Semarang, Indonesia in 2012. Concerning the repair types, there are four repair sequences as reviewed by Buckwalter (2001, they are Self-Initiated Self Repair (SISR, Self-Initiated Other Repair (SIOR, Other-Initiated Self Repair (OISR, and Other-Initiated Other Repair (OIOR. Having the repair sequences types anaysis, the students investigated the repair sequence of their peers while they performed in class conversation. The modified peer- assessment guideline as proposed by Brown (2004 was used in identifying, categorizing and classifying the types of repair sequences in their peers oral performance. While, the peer-assessment can be a valuable additional means to improve students speaking since it is one of the motives that drive peer- evaluation, along with peer- verification, also peer and self- enhancement. The analysis results were then interpreted to see whether there was significant finding related to the students’ oral performance enhancement.

Arthropod phylogenetics in light of three novel millipede (myriapoda: diplopoda mitochondrial genomes with comments on the appropriateness of mitochondrial genome sequence data for inferring deep level relationships.

Directory of Open Access Journals (Sweden)

Michael S Brewer

Full Text Available BACKGROUND: Arthropods are the most diverse group of eukaryotic organisms, but their phylogenetic relationships are poorly understood. Herein, we describe three mitochondrial genomes representing orders of millipedes for which complete genomes had not been characterized. Newly sequenced genomes are combined with existing data to characterize the protein coding regions of myriapods and to attempt to reconstruct the evolutionary relationships within the Myriapoda and Arthropoda. RESULTS: The newly sequenced genomes are similar to previously characterized millipede sequences in terms of synteny and length. Unique translocations occurred within the newly sequenced taxa, including one half of the Appalachioria falcifera genome, which is inverted with respect to other millipede genomes. Across myriapods, amino acid conservation levels are highly dependent on the gene region. Additionally, individual loci varied in the level of amino acid conservation. Overall, most gene regions showed low levels of conservation at many sites. Attempts to reconstruct the evolutionary relationships suffered from questionable relationships and low support values. Analyses of phylogenetic informativeness show the lack of signal deep in the trees (i.e., genes evolve too quickly. As a result, the myriapod tree resembles previously published results but lacks convincing support, and, within the arthropod tree, well established groups were recovered as polyphyletic. CONCLUSIONS: The novel genome sequences described herein provide useful genomic information concerning millipede groups that had not been investigated. Taken together with existing sequences, the variety of compositions and evolution of myriapod mitochondrial genomes are shown to be more complex than previously thought. Unfortunately, the use of mitochondrial protein-coding regions in deep arthropod phylogenetics appears problematic, a result consistent with previously published studies. Lack of phylogenetic

Arthropod phylogenetics in light of three novel millipede (myriapoda: diplopoda) mitochondrial genomes with comments on the appropriateness of mitochondrial genome sequence data for inferring deep level relationships.

Science.gov (United States)

Brewer, Michael S; Swafford, Lynn; Spruill, Chad L; Bond, Jason E

2013-01-01

Arthropods are the most diverse group of eukaryotic organisms, but their phylogenetic relationships are poorly understood. Herein, we describe three mitochondrial genomes representing orders of millipedes for which complete genomes had not been characterized. Newly sequenced genomes are combined with existing data to characterize the protein coding regions of myriapods and to attempt to reconstruct the evolutionary relationships within the Myriapoda and Arthropoda. The newly sequenced genomes are similar to previously characterized millipede sequences in terms of synteny and length. Unique translocations occurred within the newly sequenced taxa, including one half of the Appalachioria falcifera genome, which is inverted with respect to other millipede genomes. Across myriapods, amino acid conservation levels are highly dependent on the gene region. Additionally, individual loci varied in the level of amino acid conservation. Overall, most gene regions showed low levels of conservation at many sites. Attempts to reconstruct the evolutionary relationships suffered from questionable relationships and low support values. Analyses of phylogenetic informativeness show the lack of signal deep in the trees (i.e., genes evolve too quickly). As a result, the myriapod tree resembles previously published results but lacks convincing support, and, within the arthropod tree, well established groups were recovered as polyphyletic. The novel genome sequences described herein provide useful genomic information concerning millipede groups that had not been investigated. Taken together with existing sequences, the variety of compositions and evolution of myriapod mitochondrial genomes are shown to be more complex than previously thought. Unfortunately, the use of mitochondrial protein-coding regions in deep arthropod phylogenetics appears problematic, a result consistent with previously published studies. Lack of phylogenetic signal renders the resulting tree topologies as suspect

Performance and Feasibility Study of a Standing Column Well (SCW System Using a Deep Geothermal Well

Directory of Open Access Journals (Sweden)

Jeong-Heum Cho

2016-02-01

Full Text Available Deep geothermal heat pump systems have considerable energy saving potential for heating and cooling systems that use stable ground temperature and groundwater as their heat sources. However, deep geothermal systems have several limitations for real applications such as a very high installation cost and a lack of recognition as heating and cooling systems. In this study, we performed a feasibility assessment of a Standing Column Well (SCW system using a deep geothermal well, based on a real-scale experiment in Korea. The results showed that the temperature of the heat source increased up to 42.04 °C in the borehole after the heating experiment, which is about 30 °C higher than that of normal shallow geothermal wells. Furthermore, the coefficient of performance (COP of the heat pump during 3 months of operation was 5.8, but the system COP was only 3.6 due to the relatively high electric consumption of the pump. Moreover, the payback period of the system using a deep well for controlled horticulture in a glass greenhouse was calculated as 6 years compared with using a diesel boiler system.

Sequence-to-Sequence Prediction of Vehicle Trajectory via LSTM Encoder-Decoder Architecture

OpenAIRE

Park, Seong Hyeon; Kim, ByeongDo; Kang, Chang Mook; Chung, Chung Choo; Choi, Jun Won

2018-01-01

In this paper, we propose a deep learning based vehicle trajectory prediction technique which can generate the future trajectory sequence of surrounding vehicles in real time. We employ the encoder-decoder architecture which analyzes the pattern underlying in the past trajectory using the long short-term memory (LSTM) based encoder and generates the future trajectory sequence using the LSTM based decoder. This structure produces the $K$ most likely trajectory candidates over occupancy grid ma...

Sequence-specific bias correction for RNA-seq data using recurrent neural networks.

Science.gov (United States)

Zhang, Yao-Zhong; Yamaguchi, Rui; Imoto, Seiya; Miyano, Satoru

2017-01-25

The recent success of deep learning techniques in machine learning and artificial intelligence has stimulated a great deal of interest among bioinformaticians, who now wish to bring the power of deep learning to bare on a host of bioinformatical problems. Deep learning is ideally suited for biological problems that require automatic or hierarchical feature representation for biological data when prior knowledge is limited. In this work, we address the sequence-specific bias correction problem for RNA-seq data redusing Recurrent Neural Networks (RNNs) to model nucleotide sequences without pre-determining sequence structures. The sequence-specific bias of a read is then calculated based on the sequence probabilities estimated by RNNs, and used in the estimation of gene abundance. We explore the application of two popular RNN recurrent units for this task and demonstrate that RNN-based approaches provide a flexible way to model nucleotide sequences without knowledge of predetermined sequence structures. Our experiments show that training a RNN-based nucleotide sequence model is efficient and RNN-based bias correction methods compare well with the-state-of-the-art sequence-specific bias correction method on the commonly used MAQC-III data set. RNNs provides an alternative and flexible way to calculate sequence-specific bias without explicitly pre-determining sequence structures.

Comparative performance of the BGISEQ-500 versus Illumina HiSeq2500 sequencing platforms for palaeogenomic sequencing

DEFF Research Database (Denmark)

Mak, Sarah Siu Tze Mak; Gopalakrishnan, Shyam Sunder; Carøe, Christian

2017-01-01

on degraded DNA, then directly compared the sequencing performance and data quality of the BGISEQ-500 to the Illumina HiSeq2500 platform, on DNA extracted from eight historic and ancient dog and wolf samples. Results: The data generated was largely comparable between sequencing platforms...... difference was also observed in the mitochondrial DNA percentages recovered (p = 0.018), although we believe this is likely a stochastic effect relating to the extremely low levels of mitochondria that were sequenced from three of the samples with overall very low levels of endogenous DNA. Conclusions......: Although we acknowledge our analyses were limited to animal material, our observations suggest that the BGISEQ-500 holds the potential to represent valid and potentially valuable alternative platform for palaeogenomic data generation, that is worthy of future exploration by those interested...

Subsurface microbial diversity in deep-granitic-fracture water in Colorado

Science.gov (United States)

Sahl, J.W.; Schmidt, R.; Swanner, E.D.; Mandernack, K.W.; Templeton, A.S.; Kieft, Thomas L.; Smith, R.L.; Sanford, W.E.; Callaghan, R.L.; Mitton, J.B.; Spear, J.R.

2008-01-01

A microbial community analysis using 16S rRNA gene sequencing was performed on borehole water and a granite rock core from Henderson Mine, a >1,000-meter-deep molybdenum mine near Empire, CO. Chemical analysis of borehole water at two separate depths (1,044 m and 1,004 m below the mine entrance) suggests that a sharp chemical gradient exists, likely from the mixing of two distinct subsurface fluids, one metal rich and one relatively dilute; this has created unique niches for microorganisms. The microbial community analyzed from filtered, oxic borehole water indicated an abundance of sequences from iron-oxidizing bacteria (Gallionella spp.) and was compared to the community from the same borehole after 2 weeks of being plugged with an expandable packer. Statistical analyses with UniFrac revealed a significant shift in community structure following the addition of the packer. Phospholipid fatty acid (PLFA) analysis suggested that Nitrosomonadales dominated the oxic borehole, while PLFAs indicative of anaerobic bacteria were most abundant in the samples from the plugged borehole. Microbial sequences were represented primarily by Firmicutes, Proteobacteria, and a lineage of sequences which did not group with any identified bacterial division; phylogenetic analyses confirmed the presence of a novel candidate division. This "Henderson candidate division" dominated the clone libraries from the dilute anoxic fluids. Sequences obtained from the granitic rock core (1,740 m below the surface) were represented by the divisions Proteobacteria (primarily the family Ralstoniaceae) and Firmicutes. Sequences grouping within Ralstoniaceae were also found in the clone libraries from metal-rich fluids yet were absent in more dilute fluids. Lineage-specific comparisons, combined with phylogenetic statistical analyses, show that geochemical variance has an important effect on microbial community structure in deep, subsurface systems. Copyright ?? 2008, American Society for Microbiology

Prevalence of Hepatitis C Virus Subgenotypes 1a and 1b in Japanese Patients: Ultra-Deep Sequencing Analysis of HCV NS5B Genotype-Specific Region

Science.gov (United States)

Wu, Shuang; Kanda, Tatsuo; Nakamoto, Shingo; Jiang, Xia; Miyamura, Tatsuo; Nakatani, Sueli M.; Ono, Suzane Kioko; Takahashi-Nakaguchi, Azusa; Gonoi, Tohru; Yokosuka, Osamu

2013-01-01

Background Hepatitis C virus (HCV) subgenotypes 1a and 1b have different impacts on the treatment response to peginterferon plus ribavirin with direct-acting antivirals (DAAs) against patients infected with HCV genotype 1, as the emergence rates of resistance mutations are different between these two subgenotypes. In Japan, almost all of HCV genotype 1 belongs to subgenotype 1b. Methods and Findings To determine HCV subgenotype 1a or 1b in Japanese patients infected with HCV genotype 1, real-time PCR-based method and Sanger method were used for the HCV NS5B region. HCV subgenotypes were determined in 90% by real-time PCR-based method. We also analyzed the specific probe regions for HCV subgenotypes 1a and 1b using ultra-deep sequencing, and uncovered mutations that could not be revealed using direct-sequencing by Sanger method. We estimated the prevalence of HCV subgenotype 1a as 1.2-2.5% of HCV genotype 1 patients in Japan. Conclusions Although real-time PCR-based HCV subgenotyping method seems fair for differentiating HCV subgenotypes 1a and 1b, it may not be sufficient for clinical practice. Ultra-deep sequencing is useful for revealing the resistant strain(s) of HCV before DAA treatment as well as mixed infection with different genotypes or subgenotypes of HCV. PMID:24069214

Biogeography of Persephonella in deep-sea hydrothermal vents of the Western Pacific.

Directory of Open Access Journals (Sweden)

Sayaka eMino

2013-04-01

Full Text Available Deep-sea hydrothermal vent fields are areas on the seafloor with high biological productivity fueled by microbial chemosynthesis. Members of the Aquificales genus Persephonella are obligately chemosynthetic bacteria, and appear to be key players in carbon, sulfur, and nitrogen cycles in high temperature habitats at deep-sea vents. Although this group of bacteria has cosmopolitan distribution in deep-sea hydrothermal ecosystem around the world, little is known about their population structure such as intraspecific genomic diversity, distribution pattern, and phenotypic diversity. We developed the multi-locus sequence analysis (MLSA scheme for their genomic characterization. Sequence variation was determined in five housekeeping genes and one functional gene of 36 P. hydrogeniphila strains originated from the Okinawa Trough and the South Mariana Trough. Although the strains share > 98.7% similarities in 16S rRNA gene sequences, MLSA revealed 35 different sequence types, indicating their extensive genomic diversity. A phylogenetic tree inferred from all concatenated gene sequences revealed the clustering of isolates according to the geographic origin. In addition, the phenotypic clustering pattern inferred from whole-cell matrix-assisted laser desorption ionization-time of flight mass spectrometry (MALDI-TOF/MS analysis can be correlated to their MLSA clustering pattern. This study represents the first MLSA combined with phenotypic analysis indicative of allopatric speciation of deep-sea hydrothermal vent bacteria.

Deep Super Learner: A Deep Ensemble for Classification Problems

OpenAIRE

Young, Steven; Abdou, Tamer; Bener, Ayse

2018-01-01

Deep learning has become very popular for tasks such as predictive modeling and pattern recognition in handling big data. Deep learning is a powerful machine learning method that extracts lower level features and feeds them forward for the next layer to identify higher level features that improve performance. However, deep neural networks have drawbacks, which include many hyper-parameters and infinite architectures, opaqueness into results, and relatively slower convergence on smaller datase...

Effect of Deep Brain Stimulation on Speech Performance in Parkinson's Disease

OpenAIRE

Skodda, Sabine

2012-01-01

Deep brain stimulation (DBS) has been reported to be successful in relieving the core motor symptoms of Parkinson's disease (PD) and motor fluctuations in the more advanced stages of the disease. However, data on the effects of DBS on speech performance are inconsistent. While there are some series of patients documenting that speech function was relatively unaffected by DBS of the nucleus subthalamicus (STN), other investigators reported on improvements of distinct parameters of oral control...

Deciphering KRAS and NRAS mutated clone dynamics in MLL-AF4 paediatric leukaemia by ultra deep sequencing analysis.

Science.gov (United States)

Trentin, Luca; Bresolin, Silvia; Giarin, Emanuela; Bardini, Michela; Serafin, Valentina; Accordi, Benedetta; Fais, Franco; Tenca, Claudya; De Lorenzo, Paola; Valsecchi, Maria Grazia; Cazzaniga, Giovanni; Kronnie, Geertruy Te; Basso, Giuseppe

2016-10-04

To induce and sustain the leukaemogenic process, MLL-AF4+ leukaemia seems to require very few genetic alterations in addition to the fusion gene itself. Studies of infant and paediatric patients with MLL-AF4+ B cell precursor acute lymphoblastic leukaemia (BCP-ALL) have reported mutations in KRAS and NRAS with incidences ranging from 25 to 50%. Whereas previous studies employed Sanger sequencing, here we used next generation amplicon deep sequencing for in depth evaluation of RAS mutations in 36 paediatric patients at diagnosis of MLL-AF4+ leukaemia. RAS mutations including those in small sub-clones were detected in 63.9% of patients. Furthermore, the mutational analysis of 17 paired samples at diagnosis and relapse revealed complex RAS clone dynamics and showed that the mutated clones present at relapse were almost all originated from clones that were already detectable at diagnosis and survived to the initial therapy. Finally, we showed that mutated patients were indeed characterized by a RAS related signature at both transcriptional and protein levels and that the targeting of the RAS pathway could be of beneficial for treatment of MLL-AF4+ BCP-ALL clones carrying somatic RAS mutations.

Sleep and memory consolidation: motor performance and proactive interference effects in sequence learning.

Science.gov (United States)

Borragán, Guillermo; Urbain, Charline; Schmitz, Rémy; Mary, Alison; Peigneux, Philippe

2015-04-01

That post-training sleep supports the consolidation of sequential motor skills remains debated. Performance improvement and sensitivity to proactive interference are both putative measures of long-term memory consolidation. We tested sleep-dependent memory consolidation for visuo-motor sequence learning using a proactive interference paradigm. Thirty-three young adults were trained on sequence A on Day 1, then had Regular Sleep (RS) or were Sleep Deprived (SD) on the night after learning. After two recovery nights, they were tested on the same sequence A, then had to learn a novel, potentially competing sequence B. We hypothesized that proactive interference effects on sequence B due to the prior learning of sequence A would be higher in the RS condition, considering that proactive interference is an indirect marker of the robustness of sequence A, which should be better consolidated over post-training sleep. Results highlighted sleep-dependent improvement for sequence A, with faster RTs overnight for RS participants only. Moreover, the beneficial impact of sleep was specific to the consolidation of motor but not sequential skills. Proactive interference effects on learning a new material at Day 4 were similar between RS and SD participants. These results suggest that post-training sleep contributes to optimizing motor but not sequential components of performance in visuo-motor sequence learning. Copyright © 2015 Elsevier Inc. All rights reserved.

Quantitative analysis of the anti-noise performance of an m-sequence in an electromagnetic method

Science.gov (United States)

Yuan, Zhe; Zhang, Yiming; Zheng, Qijia

2018-02-01

An electromagnetic method with a transmitted waveform coded by an m-sequence achieved better anti-noise performance compared to the conventional manner with a square-wave. The anti-noise performance of the m-sequence varied with multiple coding parameters; hence, a quantitative analysis of the anti-noise performance for m-sequences with different coding parameters was required to optimize them. This paper proposes the concept of an identification system, with the identified Earth impulse response obtained by measuring the system output with the input of the voltage response. A quantitative analysis of the anti-noise performance of the m-sequence was achieved by analyzing the amplitude-frequency response of the corresponding identification system. The effects of the coding parameters on the anti-noise performance are summarized by numerical simulation, and their optimization is further discussed in our conclusions; the validity of the conclusions is further verified by field experiment. The quantitative analysis method proposed in this paper provides a new insight into the anti-noise mechanism of the m-sequence, and could be used to evaluate the anti-noise performance of artificial sources in other time-domain exploration methods, such as the seismic method.

Deep learning in TMVA Benchmarking Benchmarking TMVA DNN Integration of a Deep Autoencoder

CERN Document Server

Huwiler, Marc

2017-01-01

The TMVA library in ROOT is dedicated to multivariate analysis, and in partic- ular oers numerous machine learning algorithms in a standardized framework. It is widely used in High Energy Physics for data analysis, mainly to perform regression and classication. To keep up to date with the state of the art in deep learning, a new deep learning module was being developed this summer, oering deep neural net- work, convolutional neural network, and autoencoder. TMVA did not have yet any autoencoder method, and the present project consists in implementing the TMVA autoencoder class based on the deep learning module. It also includes some bench- marking performed on the actual deep neural network implementation, in comparison to the Keras framework with Tensorflow and Theano backend.

Prediction of TRISO coated particle performances for a one-pass deep burn

Energy Technology Data Exchange (ETDEWEB)

Talamo, Alberto [Nuclear Engineering Division, Argonne National Laboratory, 9700 S. Cass Ave, Argonne, IL 60439 (United States)], E-mail: alby@anl.gov

2008-02-15

In the present studies, TRISO coated particle performances have been investigated for incinerating plutonium and minor actinides by the Gas Turbine-Modular Helium Reactor, whose fresh fuel is fabricated after the uranium extraction (UREX) process applied to Light Water Reactors irradiated fuel. The analyses divide into two parts: in the first part, the latest design of the reactor core proposed by General Atomics, which takes advantage of four fuel rings, has been modeled in deep details by the Monte Carlo MCNP code and a burnup process has been simulated by the MCB code. In the second part, the TRISO coated particle performances have been investigated by the PANAMA code with the goal of verifying the design constraints proposed by General Atomics. During burnup, the refueling and shuffling schedule followed the one-pass deep burn concept, where the fuel is utilized, since fabrication for the Gas Turbine-Modular Helium Reactor, without any reprocessing until the final disposal into the geological repository. During the reactor operation, the fast fluence on all TRISO particles layers has been evaluated and the production of the key fission products monitored. During an hypothetical reactor accident scenario, the TRISO particle failure fraction has been estimated.

Prediction of TRISO coated particle performances for a one-pass deep burn

International Nuclear Information System (INIS)

Talamo, Alberto

2008-01-01

In the present studies, TRISO coated particle performances have been investigated for incinerating plutonium and minor actinides by the Gas Turbine-Modular Helium Reactor, whose fresh fuel is fabricated after the uranium extraction (UREX) process applied to Light Water Reactors irradiated fuel. The analyses divide into two parts: in the first part, the latest design of the reactor core proposed by General Atomics, which takes advantage of four fuel rings, has been modeled in deep details by the Monte Carlo MCNP code and a burnup process has been simulated by the MCB code. In the second part, the TRISO coated particle performances have been investigated by the PANAMA code with the goal of verifying the design constraints proposed by General Atomics. During burnup, the refueling and shuffling schedule followed the one-pass deep burn concept, where the fuel is utilized, since fabrication for the Gas Turbine-Modular Helium Reactor, without any reprocessing until the final disposal into the geological repository. During the reactor operation, the fast fluence on all TRISO particles layers has been evaluated and the production of the key fission products monitored. During an hypothetical reactor accident scenario, the TRISO particle failure fraction has been estimated

ComplexContact: a web server for inter-protein contact prediction using deep learning

KAUST Repository

Zeng, Hong; Wang, Sheng; Zhou, Tianming; Zhao, Feifeng; Li, Xiufeng; Wu, Qing; Xu, Jinbo

2018-01-01

ComplexContact (http://raptorx2.uchicago.edu/ComplexContact/) is a web server for sequence-based interfacial residue-residue contact prediction of a putative protein complex. Interfacial residue-residue contacts are critical for understanding how proteins form complex and interact at residue level. When receiving a pair of protein sequences, ComplexContact first searches for their sequence homologs and builds two paired multiple sequence alignments (MSA), then it applies co-evolution analysis and a CASP-winning deep learning (DL) method to predict interfacial contacts from paired MSAs and visualizes the prediction as an image. The DL method was originally developed for intra-protein contact prediction and performed the best in CASP12. Our large-scale experimental test further shows that ComplexContact greatly outperforms pure co-evolution methods for inter-protein contact prediction, regardless of the species.

ComplexContact: a web server for inter-protein contact prediction using deep learning

KAUST Repository

Zeng, Hong

2018-05-20

ComplexContact (http://raptorx2.uchicago.edu/ComplexContact/) is a web server for sequence-based interfacial residue-residue contact prediction of a putative protein complex. Interfacial residue-residue contacts are critical for understanding how proteins form complex and interact at residue level. When receiving a pair of protein sequences, ComplexContact first searches for their sequence homologs and builds two paired multiple sequence alignments (MSA), then it applies co-evolution analysis and a CASP-winning deep learning (DL) method to predict interfacial contacts from paired MSAs and visualizes the prediction as an image. The DL method was originally developed for intra-protein contact prediction and performed the best in CASP12. Our large-scale experimental test further shows that ComplexContact greatly outperforms pure co-evolution methods for inter-protein contact prediction, regardless of the species.

ComplexContact: a web server for inter-protein contact prediction using deep learning.

Science.gov (United States)

Zeng, Hong; Wang, Sheng; Zhou, Tianming; Zhao, Feifeng; Li, Xiufeng; Wu, Qing; Xu, Jinbo

2018-05-22

ComplexContact (http://raptorx2.uchicago.edu/ComplexContact/) is a web server for sequence-based interfacial residue-residue contact prediction of a putative protein complex. Interfacial residue-residue contacts are critical for understanding how proteins form complex and interact at residue level. When receiving a pair of protein sequences, ComplexContact first searches for their sequence homologs and builds two paired multiple sequence alignments (MSA), then it applies co-evolution analysis and a CASP-winning deep learning (DL) method to predict interfacial contacts from paired MSAs and visualizes the prediction as an image. The DL method was originally developed for intra-protein contact prediction and performed the best in CASP12. Our large-scale experimental test further shows that ComplexContact greatly outperforms pure co-evolution methods for inter-protein contact prediction, regardless of the species.

RNA-protein binding motifs mining with a new hybrid deep learning based cross-domain knowledge integration approach.

Science.gov (United States)

Pan, Xiaoyong; Shen, Hong-Bin

2017-02-28

RNAs play key roles in cells through the interactions with proteins known as the RNA-binding proteins (RBP) and their binding motifs enable crucial understanding of the post-transcriptional regulation of RNAs. How the RBPs correctly recognize the target RNAs and why they bind specific positions is still far from clear. Machine learning-based algorithms are widely acknowledged to be capable of speeding up this process. Although many automatic tools have been developed to predict the RNA-protein binding sites from the rapidly growing multi-resource data, e.g. sequence, structure, their domain specific features and formats have posed significant computational challenges. One of current difficulties is that the cross-source shared common knowledge is at a higher abstraction level beyond the observed data, resulting in a low efficiency of direct integration of observed data across domains. The other difficulty is how to interpret the prediction results. Existing approaches tend to terminate after outputting the potential discrete binding sites on the sequences, but how to assemble them into the meaningful binding motifs is a topic worth of further investigation. In viewing of these challenges, we propose a deep learning-based framework (iDeep) by using a novel hybrid convolutional neural network and deep belief network to predict the RBP interaction sites and motifs on RNAs. This new protocol is featured by transforming the original observed data into a high-level abstraction feature space using multiple layers of learning blocks, where the shared representations across different domains are integrated. To validate our iDeep method, we performed experiments on 31 large-scale CLIP-seq datasets, and our results show that by integrating multiple sources of data, the average AUC can be improved by 8% compared to the best single-source-based predictor; and through cross-domain knowledge integration at an abstraction level, it outperforms the state-of-the-art predictors by 6

Deep Convolutional and LSTM Recurrent Neural Networks for Multimodal Wearable Activity Recognition

Directory of Open Access Journals (Sweden)

Francisco Javier Ordóñez

2016-01-01

Full Text Available Human activity recognition (HAR tasks have traditionally been solved using engineered features obtained by heuristic processes. Current research suggests that deep convolutional neural networks are suited to automate feature extraction from raw sensor inputs. However, human activities are made of complex sequences of motor movements, and capturing this temporal dynamics is fundamental for successful HAR. Based on the recent success of recurrent neural networks for time series domains, we propose a generic deep framework for activity recognition based on convolutional and LSTM recurrent units, which: (i is suitable for multimodal wearable sensors; (ii can perform sensor fusion naturally; (iii does not require expert knowledge in designing features; and (iv explicitly models the temporal dynamics of feature activations. We evaluate our framework on two datasets, one of which has been used in a public activity recognition challenge. Our results show that our framework outperforms competing deep non-recurrent networks on the challenge dataset by 4% on average; outperforming some of the previous reported results by up to 9%. Our results show that the framework can be applied to homogeneous sensor modalities, but can also fuse multimodal sensors to improve performance. We characterise key architectural hyperparameters’ influence on performance to provide insights about their optimisation.

Deep Convolutional and LSTM Recurrent Neural Networks for Multimodal Wearable Activity Recognition.

Science.gov (United States)

Ordóñez, Francisco Javier; Roggen, Daniel

2016-01-18

Human activity recognition (HAR) tasks have traditionally been solved using engineered features obtained by heuristic processes. Current research suggests that deep convolutional neural networks are suited to automate feature extraction from raw sensor inputs. However, human activities are made of complex sequences of motor movements, and capturing this temporal dynamics is fundamental for successful HAR. Based on the recent success of recurrent neural networks for time series domains, we propose a generic deep framework for activity recognition based on convolutional and LSTM recurrent units, which: (i) is suitable for multimodal wearable sensors; (ii) can perform sensor fusion naturally; (iii) does not require expert knowledge in designing features; and (iv) explicitly models the temporal dynamics of feature activations. We evaluate our framework on two datasets, one of which has been used in a public activity recognition challenge. Our results show that our framework outperforms competing deep non-recurrent networks on the challenge dataset by 4% on average; outperforming some of the previous reported results by up to 9%. Our results show that the framework can be applied to homogeneous sensor modalities, but can also fuse multimodal sensors to improve performance. We characterise key architectural hyperparameters' influence on performance to provide insights about their optimisation.

Deep Convolutional and LSTM Recurrent Neural Networks for Multimodal Wearable Activity Recognition

Science.gov (United States)

Ordóñez, Francisco Javier; Roggen, Daniel

2016-01-01

Human activity recognition (HAR) tasks have traditionally been solved using engineered features obtained by heuristic processes. Current research suggests that deep convolutional neural networks are suited to automate feature extraction from raw sensor inputs. However, human activities are made of complex sequences of motor movements, and capturing this temporal dynamics is fundamental for successful HAR. Based on the recent success of recurrent neural networks for time series domains, we propose a generic deep framework for activity recognition based on convolutional and LSTM recurrent units, which: (i) is suitable for multimodal wearable sensors; (ii) can perform sensor fusion naturally; (iii) does not require expert knowledge in designing features; and (iv) explicitly models the temporal dynamics of feature activations. We evaluate our framework on two datasets, one of which has been used in a public activity recognition challenge. Our results show that our framework outperforms competing deep non-recurrent networks on the challenge dataset by 4% on average; outperforming some of the previous reported results by up to 9%. Our results show that the framework can be applied to homogeneous sensor modalities, but can also fuse multimodal sensors to improve performance. We characterise key architectural hyperparameters’ influence on performance to provide insights about their optimisation. PMID:26797612

High Performance Systolic Array Core Architecture Design for DNA Sequencer

Directory of Open Access Journals (Sweden)

Saiful Nurdin Dayana

2018-01-01

Full Text Available This paper presents a high performance systolic array (SA core architecture design for Deoxyribonucleic Acid (DNA sequencer. The core implements the affine gap penalty score Smith-Waterman (SW algorithm. This time-consuming local alignment algorithm guarantees optimal alignment between DNA sequences, but it requires quadratic computation time when performed on standard desktop computers. The use of linear SA decreases the time complexity from quadratic to linear. In addition, with the exponential growth of DNA databases, the SA architecture is used to overcome the timing issue. In this work, the SW algorithm has been captured using Verilog Hardware Description Language (HDL and simulated using Xilinx ISIM simulator. The proposed design has been implemented in Xilinx Virtex -6 Field Programmable Gate Array (FPGA and improved in the core area by 90% reduction.

Top tagging with deep neural networks [Vidyo

CERN Multimedia

CERN. Geneva

2017-01-01

Recent literature on deep neural networks for top tagging has focussed on image based techniques or multivariate approaches using high level jet substructure variables. Here, we take a sequential approach to this task by using anordered sequence of energy deposits as training inputs. Unlike previous approaches, this strategy does not result in a loss of information during pixelization or the calculation of high level features. We also propose new preprocessing methods that do not alter key physical quantities such as jet mass. We compare the performance of this approach to standard tagging techniques and present results evaluating the robustness of the neural network to pileup.

Melodic Priming of Motor Sequence Performance: The Role of the Dorsal Premotor Cortex

Directory of Open Access Journals (Sweden)

Marianne Anke Stephan

2016-05-01

Full Text Available The purpose of this study was to determine whether exposure to specific auditory sequences leads to the induction of new motor memories and to investigate the role of the dorsal premotor cortex (dPMC in this crossmodal learning process. Fifty-two young healthy non-musicians were familiarized with the sound to key-press mapping on a computer keyboard and tested on their baseline motor performance. Each participant received subsequently either continuous theta burst stimulation (cTBS or sham stimulation over the dPMC and was then asked to remember a 12-note melody without moving. For half of the participants, the contour of the melody memorized was congruent to a subsequently performed, but never practiced, finger movement sequence (Congruent group. For the other half, the melody memorized was incongruent to the subsequent finger movement sequence (Incongruent group. Hearing a congruent melody led to significantly faster performance of a motor sequence immediately thereafter compared to hearing an incongruent melody. In addition, cTBS speeded up motor performance in both groups, possibly by relieving motor consolidation from interference by the declarative melody memorization task. Our findings substantiate recent evidence that exposure to a movement-related tone sequence can induce specific, crossmodal encoding of a movement sequence representation. They further suggest that cTBS over the dPMC may enhance early offline procedural motor skill consolidation in cognitive states where motor consolidation would normally be disturbed by concurrent declarative memory processes. These findings may contribute to a better understanding of auditory-motor system interactions and have implications for the development of new motor rehabilitation approaches using sound and non-invasive brain stimulation as neuromodulatory tools.

Anti-slamming bulbous bow and tunnel stern applications on a novel Deep-V catamaran for improved performance

Directory of Open Access Journals (Sweden)

Mehmet Atlar

2013-06-01

Full Text Available While displacement type Deep-V mono hulls have superior seakeeping behaviour at speed, catamarans typically have modest behaviour in rough seas. It is therefore a logical progression to combine the superior seakeeping performance of a displacement type Deep-V mono-hull with the high-speed benefits of a catamaran to take the advantages of both hull forms. The displacement Deep-V catamaran concept was developed in Newcastle University and Newcastle University's own multi-purpose research vessel, which was launched in 2011, pushed the design envelope still further with the successful adoption of a novel anti-slamming bulbous bow and tunnel stern for improved efficiency. This paper presents the hullform development of this unique vessel to understand the contribution of the novel bow and stern features on the performance of the Deep-V catamaran. The study is also a further validation of the hull resistance by using advanced numerical analysis methods in conjunction with the model test. An assessment of the numerical predictions of the hull resistance is also made against physical model test results and shows a good agreement between them.

Modic Type 1 Changes: Detection Performance of Fat-Suppressed Fluid-Sensitive MRI Sequences.

Science.gov (United States)

Finkenstaedt, Tim; Del Grande, Filippo; Bolog, Nicolae; Ulrich, Nils; Tok, Sina; Kolokythas, Orpheus; Steurer, Johann; Andreisek, Gustav; Winklhofer, Sebastian

2018-02-01

 To assess the performance of fat-suppressed fluid-sensitive MRI sequences compared to T1-weighted (T1w) / T2w sequences for the detection of Modic 1 end-plate changes on lumbar spine MRI.  Sagittal T1w, T2w, and fat-suppressed fluid-sensitive MRI images of 100 consecutive patients (consequently 500 vertebral segments; 52 female, mean age 74 ± 7.4 years; 48 male, mean age 71 ± 6.3 years) were retrospectively evaluated. We recorded the presence (yes/no) and extension (i. e., Likert-scale of height, volume, and end-plate extension) of Modic I changes in T1w/T2w sequences and compared the results to fat-suppressed fluid-sensitive sequences (McNemar/Wilcoxon-signed-rank test).  Fat-suppressed fluid-sensitive sequences revealed significantly more Modic I changes compared to T1w/T2w sequences (156 vs. 93 segments, respectively; p definition of Modic I changes is not fully applicable anymore.. · Fat-suppressed fluid-sensitive MRI sequences revealed more/greater extent of Modic I changes.. · Finkenstaedt T, Del Grande F, Bolog N et al. Modic Type 1 Changes: Detection Performance of Fat-Suppressed Fluid-Sensitive MRI Sequences. Fortschr Röntgenstr 2018; 190: 152 - 160. © Georg Thieme Verlag KG Stuttgart · New York.

Evolutionary process of deep-sea bathymodiolus mussels.

Science.gov (United States)

Miyazaki, Jun-Ichi; de Oliveira Martins, Leonardo; Fujita, Yuko; Matsumoto, Hiroto; Fujiwara, Yoshihiro

2010-04-27

Since the discovery of deep-sea chemosynthesis-based communities, much work has been done to clarify their organismal and environmental aspects. However, major topics remain to be resolved, including when and how organisms invade and adapt to deep-sea environments; whether strategies for invasion and adaptation are shared by different taxa or unique to each taxon; how organisms extend their distribution and diversity; and how they become isolated to speciate in continuous waters. Deep-sea mussels are one of the dominant organisms in chemosynthesis-based communities, thus investigations of their origin and evolution contribute to resolving questions about life in those communities. We investigated worldwide phylogenetic relationships of deep-sea Bathymodiolus mussels and their mytilid relatives by analyzing nucleotide sequences of the mitochondrial cytochrome c oxidase subunit I (COI) and NADH dehydrogenase subunit 4 (ND4) genes. Phylogenetic analysis of the concatenated sequence data showed that mussels of the subfamily Bathymodiolinae from vents and seeps were divided into four groups, and that mussels of the subfamily Modiolinae from sunken wood and whale carcasses assumed the outgroup position and shallow-water modioline mussels were positioned more distantly to the bathymodioline mussels. We provisionally hypothesized the evolutionary history of Bathymodilolus mussels by estimating evolutionary time under a relaxed molecular clock model. Diversification of bathymodioline mussels was initiated in the early Miocene, and subsequently diversification of the groups occurred in the early to middle Miocene. The phylogenetic relationships support the "Evolutionary stepping stone hypothesis," in which mytilid ancestors exploited sunken wood and whale carcasses in their progressive adaptation to deep-sea environments. This hypothesis is also supported by the evolutionary transition of symbiosis in that nutritional adaptation to the deep sea proceeded from extracellular

Comparison of the live attenuated yellow fever vaccine 17D-204 strain to its virulent parental strain Asibi by deep sequencing.

Science.gov (United States)

Beck, Andrew; Tesh, Robert B; Wood, Thomas G; Widen, Steven G; Ryman, Kate D; Barrett, Alan D T

2014-02-01

The first comparison of a live RNA viral vaccine strain to its wild-type parental strain by deep sequencing is presented using as a model the yellow fever virus (YFV) live vaccine strain 17D-204 and its wild-type parental strain, Asibi. The YFV 17D-204 vaccine genome was compared to that of the parental strain Asibi by massively parallel methods. Variability was compared on multiple scales of the viral genomes. A modeled exploration of small-frequency variants was performed to reconstruct plausible regions of mutational plasticity. Overt quasispecies diversity is a feature of the parental strain, whereas the live vaccine strain lacks diversity according to multiple independent measurements. A lack of attenuating mutations in the Asibi population relative to that of 17D-204 was observed, demonstrating that the vaccine strain was derived by discrete mutation of Asibi and not by selection of genomes in the wild-type population. Relative quasispecies structure is a plausible correlate of attenuation for live viral vaccines. Analyses such as these of attenuated viruses improve our understanding of the molecular basis of vaccine attenuation and provide critical information on the stability of live vaccines and the risk of reversion to virulence.

Application of Tandem Two-Dimensional Mass Spectrometry for Top-Down Deep Sequencing of Calmodulin.

Science.gov (United States)

Floris, Federico; Chiron, Lionel; Lynch, Alice M; Barrow, Mark P; Delsuc, Marc-André; O'Connor, Peter B

2018-06-04

Two-dimensional mass spectrometry (2DMS) involves simultaneous acquisition of the fragmentation patterns of all the analytes in a mixture by correlating their precursor and fragment ions by modulating precursor ions systematically through a fragmentation zone. Tandem two-dimensional mass spectrometry (MS/2DMS) unites the ultra-high accuracy of Fourier transform ion cyclotron resonance (FT-ICR) MS/MS and the simultaneous data-independent fragmentation of 2DMS to achieve extensive inter-residue fragmentation of entire proteins. 2DMS was recently developed for top-down proteomics (TDP), and applied to the analysis of calmodulin (CaM), reporting a cleavage coverage of about ~23% using infrared multiphoton dissociation (IRMPD) as fragmentation technique. The goal of this work is to expand the utility of top-down protein analysis using MS/2DMS in order to extend the cleavage coverage in top-down proteomics further into the interior regions of the protein. In this case, using MS/2DMS, the cleavage coverage of CaM increased from ~23% to ~42%. Graphical Abstract Two-dimensional mass spectrometry, when applied to primary fragment ions from the source, allows deep-sequencing of the protein calmodulin.

Comparative performance of double-digest RAD sequencing across divergent arachnid lineages.

Science.gov (United States)

Burns, Mercedes; Starrett, James; Derkarabetian, Shahan; Richart, Casey H; Cabrero, Allan; Hedin, Marshal

2017-05-01

Next-generation sequencing technologies now allow researchers of non-model systems to perform genome-based studies without the requirement of a (often unavailable) closely related genomic reference. We evaluated the role of restriction endonuclease (RE) selection in double-digest restriction-site-associated DNA sequencing (ddRADseq) by generating reduced representation genome-wide data using four different RE combinations. Our expectation was that RE selections targeting longer, more complex restriction sites would recover fewer loci than RE with shorter, less complex sites. We sequenced a diverse sample of non-model arachnids, including five congeneric pairs of harvestmen (Opiliones) and four pairs of spiders (Araneae). Sample pairs consisted of either conspecifics or closely related congeneric taxa, and in total 26 sample pair analyses were tested. Sequence demultiplexing, read clustering and variant calling were performed in the pyRAD program. The 6-base pair cutter EcoRI combined with methylated site-specific 4-base pair cutter MspI produced, on average, the greatest numbers of intra-individual loci and shared loci per sample pair. As expected, the number of shared loci recovered for a sample pair covaried with the degree of genetic divergence, estimated with cytochrome oxidase I sequences, although this relationship was non-linear. Our comparative results will prove useful in guiding protocol selection for ddRADseq experiments on many arachnid taxa where reference genomes, even from closely related species, are unavailable. © 2016 John Wiley & Sons Ltd.

Assessment of deep tissue hyperalgesia in the groin – a method comparison of electrical vs. pressure stimulation

DEFF Research Database (Denmark)

Aasvang, E K; Werner, M U; Kehlet, H

2014-01-01

BACKGROUND: Deep pain complaints are more frequent than cutaneous in post-surgical patients, and a prevalent finding in quantitative sensory testing studies. However, the preferred assessment method - pressure algometry - is indirect and tissue unspecific, hindering advances in treatment and prev......BACKGROUND: Deep pain complaints are more frequent than cutaneous in post-surgical patients, and a prevalent finding in quantitative sensory testing studies. However, the preferred assessment method - pressure algometry - is indirect and tissue unspecific, hindering advances in treatment...... thresholds to pressure algometry, by performing identical test-retest sequences 10 days apart, in deep tissues in the groin region. Electrical stimulation was performed by five up-and-down staircase series of single impulses of 0.04 ms duration, starting from 0 mA in increments of 0.2 mA until a threshold......: The presented tissue-specific direct deep tissue electrical stimulation technique has equal or superior reliability compared with the indirect tissue-unspecific stimulation by pressure algometry. This method may facilitate advances in mechanism based preventive and treatment strategies in acute and chronic post...

A better state-of-mind: deep breathing reduces state anxiety and enhances test performance through regulating test cognitions in children.

Science.gov (United States)

Khng, Kiat Hui

2017-11-01

A pre-test/post-test, intervention-versus-control experimental design was used to examine the effects, mechanisms and moderators of deep breathing on state anxiety and test performance in 122 Primary 5 students. Taking deep breaths before a timed math test significantly reduced self-reported feelings of anxiety and improved test performance. There was a statistical trend towards greater effectiveness in reducing state anxiety for boys compared to girls, and in enhancing test performance for students with higher autonomic reactivity in test-like situations. The latter moderation was significant when comparing high-versus-low autonomic reactivity groups. Mediation analyses suggest that deep breathing reduces state anxiety in test-like situations, creating a better state-of-mind by enhancing the regulation of adaptive-maladaptive thoughts during the test, allowing for better performance. The quick and simple technique can be easily learnt and effectively applied by most children to immediately alleviate some of the adverse effects of test anxiety on psychological well-being and academic performance.

Predicting backbone Cα angles and dihedrals from protein sequences by stacked sparse auto-encoder deep neural network.

Science.gov (United States)

Lyons, James; Dehzangi, Abdollah; Heffernan, Rhys; Sharma, Alok; Paliwal, Kuldip; Sattar, Abdul; Zhou, Yaoqi; Yang, Yuedong

2014-10-30

Because a nearly constant distance between two neighbouring Cα atoms, local backbone structure of proteins can be represented accurately by the angle between C(αi-1)-C(αi)-C(αi+1) (θ) and a dihedral angle rotated about the C(αi)-C(αi+1) bond (τ). θ and τ angles, as the representative of structural properties of three to four amino-acid residues, offer a description of backbone conformations that is complementary to φ and ψ angles (single residue) and secondary structures (>3 residues). Here, we report the first machine-learning technique for sequence-based prediction of θ and τ angles. Predicted angles based on an independent test have a mean absolute error of 9° for θ and 34° for τ with a distribution on the θ-τ plane close to that of native values. The average root-mean-square distance of 10-residue fragment structures constructed from predicted θ and τ angles is only 1.9Å from their corresponding native structures. Predicted θ and τ angles are expected to be complementary to predicted ϕ and ψ angles and secondary structures for using in model validation and template-based as well as template-free structure prediction. The deep neural network learning technique is available as an on-line server called Structural Property prediction with Integrated DEep neuRal network (SPIDER) at http://sparks-lab.org. Copyright © 2014 Wiley Periodicals, Inc.

Deep shaft high rate aerobic digestion: laboratory and pilot plant performance

Energy Technology Data Exchange (ETDEWEB)

Tran, F; Gannon, D

1981-01-01

The Deep Shaft is essentially an air-lift reactor, sunk deep in the ground (100-160 m); the resulting high hydrostatic pressure together with very efficient mixing in the shaft provide extremely high O transfer efficiencies (O.T.E.) of less than or equal to 90% vs. 4-20% in other aerators. This high O.T.E. suggests real potential for Deep-Shaft technology in the aerobic digestion of sludges and animal wastes: with conventional aerobic digesters an O.T.E. over 8% is extremely difficult to achieve. Laboratory and pilot plant Deep-Shaft aerobic digester studies carried out at Eco-Research's Pointe Claire, Quebec laboratories, and at the Paris, Ontario pilot Deep-Shaft digester are described.

Deep learning evaluation using deep linguistic processing

OpenAIRE

Kuhnle, Alexander; Copestake, Ann

2017-01-01

We discuss problems with the standard approaches to evaluation for tasks like visual question answering, and argue that artificial data can be used to address these as a complement to current practice. We demonstrate that with the help of existing 'deep' linguistic processing technology we are able to create challenging abstract datasets, which enable us to investigate the language understanding abilities of multimodal deep learning models in detail, as compared to a single performance value ...

Position-specific automated processing of V3 env ultra-deep pyrosequencing data for predicting HIV-1 tropism.

Science.gov (United States)

Jeanne, Nicolas; Saliou, Adrien; Carcenac, Romain; Lefebvre, Caroline; Dubois, Martine; Cazabat, Michelle; Nicot, Florence; Loiseau, Claire; Raymond, Stéphanie; Izopet, Jacques; Delobel, Pierre

2015-11-20

HIV-1 coreceptor usage must be accurately determined before starting CCR5 antagonist-based treatment as the presence of undetected minor CXCR4-using variants can cause subsequent virological failure. Ultra-deep pyrosequencing of HIV-1 V3 env allows to detect low levels of CXCR4-using variants that current genotypic approaches miss. However, the computation of the mass of sequence data and the need to identify true minor variants while excluding artifactual sequences generated during amplification and ultra-deep pyrosequencing is rate-limiting. Arbitrary fixed cut-offs below which minor variants are discarded are currently used but the errors generated during ultra-deep pyrosequencing are sequence-dependant rather than random. We have developed an automated processing of HIV-1 V3 env ultra-deep pyrosequencing data that uses biological filters to discard artifactual or non-functional V3 sequences followed by statistical filters to determine position-specific sensitivity thresholds, rather than arbitrary fixed cut-offs. It allows to retain authentic sequences with point mutations at V3 positions of interest and discard artifactual ones with accurate sensitivity thresholds.

Trace maps for arbitrary substitution sequences

International Nuclear Information System (INIS)

Avishai, Y.

1993-01-01

The discovery of quasi-crystals and their 1-dimensional modeling have led to a deep mathematical study of Schroedinger operators with an arbitrary deterministic potential sequence. In this work we address this problem and find trace maps for an arbitrary substitution sequence. our trace maps have lower dimensionality than those of Kolar and Nori, which make them quite attractive for actual applications. (authors)

Genome-wide prediction of cis-regulatory regions using supervised deep learning methods.

Science.gov (United States)

Li, Yifeng; Shi, Wenqiang; Wasserman, Wyeth W

2018-05-31

In the human genome, 98% of DNA sequences are non-protein-coding regions that were previously disregarded as junk DNA. In fact, non-coding regions host a variety of cis-regulatory regions which precisely control the expression of genes. Thus, Identifying active cis-regulatory regions in the human genome is critical for understanding gene regulation and assessing the impact of genetic variation on phenotype. The developments of high-throughput sequencing and machine learning technologies make it possible to predict cis-regulatory regions genome wide. Based on rich data resources such as the Encyclopedia of DNA Elements (ENCODE) and the Functional Annotation of the Mammalian Genome (FANTOM) projects, we introduce DECRES based on supervised deep learning approaches for the identification of enhancer and promoter regions in the human genome. Due to their ability to discover patterns in large and complex data, the introduction of deep learning methods enables a significant advance in our knowledge of the genomic locations of cis-regulatory regions. Using models for well-characterized cell lines, we identify key experimental features that contribute to the predictive performance. Applying DECRES, we delineate locations of 300,000 candidate enhancers genome wide (6.8% of the genome, of which 40,000 are supported by bidirectional transcription data), and 26,000 candidate promoters (0.6% of the genome). The predicted annotations of cis-regulatory regions will provide broad utility for genome interpretation from functional genomics to clinical applications. The DECRES model demonstrates potentials of deep learning technologies when combined with high-throughput sequencing data, and inspires the development of other advanced neural network models for further improvement of genome annotations.

High-throughput deep sequencing reveals that microRNAs play important roles in salt tolerance of euhalophyte Salicornia europaea.

Science.gov (United States)

Feng, Juanjuan; Wang, Jinhui; Fan, Pengxiang; Jia, Weitao; Nie, Lingling; Jiang, Ping; Chen, Xianyang; Lv, Sulian; Wan, Lichuan; Chang, Sandra; Li, Shizhong; Li, Yinxin

2015-02-26

microRNAs (miRNAs) are implicated in plant development processes and play pivotal roles in plant adaptation to environmental stresses. Salicornia europaea, a salt mash euhalophyte, is a suitable model plant to study salt adaptation mechanisms. S. europaea is also a vegetable, forage, and oilseed that can be used for saline land reclamation and biofuel precursor production on marginal lands. Despite its importance, no miRNA has been identified from S. europaea thus far. Deep sequencing was performed to investigate small RNA transcriptome of S. europaea. Two hundred and ten conserved miRNAs comprising 51 families and 31 novel miRNAs (including seven miRNA star sequences) belonging to 30 families were identified. About half (13 out of 31) of the novel miRNAs were only detected in salt-treated samples. The expression of 43 conserved and 13 novel miRNAs significantly changed in response to salinity. In addition, 53 conserved and 13 novel miRNAs were differentially expressed between the shoots and roots. Furthermore, 306 and 195 S. europaea unigenes were predicted to be targets of 41 conserved and 29 novel miRNA families, respectively. These targets encoded a wide range of proteins, and genes involved in transcription regulation constituted the largest category. Four of these genes encoding laccase, F-box family protein, SAC3/GANP family protein, and NADPH cytochrome P-450 reductase were validated using 5'-RACE. Our results indicate that specific miRNAs are tightly regulated by salinity in the shoots and/or roots of S. europaea, which may play important roles in salt tolerance of this euhalophyte. The S. europaea salt-responsive miRNAs and miRNAs that target transcription factors, nucleotide binding site-leucine-rich repeat proteins and enzymes involved in lignin biosynthesis as well as carbon and nitrogen metabolism may be applied in genetic engineering of crops with high stress tolerance, and genetic modification of biofuel crops with high biomass and regulatable

Deep Recurrent Model for Server Load and Performance Prediction in Data Center

Directory of Open Access Journals (Sweden)

Zheng Huang

2017-01-01

Full Text Available Recurrent neural network (RNN has been widely applied to many sequential tagging tasks such as natural language process (NLP and time series analysis, and it has been proved that RNN works well in those areas. In this paper, we propose using RNN with long short-term memory (LSTM units for server load and performance prediction. Classical methods for performance prediction focus on building relation between performance and time domain, which makes a lot of unrealistic hypotheses. Our model is built based on events (user requests, which is the root cause of server performance. We predict the performance of the servers using RNN-LSTM by analyzing the log of servers in data center which contains user’s access sequence. Previous work for workload prediction could not generate detailed simulated workload, which is useful in testing the working condition of servers. Our method provides a new way to reproduce user request sequence to solve this problem by using RNN-LSTM. Experiment result shows that our models get a good performance in generating load and predicting performance on the data set which has been logged in online service. We did experiments with nginx web server and mysql database server, and our methods can been easily applied to other servers in data center.

Performance of cellulose derivatives in deep-fried battered snacks: Oil barrier and crispy properties

NARCIS (Netherlands)

Primo-Martín, C.; Sanz, T.; Steringa, D.W.; Salvador, A.; Fiszman, S.M.; Vliet, T. van

2010-01-01

The performance of batters containing cellulose derivatives (methyl cellulose (A4M), three hydroxypropylmethyl celluloses (E4M, F4M and K4M) with different degree of hydroxypropyl and/or methyl substitution and carboxymethyl cellulose (CMC)) to produce crispy deep-fried snacks crusts was studied by

Uniform, optimal signal processing of mapped deep-sequencing data.

Science.gov (United States)

Kumar, Vibhor; Muratani, Masafumi; Rayan, Nirmala Arul; Kraus, Petra; Lufkin, Thomas; Ng, Huck Hui; Prabhakar, Shyam

2013-07-01

Despite their apparent diversity, many problems in the analysis of high-throughput sequencing data are merely special cases of two general problems, signal detection and signal estimation. Here we adapt formally optimal solutions from signal processing theory to analyze signals of DNA sequence reads mapped to a genome. We describe DFilter, a detection algorithm that identifies regulatory features in ChIP-seq, DNase-seq and FAIRE-seq data more accurately than assay-specific algorithms. We also describe EFilter, an estimation algorithm that accurately predicts mRNA levels from as few as 1-2 histone profiles (R ∼0.9). Notably, the presence of regulatory motifs in promoters correlates more with histone modifications than with mRNA levels, suggesting that histone profiles are more predictive of cis-regulatory mechanisms. We show by applying DFilter and EFilter to embryonic forebrain ChIP-seq data that regulatory protein identification and functional annotation are feasible despite tissue heterogeneity. The mathematical formalism underlying our tools facilitates integrative analysis of data from virtually any sequencing-based functional profile.

MUFOLD-SS: New deep inception-inside-inception networks for protein secondary structure prediction.

Science.gov (United States)

Fang, Chao; Shang, Yi; Xu, Dong

2018-05-01

Protein secondary structure prediction can provide important information for protein 3D structure prediction and protein functions. Deep learning offers a new opportunity to significantly improve prediction accuracy. In this article, a new deep neural network architecture, named the Deep inception-inside-inception (Deep3I) network, is proposed for protein secondary structure prediction and implemented as a software tool MUFOLD-SS. The input to MUFOLD-SS is a carefully designed feature matrix corresponding to the primary amino acid sequence of a protein, which consists of a rich set of information derived from individual amino acid, as well as the context of the protein sequence. Specifically, the feature matrix is a composition of physio-chemical properties of amino acids, PSI-BLAST profile, and HHBlits profile. MUFOLD-SS is composed of a sequence of nested inception modules and maps the input matrix to either eight states or three states of secondary structures. The architecture of MUFOLD-SS enables effective processing of local and global interactions between amino acids in making accurate prediction. In extensive experiments on multiple datasets, MUFOLD-SS outperformed the best existing methods and other deep neural networks significantly. MUFold-SS can be downloaded from http://dslsrv8.cs.missouri.edu/~cf797/MUFoldSS/download.html. © 2018 Wiley Periodicals, Inc.

Human Splice-Site Prediction with Deep Neural Networks.

Science.gov (United States)

Naito, Tatsuhiko

2018-04-18

Accurate splice-site prediction is essential to delineate gene structures from sequence data. Several computational techniques have been applied to create a system to predict canonical splice sites. For classification tasks, deep neural networks (DNNs) have achieved record-breaking results and often outperformed other supervised learning techniques. In this study, a new method of splice-site prediction using DNNs was proposed. The proposed system receives an input sequence data and returns an answer as to whether it is splice site. The length of input is 140 nucleotides, with the consensus sequence (i.e., "GT" and "AG" for the donor and acceptor sites, respectively) in the middle. Each input sequence model is applied to the pretrained DNN model that determines the probability that an input is a splice site. The model consists of convolutional layers and bidirectional long short-term memory network layers. The pretraining and validation were conducted using the data set tested in previously reported methods. The performance evaluation results showed that the proposed method can outperform the previous methods. In addition, the pattern learned by the DNNs was visualized as position frequency matrices (PFMs). Some of PFMs were very similar to the consensus sequence. The trained DNN model and the brief source code for the prediction system are uploaded. Further improvement will be achieved following the further development of DNNs.

Probing the Rare Biosphere of the North-West Mediterranean Sea: An Experiment with High Sequencing Effort.

Directory of Open Access Journals (Sweden)

Bibiana G Crespo

Full Text Available High-throughput sequencing (HTS techniques have suggested the existence of a wealth of species with very low relative abundance: the rare biosphere. We attempted to exhaustively map this rare biosphere in two water samples by performing an exceptionally deep pyrosequencing analysis (~500,000 final reads per sample. Species data were derived by a 97% identity criterion and various parametric distributions were fitted to the observed counts. Using the best-fitting Sichel distribution we estimate a total species richness of 1,568-1,669 (95% Credible Interval and 5,027-5,196 for surface and deep water samples respectively, implying that 84-89% of the total richness in those two samples was sequenced, and we predict that a quadrupling of the present sequencing effort would suffice to observe 90% of the total richness in both samples. Comparing the HTS results with a culturing approach we found that most of the cultured taxa were not obtained by HTS, despite the high sequencing effort. Culturing therefore remains a useful tool for uncovering marine bacterial diversity, in addition to its other uses for studying the ecology of marine bacteria.

Probing the Rare Biosphere of the North-West Mediterranean Sea: An Experiment with High Sequencing Effort.

Science.gov (United States)

Crespo, Bibiana G; Wallhead, Philip J; Logares, Ramiro; Pedrós-Alió, Carlos

2016-01-01

High-throughput sequencing (HTS) techniques have suggested the existence of a wealth of species with very low relative abundance: the rare biosphere. We attempted to exhaustively map this rare biosphere in two water samples by performing an exceptionally deep pyrosequencing analysis (~500,000 final reads per sample). Species data were derived by a 97% identity criterion and various parametric distributions were fitted to the observed counts. Using the best-fitting Sichel distribution we estimate a total species richness of 1,568-1,669 (95% Credible Interval) and 5,027-5,196 for surface and deep water samples respectively, implying that 84-89% of the total richness in those two samples was sequenced, and we predict that a quadrupling of the present sequencing effort would suffice to observe 90% of the total richness in both samples. Comparing the HTS results with a culturing approach we found that most of the cultured taxa were not obtained by HTS, despite the high sequencing effort. Culturing therefore remains a useful tool for uncovering marine bacterial diversity, in addition to its other uses for studying the ecology of marine bacteria.

Sequence Capture versus Restriction Site Associated DNA Sequencing for Shallow Systematics.

Science.gov (United States)

Harvey, Michael G; Smith, Brian Tilston; Glenn, Travis C; Faircloth, Brant C; Brumfield, Robb T

2016-09-01

Sequence capture and restriction site associated DNA sequencing (RAD-Seq) are two genomic enrichment strategies for applying next-generation sequencing technologies to systematics studies. At shallow timescales, such as within species, RAD-Seq has been widely adopted among researchers, although there has been little discussion of the potential limitations and benefits of RAD-Seq and sequence capture. We discuss a series of issues that may impact the utility of sequence capture and RAD-Seq data for shallow systematics in non-model species. We review prior studies that used both methods, and investigate differences between the methods by re-analyzing existing RAD-Seq and sequence capture data sets from a Neotropical bird (Xenops minutus). We suggest that the strengths of RAD-Seq data sets for shallow systematics are the wide dispersion of markers across the genome, the relative ease and cost of laboratory work, the deep coverage and read overlap at recovered loci, and the high overall information that results. Sequence capture's benefits include flexibility and repeatability in the genomic regions targeted, success using low-quality samples, more straightforward read orthology assessment, and higher per-locus information content. The utility of a method in systematics, however, rests not only on its performance within a study, but on the comparability of data sets and inferences with those of prior work. In RAD-Seq data sets, comparability is compromised by low overlap of orthologous markers across species and the sensitivity of genetic diversity in a data set to an interaction between the level of natural heterozygosity in the samples examined and the parameters used for orthology assessment. In contrast, sequence capture of conserved genomic regions permits interrogation of the same loci across divergent species, which is preferable for maintaining comparability among data sets and studies for the purpose of drawing general conclusions about the impact of

Deep sequencing reveals different compositions of mRNA transcribed from the F8 gene in a panel of FVIII-producing CHO cell lines

DEFF Research Database (Denmark)

Kaas, Christian Schrøder; Bolt, Gert; Hansen, Jens J

2015-01-01

orders of magnitude lower than for antibodies. In the present study we investigated CHO DXB11 cells transfected with a plasmid encoding human coagulation factor VIII. Single cell clones were isolated from the pool of transfectants and a panel of 14 clones representing a dynamic range of FVIII...... FVIII productivity. It was found that three MTX resistant, nonproducing clones had different truncations of the F8 transcripts. We find that by using deep sequencing, in contrast to microarray technology, for determining the transcriptome from CHO transfectants, we are able to accurately deduce...

Performance Results for Massachusetts and Rhode Island Deep Energy Retrofit Pilot Community

Energy Technology Data Exchange (ETDEWEB)

Gates, C. [Building Science Corporation, Somerville, MA (United States); Neuhauser, K. [Building Science Corporation, Somerville, MA (United States)

2014-03-01

Between December, 2009 and December, 2012, 42 deep energy retrofit (DER) projects were completed through a pilot program sponsored by National Grid and conducted in Massachusetts and Rhode Island. Thirty-seven of these projects were comprehensive retrofits while five were partial DERs, meaning that high performance retrofit was implemented for a single major enclosure component or a limited number of major enclosure components. Building Science Corporation developed a consistent "package" of measures in terms of the performance targeted for major building components. Based on the community experience, this DER package is expected to result in yearly source energy use near 110 MMBtu/year or approximately 40% below the Northeast regional average.

Improving Protein Fold Recognition by Deep Learning Networks

Science.gov (United States)

Jo, Taeho; Hou, Jie; Eickholt, Jesse; Cheng, Jianlin

2015-12-01

For accurate recognition of protein folds, a deep learning network method (DN-Fold) was developed to predict if a given query-template protein pair belongs to the same structural fold. The input used stemmed from the protein sequence and structural features extracted from the protein pair. We evaluated the performance of DN-Fold along with 18 different methods on Lindahl’s benchmark dataset and on a large benchmark set extracted from SCOP 1.75 consisting of about one million protein pairs, at three different levels of fold recognition (i.e., protein family, superfamily, and fold) depending on the evolutionary distance between protein sequences. The correct recognition rate of ensembled DN-Fold for Top 1 predictions is 84.5%, 61.5%, and 33.6% and for Top 5 is 91.2%, 76.5%, and 60.7% at family, superfamily, and fold levels, respectively. We also evaluated the performance of single DN-Fold (DN-FoldS), which showed the comparable results at the level of family and superfamily, compared to ensemble DN-Fold. Finally, we extended the binary classification problem of fold recognition to real-value regression task, which also show a promising performance. DN-Fold is freely available through a web server at http://iris.rnet.missouri.edu/dnfold.

Improving Protein Fold Recognition by Deep Learning Networks.

Science.gov (United States)

Jo, Taeho; Hou, Jie; Eickholt, Jesse; Cheng, Jianlin

2015-12-04

For accurate recognition of protein folds, a deep learning network method (DN-Fold) was developed to predict if a given query-template protein pair belongs to the same structural fold. The input used stemmed from the protein sequence and structural features extracted from the protein pair. We evaluated the performance of DN-Fold along with 18 different methods on Lindahl's benchmark dataset and on a large benchmark set extracted from SCOP 1.75 consisting of about one million protein pairs, at three different levels of fold recognition (i.e., protein family, superfamily, and fold) depending on the evolutionary distance between protein sequences. The correct recognition rate of ensembled DN-Fold for Top 1 predictions is 84.5%, 61.5%, and 33.6% and for Top 5 is 91.2%, 76.5%, and 60.7% at family, superfamily, and fold levels, respectively. We also evaluated the performance of single DN-Fold (DN-FoldS), which showed the comparable results at the level of family and superfamily, compared to ensemble DN-Fold. Finally, we extended the binary classification problem of fold recognition to real-value regression task, which also show a promising performance. DN-Fold is freely available through a web server at http://iris.rnet.missouri.edu/dnfold.

Deep Space Network equipment performance, reliability, and operations management information system

Science.gov (United States)

Cooper, T.; Lin, J.; Chatillon, M.

2002-01-01

The Deep Space Mission System (DSMS) Operations Program Office and the DeepSpace Network (DSN) facilities utilize the Discrepancy Reporting Management System (DRMS) to collect, process, communicate and manage data discrepancies, equipment resets, physical equipment status, and to maintain an internal Station Log. A collaborative effort development between JPL and the Canberra Deep Space Communication Complex delivered a system to support DSN Operations.

Deep Sequencing of Plant and Animal DNA Contained within Traditional Chinese Medicines Reveals Legality Issues and Health Safety Concerns

Science.gov (United States)

Coghlan, Megan L.; Haile, James; Houston, Jayne; Murray, Dáithí C.; White, Nicole E.; Moolhuijzen, Paula; Bellgard, Matthew I.; Bunce, Michael

2012-01-01

Traditional Chinese medicine (TCM) has been practiced for thousands of years, but only within the last few decades has its use become more widespread outside of Asia. Concerns continue to be raised about the efficacy, legality, and safety of many popular complementary alternative medicines, including TCMs. Ingredients of some TCMs are known to include derivatives of endangered, trade-restricted species of plants and animals, and therefore contravene the Convention on International Trade in Endangered Species (CITES) legislation. Chromatographic studies have detected the presence of heavy metals and plant toxins within some TCMs, and there are numerous cases of adverse reactions. It is in the interests of both biodiversity conservation and public safety that techniques are developed to screen medicinals like TCMs. Targeting both the p-loop region of the plastid trnL gene and the mitochondrial 16S ribosomal RNA gene, over 49,000 amplicon sequence reads were generated from 15 TCM samples presented in the form of powders, tablets, capsules, bile flakes, and herbal teas. Here we show that second-generation, high-throughput sequencing (HTS) of DNA represents an effective means to genetically audit organic ingredients within complex TCMs. Comparison of DNA sequence data to reference databases revealed the presence of 68 different plant families and included genera, such as Ephedra and Asarum, that are potentially toxic. Similarly, animal families were identified that include genera that are classified as vulnerable, endangered, or critically endangered, including Asiatic black bear (Ursus thibetanus) and Saiga antelope (Saiga tatarica). Bovidae, Cervidae, and Bufonidae DNA were also detected in many of the TCM samples and were rarely declared on the product packaging. This study demonstrates that deep sequencing via HTS is an efficient and cost-effective way to audit highly processed TCM products and will assist in monitoring their legality and safety especially when

Deep sequencing of the Camellia sinensis transcriptome revealed candidate genes for major metabolic pathways of tea-specific compounds

Energy Technology Data Exchange (ETDEWEB)

Shi, CY; Yang, H; Wei, CL; Yu, O; Zhang, ZZ; Sun, J; Wan, XC

2011-01-01

time PCR (qRT-PCR). An extensive transcriptome dataset has been obtained from the deep sequencing of tea plant. The coverage of the transcriptome is comprehensive enough to discover all known genes of several major metabolic pathways. This transcriptome dataset can serve as an important public information platform for gene expression, genomics, and functional genomic studies in C. sinensis.

Deep sequencing of the Camellia sinensis transcriptome revealed candidate genes for major metabolic pathways of tea-specific compounds

Directory of Open Access Journals (Sweden)

Chen Qi

2011-02-01

analyzed by RT-PCR and quantitative real time PCR (qRT-PCR. Conclusions An extensive transcriptome dataset has been obtained from the deep sequencing of tea plant. The coverage of the transcriptome is comprehensive enough to discover all known genes of several major metabolic pathways. This transcriptome dataset can serve as an important public information platform for gene expression, genomics, and functional genomic studies in C. sinensis.

Evolutionary process of deep-sea bathymodiolus mussels.

Directory of Open Access Journals (Sweden)

Jun-Ichi Miyazaki

Full Text Available BACKGROUND: Since the discovery of deep-sea chemosynthesis-based communities, much work has been done to clarify their organismal and environmental aspects. However, major topics remain to be resolved, including when and how organisms invade and adapt to deep-sea environments; whether strategies for invasion and adaptation are shared by different taxa or unique to each taxon; how organisms extend their distribution and diversity; and how they become isolated to speciate in continuous waters. Deep-sea mussels are one of the dominant organisms in chemosynthesis-based communities, thus investigations of their origin and evolution contribute to resolving questions about life in those communities. METHODOLOGY/PRINCIPAL FINDING: We investigated worldwide phylogenetic relationships of deep-sea Bathymodiolus mussels and their mytilid relatives by analyzing nucleotide sequences of the mitochondrial cytochrome c oxidase subunit I (COI and NADH dehydrogenase subunit 4 (ND4 genes. Phylogenetic analysis of the concatenated sequence data showed that mussels of the subfamily Bathymodiolinae from vents and seeps were divided into four groups, and that mussels of the subfamily Modiolinae from sunken wood and whale carcasses assumed the outgroup position and shallow-water modioline mussels were positioned more distantly to the bathymodioline mussels. We provisionally hypothesized the evolutionary history of Bathymodilolus mussels by estimating evolutionary time under a relaxed molecular clock model. Diversification of bathymodioline mussels was initiated in the early Miocene, and subsequently diversification of the groups occurred in the early to middle Miocene. CONCLUSIONS/SIGNIFICANCE: The phylogenetic relationships support the "Evolutionary stepping stone hypothesis," in which mytilid ancestors exploited sunken wood and whale carcasses in their progressive adaptation to deep-sea environments. This hypothesis is also supported by the evolutionary transition of

Deep sequencing of the viral phoH gene reveals temporal variation, depth-specific composition, and persistent dominance of the same viral phoH genes in the Sargasso Sea

Directory of Open Access Journals (Sweden)

Dawn B. Goldsmith

2015-06-01

Full Text Available Deep sequencing of the viral phoH gene, a host-derived auxiliary metabolic gene, was used to track viral diversity throughout the water column at the Bermuda Atlantic Time-series Study (BATS site in the summer (September and winter (March of three years. Viral phoH sequences reveal differences in the viral communities throughout a depth profile and between seasons in the same year. Variation was also detected between the same seasons in subsequent years, though these differences were not as great as the summer/winter distinctions. Over 3,600 phoH operational taxonomic units (OTUs; 97% sequence identity were identified. Despite high richness, most phoH sequences belong to a few large, common OTUs whereas the majority of the OTUs are small and rare. While many OTUs make sporadic appearances at just a few times or depths, a small number of OTUs dominate the community throughout the seasons, depths, and years.

Procedural Memory Consolidation in the Performance of Brief Keyboard Sequences

Science.gov (United States)

Duke, Robert A.; Davis, Carla M.

2006-01-01

Using two sequential key press sequences, we tested the extent to which subjects' performance on a digital piano keyboard changed between the end of training and retest on subsequent days. We found consistent, significant improvements attributable to sleep-based consolidation effects, indicating that learning continued after the cessation of…

Deep-tissue reporter-gene imaging with fluorescence and optoacoustic tomography: a performance overview.

Science.gov (United States)

Deliolanis, Nikolaos C; Ale, Angelique; Morscher, Stefan; Burton, Neal C; Schaefer, Karin; Radrich, Karin; Razansky, Daniel; Ntziachristos, Vasilis

2014-10-01

A primary enabling feature of near-infrared fluorescent proteins (FPs) and fluorescent probes is the ability to visualize deeper in tissues than in the visible. The purpose of this work is to find which is the optimal visualization method that can exploit the advantages of this novel class of FPs in full-scale pre-clinical molecular imaging studies. Nude mice were stereotactically implanted with near-infrared FP expressing glioma cells to from brain tumors. The feasibility and performance metrics of FPs were compared between planar epi-illumination and trans-illumination fluorescence imaging, as well as to hybrid Fluorescence Molecular Tomography (FMT) system combined with X-ray CT and Multispectral Optoacoustic (or Photoacoustic) Tomography (MSOT). It is shown that deep-seated glioma brain tumors are possible to visualize both with fluorescence and optoacoustic imaging. Fluorescence imaging is straightforward and has good sensitivity; however, it lacks resolution. FMT-XCT can provide an improved rough resolution of ∼1 mm in deep tissue, while MSOT achieves 0.1 mm resolution in deep tissue and has comparable sensitivity. We show imaging capacity that can shift the visualization paradigm in biological discovery. The results are relevant not only to reporter gene imaging, but stand as cross-platform comparison for all methods imaging near infrared fluorescent contrast agents.

Deep sequencing of Salmonella RNA associated with heterologous Hfq proteins in vivo reveals small RNAs as a major target class and identifies RNA processing phenotypes.

Science.gov (United States)

Sittka, Alexandra; Sharma, Cynthia M; Rolle, Katarzyna; Vogel, Jörg

2009-01-01

The bacterial Sm-like protein, Hfq, is a key factor for the stability and function of small non-coding RNAs (sRNAs) in Escherichia coli. Homologues of this protein have been predicted in many distantly related organisms yet their functional conservation as sRNA-binding proteins has not entirely been clear. To address this, we expressed in Salmonella the Hfq proteins of two eubacteria (Neisseria meningitides, Aquifex aeolicus) and an archaeon (Methanocaldococcus jannaschii), and analyzed the associated RNA by deep sequencing. This in vivo approach identified endogenous Salmonella sRNAs as a major target of the foreign Hfq proteins. New Salmonella sRNA species were also identified, and some of these accumulated specifically in the presence of a foreign Hfq protein. In addition, we observed specific RNA processing defects, e.g., suppression of precursor processing of SraH sRNA by Methanocaldococcus Hfq, or aberrant accumulation of extracytoplasmic target mRNAs of the Salmonella GcvB, MicA or RybB sRNAs. Taken together, our study provides evidence of a conserved inherent sRNA-binding property of Hfq, which may facilitate the lateral transmission of regulatory sRNAs among distantly related species. It also suggests that the expression of heterologous RNA-binding proteins combined with deep sequencing analysis of RNA ligands can be used as a molecular tool to dissect individual steps of RNA metabolism in vivo.

Occipital deep white matter hyperintensity as seen by MRI, 1

International Nuclear Information System (INIS)

Miyazaki, Masahito; Hashimoto, Toshiaki; Tayama, Masanobu; Kuroda, Yasuhiro

1992-01-01

Magnetic resonance imaging was performed in 270 patients with various neurologic complaints (1-15Y) with a 0.5 tesla superconducting imaging system using a field echo T1-weighted sequence and spin echo T2-weighted and PD-weighted sequences. Twenty-seven of them had deep white matter hyperintensity (DWMH) in the occipital lobe on T2-weighted images. The frequency of mild DWMH differed in different age groups, suggesting that mild DWMH may result from delayed myelination in the central nervous system. However, the frequency of severe DWMH, which was revealed as isointense relative to cerebrospinal fluid, did not differ in different age groups and it was significantly more common in severely retarded patients. Classification of DWMH based on the signal intensity is valuable to distinguish white matter abnormalities in the occipital lobe from delayed myelination in the same site. (author)

MicroRNAs in Amoebozoa: deep sequencing of the small RNA population in the social amoeba Dictyostelium discoideum reveals developmentally regulated microRNAs.

Science.gov (United States)

Avesson, Lotta; Reimegård, Johan; Wagner, E Gerhart H; Söderbom, Fredrik

2012-10-01

The RNA interference machinery has served as a guardian of eukaryotic genomes since the divergence from prokaryotes. Although the basic components have a shared origin, silencing pathways directed by small RNAs have evolved in diverse directions in different eukaryotic lineages. Micro (mi)RNAs regulate protein-coding genes and play vital roles in plants and animals, but less is known about their functions in other organisms. Here, we report, for the first time, deep sequencing of small RNAs from the social amoeba Dictyostelium discoideum. RNA from growing single-cell amoebae as well as from two multicellular developmental stages was sequenced. Computational analyses combined with experimental data reveal the expression of miRNAs, several of them exhibiting distinct expression patterns during development. To our knowledge, this is the first report of miRNAs in the Amoebozoa supergroup. We also show that overexpressed miRNA precursors generate miRNAs and, in most cases, miRNA* sequences, whose biogenesis is dependent on the Dicer-like protein DrnB, further supporting the presence of miRNAs in D. discoideum. In addition, we find miRNAs processed from hairpin structures originating from an intron as well as from a class of repetitive elements. We believe that these repetitive elements are sources for newly evolved miRNAs.

Auxiliary Deep Generative Models

DEFF Research Database (Denmark)

Maaløe, Lars; Sønderby, Casper Kaae; Sønderby, Søren Kaae

2016-01-01

Deep generative models parameterized by neural networks have recently achieved state-of-the-art performance in unsupervised and semi-supervised learning. We extend deep generative models with auxiliary variables which improves the variational approximation. The auxiliary variables leave...... the generative model unchanged but make the variational distribution more expressive. Inspired by the structure of the auxiliary variable we also propose a model with two stochastic layers and skip connections. Our findings suggest that more expressive and properly specified deep generative models converge...... faster with better results. We show state-of-the-art performance within semi-supervised learning on MNIST (0.96%), SVHN (16.61%) and NORB (9.40%) datasets....

De novo transcriptome assembly and positive selection analysis of an individual deep-sea fish.

Science.gov (United States)

Lan, Yi; Sun, Jin; Xu, Ting; Chen, Chong; Tian, Renmao; Qiu, Jian-Wen; Qian, Pei-Yuan

2018-05-24

High hydrostatic pressure and low temperatures make the deep sea a harsh environment for life forms. Actin organization and microtubules assembly, which are essential for intracellular transport and cell motility, can be disrupted by high hydrostatic pressure. High hydrostatic pressure can also damage DNA. Nucleic acids exposed to low temperatures can form secondary structures that hinder genetic information processing. To study how deep-sea creatures adapt to such a hostile environment, one of the most straightforward ways is to sequence and compare their genes with those of their shallow-water relatives. We captured an individual of the fish species Aldrovandia affinis, which is a typical deep-sea inhabitant, from the Okinawa Trough at a depth of 1550 m using a remotely operated vehicle (ROV). We sequenced its transcriptome and analyzed its molecular adaptation. We obtained 27,633 protein coding sequences using an Illumina platform and compared them with those of several shallow-water fish species. Analysis of 4918 single-copy orthologs identified 138 positively selected genes in A. affinis, including genes involved in microtubule regulation. Particularly, functional domains related to cold shock as well as DNA repair are exposed to positive selection pressure in both deep-sea fish and hadal amphipod. Overall, we have identified a set of positively selected genes related to cytoskeleton structures, DNA repair and genetic information processing, which shed light on molecular adaptation to the deep sea. These results suggest that amino acid substitutions of these positively selected genes may contribute crucially to the adaptation of deep-sea animals. Additionally, we provide a high-quality transcriptome of a deep-sea fish for future deep-sea studies.

Deep Energy Retrofit Performance Metric Comparison: Eight California Case Studies

Energy Technology Data Exchange (ETDEWEB)

Walker, Iain [Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States); Fisher, Jeremy [Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States); Less, Brennan [Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States)

2014-06-01

In this paper we will present the results of monitored annual energy use data from eight residential Deep Energy Retrofit (DER) case studies using a variety of performance metrics. For each home, the details of the retrofits were analyzed, diagnostic tests to characterize the home were performed and the homes were monitored for total and individual end-use energy consumption for approximately one year. Annual performance in site and source energy, as well as carbon dioxide equivalent (CO₂e) emissions were determined on a per house, per person and per square foot basis to examine the sensitivity to these different metrics. All eight DERs showed consistent success in achieving substantial site energy and CO₂e reductions, but some projects achieved very little, if any source energy reduction. This problem emerged in those homes that switched from natural gas to electricity for heating and hot water, resulting in energy consumption dominated by electricity use. This demonstrates the crucial importance of selecting an appropriate metric to be used in guiding retrofit decisions. Also, due to the dynamic nature of DERs, with changes in occupancy, size, layout, and comfort, several performance metrics might be necessary to understand a project’s success.

Deep sequencing-based identification of small regulatory RNAs in Synechocystis sp. PCC 6803.

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Wen Xu

Full Text Available Synechocystis sp. PCC 6803 is a genetically tractable model organism for photosynthesis research. The genome of Synechocystis sp. PCC 6803 consists of a circular chromosome and seven plasmids. The importance of small regulatory RNAs (sRNAs as mediators of a number of cellular processes in bacteria has begun to be recognized. However, little is known regarding sRNAs in Synechocystis sp. PCC 6803. To provide a comprehensive overview of sRNAs in this model organism, the sRNAs of Synechocystis sp. PCC 6803 were analyzed using deep sequencing, and 7,951,189 reads were obtained. High quality mapping reads (6,127,890 were mapped onto the genome and assembled into 16,192 transcribed regions (clusters based on read overlap. A total number of 5211 putative sRNAs were revealed from the genome and the 4 megaplasmids, and 27 of these molecules, including four from plasmids, were confirmed by RT-PCR. In addition, possible target genes regulated by all of the putative sRNAs identified in this study were predicted by IntaRNA and analyzed for functional categorization and biological pathways, which provided evidence that sRNAs are indeed involved in many different metabolic pathways, including basic metabolic pathways, such as glycolysis/gluconeogenesis, the citrate cycle, fatty acid metabolism and adaptations to environmentally stress-induced changes. The information from this study provides a valuable reservoir for understanding the sRNA-mediated regulation of the complex physiology and metabolic processes of cyanobacteria.

Deep Sequence Analysis of AgoshRNA Processing Reveals 3' A Addition and Trimming.

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Harwig, Alex; Herrera-Carrillo, Elena; Jongejan, Aldo; van Kampen, Antonius Hubertus; Berkhout, Ben

2015-07-14

The RNA interference (RNAi) pathway, in which microprocessor and Dicer collaborate to process microRNAs (miRNA), was recently expanded by the description of alternative processing routes. In one of these noncanonical pathways, Dicer action is replaced by the Argonaute2 (Ago2) slicer function. It was recently shown that the stem-length of precursor-miRNA or short hairpin RNA (shRNA) molecules is a major determinant for Dicer versus Ago2 processing. Here we present the results of a deep sequence study on the processing of shRNAs with different stem length and a top G·U wobble base pair (bp). This analysis revealed some unexpected properties of these so-called AgoshRNA molecules that are processed by Ago2 instead of Dicer. First, we confirmed the gradual shift from Dicer to Ago2 processing upon shortening of the hairpin length. Second, hairpins with a stem larger than 19 base pair are inefficiently cleaved by Ago2 and we noticed a shift in the cleavage site. Third, the introduction of a top G·U bp in a regular shRNA can promote Ago2-cleavage, which coincides with a loss of Ago2-loading of the Dicer-cleaved 3' strand. Fourth, the Ago2-processed AgoshRNAs acquire a short 3' tail of 1-3 A-nucleotides (nt) and we present evidence that this product is subsequently trimmed by the poly(A)-specific ribonuclease (PARN).

MicroRNA discovery and analysis of pinewood nematode Bursaphelenchus xylophilus by deep sequencing.

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Qi-Xing Huang

Full Text Available BACKGROUND: MicroRNAs (miRNAs are considered to be very important in regulating the growth, development, behavior and stress response in animals and plants in post-transcriptional gene regulation. Pinewood nematode, Bursaphelenchus xylophilus, is an important invasive plant parasitic nematode in Asia. To have a comprehensive knowledge about miRNAs of the nematode is necessary for further in-depth study on roles of miRNAs in the ecological adaptation of the invasive species. METHODS AND FINDINGS: Five small RNA libraries were constructed and sequenced by Illumina/Solexa deep-sequencing technology. A total of 810 miRNA candidates (49 conserved and 761 novel were predicted by a computational pipeline, of which 57 miRNAs (20 conserved and 37 novel encoded by 53 miRNA precursors were identified by experimental methods. Ten novel miRNAs were considered to be species-specific miRNAs of B. xylophilus. Comparison of expression profiles of miRNAs in the five small RNA libraries showed that many miRNAs exhibited obviously different expression levels in the third-stage dispersal juvenile and at a cold-stressed status. Most of the miRNAs exhibited obviously down-regulated expression in the dispersal stage. But differences among the three geographic libraries were not prominent. A total of 979 genes were predicted to be targets of these authentic miRNAs. Among them, seven heat shock protein genes were targeted by 14 miRNAs, and six FMRFamide-like neuropeptides genes were targeted by 17 miRNAs. A real-time quantitative polymerase chain reaction was used to quantify the mRNA expression levels of target genes. CONCLUSIONS: Basing on the fact that a negative correlation existed between the expression profiles of miRNAs and the mRNA expression profiles of their target genes (hsp, flp by comparing those of the nematodes at a cold stressed status and a normal status, we suggested that miRNAs might participate in ecological adaptation and behavior regulation of the

Improved oxygenation during standing performance of deep breathing exercises with positive expiratory pressure after cardiac surgery: A randomized controlled trial.

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Pettersson, Henrik; Faager, Gun; Westerdahl, Elisabeth

2015-09-01

Breathing exercises after cardiac surgery are often performed in a sitting position. It is unknown whether oxygenation would be better in the standing position. The aim of this study was to evaluate oxygenation and subjective breathing ability during sitting vs standing performance of deep breathing exercises on the second day after cardiac surgery. Patients undergoing coronary artery bypass grafting (n = 189) were randomized to sitting (controls) or standing. Both groups performed 3 × 10 deep breaths with a positive expiratory pressure device. Peripheral oxygen saturation was measured before, directly after, and 15 min after the intervention. Subjective breathing ability, blood pressure, heart rate, and pain were assessed. Oxygenation improved significantly in the standing group compared with controls directly after the breathing exercises (p < 0.001) and after 15 min rest (p = 0.027). The standing group reported better deep breathing ability compared with controls (p = 0.004). A slightly increased heart rate was found in the standing group (p = 0.047). After cardiac surgery, breathing exercises with positive expiratory pressure, performed in a standing position, significantly improved oxygenation and subjective breathing ability compared with sitting performance. Performance of breathing exercises in the standing position is feasible and could be a valuable treatment for patients with postoperative hypoxaemia.

Metagenomes obtained by "deep sequencing" - what do they tell about the EBPR communities?

DEFF Research Database (Denmark)

Albertsen, Mads; Saunders, Aaron Marc; Nielsen, Kåre Lehmann

2013-01-01

Metagenomics enables studies of the genomic potential of complex microbial communities by sequencing bulk genomic DNA directly from the environment. Knowledge of the genetic potential of a community can be used to formulate and test ecological hypotheses about stability and performance...... demonstrate that metagenomics can be used as a powerful tool for system wide characterization of the EBPR community as well as for a deeper understanding of the function of specific community members. Furthermore, we discuss and illustrate some of the general pitfalls in metagenomics and stress the need...

cTBS disruption of the supplementary motor area perturbs cortical sequence representation but not behavioural performance.

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Solopchuk, Oleg; Alamia, Andrea; Dricot, Laurence; Duque, Julie; Zénon, Alexandre

2017-12-01

Neuroimaging studies have repeatedly emphasized the role of the supplementary motor area (SMA) in motor sequence learning, but interferential approaches have led to inconsistent findings. Here, we aimed to test the role of the SMA in motor skill learning by combining interferential and neuroimaging techniques. Sixteen subjects were trained on simple finger movement sequences for 4 days. Afterwards, they underwent two neuroimaging sessions, in which they executed both trained and novel sequences. Prior to entering the scanner, the subjects received inhibitory transcranial magnetic stimulation (TMS) over the SMA or a control site. Using multivariate fMRI analysis, we confirmed that motor training enhances the neural representation of motor sequences in the SMA, in accordance with previous findings. However, although SMA inhibition altered sequence representation (i.e. between-sequence decoding accuracy) in this area, behavioural performance remained unimpaired. Our findings question the causal link between the neuroimaging correlate of elementary motor sequence representation in the SMA and sequence generation, calling for a more thorough investigation of the role of this region in performance of learned motor sequences. Copyright © 2017 Elsevier Inc. All rights reserved.

Deep sequencing of the Trypanosoma cruzi GP63 surface proteases reveals diversity and diversifying selection among chronic and congenital Chagas disease patients.

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Llewellyn, Martin S; Messenger, Louisa A; Luquetti, Alejandro O; Garcia, Lineth; Torrico, Faustino; Tavares, Suelene B N; Cheaib, Bachar; Derome, Nicolas; Delepine, Marc; Baulard, Céline; Deleuze, Jean-Francois; Sauer, Sascha; Miles, Michael A

2015-04-01

Chagas disease results from infection with the diploid protozoan parasite Trypanosoma cruzi. T. cruzi is highly genetically diverse, and multiclonal infections in individual hosts are common, but little studied. In this study, we explore T. cruzi infection multiclonality in the context of age, sex and clinical profile among a cohort of chronic patients, as well as paired congenital cases from Cochabamba, Bolivia and Goias, Brazil using amplicon deep sequencing technology. A 450bp fragment of the trypomastigote TcGP63I surface protease gene was amplified and sequenced across 70 chronic and 22 congenital cases on the Illumina MiSeq platform. In addition, a second, mitochondrial target--ND5--was sequenced across the same cohort of cases. Several million reads were generated, and sequencing read depths were normalized within patient cohorts (Goias chronic, n = 43, Goias congenital n = 2, Bolivia chronic, n = 27; Bolivia congenital, n = 20), Among chronic cases, analyses of variance indicated no clear correlation between intra-host sequence diversity and age, sex or symptoms, while principal coordinate analyses showed no clustering by symptoms between patients. Between congenital pairs, we found evidence for the transmission of multiple sequence types from mother to infant, as well as widespread instances of novel genotypes in infants. Finally, non-synonymous to synonymous (dn:ds) nucleotide substitution ratios among sequences of TcGP63Ia and TcGP63Ib subfamilies within each cohort provided powerful evidence of strong diversifying selection at this locus. Our results shed light on the diversity of parasite DTUs within each patient, as well as the extent to which parasite strains pass between mother and foetus in congenital cases. Although we were unable to find any evidence that parasite diversity accumulates with age in our study cohorts, putative diversifying selection within members of the TcGP63I gene family suggests a link between genetic diversity within this gene

Deep sequencing of ESTs from nacreous and prismatic layer producing tissues and a screen for novel shell formation-related genes in the pearl oyster.

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Shigeharu Kinoshita

Full Text Available BACKGROUND: Despite its economic importance, we have a limited understanding of the molecular mechanisms underlying shell formation in pearl oysters, wherein the calcium carbonate crystals, nacre and prism, are formed in a highly controlled manner. We constructed comprehensive expressed gene profiles in the shell-forming tissues of the pearl oyster Pinctada fucata and identified novel shell formation-related genes candidates. PRINCIPAL FINDINGS: We employed the GS FLX 454 system and constructed transcriptome data sets from pallial mantle and pearl sac, which form the nacreous layer, and from the mantle edge, which forms the prismatic layer in P. fucata. We sequenced 260477 reads and obtained 29682 unique sequences. We also screened novel nacreous and prismatic gene candidates by a combined analysis of sequence and expression data sets, and identified various genes encoding lectin, protease, protease inhibitors, lysine-rich matrix protein, and secreting calcium-binding proteins. We also examined the expression of known nacreous and prismatic genes in our EST library and identified novel isoforms with tissue-specific expressions. CONCLUSIONS: We constructed EST data sets from the nacre- and prism-producing tissues in P. fucata and found 29682 unique sequences containing novel gene candidates for nacreous and prismatic layer formation. This is the first report of deep sequencing of ESTs in the shell-forming tissues of P. fucata and our data provide a powerful tool for a comprehensive understanding of the molecular mechanisms of molluscan biomineralization.

Deep Illumina-based shotgun sequencing reveals dietary effects on the structure and function of the fecal microbiome of growing kittens.

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Oliver Deusch

Full Text Available Previously, we demonstrated that dietary protein:carbohydrate ratio dramatically affects the fecal microbial taxonomic structure of kittens using targeted 16S gene sequencing. The present study, using the same fecal samples, applied deep Illumina shotgun sequencing to identify the diet-associated functional potential and analyze taxonomic changes of the feline fecal microbiome.Fecal samples from kittens fed one of two diets differing in protein and carbohydrate content (high-protein, low-carbohydrate, HPLC; and moderate-protein, moderate-carbohydrate, MPMC were collected at 8, 12 and 16 weeks of age (n = 6 per group. A total of 345.3 gigabases of sequence were generated from 36 samples, with 99.75% of annotated sequences identified as bacterial. At the genus level, 26% and 39% of reads were annotated for HPLC- and MPMC-fed kittens, with HPLC-fed cats showing greater species richness and microbial diversity. Two phyla, ten families and fifteen genera were responsible for more than 80% of the sequences at each taxonomic level for both diet groups, consistent with the previous taxonomic study. Significantly different abundances between diet groups were observed for 324 genera (56% of all genera identified demonstrating widespread diet-induced changes in microbial taxonomic structure. Diversity was not affected over time. Functional analysis identified 2,013 putative enzyme function groups were different (p<0.000007 between the two dietary groups and were associated to 194 pathways, which formed five discrete clusters based on average relative abundance. Of those, ten contained more (p<0.022 enzyme functions with significant diet effects than expected by chance. Six pathways were related to amino acid biosynthesis and metabolism linking changes in dietary protein with functional differences of the gut microbiome.These data indicate that feline feces-derived microbiomes have large structural and functional differences relating to the dietary

The induced earthquake sequence related to the St. Gallen deep geothermal project (Switzerland): Fault reactivation and fluid interactions imaged by microseismicity

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Diehl, T.; Kraft, T.; Kissling, E.; Wiemer, S.

2017-09-01

In July 2013, a sequence of more than 340 earthquakes was induced by reservoir stimulations and well-control procedures following a gas kick at a deep geothermal drilling project close to the city of St. Gallen, Switzerland. The sequence culminated in an ML 3.5 earthquake, which was felt within 10-15 km from the epicenter. High-quality earthquake locations and 3-D reflection seismic data acquired in the St. Gallen project provide a unique data set, which allows high-resolution studies of earthquake triggering related to the injection of fluids into macroscopic fault zones. In this study, we present a high-precision earthquake catalog of the induced sequence. Absolute locations are constrained by a coupled hypocenter-velocity inversion, and subsequent double-difference relocations image the geometry of the ML 3.5 rupture and resolve the spatiotemporal evolution of seismicity. A joint interpretation of earthquake and seismic data shows that the majority of the seismicity occurred in the pre-Mesozoic basement, hundreds of meters below the borehole and the targeted Mesozoic sequence. We propose a hydraulic connectivity between the reactivated fault and the borehole, likely through faults mapped by seismic data. Despite the excellent quality of the seismic data, the association of seismicity with mapped faults remains ambiguous. In summary, our results document that the actual hydraulic properties of a fault system and hydraulic connections between its fault segments are complex and may not be predictable upfront. Incomplete knowledge of fault structures and stress heterogeneities within highly complex fault systems additionally challenge the degree of predictability of induced seismicity related to underground fluid injections.

The 2007 Nazko, British Columbia, earthquake sequence: Injection of magma deep in the crust beneath the Anahim volcanic belt

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Cassidy, J.F.; Balfour, N.; Hickson, C.; Kao, H.; White, Rickie; Caplan-Auerbach, J.; Mazzotti, S.; Rogers, Gary C.; Al-Khoubbi, I.; Bird, A.L.; Esteban, L.; Kelman, M.; Hutchinson, J.; McCormack, D.

2011-01-01

On 9 October 2007, an unusual sequence of earthquakes began in central British Columbia about 20 km west of the Nazko cone, the most recent (circa 7200 yr) volcanic center in the Anahim volcanic belt. Within 25 hr, eight earthquakes of magnitude 2.3-2.9 occurred in a region where no earthquakes had previously been recorded. During the next three weeks, more than 800 microearthquakes were located (and many more detected), most at a depth of 25-31 km and within a radius of about 5 km. After about two months, almost all activity ceased. The clear P- and S-wave arrivals indicated that these were high-frequency (volcanic-tectonic) earthquakes and the b value of 1.9 that we calculated is anomalous for crustal earthquakes but consistent with volcanic-related events. Analysis of receiver functions at a station immediately above the seismicity indicated a Moho near 30 km depth. Precise relocation of the seismicity using a double-difference method suggested a horizontal migration at the rate of about 0:5 km=d, with almost all events within the lowermost crust. Neither harmonic tremor nor long-period events were observed; however, some spasmodic bursts were recorded and determined to be colocated with the earthquake hypocenters. These observations are all very similar to a deep earthquake sequence recorded beneath Lake Tahoe, California, in 2003-2004. Based on these remarkable similarities, we interpret the Nazko sequence as an indication of an injection of magma into the lower crust beneath the Anahim volcanic belt. This magma injection fractures rock, producing high-frequency, volcanic-tectonic earthquakes and spasmodic bursts.

DeepSurv: personalized treatment recommender system using a Cox proportional hazards deep neural network.

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Katzman, Jared L; Shaham, Uri; Cloninger, Alexander; Bates, Jonathan; Jiang, Tingting; Kluger, Yuval

2018-02-26

Medical practitioners use survival models to explore and understand the relationships between patients' covariates (e.g. clinical and genetic features) and the effectiveness of various treatment options. Standard survival models like the linear Cox proportional hazards model require extensive feature engineering or prior medical knowledge to model treatment interaction at an individual level. While nonlinear survival methods, such as neural networks and survival forests, can inherently model these high-level interaction terms, they have yet to be shown as effective treatment recommender systems. We introduce DeepSurv, a Cox proportional hazards deep neural network and state-of-the-art survival method for modeling interactions between a patient's covariates and treatment effectiveness in order to provide personalized treatment recommendations. We perform a number of experiments training DeepSurv on simulated and real survival data. We demonstrate that DeepSurv performs as well as or better than other state-of-the-art survival models and validate that DeepSurv successfully models increasingly complex relationships between a patient's covariates and their risk of failure. We then show how DeepSurv models the relationship between a patient's features and effectiveness of different treatment options to show how DeepSurv can be used to provide individual treatment recommendations. Finally, we train DeepSurv on real clinical studies to demonstrate how it's personalized treatment recommendations would increase the survival time of a set of patients. The predictive and modeling capabilities of DeepSurv will enable medical researchers to use deep neural networks as a tool in their exploration, understanding, and prediction of the effects of a patient's characteristics on their risk of failure.

Identifying genomic changes associated with insecticide resistance in the dengue mosquito Aedes aegypti by deep targeted sequencing

Science.gov (United States)

Faucon, Frederic; Dusfour, Isabelle; Gaude, Thierry; Navratil, Vincent; Boyer, Frederic; Chandre, Fabrice; Sirisopa, Patcharawan; Thanispong, Kanutcharee; Juntarajumnong, Waraporn; Poupardin, Rodolphe; Chareonviriyaphap, Theeraphap; Girod, Romain; Corbel, Vincent; Reynaud, Stephane; David, Jean-Philippe

2015-01-01

The capacity of mosquitoes to resist insecticides threatens the control of diseases such as dengue and malaria. Until alternative control tools are implemented, characterizing resistance mechanisms is crucial for managing resistance in natural populations. Insecticide biodegradation by detoxification enzymes is a common resistance mechanism; however, the genomic changes underlying this mechanism have rarely been identified, precluding individual resistance genotyping. In particular, the role of copy number variations (CNVs) and polymorphisms of detoxification enzymes have never been investigated at the genome level, although they can represent robust markers of metabolic resistance. In this context, we combined target enrichment with high-throughput sequencing for conducting the first comprehensive screening of gene amplifications and polymorphisms associated with insecticide resistance in mosquitoes. More than 760 candidate genes were captured and deep sequenced in several populations of the dengue mosquito Ae. aegypti displaying distinct genetic backgrounds and contrasted resistance levels to the insecticide deltamethrin. CNV analysis identified 41 gene amplifications associated with resistance, most affecting cytochrome P450s overtranscribed in resistant populations. Polymorphism analysis detected more than 30,000 variants and strong selection footprints in specific genomic regions. Combining Bayesian and allele frequency filtering approaches identified 55 nonsynonymous variants strongly associated with resistance. Both CNVs and polymorphisms were conserved within regions but differed across continents, confirming that genomic changes underlying metabolic resistance to insecticides are not universal. By identifying novel DNA markers of insecticide resistance, this study opens the way for tracking down metabolic changes developed by mosquitoes to resist insecticides within and among populations. PMID:26206155

Generic Amplicon Deep Sequencing to Determine Ilarvirus Species Diversity in Australian Prunus.

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Kinoti, Wycliff M; Constable, Fiona E; Nancarrow, Narelle; Plummer, Kim M; Rodoni, Brendan

2017-01-01

The distribution of Ilarvirus species populations amongst 61 Australian Prunus trees was determined by next generation sequencing (NGS) of amplicons generated using a genus-based generic RT-PCR targeting a conserved region of the Ilarvirus RNA2 component that encodes the RNA dependent RNA polymerase (RdRp) gene. Presence of Ilarvirus sequences in each positive sample was further validated by Sanger sequencing of cloned amplicons of regions of each of RNA1, RNA2 and/or RNA3 that were generated by species specific PCRs and by metagenomic NGS. Prunus necrotic ringspot virus (PNRSV) was the most frequently detected Ilarvirus , occurring in 48 of the 61 Ilarvirus -positive trees and Prune dwarf virus (PDV) and Apple mosaic virus (ApMV) were detected in three trees and one tree, respectively. American plum line pattern virus (APLPV) was detected in three trees and represents the first report of APLPV detection in Australia. Two novel and distinct groups of Ilarvirus -like RNA2 amplicon sequences were also identified in several trees by the generic amplicon NGS approach. The high read depth from the amplicon NGS of the generic PCR products allowed the detection of distinct RNA2 RdRp sequence variant populations of PNRSV, PDV, ApMV, APLPV and the two novel Ilarvirus -like sequences. Mixed infections of ilarviruses were also detected in seven Prunus trees. Sanger sequencing of specific RNA1, RNA2, and/or RNA3 genome segments of each virus and total nucleic acid metagenomics NGS confirmed the presence of PNRSV, PDV, ApMV and APLPV detected by RNA2 generic amplicon NGS. However, the two novel groups of Ilarvirus -like RNA2 amplicon sequences detected by the generic amplicon NGS could not be associated to the presence of sequence from RNA1 or RNA3 genome segments or full Ilarvirus genomes, and their origin is unclear. This work highlights the sensitivity of genus-specific amplicon NGS in detection of virus sequences and their distinct populations in multiple samples, and the

Generic Amplicon Deep Sequencing to Determine Ilarvirus Species Diversity in Australian Prunus

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Wycliff M. Kinoti

2017-06-01

Full Text Available The distribution of Ilarvirus species populations amongst 61 Australian Prunus trees was determined by next generation sequencing (NGS of amplicons generated using a genus-based generic RT-PCR targeting a conserved region of the Ilarvirus RNA2 component that encodes the RNA dependent RNA polymerase (RdRp gene. Presence of Ilarvirus sequences in each positive sample was further validated by Sanger sequencing of cloned amplicons of regions of each of RNA1, RNA2 and/or RNA3 that were generated by species specific PCRs and by metagenomic NGS. Prunus necrotic ringspot virus (PNRSV was the most frequently detected Ilarvirus, occurring in 48 of the 61 Ilarvirus-positive trees and Prune dwarf virus (PDV and Apple mosaic virus (ApMV were detected in three trees and one tree, respectively. American plum line pattern virus (APLPV was detected in three trees and represents the first report of APLPV detection in Australia. Two novel and distinct groups of Ilarvirus-like RNA2 amplicon sequences were also identified in several trees by the generic amplicon NGS approach. The high read depth from the amplicon NGS of the generic PCR products allowed the detection of distinct RNA2 RdRp sequence variant populations of PNRSV, PDV, ApMV, APLPV and the two novel Ilarvirus-like sequences. Mixed infections of ilarviruses were also detected in seven Prunus trees. Sanger sequencing of specific RNA1, RNA2, and/or RNA3 genome segments of each virus and total nucleic acid metagenomics NGS confirmed the presence of PNRSV, PDV, ApMV and APLPV detected by RNA2 generic amplicon NGS. However, the two novel groups of Ilarvirus-like RNA2 amplicon sequences detected by the generic amplicon NGS could not be associated to the presence of sequence from RNA1 or RNA3 genome segments or full Ilarvirus genomes, and their origin is unclear. This work highlights the sensitivity of genus-specific amplicon NGS in detection of virus sequences and their distinct populations in multiple samples

Revealing Holobiont Structure and Function of Three Red Sea Deep-Sea Corals

KAUST Repository

Yum, Lauren

2014-12-01

Deep-sea corals have long been regarded as cold-water coral; however a reevaluation of their habitat limitations has been suggested after the discovery of deep-sea coral in the Red Sea where temperatures exceed 20˚C. To gain further insight into the biology of deep-sea corals at these temperatures, the work in this PhD employed a holotranscriptomic approach, looking at coral animal host and bacterial symbiont gene expression in Dendrophyllia sp., Eguchipsammia fistula, and Rhizotrochus sp. sampled from the deep Red Sea. Bacterial community composition was analyzed via amplicon-based 16S surveys and cultured bacterial strains were subjected to bioprospecting in order to gauge the pharmaceutical potential of coralassociated microbes. Coral host transcriptome data suggest that coral can employ mitochondrial hypometabolism, anaerobic glycolysis, and surface cilia to enhance mass transport rates to manage the low oxygen and highly oligotrophic Red Sea waters. In the microbial community associated with these corals, ribokinases and retron-type reverse transcriptases are abundantly expressed. In its first application to deep-sea coral associated microbial communities, 16S-based next-generation sequencing found that a single operational taxonomic unit can comprise the majority of sequence reads and that a large number of low abundance populations are present, which cannot be visualized with first generation sequencing. Bioactivity testing of selected bacterial isolates was surveyed over 100 cytological parameters with high content screening, covering several major organelles and key proteins involved in a variety of signaling cascades. Some of these cytological profiles were similar to those of several reference pharmacologically active compounds, which suggest that the bacteria isolates produce compounds with similar mechanisms of action as the reference compounds. The sum of this work offers several mechanisms by which Red Sea deep-sea corals cope with environmental

DeepQA: improving the estimation of single protein model quality with deep belief networks.

Science.gov (United States)

Cao, Renzhi; Bhattacharya, Debswapna; Hou, Jie; Cheng, Jianlin

2016-12-05

Protein quality assessment (QA) useful for ranking and selecting protein models has long been viewed as one of the major challenges for protein tertiary structure prediction. Especially, estimating the quality of a single protein model, which is important for selecting a few good models out of a large model pool consisting of mostly low-quality models, is still a largely unsolved problem. We introduce a novel single-model quality assessment method DeepQA based on deep belief network that utilizes a number of selected features describing the quality of a model from different perspectives, such as energy, physio-chemical characteristics, and structural information. The deep belief network is trained on several large datasets consisting of models from the Critical Assessment of Protein Structure Prediction (CASP) experiments, several publicly available datasets, and models generated by our in-house ab initio method. Our experiments demonstrate that deep belief network has better performance compared to Support Vector Machines and Neural Networks on the protein model quality assessment problem, and our method DeepQA achieves the state-of-the-art performance on CASP11 dataset. It also outperformed two well-established methods in selecting good outlier models from a large set of models of mostly low quality generated by ab initio modeling methods. DeepQA is a useful deep learning tool for protein single model quality assessment and protein structure prediction. The source code, executable, document and training/test datasets of DeepQA for Linux is freely available to non-commercial users at http://cactus.rnet.missouri.edu/DeepQA/ .

Microbial investigations of deep geological compartments

International Nuclear Information System (INIS)

Barsotti, V.; Sergeant, C.; Vesvres, M.H.; Joulian, C.; Coulon, S.; Le Marrec, C.; Garrido, F.

2010-01-01

Document available in extended abstract form only. Deep sedimentary rocks are now considered to contain a significant part of the total bacterial population, but are microbiologically unexplored. The drilling down to the base of the Triassic (1980 meters deep) in the geological formations of the eastern Paris Basin performed by ANDRA (EST433) in 2008 provides us a good opportunity to explore the deep biosphere. We conditioned and sub-sampled on the coring site, in as aseptic conditions as possible, the nine cores: two in the Callovo-Oxfordian clay, two in the Dogger, five in the Triassic compartments. In addition to storage at atmospheric pressure, a portion of the five Triassic samples was placed in a 190 bars pressurized bars chamber to investigate the influence of the conservation pressure factor on the found microflora. In parallel, in order to evaluate a potential bacterial contamination of the core by the drilling fluids, samples of mud just before each sample drilling were taken and analysed. The microbial exploration we started can be divided in two parts: - A cultural approach in different culture media for six metabolic groups to try to find microbial cells still viable. This type of experiment is difficult because of the small proportion of cultivable species, especially in these extreme environmental samples. - A molecular approach by direct extraction of genomic DNA from the geological samples to explore a larger biodiversity. Here, the limits are the difficulties to extract DNA from these low biomass containing rocks. The five Triassic samples were partly crushed in powder and inoculated in the six culture media with four NaCl concentrations, because this type of rock is known as saline or hyper-saline, and incubated at three temperatures: 30 deg. C, 55 deg. C under agitation and 70 deg. C. First results will be presented. The direct extraction of DNA needs a complete method optimisation to adapt existent procedures (using commercial kit and classical

Large-Scale Genotyping-by-Sequencing Indicates High Levels of Gene Flow in the Deep-Sea Octocoral Swiftia simplex (Nutting 1909 on the West Coast of the United States.

Directory of Open Access Journals (Sweden)

Meredith V Everett

Full Text Available Deep-sea corals are a critical component of habitat in the deep-sea, existing as regional hotspots for biodiversity, and are associated with increased assemblages of fish, including commercially important species. Because sampling these species is so difficult, little is known about the connectivity and life history of deep-sea octocoral populations. This study evaluates the genetic connectivity among 23 individuals of the deep-sea octocoral Swiftia simplex collected from Eastern Pacific waters along the west coast of the United States. We utilized high-throughput restriction-site associated DNA (RAD-tag sequencing to develop the first molecular genetic resource for the deep-sea octocoral, Swiftia simplex. Using this technique we discovered thousands of putative genome-wide SNPs in this species, and after quality control, successfully genotyped 1,145 SNPs across individuals sampled from California to Washington. These SNPs were used to assess putative population structure across the region. A STRUCTURE analysis as well as a principal coordinates analysis both failed to detect any population differentiation across all geographic areas in these collections. Additionally, after assigning individuals to putative population groups geographically, no significant FST values could be detected (FST for the full data set 0.0056, and no significant isolation by distance could be detected (p = 0.999. Taken together, these results indicate a high degree of connectivity and potential panmixia in S. simplex along this portion of the continental shelf.

Evolution of developmental sequences in lepidosaurs

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Tomasz Skawiński

2017-04-01

Full Text Available Background Lepidosaurs, a group including rhynchocephalians and squamates, are one of the major clades of extant vertebrates. Although there has been extensive phylogenetic work on this clade, its interrelationships are a matter of debate. Morphological and molecular data suggest very different relationships within squamates. Despite this, relatively few studies have assessed the utility of other types of data for inferring squamate phylogeny. Methods We used developmental sequences of 20 events in 29 species of lepidosaurs. These sequences were analysed using event-pairing and continuous analysis. They were transformed into cladistic characters and analysed in TNT. Ancestral state reconstructions were performed on two main phylogenetic hypotheses of squamates (morphological and molecular. Results Cladistic analyses conducted using characters generated by these methods do not resemble any previously published phylogeny. Ancestral state reconstructions are equally consistent with both morphological and molecular hypotheses of squamate phylogeny. Only several inferred heterochronic events are common to all methods and phylogenies. Discussion Results of the cladistic analyses, and the fact that reconstructions of heterochronic events show more similarities between certain methods rather than phylogenetic hypotheses, suggest that phylogenetic signal is at best weak in the studied developmental events. Possibly the developmental sequences analysed here evolve too quickly to recover deep divergences within Squamata.

Integrative analysis of deep sequencing data identifies estrogen receptor early response genes and links ATAD3B to poor survival in breast cancer.

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Kristian Ovaska

Full Text Available Identification of responsive genes to an extra-cellular cue enables characterization of pathophysiologically crucial biological processes. Deep sequencing technologies provide a powerful means to identify responsive genes, which creates a need for computational methods able to analyze dynamic and multi-level deep sequencing data. To answer this need we introduce here a data-driven algorithm, SPINLONG, which is designed to search for genes that match the user-defined hypotheses or models. SPINLONG is applicable to various experimental setups measuring several molecular markers in parallel. To demonstrate the SPINLONG approach, we analyzed ChIP-seq data reporting PolII, estrogen receptor α (ERα, H3K4me3 and H2A.Z occupancy at five time points in the MCF-7 breast cancer cell line after estradiol stimulus. We obtained 777 ERa early responsive genes and compared the biological functions of the genes having ERα binding within 20 kb of the transcription start site (TSS to genes without such binding site. Our results show that the non-genomic action of ERα via the MAPK pathway, instead of direct ERa binding, may be responsible for early cell responses to ERα activation. Our results also indicate that the ERα responsive genes triggered by the genomic pathway are transcribed faster than those without ERα binding sites. The survival analysis of the 777 ERα responsive genes with 150 primary breast cancer tumors and in two independent validation cohorts indicated the ATAD3B gene, which does not have ERα binding site within 20 kb of its TSS, to be significantly associated with poor patient survival.

Compositional Bias in Naïve and Chemically-modified Phage-Displayed Libraries uncovered by Paired-end Deep Sequencing.

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He, Bifang; Tjhung, Katrina F; Bennett, Nicholas J; Chou, Ying; Rau, Andrea; Huang, Jian; Derda, Ratmir

2018-01-19

Understanding the composition of a genetically-encoded (GE) library is instrumental to the success of ligand discovery. In this manuscript, we investigate the bias in GE-libraries of linear, macrocyclic and chemically post-translationally modified (cPTM) tetrapeptides displayed on the M13KE platform, which are produced via trinucleotide cassette synthesis (19 codons) and NNK-randomized codon. Differential enrichment of synthetic DNA {S}, ligated vector {L} (extension and ligation of synthetic DNA into the vector), naïve libraries {N} (transformation of the ligated vector into the bacteria followed by expression of the library for 4.5 hours to yield a "naïve" library), and libraries chemically modified by aldehyde ligation and cysteine macrocyclization {M} characterized by paired-end deep sequencing, detected a significant drop in diversity in {L} → {N}, but only a minor compositional difference in {S} → {L} and {N} → {M}. Libraries expressed at the N-terminus of phage protein pIII censored positively charged amino acids Arg and Lys; libraries expressed between pIII domains N1 and N2 overcame Arg/Lys-censorship but introduced new bias towards Gly and Ser. Interrogation of biases arising from cPTM by aldehyde ligation and cysteine macrocyclization unveiled censorship of sequences with Ser/Phe. Analogous analysis can be used to explore library diversity in new display platforms and optimize cPTM of these libraries.

The Impact of RLC Delivery Sequence on FTP Performance in UMTS

DEFF Research Database (Denmark)

Teyeb, Oumer; Boussif, Malek; Sørensen, Troels Bundgaard

2005-01-01

The Radio Link Control (RLC) protocol of Universal Mobile Telecommunication System (UMTS) provides an option for in- or out-of-sequence delivery of Service Data Units (SDUs) to upper layers. In this paper, the impact of this setting on the performance of File Transport Protocol (FTP) sessions...

Predicting DNA Methylation State of CpG Dinucleotide Using Genome Topological Features and Deep Networks.

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Wang, Yiheng; Liu, Tong; Xu, Dong; Shi, Huidong; Zhang, Chaoyang; Mo, Yin-Yuan; Wang, Zheng

2016-01-22

The hypo- or hyper-methylation of the human genome is one of the epigenetic features of leukemia. However, experimental approaches have only determined the methylation state of a small portion of the human genome. We developed deep learning based (stacked denoising autoencoders, or SdAs) software named "DeepMethyl" to predict the methylation state of DNA CpG dinucleotides using features inferred from three-dimensional genome topology (based on Hi-C) and DNA sequence patterns. We used the experimental data from immortalised myelogenous leukemia (K562) and healthy lymphoblastoid (GM12878) cell lines to train the learning models and assess prediction performance. We have tested various SdA architectures with different configurations of hidden layer(s) and amount of pre-training data and compared the performance of deep networks relative to support vector machines (SVMs). Using the methylation states of sequentially neighboring regions as one of the learning features, an SdA achieved a blind test accuracy of 89.7% for GM12878 and 88.6% for K562. When the methylation states of sequentially neighboring regions are unknown, the accuracies are 84.82% for GM12878 and 72.01% for K562. We also analyzed the contribution of genome topological features inferred from Hi-C. DeepMethyl can be accessed at http://dna.cs.usm.edu/deepmethyl/.

Is Multitask Deep Learning Practical for Pharma?

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Ramsundar, Bharath; Liu, Bowen; Wu, Zhenqin; Verras, Andreas; Tudor, Matthew; Sheridan, Robert P; Pande, Vijay

2017-08-28

Multitask deep learning has emerged as a powerful tool for computational drug discovery. However, despite a number of preliminary studies, multitask deep networks have yet to be widely deployed in the pharmaceutical and biotech industries. This lack of acceptance stems from both software difficulties and lack of understanding of the robustness of multitask deep networks. Our work aims to resolve both of these barriers to adoption. We introduce a high-quality open-source implementation of multitask deep networks as part of the DeepChem open-source platform. Our implementation enables simple python scripts to construct, fit, and evaluate sophisticated deep models. We use our implementation to analyze the performance of multitask deep networks and related deep models on four collections of pharmaceutical data (three of which have not previously been analyzed in the literature). We split these data sets into train/valid/test using time and neighbor splits to test multitask deep learning performance under challenging conditions. Our results demonstrate that multitask deep networks are surprisingly robust and can offer strong improvement over random forests. Our analysis and open-source implementation in DeepChem provide an argument that multitask deep networks are ready for widespread use in commercial drug discovery.

Differential genomic arrangements in Caryophyllales through deep transcriptome sequencing of A. hypochondriacus.

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Meeta Sunil

Full Text Available Genome duplication event in edible dicots under the orders Rosid and Asterid, common during the oligocene period, is missing for species under the order Caryophyllales. Despite this, grain amaranths not only survived this period but display many desirable traits missing in species under rosids and asterids. For example, grain amaranths display traits like C4 photosynthesis, high-lysine seeds, high-yield, drought resistance, tolerance to infection and resilience to stress. It is, therefore, of interest to look for minor genome rearrangements with potential functional implications that are unique to grain amaranths. Here, by deep sequencing and assembly of 16 transcriptomes (86.8 billion bases we have interrogated differential genome rearrangement unique to Amaranthus hypochondriacus with potential links to these phenotypes. We have predicted 125,581 non-redundant transcripts including 44,529 protein coding transcripts identified based on homology to known proteins and 13,529 predicted as novel/amaranth specific coding transcripts. Of the protein coding de novo assembled transcripts, we have identified 1810 chimeric transcripts. More than 30% and 19% of the gene pairs within the chimeric transcripts are found within the same loci in the genomes of A. hypochondriacus and Beta vulgaris respectively and are considered real positives. Interestingly, one of the chimeric transcripts comprises two important genes, namely DHDPS1, a key enzyme implicated in the biosynthesis of lysine, and alpha-glucosidase, an enzyme involved in sucrose catabolism, in close proximity to each other separated by a distance of 612 bases in the genome of A. hypochondriacus in a convergent configuration. We have experimentally validated that transcripts of these two genes are also overlapping in the 3' UTR with their expression negatively correlated from bud to mature seed, suggesting a potential link between the high seed lysine trait and unique genome organization.

Same-day genomic and epigenomic diagnosis of brain tumors using real-time nanopore sequencing.

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Euskirchen, Philipp; Bielle, Franck; Labreche, Karim; Kloosterman, Wigard P; Rosenberg, Shai; Daniau, Mailys; Schmitt, Charlotte; Masliah-Planchon, Julien; Bourdeaut, Franck; Dehais, Caroline; Marie, Yannick; Delattre, Jean-Yves; Idbaih, Ahmed

2017-11-01

Molecular classification of cancer has entered clinical routine to inform diagnosis, prognosis, and treatment decisions. At the same time, new tumor entities have been identified that cannot be defined histologically. For central nervous system tumors, the current World Health Organization classification explicitly demands molecular testing, e.g., for 1p/19q-codeletion or IDH mutations, to make an integrated histomolecular diagnosis. However, a plethora of sophisticated technologies is currently needed to assess different genomic and epigenomic alterations and turnaround times are in the range of weeks, which makes standardized and widespread implementation difficult and hinders timely decision making. Here, we explored the potential of a pocket-size nanopore sequencing device for multimodal and rapid molecular diagnostics of cancer. Low-pass whole genome sequencing was used to simultaneously generate copy number (CN) and methylation profiles from native tumor DNA in the same sequencing run. Single nucleotide variants in IDH1, IDH2, TP53, H3F3A, and the TERT promoter region were identified using deep amplicon sequencing. Nanopore sequencing yielded ~0.1X genome coverage within 6 h and resulting CN and epigenetic profiles correlated well with matched microarray data. Diagnostically relevant alterations, such as 1p/19q codeletion, and focal amplifications could be recapitulated. Using ad hoc random forests, we could perform supervised pan-cancer classification to distinguish gliomas, medulloblastomas, and brain metastases of different primary sites. Single nucleotide variants in IDH1, IDH2, and H3F3A were identified using deep amplicon sequencing within minutes of sequencing. Detection of TP53 and TERT promoter mutations shows that sequencing of entire genes and GC-rich regions is feasible. Nanopore sequencing allows same-day detection of structural variants, point mutations, and methylation profiling using a single device with negligible capital cost. It

A sparse autoencoder-based deep neural network for protein solvent accessibility and contact number prediction.

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Deng, Lei; Fan, Chao; Zeng, Zhiwen

2017-12-28

Direct prediction of the three-dimensional (3D) structures of proteins from one-dimensional (1D) sequences is a challenging problem. Significant structural characteristics such as solvent accessibility and contact number are essential for deriving restrains in modeling protein folding and protein 3D structure. Thus, accurately predicting these features is a critical step for 3D protein structure building. In this study, we present DeepSacon, a computational method that can effectively predict protein solvent accessibility and contact number by using a deep neural network, which is built based on stacked autoencoder and a dropout method. The results demonstrate that our proposed DeepSacon achieves a significant improvement in the prediction quality compared with the state-of-the-art methods. We obtain 0.70 three-state accuracy for solvent accessibility, 0.33 15-state accuracy and 0.74 Pearson Correlation Coefficient (PCC) for the contact number on the 5729 monomeric soluble globular protein dataset. We also evaluate the performance on the CASP11 benchmark dataset, DeepSacon achieves 0.68 three-state accuracy and 0.69 PCC for solvent accessibility and contact number, respectively. We have shown that DeepSacon can reliably predict solvent accessibility and contact number with stacked sparse autoencoder and a dropout approach.

DeepBipolar: Identifying genomic mutations for bipolar disorder via deep learning.

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Laksshman, Sundaram; Bhat, Rajendra Rana; Viswanath, Vivek; Li, Xiaolin

2017-09-01

Bipolar disorder, also known as manic depression, is a brain disorder that affects the brain structure of a patient. It results in extreme mood swings, severe states of depression, and overexcitement simultaneously. It is estimated that roughly 3% of the population of the United States (about 5.3 million adults) suffers from bipolar disorder. Recent research efforts like the Twin studies have demonstrated a high heritability factor for the disorder, making genomics a viable alternative for detecting and treating bipolar disorder, in addition to the conventional lengthy and costly postsymptom clinical diagnosis. Motivated by this study, leveraging several emerging deep learning algorithms, we design an end-to-end deep learning architecture (called DeepBipolar) to predict bipolar disorder based on limited genomic data. DeepBipolar adopts the Deep Convolutional Neural Network (DCNN) architecture that automatically extracts features from genotype information to predict the bipolar phenotype. We participated in the Critical Assessment of Genome Interpretation (CAGI) bipolar disorder challenge and DeepBipolar was considered the most successful by the independent assessor. In this work, we thoroughly evaluate the performance of DeepBipolar and analyze the type of signals we believe could have affected the classifier in distinguishing the case samples from the control set. © 2017 Wiley Periodicals, Inc.

Whole-genome and Transcriptome Sequencing of Prostate Cancer Identify New Genetic Alterations Driving Disease Progression

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Ren, Shancheng; Wei, Gong-Hong; Liu, Dongbing

2018-01-01

BACKGROUND: Global disparities in prostate cancer (PCa) incidence highlight the urgent need to identify genomic abnormalities in prostate tumors in different ethnic populations including Asian men. OBJECTIVE: To systematically explore the genomic complexity and define disease-driven genetic......-scale and comprehensive genomic data of prostate cancer from Asian population. Identification of these genetic alterations may help advance prostate cancer diagnosis, prognosis, and treatment....... alterations in PCa. DESIGN, SETTING, AND PARTICIPANTS: The study sequenced whole-genome and transcriptome of tumor-benign paired tissues from 65 treatment-naive Chinese PCa patients. Subsequent targeted deep sequencing of 293 PCa-relevant genes was performed in another cohort of 145 prostate tumors. OUTCOME...

Analysis of hepatitis C NS5A resistance associated polymorphisms using ultra deep single molecule real time (SMRT) sequencing.

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Bergfors, Assar; Leenheer, Daniël; Bergqvist, Anders; Ameur, Adam; Lennerstrand, Johan

2016-02-01

Development of Hepatitis C virus (HCV) resistance against direct-acting antivirals (DAAs), including NS5A inhibitors, is an obstacle to successful treatment of HCV when DAAs are used in sub-optimal combinations. Furthermore, it has been shown that baseline (pre-existing) resistance against DAAs is present in treatment naïve-patients and this will potentially complicate future treatment strategies in different HCV genotypes (GTs). Thus the aim was to detect low levels of NS5A resistant associated variants (RAVs) in a limited sample set of treatment-naïve patients of HCV GT1a and 3a, since such polymorphisms can display in vitro resistance as high as 60000 fold. Ultra-deep single molecule real time (SMRT) sequencing with the Pacific Biosciences (PacBio) RSII instrument was used to detect these RAVs. The SMRT sequencing was conducted on ten samples; three of them positive with Sanger sequencing (GT1a Q30H and Y93N, and GT3a Y93H), five GT1a samples, and two GT3a non-positive samples. The same methods were applied to the HCV GT1a H77-plasmid in a dilution series, in order to determine the error rates of replication, which in turn was used to determine the limit of detection (LOD), as defined by mean + 3SD, of minority variants down to 0.24%. We found important baseline NS5A RAVs at levels between 0.24 and 0.5%, which could potentially have clinical relevance. This new method with low level detection of baseline RAVs could be useful in predicting the most cost-efficient combination of DAA treatment, and reduce the treatment duration for an HCV infected individual. Copyright © 2015 Elsevier B.V. All rights reserved.

Gene expression in the deep biosphere.

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Orsi, William D; Edgcomb, Virginia P; Christman, Glenn D; Biddle, Jennifer F

2013-07-11

Scientific ocean drilling has revealed a deep biosphere of widespread microbial life in sub-seafloor sediment. Microbial metabolism in the marine subsurface probably has an important role in global biogeochemical cycles, but deep biosphere activities are not well understood. Here we describe and analyse the first sub-seafloor metatranscriptomes from anaerobic Peru Margin sediment up to 159 metres below the sea floor, represented by over 1 billion complementary DNA (cDNA) sequence reads. Anaerobic metabolism of amino acids, carbohydrates and lipids seem to be the dominant metabolic processes, and profiles of dissimilatory sulfite reductase (dsr) transcripts are consistent with pore-water sulphate concentration profiles. Moreover, transcripts involved in cell division increase as a function of microbial cell concentration, indicating that increases in sub-seafloor microbial abundance are a function of cell division across all three domains of life. These data support calculations and models of sub-seafloor microbial metabolism and represent the first holistic picture of deep biosphere activities.

Deep Sequence Analysis of AgoshRNA Processing Reveals 3’ A Addition and Trimming

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Alex Harwig

2015-01-01

Full Text Available The RNA interference (RNAi pathway, in which microprocessor and Dicer collaborate to process microRNAs (miRNA, was recently expanded by the description of alternative processing routes. In one of these noncanonical pathways, Dicer action is replaced by the Argonaute2 (Ago2 slicer function. It was recently shown that the stem-length of precursor-miRNA or short hairpin RNA (shRNA molecules is a major determinant for Dicer versus Ago2 processing. Here we present the results of a deep sequence study on the processing of shRNAs with different stem length and a top G·U wobble base pair (bp. This analysis revealed some unexpected properties of these so-called AgoshRNA molecules that are processed by Ago2 instead of Dicer. First, we confirmed the gradual shift from Dicer to Ago2 processing upon shortening of the hairpin length. Second, hairpins with a stem larger than 19 base pair are inefficiently cleaved by Ago2 and we noticed a shift in the cleavage site. Third, the introduction of a top G·U bp in a regular shRNA can promote Ago2-cleavage, which coincides with a loss of Ago2-loading of the Dicer-cleaved 3’ strand. Fourth, the Ago2-processed AgoshRNAs acquire a short 3’ tail of 1–3 A-nucleotides (nt and we present evidence that this product is subsequently trimmed by the poly(A-specific ribonuclease (PARN.

Modic type 1 changes. Detection performance of fat-suppressed fluid-sensitive MRI sequences

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Finkenstaedt, Tim; Andreisek, Gustav [University Hospital Zurich (Switzerland). Inst. of Diagnostic and Interventional Radiology; Del Grande, Filippo [Ospedale Regionale di Lugano (Switzerland). Inst. of Diagnostic and Interventional Radiology; Bolog, Nicolae [Phoenix Diagnostic Clinic, Bucharest (Romania); Ulrich, Nils; Tok, Sina [Schulthess Clinic, Zurich (Switzerland). Dept. of Neurosurgery; Kolokythas, Orpheus [Kantonsspital Winterthur (Switzerland). Inst. for Radiology and Nuclear Medicine; Steurer, Johann [University Hospital Zurich (Switzerland). Horten Center for Patient Oriented Research and Knowledge Transfer; Winklhofer, Sebastian [University Hospital Zurich (Switzerland). Inst. of Diagnostic and Interventional Radiology; University Hospital Zurich (Switzerland). Dept. of Neuroradiology; Collaboration: LSOS Study Group

2018-02-15

To assess the performance of fat-suppressed fluid-sensitive MRI sequences compared to T1-weighted (T1w) / T2w sequences for the detection of Modic 1 end-plate changes on lumbar spine MRI. Sagittal T1w, T2w, and fat-suppressed fluid-sensitive MRI images of 100 consecutive patients (consequently 500 vertebral segments; 52 female, mean age 74 ± 7.4 years; 48 male, mean age 71 ± 6.3 years) were retrospectively evaluated. We recorded the presence (yes/no) and extension (i.e., Likert-scale of height, volume, and end-plate extension) of Modic I changes in T1w/T2w sequences and compared the results to fat-suppressed fluid-sensitive sequences (McNemar/Wilcoxon-signed-rank test). Fat-suppressed fluid-sensitive sequences revealed significantly more Modic I changes compared to T1w/T2w sequences (156 vs. 93 segments, respectively; p < 0.001). The extension of Modic I changes in fat-suppressed fluid-sensitive sequences was significantly larger compared to T1w/T2w sequences (height: 2.53 ± 0.82 vs. 2.27 ± 0.79, volume: 2.35 ± 0.76 vs. 2.1 ± 0.65, end-plate: 2.46 ± 0.76 vs. 2.19 ± 0.81), (p < 0.05). Modic I changes that were only visible in fat-suppressed fluid-sensitive sequences but not in T1w/T2w sequences were significantly smaller compared to Modic I changes that were also visible in T1w/T2w sequences (p < 0.05). In conclusion, fat-suppressed fluid-sensitive MRI sequences revealed significantly more Modic I end-plate changes and demonstrated a greater extent compared to standard T1w/T2w imaging.

The Deepwater Horizon Oil Spill: Ecogenomics of the Deep-Sea Plume

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Hazen, T. C.

2012-12-01

The explosion on April 20, 2010 at the BP-leased Deepwater Horizon drilling rig in the Gulf of Mexico off the coast of Louisiana, resulted in oil and gas rising to the surface and the oil coming ashore in many parts of the Gulf, it also resulted in the dispersment of an immense oil plume 4,000 feet below the surface of the water. Despite spanning more than 600 feet in the water column and extending more than 10 miles from the wellhead, the dispersed oil plume was gone within weeks after the wellhead was capped - degraded and diluted to undetectable levels. Furthermore, this degradation took place without significant oxygen depletion. Ecogenomics enabled discovery of new and unclassified species of oil-eating bacteria that apparently lives in the deep Gulf where oil seeps are common. Using 16s microarrays, functional gene arrays, clone libraries, lipid analysis and a variety of hydrocarbon and micronutrient analyses we were able to characterize the oil degraders. Metagenomic sequence data was obtained for the deep-water samples using the Illumina platform. In addition, single cells were sorted and sequenced for the some of the most dominant bacteria that were represented in the oil plume; namely uncultivated representatives of Colwellia and Oceanospirillum. In addition, we performed laboratory microcosm experiments using uncontaminated water collected from The Gulf at the depth of the oil plume to which we added oil and COREXIT. These samples were characterized by 454 pyrotag. The results provide information about the key players and processes involved in degradation of oil, with and without COREXIT, in different impacted environments in The Gulf of Mexico. We are also extending these studies to explore dozens of deep sediment samples that were also collected after the oil spill around the wellhead. This data suggests that a great potential for intrinsic bioremediation of oil plumes exists in the deep-sea and other environs in the Gulf of Mexico.

Pulmonary embolism and pelvic-lower limb deep venous thrombosis: initial experience with magnetic resonance angiography

International Nuclear Information System (INIS)

Jiang Tao; Qiu Chuanya; Jiang Hua

2004-01-01

Objective: To evaluate the usefulness of combined three-dimensional (3D) and two-dimensional (2D) contrast enhanced magnetic resonance angiography (CE-MRA) for checking the thrombus embolism of different positions within single examination on the pulmonary artery and pelvic-lower limb deep veins. Methods: Fifteen patients with suspected pulmonary embolism and pelvic-lower limb deep venous thrombosis (DVT) were evaluate with combined 3D MRA and 2D CE-MRA. 3D spoiled gradient-recalled-echo bolus chase MR angiograms were obtained in four stations from the pulmonary artery to the ankle. Thereafter, 3D CE MRA was reversely scanned from the ankle to the pelvic. 2D contrast-enhanced MRI was obtained in pelvis, thigh, and calf. Pulmonary CT angiography (CTA) and/or DSA were performed in 15 patients, and duplex ultrasonography of lower-limb vein was performed in 12 patients. Results: Of the 15 cases, acceptable imaging of pulmonary vessel was acquired with 3D CE-MRA in 12 cases. The signal intensity was lower in the deep iliac vein and lower extremities than that in the artery, but vein frame was distinct after post processing. The artery and deep vein were clearly revealed with contrast enhanced FSPGR sequence in 15 cases. 3D CD-MRA imaging disclosed pulmonary embolism in fourteen patients and pelvis-lower limb DVT with multi-place involvement in nine patients. 2D contrast-enhanced MR imaging proved DVT in pelvis-lower limb. 2D contrast-enhanced FSPGR sequence was a complementation of 3D CE-MRA and it had larger scan field. Thrombus presented as low signals and eccentral or intraluminal filling defect. Local caliber of vein thrombus in 6 cases was evidently broadened. Conclusion: Within only one MR examination procedure, it is capable of examining the pulmonary embolism and DVT of pelvis-lower limb with combined 3D MRA and 2D contrast enhanced MR. The results are promising as a non-invasion 'on-stop shopping' tool in the evaluation of thromboembolic disease

Expression profiles of mRNA and long noncoding RNA in the ovaries of letrozole-induced polycystic ovary syndrome rat model through deep sequencing.

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Fu, Lu-Lu; Xu, Ying; Li, Dan-Dan; Dai, Xiao-Wei; Xu, Xin; Zhang, Jing-Shun; Ming, Hao; Zhang, Xue-Ying; Zhang, Guo-Qing; Ma, Ya-Lan; Zheng, Lian-Wen

2018-05-30

Polycystic ovary syndrome (PCOS) is one of the most common endocrine disorders in reproductive-aged women. However, the exact pathophysiology of PCOS remains largely unclear. We performed deep sequencing to investigate the mRNA and long noncoding RNA (lncRNA) expression profiles in the ovarian tissues of letrozole-induced PCOS rat model and control rats. A total of 2147 mRNAs and 158 lncRNAs were differentially expressed between the PCOS models and control. Gene ontology analysis indicated that differentially expressed mRNAs were associated with biological adhesion, reproduction, and metabolic process. Pathway analysis results indicated that these aberrantly expressed mRNAs were related to several specific signaling pathways, including insulin resistance, steroid hormone biosynthesis, PPAR signaling pathway, cell adhesion molecules, autoimmune thyroid disease, and AMPK signaling pathway. The relative expression levels of mRNAs and lncRNAs were validated through qRT-PCR. LncRNA-miRNA-mRNA network was constructed to explore ceRNAs involved in the PCOS model and were also verified by qRTPCR experiment. These findings may provide insight into the pathogenesis of PCOS and clues to find key diagnostic and therapeutic roles of lncRNA in PCOS. Copyright © 2018 Elsevier B.V. All rights reserved.

Fungal diversity in deep-sea sediments of a hydrothermal vent system in the Southwest Indian Ridge

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Xu, Wei; Gong, Lin-feng; Pang, Ka-Lai; Luo, Zhu-Hua

2018-01-01

Deep-sea hydrothermal sediment is known to support remarkably diverse microbial consortia. In deep sea environments, fungal communities remain less studied despite their known taxonomic and functional diversity. High-throughput sequencing methods have augmented our capacity to assess eukaryotic diversity and their functions in microbial ecology. Here we provide the first description of the fungal community diversity found in deep sea sediments collected at the Southwest Indian Ridge (SWIR) using culture-dependent and high-throughput sequencing approaches. A total of 138 fungal isolates were cultured from seven different sediment samples using various nutrient media, and these isolates were identified to 14 fungal taxa, including 11 Ascomycota taxa (7 genera) and 3 Basidiomycota taxa (2 genera) based on internal transcribed spacers (ITS1, ITS2 and 5.8S) of rDNA. Using illumina HiSeq sequencing, a total of 757,467 fungal ITS2 tags were recovered from the samples and clustered into 723 operational taxonomic units (OTUs) belonging to 79 taxa (Ascomycota and Basidiomycota contributed to 99% of all samples) based on 97% sequence similarity. Results from both approaches suggest that there is a high fungal diversity in the deep-sea sediments collected in the SWIR and fungal communities were shown to be slightly different by location, although all were collected from adjacent sites at the SWIR. This study provides baseline data of the fungal diversity and biogeography, and a glimpse to the microbial ecology associated with the deep-sea sediments of the hydrothermal vent system of the Southwest Indian Ridge.

The cortisol awakening response is associated with performance of a serial sequence reaction time task.

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Hodyl, Nicolette A; Schneider, Luke; Vallence, Ann-Maree; Clow, Angela; Ridding, Michael C; Pitcher, Julia B

2016-02-01

There is emerging evidence of a relationship between the cortisol awakening response (CAR) and the neural mechanisms underlying learning and memory. The aim of this study was to determine whether the CAR is associated with acquisition, retention and overnight consolidation or improvement of a serial sequence reaction time task. Salivary samples were collected at 0, 15, 30 and 45 min after awakening in 39 healthy adults on 2 consecutive days. The serial sequence reaction time task was repeated each afternoon. Participants completed the perceived stress scale and provided salivary samples prior to testing for cortisol assessment. While the magnitude of the CAR (Z score) was not associated with either baseline performance or the timed improvement during task acquisition of the serial sequence task, a positive correlation was observed with reaction times during the stable performance phase on day 1 (r=0.373, p=0.019). Residuals derived from the relationship between baseline and stable phase reaction times on day 1 were used as a surrogate for the degree of learning: these residuals were also correlated with the CAR mean increase on day 1 (r=0.357, p=0.048). Task performance on day 2 was not associated with the CAR obtained on this same day. No association was observed between the perceived stress score, cortisol at testing or task performance. These data indicate that a smaller CAR in healthy adults is associated with a greater degree of learning and faster performance of a serial sequence reaction time task. These results support recognition of the CAR as an important factor contributing to cognitive performance throughout the day. Copyright © 2015 Elsevier B.V. All rights reserved.

Deep Unfolding for Topic Models.

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Chien, Jen-Tzung; Lee, Chao-Hsi

2018-02-01

Deep unfolding provides an approach to integrate the probabilistic generative models and the deterministic neural networks. Such an approach is benefited by deep representation, easy interpretation, flexible learning and stochastic modeling. This study develops the unsupervised and supervised learning of deep unfolded topic models for document representation and classification. Conventionally, the unsupervised and supervised topic models are inferred via the variational inference algorithm where the model parameters are estimated by maximizing the lower bound of logarithm of marginal likelihood using input documents without and with class labels, respectively. The representation capability or classification accuracy is constrained by the variational lower bound and the tied model parameters across inference procedure. This paper aims to relax these constraints by directly maximizing the end performance criterion and continuously untying the parameters in learning process via deep unfolding inference (DUI). The inference procedure is treated as the layer-wise learning in a deep neural network. The end performance is iteratively improved by using the estimated topic parameters according to the exponentiated updates. Deep learning of topic models is therefore implemented through a back-propagation procedure. Experimental results show the merits of DUI with increasing number of layers compared with variational inference in unsupervised as well as supervised topic models.

DeepCNF-D: Predicting Protein Order/Disorder Regions by Weighted Deep Convolutional Neural Fields

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Sheng Wang

2015-07-01

Full Text Available Intrinsically disordered proteins or protein regions are involved in key biological processes including regulation of transcription, signal transduction, and alternative splicing. Accurately predicting order/disorder regions ab initio from the protein sequence is a prerequisite step for further analysis of functions and mechanisms for these disordered regions. This work presents a learning method, weighted DeepCNF (Deep Convolutional Neural Fields, to improve the accuracy of order/disorder prediction by exploiting the long-range sequential information and the interdependency between adjacent order/disorder labels and by assigning different weights for each label during training and prediction to solve the label imbalance issue. Evaluated by the CASP9 and CASP10 targets, our method obtains 0.855 and 0.898 AUC values, which are higher than the state-of-the-art single ab initio predictors.

Deep Sequencing Analysis of miRNA Expression in Breast Muscle of Fast-Growing and Slow-Growing Broilers

Directory of Open Access Journals (Sweden)

Hongjia Ouyang

2015-07-01

Full Text Available Growth performance is an important economic trait in chicken. MicroRNAs (miRNAs have been shown to play important roles in various biological processes, but their functions in chicken growth are not yet clear. To investigate the function of miRNAs in chicken growth, breast muscle tissues of the two-tail samples (highest and lowest body weight from Recessive White Rock (WRR and Xinghua Chickens (XH were performed on high throughput small RNA deep sequencing. In this study, a total of 921 miRNAs were identified, including 733 known mature miRNAs and 188 novel miRNAs. There were 200, 279, 257 and 297 differentially expressed miRNAs in the comparisons of WRRh vs. WRRl, WRRh vs. XHh, WRRl vs. XHl, and XHh vs. XHl group, respectively. A total of 22 highly differentially expressed miRNAs (fold change > 2 or < 0.5; p-value < 0.05; q-value < 0.01, which also have abundant expression (read counts > 1000 were found in our comparisons. As far as two analyses (WRRh vs. WRRl, and XHh vs. XHl are concerned, we found 80 common differentially expressed miRNAs, while 110 miRNAs were found in WRRh vs. XHh and WRRl vs. XHl. Furthermore, 26 common miRNAs were identified among all four comparisons. Four differentially expressed miRNAs (miR-223, miR-16, miR-205a and miR-222b-5p were validated by quantitative real-time RT-PCR (qRT-PCR. Regulatory networks of interactions among miRNAs and their targets were constructed using integrative miRNA target-prediction and network-analysis. Growth hormone receptor (GHR was confirmed as a target of miR-146b-3p by dual-luciferase assay and qPCR, indicating that miR-34c, miR-223, miR-146b-3p, miR-21 and miR-205a are key growth-related target genes in the network. These miRNAs are proposed as candidate miRNAs for future studies concerning miRNA-target function on regulation of chicken growth.

Comparison between Laying Hen Performance in the Cage System and the Deep Litter System on a Diet Free from Animal Protein

Directory of Open Access Journals (Sweden)

E. Voslářová

2006-01-01

Full Text Available Battery cage systems for housing laying hens are being replaced by alternative systems including the deep litter system. At the same time, the substitution of meat and bone meal by vegetable matter in poultry feed mixtures is sought in the nutrition of laying hens. In the experiment, we compared the performance of laying hens of the ISA BROWN hybrid in both the cage system and the deep litter system, on a diet with the meat and bone meal content replaced by vegetable feeds (based on lupin. In the first group, 36 laying hens were kept in the deep litter system; in the second group, 36 laying hens were kept in cages. Over the period of nine months, the number of eggs laid, their weight, shell quality, the clinical state of the laying hens and incidence of their mortality were monitored daily. We found that in the cage system a higher number of eggs was obtained; a lower mean egg weight (p p p p p > 0.05, and the number of laying hens which died was lower (p < 0.05 in comparison with the deep litter system. The results of the experiment demonstrate that, with the substitution of meat and bone meal by vegetable matter in the feed mixtures for laying hens, there are differences between the performance of laying hens from the deep litter system as compared to the laying hens from the cage system. The deep litter system better meets the requirements for the welfare of laying hens; however, it provides a lower yield.

New optimized drill pipe size for deep-water, extended reach and ultra-deep drilling

Energy Technology Data Exchange (ETDEWEB)

Jellison, Michael J.; Delgado, Ivanni [Grant Prideco, Inc., Hoston, TX (United States); Falcao, Jose Luiz; Sato, Ademar Takashi [PETROBRAS, Rio de Janeiro, RJ (Brazil); Moura, Carlos Amsler [Comercial Perfuradora Delba Baiana Ltda., Rio de Janeiro, RJ (Brazil)

2004-07-01

A new drill pipe size, 5-7/8 in. OD, represents enabling technology for Extended Reach Drilling (ERD), deep water and other deep well applications. Most world-class ERD and deep water wells have traditionally been drilled with 5-1/2 in. drill pipe or a combination of 6-5/8 in. and 5-1/2 in. drill pipe. The hydraulic performance of 5-1/2 in. drill pipe can be a major limitation in substantial ERD and deep water wells resulting in poor cuttings removal, slower penetration rates, diminished control over well trajectory and more tendency for drill pipe sticking. The 5-7/8 in. drill pipe provides a significant improvement in hydraulic efficiency compared to 5-1/2 in. drill pipe and does not suffer from the disadvantages associated with use of 6-5/8 in. drill pipe. It represents a drill pipe assembly that is optimized dimensionally and on a performance basis for casing and bit programs that are commonly used for ERD, deep water and ultra-deep wells. The paper discusses the engineering philosophy behind 5-7/8 in. drill pipe, the design challenges associated with development of the product and reviews the features and capabilities of the second-generation double-shoulder connection. The paper provides drilling case history information on significant projects where the pipe has been used and details results achieved with the pipe. (author)

Deep sequencing reveals a novel closterovirus associated with wild rose leaf rosette disease.

Science.gov (United States)

He, Yan; Yang, Zuokun; Hong, Ni; Wang, Guoping; Ning, Guogui; Xu, Wenxing

2015-06-01

A bizarre virus-like symptom of a leaf rosette formed by dense small leaves on branches of wild roses (Rosa multiflora Thunb.), designated as 'wild rose leaf rosette disease' (WRLRD), was observed in China. To investigate the presumed causal virus, a wild rose sample affected by WRLRD was subjected to deep sequencing of small interfering RNAs (siRNAs) for a complete survey of the infecting viruses and viroids. The assembly of siRNAs led to the reconstruction of the complete genomes of three known viruses, namely Apple stem grooving virus (ASGV), Blackberry chlorotic ringspot virus (BCRV) and Prunus necrotic ringspot virus (PNRSV), and of a novel virus provisionally named 'rose leaf rosette-associated virus' (RLRaV). Phylogenetic analysis clearly placed RLRaV alongside members of the genus Closterovirus, family Closteroviridae. Genome organization of RLRaV RNA (17,653 nucleotides) showed 13 open reading frames (ORFs), except ORF1 and the quintuple gene block, most of which showed no significant similarities with known viral proteins, but, instead, had detectable identities to fungal or bacterial proteins. Additional novel molecular features indicated that RLRaV seems to be the most complex virus among the known genus members. To our knowledge, this is the first report of WRLRD and its associated closterovirus, as well as two ilarviruses and one capilovirus, infecting wild roses. Our findings present novel information about the closterovirus and the aetiology of this rose disease which should facilitate its control. More importantly, the novel features of RLRaV help to clarify the molecular and evolutionary features of the closterovirus. © 2014 BSPP AND JOHN WILEY & SONS LTD.

Micropathogen Community Analysis in Hyalomma rufipes via High-Throughput Sequencing of Small RNAs

Science.gov (United States)

Luo, Jin; Liu, Min-Xuan; Ren, Qiao-Yun; Chen, Ze; Tian, Zhan-Cheng; Hao, Jia-Wei; Wu, Feng; Liu, Xiao-Cui; Luo, Jian-Xun; Yin, Hong; Wang, Hui; Liu, Guang-Yuan

2017-01-01

Ticks are important vectors in the transmission of a broad range of micropathogens to vertebrates, including humans. Because of the role of ticks in disease transmission, identifying and characterizing the micropathogen profiles of tick populations have become increasingly important. The objective of this study was to survey the micropathogens of Hyalomma rufipes ticks. Illumina HiSeq2000 technology was utilized to perform deep sequencing of small RNAs (sRNAs) extracted from field-collected H. rufipes ticks in Gansu Province, China. The resultant sRNA library data revealed that the surveyed tick populations produced reads that were homologous to St. Croix River Virus (SCRV) sequences. We also observed many reads that were homologous to microbial and/or pathogenic isolates, including bacteria, protozoa, and fungi. As part of this analysis, a phylogenetic tree was constructed to display the relationships among the homologous sequences that were identified. The study offered a unique opportunity to gain insight into the micropathogens of H. rufipes ticks. The effective control of arthropod vectors in the future will require knowledge of the micropathogen composition of vectors harboring infectious agents. Understanding the ecological factors that regulate vector propagation in association with the prevalence and persistence of micropathogen lineages is also imperative. These interactions may affect the evolution of micropathogen lineages, especially if the micropathogens rely on the vector or host for dispersal. The sRNA deep-sequencing approach used in this analysis provides an intuitive method to survey micropathogen prevalence in ticks and other vector species. PMID:28861401

Insertion sequences enrichment in extreme Red sea brine pool vent

KAUST Repository

Elbehery, Ali H. A.

2016-12-03

Mobile genetic elements are major agents of genome diversification and evolution. Limited studies addressed their characteristics, including abundance, and role in extreme habitats. One of the rare natural habitats exposed to multiple-extreme conditions, including high temperature, salinity and concentration of heavy metals, are the Red Sea brine pools. We assessed the abundance and distribution of different mobile genetic elements in four Red Sea brine pools including the world’s largest known multiple-extreme deep-sea environment, the Red Sea Atlantis II Deep. We report a gradient in the abundance of mobile genetic elements, dramatically increasing in the harshest environment of the pool. Additionally, we identified a strong association between the abundance of insertion sequences and extreme conditions, being highest in the harshest and deepest layer of the Red Sea Atlantis II Deep. Our comparative analyses of mobile genetic elements in secluded, extreme and relatively non-extreme environments, suggest that insertion sequences predominantly contribute to polyextremophiles genome plasticity.

Deep Sequencing of Three Loci Implicated in Large-Scale Genome-Wide Association Study Smoking Meta-Analyses.

Science.gov (United States)

Clark, Shaunna L; McClay, Joseph L; Adkins, Daniel E; Aberg, Karolina A; Kumar, Gaurav; Nerella, Sri; Xie, Linying; Collins, Ann L; Crowley, James J; Quakenbush, Corey R; Hillard, Christopher E; Gao, Guimin; Shabalin, Andrey A; Peterson, Roseann E; Copeland, William E; Silberg, Judy L; Maes, Hermine; Sullivan, Patrick F; Costello, Elizabeth J; van den Oord, Edwin J

2016-05-01

Genome-wide association study meta-analyses have robustly implicated three loci that affect susceptibility for smoking: CHRNA5\\CHRNA3\\CHRNB4, CHRNB3\\CHRNA6 and EGLN2\\CYP2A6. Functional follow-up studies of these loci are needed to provide insight into biological mechanisms. However, these efforts have been hampered by a lack of knowledge about the specific causal variant(s) involved. In this study, we prioritized variants in terms of the likelihood they account for the reported associations. We employed targeted capture of the CHRNA5\\CHRNA3\\CHRNB4, CHRNB3\\CHRNA6, and EGLN2\\CYP2A6 loci and flanking regions followed by next-generation deep sequencing (mean coverage 78×) to capture genomic variation in 363 individuals. We performed single locus tests to determine if any single variant accounts for the association, and examined if sets of (rare) variants that overlapped with biologically meaningful annotations account for the associations. In total, we investigated 963 variants, of which 71.1% were rare (minor allele frequency < 0.01), 6.02% were insertion/deletions, and 51.7% were catalogued in dbSNP141. The single variant results showed that no variant fully accounts for the association in any region. In the variant set results, CHRNB4 accounts for most of the signal with significant sets consisting of directly damaging variants. CHRNA6 explains most of the signal in the CHRNB3\\CHRNA6 locus with significant sets indicating a regulatory role for CHRNA6. Significant sets in CYP2A6 involved directly damaging variants while the significant variant sets suggested a regulatory role for EGLN2. We found that multiple variants implicating multiple processes explain the signal. Some variants can be prioritized for functional follow-up. © The Author 2015. Published by Oxford University Press on behalf of the Society for Research on Nicotine and Tobacco. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.

Diagnosing upper extremity deep vein thrombosis with non-contrast-enhanced Magnetic Resonance Direct Thrombus Imaging: A pilot study.

Science.gov (United States)

Dronkers, C E A; Klok, F A; van Haren, G R; Gleditsch, J; Westerlund, E; Huisman, M V; Kroft, L J M

2018-03-01

Diagnosing upper extremity deep vein thrombosis (UEDVT) can be challenging. Compression ultrasonography is often inconclusive because of overlying anatomic structures that hamper compressing veins. Contrast venography is invasive and has a risk of contrast allergy. Magnetic Resonance Direct Thrombus Imaging (MRDTI) and Three Dimensional Turbo Spin-echo Spectral Attenuated Inversion Recovery (3D TSE-SPAIR) are both non-contrast-enhanced Magnetic Resonance Imaging (MRI) sequences that can visualize a thrombus directly by the visualization of methemoglobin, which is formed in a fresh blood clot. MRDTI has been proven to be accurate in diagnosing deep venous thrombosis (DVT) of the leg. The primary aim of this pilot study was to test the feasibility of diagnosing UEDVT with these MRI techniques. MRDTI and 3D TSE-SPAIR were performed in 3 pilot patients who were already diagnosed with UEDVT by ultrasonography or contrast venography. In all patients, UEDVT diagnosis could be confirmed by MRDTI and 3D TSE-SPAIR in all vein segments. In conclusion, this study showed that non-contrast MRDTI and 3D TSE-SPAIR sequences may be feasible tests to diagnose UEDVT. However diagnostic accuracy and management studies have to be performed before these techniques can be routinely used in clinical practice. Copyright © 2018 Elsevier Ltd. All rights reserved.

Performance evaluation of enterprise architecture using fuzzy sequence diagram

Directory of Open Access Journals (Sweden)

Mohammad Atasheneh

2014-01-01

Full Text Available Developing an Enterprise Architecture is a complex task and to control the complexity of the regulatory framework we need to measure the relative performance of one system against other available systems. On the other hand, enterprise architecture cannot be organized without the use of a logical structure. The framework provides a logical structure for classifying architectural output. Among the common architectural framework, the C4ISR framework and methodology of the product is one of the most popular techniques. In this paper, given the existing uncertainties in system development and information systems, a new version of UML called Fuzzy-UML is proposed for enterprise architecture development based on fuzzy Petri nets. In addition, the performance of the system is also evaluated based on Fuzzy sequence diagram.

Deep learning in bioinformatics.

Science.gov (United States)

Min, Seonwoo; Lee, Byunghan; Yoon, Sungroh

2017-09-01

In the era of big data, transformation of biomedical big data into valuable knowledge has been one of the most important challenges in bioinformatics. Deep learning has advanced rapidly since the early 2000s and now demonstrates state-of-the-art performance in various fields. Accordingly, application of deep learning in bioinformatics to gain insight from data has been emphasized in both academia and industry. Here, we review deep learning in bioinformatics, presenting examples of current research. To provide a useful and comprehensive perspective, we categorize research both by the bioinformatics domain (i.e. omics, biomedical imaging, biomedical signal processing) and deep learning architecture (i.e. deep neural networks, convolutional neural networks, recurrent neural networks, emergent architectures) and present brief descriptions of each study. Additionally, we discuss theoretical and practical issues of deep learning in bioinformatics and suggest future research directions. We believe that this review will provide valuable insights and serve as a starting point for researchers to apply deep learning approaches in their bioinformatics studies. © The Author 2016. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oup.com.

Regular Routes: Deep Mapping a Performative Counterpractice for the Daily Commute 1

Directory of Open Access Journals (Sweden)

Laura Bissell

2015-09-01

Full Text Available This article offers a textual “deep map” of a series of experimental commutes undertaken in the west of Scotland in 2014. Recent developments in the field of transport studies have reconceived travel time as a far richer cultural experience than in previously utilitarian and economic approaches to the “problem” of commuting. Understanding their own commutes in these terms—as spaces of creativity, productivity and transformation—the authors trace the development of a performative “counterpractice” for their daily journeys between home and work. Deep mapping—as a form of “theory-informed story-telling”—is employed as a productive strategy to document this reimagination of ostensibly quotidian and functional travel. Importantly, this particular stage of the project is not presented as an end-point. Striving to develop an ongoing creative engagement with landscape, the authors continue this exploratory mobile research by connecting to other commuters’ journeys, and proposing a series of “strategies” for reimagining the daily commute; a list of prompts for future action within the routines and spaces of commuting. A range of alternative approaches to commuting are offered here to anyone who regularly travels to and from work to employ or develop as they wish, extending the mapping process to other routes and contexts.

Real-Time Human Detection for Aerial Captured Video Sequences via Deep Models

Directory of Open Access Journals (Sweden)

Nouar AlDahoul

2018-01-01

Full Text Available Human detection in videos plays an important role in various real life applications. Most of traditional approaches depend on utilizing handcrafted features which are problem-dependent and optimal for specific tasks. Moreover, they are highly susceptible to dynamical events such as illumination changes, camera jitter, and variations in object sizes. On the other hand, the proposed feature learning approaches are cheaper and easier because highly abstract and discriminative features can be produced automatically without the need of expert knowledge. In this paper, we utilize automatic feature learning methods which combine optical flow and three different deep models (i.e., supervised convolutional neural network (S-CNN, pretrained CNN feature extractor, and hierarchical extreme learning machine for human detection in videos captured using a nonstatic camera on an aerial platform with varying altitudes. The models are trained and tested on the publicly available and highly challenging UCF-ARG aerial dataset. The comparison between these models in terms of training, testing accuracy, and learning speed is analyzed. The performance evaluation considers five human actions (digging, waving, throwing, walking, and running. Experimental results demonstrated that the proposed methods are successful for human detection task. Pretrained CNN produces an average accuracy of 98.09%. S-CNN produces an average accuracy of 95.6% with soft-max and 91.7% with Support Vector Machines (SVM. H-ELM has an average accuracy of 95.9%. Using a normal Central Processing Unit (CPU, H-ELM’s training time takes 445 seconds. Learning in S-CNN takes 770 seconds with a high performance Graphical Processing Unit (GPU.

Human-associated fungi in deep subseafloor sediment?

Science.gov (United States)

Fulfer, V. M.; Kirkpatrick, J. B.; D'Hondt, S.

2015-12-01

Recent studies have reported fungi in marine sediment samples from depths as great as 1740 meters below seafloor (mbsf) (Rédou et al., 2014). Such studies have utilized a variety of techniques to identify fungi, including cultivation of isolates, amplicon sequencing, and metagenomics. Six recent studies of marine sediment collectively identify nearly 100 fungal taxa at the genus and species levels (Damare et al., 2006; Lai et al., 2007; Edgcomb et al., 2010; Singh et al., 2010; Orsi et al., 2013; Rédou et al., 2014). Known marine taxa are rarely identified by these studies. For individual studies with more than two taxa, between 16% and 57% of the fungal taxa are human microflora or associated with human environments (e.g., human skin or indoor air). For example, three of the six studies identified Malassezia species that are common skin inhabitants of humans and dogs. Although human-associated taxa have been identified in both shallow and deep sediment, they pose a particularly acute problem for deep subseafloor samples, where claims of a eukaryotic deep biosphere are most striking; depending on the study, 25% to 38% of species identified in sediment taken at depths greater than 40 meters are human-associated. Only one to three species have been reported from each of the four samples taken at depths greater than one km (eight species total; Rédou et al., 2014). Of these eight species, three are human-associated. This ubiquity of human-associated microflora is very problematic for interpretations of an indigenous deep subseafloor fungal community; either human-associated taxa comprise a large fraction of marine sedimentary fungi, or sample and analytical contamination is so widespread that the extent and ubiquity of a deep subseafloor fungal community remains uncertain. This highlights the need for stringent quality control measures throughout coring, sampling, and recovery of marine sediment, and when cultivating, extracting, and/or sequencing fungi from

Deep sequencing of uveal melanoma identifies a recurrent mutation in PLCB4

DEFF Research Database (Denmark)

Johansson, Peter; Aoude, Lauren G; Wadt, Karin

2016-01-01

Next generation sequencing of uveal melanoma (UM) samples has identified a number of recurrent oncogenic or loss-of-function mutations in key driver genes including: GNAQ, GNA11, EIF1AX, SF3B1 and BAP1. To search for additional driver mutations in this tumor type we carried out whole......, instead, a BRCA mutation signature predominated. In addition to mutations in the known UM driver genes, we found a recurrent mutation in PLCB4 (c.G1888T, p.D630Y, NM_000933), which was validated using Sanger sequencing. The identical mutation was also found in published UM sequence data (1 of 56 tumors......-genome or whole-exome sequencing of 28 tumors or primary cell lines. These samples have a low mutation burden, with a mean of 10.6 protein changing mutations per sample (range 0 to 53). As expected for these sun-shielded melanomas the mutation spectrum was not consistent with an ultraviolet radiation signature...

Pitch Sequence Complexity and Long-Term Pitcher Performance

Directory of Open Access Journals (Sweden)

Joel R. Bock

2015-03-01

Full Text Available Winning one or two games during a Major League Baseball (MLB season is often the difference between a team advancing to post-season play, or “waiting until next year”. Technology advances have made it feasible to augment historical data with in-game contextual data to provide managers immediate insights regarding an opponent’s next move, thereby providing a competitive edge. We developed statistical models of pitcher behavior using pitch sequences thrown during three recent MLB seasons (2011–2013. The purpose of these models was to predict the next pitch type, for each pitcher, based on data available at the immediate moment, in each at-bat. Independent models were developed for each player’s most frequent four pitches. The overall predictability of next pitch type is 74:5%. Additional analyses on pitcher predictability within specific game situations are discussed. Finally, using linear regression analysis, we show that an index of pitch sequence predictability may be used to project player performance in terms of Earned Run Average (ERA and Fielding Independent Pitching (FIP over a longer term. On a restricted range of the independent variable, reducing complexity in selection of pitches is correlated with higher values of both FIP and ERA for the players represented in the sample. Both models were significant at the α = 0.05 level (ERA: p = 0.022; FIP: p = 0.0114. With further development, such models may reduce risk faced by management in evaluation of potential trades, or to scouts assessing unproven emerging talent. Pitchers themselves might benefit from awareness of their individual statistical tendencies, and adapt their behavior on the mound accordingly. To our knowledge, the predictive model relating pitch-wise complexity and long-term performance appears to be novel.

Viral metagenomics: Analysis of begomoviruses by illumina high-throughput sequencing

KAUST Repository

Idris, Ali

2014-03-12

Traditional DNA sequencing methods are inefficient, lack the ability to discern the least abundant viral sequences, and ineffective for determining the extent of variability in viral populations. Here, populations of single-stranded DNA plant begomoviral genomes and their associated beta- and alpha-satellite molecules (virus-satellite complexes) (genus, Begomovirus; family, Geminiviridae) were enriched from total nucleic acids isolated from symptomatic, field-infected plants, using rolling circle amplification (RCA). Enriched virus-satellite complexes were subjected to Illumina-Next Generation Sequencing (NGS). CASAVA and SeqMan NGen programs were implemented, respectively, for quality control and for de novo and reference-guided contig assembly of viral-satellite sequences. The authenticity of the begomoviral sequences, and the reproducibility of the Illumina-NGS approach for begomoviral deep sequencing projects, were validated by comparing NGS results with those obtained using traditional molecular cloning and Sanger sequencing of viral components and satellite DNAs, also enriched by RCA or amplified by polymerase chain reaction. As the use of NGS approaches, together with advances in software development, make possible deep sequence coverage at a lower cost; the approach described herein will streamline the exploration of begomovirus diversity and population structure from naturally infected plants, irrespective of viral abundance. This is the first report of the implementation of Illumina-NGS to explore the diversity and identify begomoviral-satellite SNPs directly from plants naturally-infected with begomoviruses under field conditions. 2014 by the authors; licensee MDPI, Basel, Switzerland.

Viral Metagenomics: Analysis of Begomoviruses by Illumina High-Throughput Sequencing

Directory of Open Access Journals (Sweden)

Ali Idris

2014-03-01

Full Text Available Traditional DNA sequencing methods are inefficient, lack the ability to discern the least abundant viral sequences, and ineffective for determining the extent of variability in viral populations. Here, populations of single-stranded DNA plant begomoviral genomes and their associated beta- and alpha-satellite molecules (virus-satellite complexes (genus, Begomovirus; family, Geminiviridae were enriched from total nucleic acids isolated from symptomatic, field-infected plants, using rolling circle amplification (RCA. Enriched virus-satellite complexes were subjected to Illumina-Next Generation Sequencing (NGS. CASAVA and SeqMan NGen programs were implemented, respectively, for quality control and for de novo and reference-guided contig assembly of viral-satellite sequences. The authenticity of the begomoviral sequences, and the reproducibility of the Illumina-NGS approach for begomoviral deep sequencing projects, were validated by comparing NGS results with those obtained using traditional molecular cloning and Sanger sequencing of viral components and satellite DNAs, also enriched by RCA or amplified by polymerase chain reaction. As the use of NGS approaches, together with advances in software development, make possible deep sequence coverage at a lower cost; the approach described herein will streamline the exploration of begomovirus diversity and population structure from naturally infected plants, irrespective of viral abundance. This is the first report of the implementation of Illumina-NGS to explore the diversity and identify begomoviral-satellite SNPs directly from plants naturally-infected with begomoviruses under field conditions.

A THM stress-strain framework for modelling the performance of argillaceous materials in deep repositories for radioactive waste

International Nuclear Information System (INIS)

Laloui, L.; Francois, B.

2007-01-01

In the scenarios for deep, geological nuclear-waste repositories, clayey soils will be hydrated, heated, cooled and dried. The numerical modelling of these mechanical processes is a key issue. Performance assessment of deep repositories for heat-generating radioactive waste would benefit from improvements in mechanical stress-strain constitutive modelling of the coupled thermo-hydro-mechanical behaviour. The presented framework allows progress in understanding the most involved phenomena relevant to nuclear-waste repositories and their coupled nature. It could be used both in the design and in the performance assessment of repositories. It may be applied to disposal in clay formations and to hard-rock repositories where artificially compacted clay is to be used as buffer and backfill. Such a constitutive framework may help in understanding some unexplained or controversial behaviours and in defining experimental programmes to answer key questions. (author)

Complete genome sequence of a tomato infecting tomato mottle mosaic virus in New York

Science.gov (United States)

Complete genome sequence of an emerging isolate of tomato mottle mosaic virus (ToMMV) infecting experimental nicotianan benthamiana plants in up-state New York was obtained using small RNA deep sequencing. ToMMV_NY-13 shared 99% sequence identity to ToMMV isolates from Mexico and Florida. Broader d...

The complete mitochondrial genome of the deep-sea sponge Poecillastra laminaris (Astrophorida, Vulcanellidae).

Science.gov (United States)

Zeng, Cong; Thomas, Leighton J; Kelly, Michelle; Gardner, Jonathan P A

2016-05-01

The complete mitochondrial genome of a New Zealand specimen of the deep-sea sponge Poecillastra laminaris (Sollas, 1886) (Astrophorida, Vulcanellidae), from the Colville Ridge, New Zealand, was sequenced using the 454 Life Science pyrosequencing system. To identify homologous mitochondrial sequences, the 454 reads were mapped to the complete mitochondrial genome sequence of Geodia neptuni (GeneBank No. NC_006990). The P. laminaris genome is 18,413 bp in length and includes 14 protein-coding genes, 24 transfer RNA genes and 2 ribosomal RNA genes. Gene order resembled that of other demosponges. The base composition of the genome is A (29.1%), T (35.2%), C (14.0%) and G (21.7%). This is the second published mitogenome for a sponge of the order Astrophorida and will be useful in future phylogenetic analysis of deep-sea sponges.

Effect of the sequence data deluge on the performance of methods for detecting protein functional residues.

Science.gov (United States)

Garrido-Martín, Diego; Pazos, Florencio

2018-02-27

The exponential accumulation of new sequences in public databases is expected to improve the performance of all the approaches for predicting protein structural and functional features. Nevertheless, this was never assessed or quantified for some widely used methodologies, such as those aimed at detecting functional sites and functional subfamilies in protein multiple sequence alignments. Using raw protein sequences as only input, these approaches can detect fully conserved positions, as well as those with a family-dependent conservation pattern. Both types of residues are routinely used as predictors of functional sites and, consequently, understanding how the sequence content of the databases affects them is relevant and timely. In this work we evaluate how the growth and change with time in the content of sequence databases affect five sequence-based approaches for detecting functional sites and subfamilies. We do that by recreating historical versions of the multiple sequence alignments that would have been obtained in the past based on the database contents at different time points, covering a period of 20 years. Applying the methods to these historical alignments allows quantifying the temporal variation in their performance. Our results show that the number of families to which these methods can be applied sharply increases with time, while their ability to detect potentially functional residues remains almost constant. These results are informative for the methods' developers and final users, and may have implications in the design of new sequencing initiatives.

Effects of hydrostatic pressure on yeasts isolated from deep-sea hydrothermal vents.

Science.gov (United States)

Burgaud, Gaëtan; Hué, Nguyen Thi Minh; Arzur, Danielle; Coton, Monika; Perrier-Cornet, Jean-Marie; Jebbar, Mohamed; Barbier, Georges

2015-11-01

Hydrostatic pressure plays a significant role in the distribution of life in the biosphere. Knowledge of deep-sea piezotolerant and (hyper)piezophilic bacteria and archaea diversity has been well documented, along with their specific adaptations to cope with high hydrostatic pressure (HHP). Recent investigations of deep-sea microbial community compositions have shown unexpected micro-eukaryotic communities, mainly dominated by fungi. Molecular methods such as next-generation sequencing have been used for SSU rRNA gene sequencing to reveal fungal taxa. Currently, a difficult but fascinating challenge for marine mycologists is to create deep-sea marine fungus culture collections and assess their ability to cope with pressure. Indeed, although there is no universal genetic marker for piezoresistance, physiological analyses provide concrete relevant data for estimating their adaptations and understanding the role of fungal communities in the abyss. The present study investigated morphological and physiological responses of fungi to HHP using a collection of deep-sea yeasts as a model. The aim was to determine whether deep-sea yeasts were able to tolerate different HHP and if they were metabolically active. Here we report an unexpected taxonomic-based dichotomic response to pressure with piezosensitve ascomycetes and piezotolerant basidiomycetes, and distinct morphological switches triggered by pressure for certain strains. Copyright © 2015 Institut Pasteur. Published by Elsevier Masson SAS. All rights reserved.

Prediction of Bispectral Index during Target-controlled Infusion of Propofol and Remifentanil: A Deep Learning Approach.

Science.gov (United States)

Lee, Hyung-Chul; Ryu, Ho-Geol; Chung, Eun-Jin; Jung, Chul-Woo

2018-03-01

The discrepancy between predicted effect-site concentration and measured bispectral index is problematic during intravenous anesthesia with target-controlled infusion of propofol and remifentanil. We hypothesized that bispectral index during total intravenous anesthesia would be more accurately predicted by a deep learning approach. Long short-term memory and the feed-forward neural network were sequenced to simulate the pharmacokinetic and pharmacodynamic parts of an empirical model, respectively, to predict intraoperative bispectral index during combined use of propofol and remifentanil. Inputs of long short-term memory were infusion histories of propofol and remifentanil, which were retrieved from target-controlled infusion pumps for 1,800 s at 10-s intervals. Inputs of the feed-forward network were the outputs of long short-term memory and demographic data such as age, sex, weight, and height. The final output of the feed-forward network was the bispectral index. The performance of bispectral index prediction was compared between the deep learning model and previously reported response surface model. The model hyperparameters comprised 8 memory cells in the long short-term memory layer and 16 nodes in the hidden layer of the feed-forward network. The model training and testing were performed with separate data sets of 131 and 100 cases. The concordance correlation coefficient (95% CI) were 0.561 (0.560 to 0.562) in the deep learning model, which was significantly larger than that in the response surface model (0.265 [0.263 to 0.266], P deep learning model-predicted bispectral index during target-controlled infusion of propofol and remifentanil more accurately compared to the traditional model. The deep learning approach in anesthetic pharmacology seems promising because of its excellent performance and extensibility.

Fungal diversity in deep-sea sediments associated with asphalt seeps at the Sao Paulo Plateau

Science.gov (United States)

Nagano, Yuriko; Miura, Toshiko; Nishi, Shinro; Lima, Andre O.; Nakayama, Cristina; Pellizari, Vivian H.; Fujikura, Katsunori

2017-12-01

We investigated the fungal diversity in a total of 20 deep-sea sediment samples (of which 14 samples were associated with natural asphalt seeps and 6 samples were not associated) collected from two different sites at the Sao Paulo Plateau off Brazil by Ion Torrent PGM targeting ITS region of ribosomal RNA. Our results suggest that diverse fungi (113 operational taxonomic units (OTUs) based on clustering at 97% sequence similarity assigned into 9 classes and 31 genus) are present in deep-sea sediment samples collected at the Sao Paulo Plateau, dominated by Ascomycota (74.3%), followed by Basidiomycota (11.5%), unidentified fungi (7.1%), and sequences with no affiliation to any organisms in the public database (7.1%). However, it was revealed that only three species, namely Penicillium sp., Cadophora malorum and Rhodosporidium diobovatum, were dominant, with the majority of OTUs remaining a minor community. Unexpectedly, there was no significant difference in major fungal community structure between the asphalt seep and non-asphalt seep sites, despite the presence of mass hydrocarbon deposits and the high amount of macro organisms surrounding the asphalt seeps. However, there were some differences in the minor fungal communities, with possible asphalt degrading fungi present specifically in the asphalt seep sites. In contrast, some differences were found between the two different sampling sites. Classification of OTUs revealed that only 47 (41.6%) fungal OTUs exhibited >97% sequence similarity, in comparison with pre-existing ITS sequences in public databases, indicating that a majority of deep-sea inhabiting fungal taxa still remain undescribed. Although our knowledge on fungi and their role in deep-sea environments is still limited and scarce, this study increases our understanding of fungal diversity and community structure in deep-sea environments.

Contemporary deep recurrent learning for recognition

Science.gov (United States)

Iftekharuddin, K. M.; Alam, M.; Vidyaratne, L.

2017-05-01

Large-scale feed-forward neural networks have seen intense application in many computer vision problems. However, these networks can get hefty and computationally intensive with increasing complexity of the task. Our work, for the first time in literature, introduces a Cellular Simultaneous Recurrent Network (CSRN) based hierarchical neural network for object detection. CSRN has shown to be more effective to solving complex tasks such as maze traversal and image processing when compared to generic feed forward networks. While deep neural networks (DNN) have exhibited excellent performance in object detection and recognition, such hierarchical structure has largely been absent in neural networks with recurrency. Further, our work introduces deep hierarchy in SRN for object recognition. The simultaneous recurrency results in an unfolding effect of the SRN through time, potentially enabling the design of an arbitrarily deep network. This paper shows experiments using face, facial expression and character recognition tasks using novel deep recurrent model and compares recognition performance with that of generic deep feed forward model. Finally, we demonstrate the flexibility of incorporating our proposed deep SRN based recognition framework in a humanoid robotic platform called NAO.

A fuel performance analysis for a 450 MWth deep burn-high temperature reactor

International Nuclear Information System (INIS)

Kim, Young Min; Jo, Chang Keun; Jun, Ji Su; Cho, Moon Sung; Venneri, Francesco

2011-01-01

Highlights: → We have checked, through a fuel performance analysis, if a 450 MW th high temperature reactor was safe for the deep burn of a TRU fuel. → During a core heat-up event, the fuel temperature was below 1600 deg. C and the maximum gas pressure in the void of coated fuel particle was about 90 MPa. → At elevated temperatures of the accident event, the failure fraction of coated fuel particles resulted from the mechanical failure and the thermal decomposition of the SiC barrier was 3.30 x 10 -3 . - Abstract: A performance analysis for a 450 MW th deep burn-high temperature reactor (DB-HTR) fuel was performed using COPA, a fuel performance analysis code of Korea Atomic Energy Research Institute (KAERI). The code computes gas pressure buildup in the void volume of a tri-isotropic coated fuel particle (TRISO), temperature distribution in a DB-HTR fuel, thermo-mechanical stress in a coated fuel particle (CFP), failure fractions of a batch of CFPs, and fission product (FP) releases into the coolant. The 350 μm DB-HTR kernel is composed of 30% UO 2 + 70% (5% NpO 2 + 95% PuO 1.8 ) mixed with 0.6 moles of silicon carbide (SiC) per mole of heavy metal. The DB-HTR is operated at the constant temperature and power of 858 deg. C and 39.02 mW per CFP for 1395 effective full power days (EFPD) and is subjected to a core heat-up event for 250 h during which the maximum coolant temperature reaches 1548.70 deg. C. Within the normal operating temperature, the fuel showed good thermal and mechanical integrity. At elevated temperatures of the accident event, the failure fraction of CFPs resulted from the mechanical failure (MF) and the thermal decomposition (TD) of the SiC barrier is 3.30 x 10 -3 .

Deep developmental transcriptome sequencing uncovers numerous new genes and enhances gene annotation in the sponge Amphimedon queenslandica.

Science.gov (United States)

Fernandez-Valverde, Selene L; Calcino, Andrew D; Degnan, Bernard M

2015-05-15

The demosponge Amphimedon queenslandica is amongst the few early-branching metazoans with an assembled and annotated draft genome, making it an important species in the study of the origin and early evolution of animals. Current gene models in this species are largely based on in silico predictions and low coverage expressed sequence tag (EST) evidence. Amphimedon queenslandica protein-coding gene models are improved using deep RNA-Seq data from four developmental stages and CEL-Seq data from 82 developmental samples. Over 86% of previously predicted genes are retained in the new gene models, although 24% have additional exons; there is also a marked increase in the total number of annotated 3' and 5' untranslated regions (UTRs). Importantly, these new developmental transcriptome data reveal numerous previously unannotated protein-coding genes in the Amphimedon genome, increasing the total gene number by 25%, from 30,060 to 40,122. In general, Amphimedon genes have introns that are markedly smaller than those in other animals and most of the alternatively spliced genes in Amphimedon undergo intron-retention; exon-skipping is the least common mode of alternative splicing. Finally, in addition to canonical polyadenylation signal sequences, Amphimedon genes are enriched in a number of unique AT-rich motifs in their 3' UTRs. The inclusion of developmental transcriptome data has substantially improved the structure and composition of protein-coding gene models in Amphimedon queenslandica, providing a more accurate and comprehensive set of genes for functional and comparative studies. These improvements reveal the Amphimedon genome is comprised of a remarkably high number of tightly packed genes. These genes have small introns and there is pervasive intron retention amongst alternatively spliced transcripts. These aspects of the sponge genome are more similar unicellular opisthokont genomes than to other animal genomes.

Deep sequencing shows that oocytes are not prone to accumulate mtDNA heteroplasmic mutations during ovarian ageing.

Science.gov (United States)

Boucret, L; Bris, C; Seegers, V; Goudenège, D; Desquiret-Dumas, V; Domin-Bernhard, M; Ferré-L'Hotellier, V; Bouet, P E; Descamps, P; Reynier, P; Procaccio, V; May-Panloup, P

2017-10-01

Does ovarian ageing increase the number of heteroplasmic mitochondrial DNA (mtDNA) point mutations in oocytes? Our results suggest that oocytes are not subject to the accumulation of mtDNA point mutations during ovarian ageing. Ageing is associated with the alteration of mtDNA integrity in various tissues. Primary oocytes, present in the ovary since embryonic life, may accumulate mtDNA mutations during the process of ovarian ageing. This was an observational study of 53 immature oocyte-cumulus complexes retrieved from 35 women undergoing IVF at the University Hospital of Angers, France, from March 2013 to March 2014. The women were classified in two groups, one including 19 women showing signs of ovarian ageing objectified by a diminished ovarian reserve (DOR), and the other, including 16 women with a normal ovarian reserve (NOR), which served as a control group. mtDNA was extracted from isolated oocytes, and from their corresponding cumulus cells (CCs) considered as a somatic cell compartment. The average mtDNA content of each sample was assessed by using a quantitative real-time PCR technique. Deep sequencing was performed using the Ion Torrent Proton for Next-Generation Sequencing. Signal processing and base calling were done by the embedded pre-processing pipeline and the variants were analyzed using an in-house workflow. The distribution of the different variants between DOR and NOR patients, on one hand, and oocyte and CCs, on the other, was analyzed with the generalized mixed linear model to take into account the cluster of cells belonging to a given mother. There were no significant differences between the numbers of mtDNA variants between the DOR and the NOR patients, either in the oocytes (P = 0.867) or in the surrounding CCs (P = 0.154). There were also no differences in terms of variants with potential functional consequences. De-novo mtDNA variants were found in 28% of the oocytes and in 66% of the CCs with the mean number of variants being

DeepMitosis: Mitosis detection via deep detection, verification and segmentation networks.

Science.gov (United States)

Li, Chao; Wang, Xinggang; Liu, Wenyu; Latecki, Longin Jan

2018-04-01

Mitotic count is a critical predictor of tumor aggressiveness in the breast cancer diagnosis. Nowadays mitosis counting is mainly performed by pathologists manually, which is extremely arduous and time-consuming. In this paper, we propose an accurate method for detecting the mitotic cells from histopathological slides using a novel multi-stage deep learning framework. Our method consists of a deep segmentation network for generating mitosis region when only a weak label is given (i.e., only the centroid pixel of mitosis is annotated), an elaborately designed deep detection network for localizing mitosis by using contextual region information, and a deep verification network for improving detection accuracy by removing false positives. We validate the proposed deep learning method on two widely used Mitosis Detection in Breast Cancer Histological Images (MITOSIS) datasets. Experimental results show that we can achieve the highest F-score on the MITOSIS dataset from ICPR 2012 grand challenge merely using the deep detection network. For the ICPR 2014 MITOSIS dataset that only provides the centroid location of mitosis, we employ the segmentation model to estimate the bounding box annotation for training the deep detection network. We also apply the verification model to eliminate some false positives produced from the detection model. By fusing scores of the detection and verification models, we achieve the state-of-the-art results. Moreover, our method is very fast with GPU computing, which makes it feasible for clinical practice. Copyright © 2018 Elsevier B.V. All rights reserved.

Bioinformatics for whole-genome shotgun sequencing of microbial communities.

Directory of Open Access Journals (Sweden)

Kevin Chen

2005-07-01

Full Text Available The application of whole-genome shotgun sequencing to microbial communities represents a major development in metagenomics, the study of uncultured microbes via the tools of modern genomic analysis. In the past year, whole-genome shotgun sequencing projects of prokaryotic communities from an acid mine biofilm, the Sargasso Sea, Minnesota farm soil, three deep-sea whale falls, and deep-sea sediments have been reported, adding to previously published work on viral communities from marine and fecal samples. The interpretation of this new kind of data poses a wide variety of exciting and difficult bioinformatics problems. The aim of this review is to introduce the bioinformatics community to this emerging field by surveying existing techniques and promising new approaches for several of the most interesting of these computational problems.

Study of the sealing performance of tubing adapters in gas-tight deep-sea water sampler

Directory of Open Access Journals (Sweden)

Huang Haocai

2014-09-01

Full Text Available Tubing adapter is a key connection device in Gas-Tight Deep-Sea Water Sampler (GTWS. The sealing performance of the tubing adapter directly affects the GTWS’s overall gas tightness. Tubing adapters with good sealing performance can ensure the transmission of seawater samples without gas leakage and can be repeatedly used. However, the sealing performance of tubing adapters made of different materials was not studied sufficiently. With the research discussed in this paper, the materials match schemes of the tubing adapters were proposed. With non-linear finite element contact analysis and sea trials in the South China Sea, it is expected that the recommended materials match schemes not only meet the requirements of tubing adapters' sealing performance but also provide the feasible options for the following research on tubing adapters in GTWS

Study of the sealing performance of tubing adapters in gas-tight deep-sea water sampler

Directory of Open Access Journals (Sweden)

Haocai Huang

2014-09-01

Full Text Available Tubing adapter is a key connection device in Gas-Tight Deep-Sea Water Sampler (GTWS. The sealing performance of the tubing adapter directly affects the GTWS's overall gas tightness. Tubing adapters with good sealing performance can ensure the transmission of seawater samples without gas leakage and can be repeatedly used. However, the sealing performance of tubing adapters made of different materials was not studied sufficiently. With the research discussed in this paper, the materials match schemes of the tubing adapters were proposed. With non-linear finite element contact analysis and sea trials in the South China Sea, it is expected that the recommended materials match schemes not only meet the requirements of tubing adapters’ sealing performance but also provide the feasible options for the following research on tubing adapters in GTWS.

Amplicon-based semiconductor sequencing of human exomes: performance evaluation and optimization strategies.

Science.gov (United States)

Damiati, E; Borsani, G; Giacopuzzi, Edoardo

2016-05-01

The Ion Proton platform allows to perform whole exome sequencing (WES) at low cost, providing rapid turnaround time and great flexibility. Products for WES on Ion Proton system include the AmpliSeq Exome kit and the recently introduced HiQ sequencing chemistry. Here, we used gold standard variants from GIAB consortium to assess the performances in variants identification, characterize the erroneous calls and develop a filtering strategy to reduce false positives. The AmpliSeq Exome kit captures a large fraction of bases (>94 %) in human CDS, ClinVar genes and ACMG genes, but with 2,041 (7 %), 449 (13 %) and 11 (19 %) genes not fully represented, respectively. Overall, 515 protein coding genes contain hard-to-sequence regions, including 90 genes from ClinVar. Performance in variants detection was maximum at mean coverage >120×, while at 90× and 70× we measured a loss of variants of 3.2 and 4.5 %, respectively. WES using HiQ chemistry showed ~71/97.5 % sensitivity, ~37/2 % FDR and ~0.66/0.98 F1 score for indels and SNPs, respectively. The proposed low, medium or high-stringency filters reduced the amount of false positives by 10.2, 21.2 and 40.4 % for indels and 21.2, 41.9 and 68.2 % for SNP, respectively. Amplicon-based WES on Ion Proton platform using HiQ chemistry emerged as a competitive approach, with improved accuracy in variants identification. False-positive variants remain an issue for the Ion Torrent technology, but our filtering strategy can be applied to reduce erroneous variants.

Detecting very low allele fraction variants using targeted DNA sequencing and a novel molecular barcode-aware variant caller.

Science.gov (United States)

Xu, Chang; Nezami Ranjbar, Mohammad R; Wu, Zhong; DiCarlo, John; Wang, Yexun

2017-01-03

Detection of DNA mutations at very low allele fractions with high accuracy will significantly improve the effectiveness of precision medicine for cancer patients. To achieve this goal through next generation sequencing, researchers need a detection method that 1) captures rare mutation-containing DNA fragments efficiently in the mix of abundant wild-type DNA; 2) sequences the DNA library extensively to deep coverage; and 3) distinguishes low level true variants from amplification and sequencing errors with high accuracy. Targeted enrichment using PCR primers provides researchers with a convenient way to achieve deep sequencing for a small, yet most relevant region using benchtop sequencers. Molecular barcoding (or indexing) provides a unique solution for reducing sequencing artifacts analytically. Although different molecular barcoding schemes have been reported in recent literature, most variant calling has been done on limited targets, using simple custom scripts. The analytical performance of barcode-aware variant calling can be significantly improved by incorporating advanced statistical models. We present here a highly efficient, simple and scalable enrichment protocol that integrates molecular barcodes in multiplex PCR amplification. In addition, we developed smCounter, an open source, generic, barcode-aware variant caller based on a Bayesian probabilistic model. smCounter was optimized and benchmarked on two independent read sets with SNVs and indels at 5 and 1% allele fractions. Variants were called with very good sensitivity and specificity within coding regions. We demonstrated that we can accurately detect somatic mutations with allele fractions as low as 1% in coding regions using our enrichment protocol and variant caller.

Joint Training of Deep Boltzmann Machines

OpenAIRE

Goodfellow, Ian; Courville, Aaron; Bengio, Yoshua

2012-01-01

We introduce a new method for training deep Boltzmann machines jointly. Prior methods require an initial learning pass that trains the deep Boltzmann machine greedily, one layer at a time, or do not perform well on classifi- cation tasks.

U.V. repair in deep-sea bacteria

International Nuclear Information System (INIS)

Lutz, L.; Yayanos, A.A.

1986-01-01

Exposure of cells to light of less than 320 nanometers wavelengths may lead to lethal lesions and perhaps carcinogenesis. Many organisms have evolved mechanisms to repair U.V. light-induced damage. Organisms such as deep-sea bacteria are presumably never exposed to U.V. light and perhaps occasionally to visible from bioluminescence. Thus, the repair of U.V. damage in deep-sea bacterial DNA might be inefficient and repair by photoreactivation unlikely. The bacteria utilized in this investigation are temperature sensitive and barophilic. Four deep-sea isolates were chosen for this study: PE-36 from 3584 m, CNPT-3 from 5782 m, HS-34 from 5682 m, and MT-41 from 10,476 m, all are from the North Pacific ocean. The deep-sea extends from 1100 m to depths greater than 7000 m. It is a region of relatively uniform conditions. The temperature ranges from 5 to -1 0 C. There is no solar light in the deep-sea. Deep-sea bacteria are sensitive to U.V. light; in fact more sensitive than a variety of terrestrial and sea-surface bacteria. All four isolates demonstrate thymine dimer repair. Photoreactivation was observed in only MT-41. The other strains from shallower depths displayed no photoreactivation. The presence of DNA sequences homologous to the rec A, uvr A, B, and C and phr genes of E. coli have been examined by Southern hybridization techniques

Artificial neural network based modeling of performance characteristics of deep well pumps with splitter blade

International Nuclear Information System (INIS)

Goelcue, Mustafa

2006-01-01

Experimental studies were made to investigate the effects of splitter blade length (25%, 35%, 50%, 60% and 80% of the main blade length) on the pump characteristics of deep well pumps for different blade numbers (z=3, 4, 5, 6 and 7). In this study, an artificial neural network (ANN) was used for modeling the performance of deep well pumps with splitter blades. Two hundred and ten experimental results were used to train and test. Forty-two patterns have been randomly selected and used as the test data. The main parameters for the experiments are the blade number (z), non-dimensional splitter blade length (L-bar ), flow rate (Q, l/s), head (H m , m), efficiency (η, %) and power (P e , kW). z, L-bar and Q have been used as the input layer, and H m and η have also been used as the output layer. The best training algorithm and number of neurons were obtained. Training of the network was performed using the Levenberg-Marquardt (LM) algorithm. To determine the effect of the transfer function, different ANN models are trained, and the results of these ANN models are compared. Some statistical methods; fraction of variance (R 2 ) and root mean squared error (RMSE) values, have been used for comparison

Multiplexed enrichment of rare DNA variants via sequence-selective and temperature-robust amplification

Science.gov (United States)

Wu, Lucia R.; Chen, Sherry X.; Wu, Yalei; Patel, Abhijit A.; Zhang, David Yu

2018-01-01

Rare DNA-sequence variants hold important clinical and biological information, but existing detection techniques are expensive, complex, allele-specific, or don’t allow for significant multiplexing. Here, we report a temperature-robust polymerase-chain-reaction method, which we term blocker displacement amplification (BDA), that selectively amplifies all sequence variants, including single-nucleotide variants (SNVs), within a roughly 20-nucleotide window by 1,000-fold over wild-type sequences. This allows for easy detection and quantitation of hundreds of potential variants originally at ≤0.1% in allele frequency. BDA is compatible with inexpensive thermocycler instrumentation and employs a rationally designed competitive hybridization reaction to achieve comparable enrichment performance across annealing temperatures ranging from 56 °C to 64 °C. To show the sequence generality of BDA, we demonstrate enrichment of 156 SNVs and the reliable detection of single-digit copies. We also show that the BDA detection of rare driver mutations in cell-free DNA samples extracted from the blood plasma of lung-cancer patients is highly consistent with deep sequencing using molecular lineage tags, with a receiver operator characteristic accuracy of 95%. PMID:29805844

Deep Mapping and Spatial Anthropology

Directory of Open Access Journals (Sweden)

Les Roberts

2016-01-01

Full Text Available This paper provides an introduction to the Humanities Special Issue on “Deep Mapping”. It sets out the rationale for the collection and explores the broad-ranging nature of perspectives and practices that fall within the “undisciplined” interdisciplinary domain of spatial humanities. Sketching a cross-current of ideas that have begun to coalesce around the concept of “deep mapping”, the paper argues that rather than attempting to outline a set of defining characteristics and “deep” cartographic features, a more instructive approach is to pay closer attention to the multivalent ways deep mapping is performatively put to work. Casting a critical and reflexive gaze over the developing discourse of deep mapping, it is argued that what deep mapping “is” cannot be reduced to the otherwise a-spatial and a-temporal fixity of the “deep map”. In this respect, as an undisciplined survey of this increasing expansive field of study and practice, the paper explores the ways in which deep mapping can engage broader discussion around questions of spatial anthropology.

DeepGait: A Learning Deep Convolutional Representation for View-Invariant Gait Recognition Using Joint Bayesian

Directory of Open Access Journals (Sweden)

Chao Li

2017-02-01

Full Text Available Human gait, as a soft biometric, helps to recognize people through their walking. To further improve the recognition performance, we propose a novel video sensor-based gait representation, DeepGait, using deep convolutional features and introduce Joint Bayesian to model view variance. DeepGait is generated by using a pre-trained “very deep” network “D-Net” (VGG-D without any fine-tuning. For non-view setting, DeepGait outperforms hand-crafted representations (e.g., Gait Energy Image, Frequency-Domain Feature and Gait Flow Image, etc.. Furthermore, for cross-view setting, 256-dimensional DeepGait after PCA significantly outperforms the state-of-the-art methods on the OU-ISR large population (OULP dataset. The OULP dataset, which includes 4007 subjects, makes our result reliable in a statistically reliable way.

Information performances and illative sequences: Sequential organization of explanations of chemical phase equilibrium

Science.gov (United States)

Brown, Nathaniel James Swanton

While there is consensus that conceptual change is surprisingly difficult, many competing theories of conceptual change co-exist in the literature. This dissertation argues that this discord is partly the result of an inadequate account of the unwritten rules of human social interaction that underlie the field's preferred methodology---semi-structured interviewing. To better understand the contributions of interaction during explanations, I analyze eight undergraduate general chemistry students as they attempt to explain to various people, for various reasons, why phenomena involving chemical phase equilibrium occur. Using the methods of interaction analysis, I characterize the unwritten, but systematic, rules that these participants follow as they explain. The result is a description of the contributions of interaction to explaining. Each step in each explanation is a jointly performed expression of a subject-predicate relation, an interactive accomplishment I call an information performance (in-form, for short). Unlike clauses, in-forms need not have a coherent grammatical structure. Unlike speaker turns, in-forms have the clear function of expressing information. Unlike both clauses and speaker turns, in-forms are a co-construction, jointly performed by both the primary speaker and the other interlocutor. The other interlocutor strongly affects the form and content of each explanation by giving or withholding feedback at the end of each in-form, moments I call feedback-relevant places. While in-forms are the bricks out of which the explanation is constructed, they are secured by a series of inferential links I call an illative sequence. Illative sequences are forward-searching, starting with a remembered fact or observation and following a chain of inferences in the hope it leads to the target phenomenon. The participants treat an explanation as a success if the illative sequence generates an in-form that describes the phenomenon. If the illative sequence does

Risk of Breast Cancer with CXCR4-using HIV Defined by V3-Loop Sequencing

Science.gov (United States)

Goedert, James J.; Swenson, Luke C.; Napolitano, Laura A.; Haddad, Mojgan; Anastos, Kathryn; Minkoff, Howard; Young, Mary; Levine, Alexandra; Adeyemi, Oluwatoyin; Seaberg, Eric C.; Aouizerat, Bradley; Rabkin, Charles S.; Harrigan, P. Richard; Hessol, Nancy A.

2014-01-01

Objective Evaluate the risk of female breast cancer associated with HIV-CXCR4 (X4) tropism as determined by various genotypic measures. Methods A breast cancer case-control study, with pairwise comparisons of tropism determination methods, was conducted. From the Women's Interagency HIV Study repository, one stored plasma specimen was selected from 25 HIV-infected cases near the breast cancer diagnosis date and 75 HIV-infected control women matched for age and calendar date. HIVgp120-V3 sequences were derived by Sanger population sequencing (PS) and 454-pyro deep sequencing (DS). Sequencing-based HIV-X4 tropism was defined using the geno2pheno algorithm, with both high-stringency DS [False-Positive-Rate (FPR 3.5) and 2% X4 cutoff], and lower stringency DS (FPR 5.75, 15% X4 cut-off). Concordance of tropism results by PS, DS, and previously performed phenotyping was assessed with kappa (κ) statistics. Case-control comparisons used exact P-values and conditional logistic regression. Results In 74 women (19 cases, 55 controls) with complete results, prevalence of HIV-X4 by PS was 5% in cases vs 29% in controls (P=0.06, odds ratio 0.14, confidence interval 0.003-1.03). Smaller case-control prevalence differences were found with high-stringency DS (21% vs 36%, P=0.32), lower-stringency DS (16% vs 35%, P=0.18), and phenotyping (11% vs 31%, P=0.10). HIV-X4-tropism concordance was best between PS and lower-stringency DS (93%, κ=0.83). Other pairwise concordances were 82%-92% (κ=0.56-0.81). Concordance was similar among cases and controls. Conclusions HIV-X4 defined by population sequencing (PS) had good agreement with lower stringency deep sequencing and was significantly associated with lower odds of breast cancer. PMID:25321183

DeepPy: Pythonic deep learning

DEFF Research Database (Denmark)

Larsen, Anders Boesen Lindbo

This technical report introduces DeepPy – a deep learning framework built on top of NumPy with GPU acceleration. DeepPy bridges the gap between highperformance neural networks and the ease of development from Python/NumPy. Users with a background in scientific computing in Python will quickly...... be able to understand and change the DeepPy codebase as it is mainly implemented using high-level NumPy primitives. Moreover, DeepPy supports complex network architectures by letting the user compose mathematical expressions as directed graphs. The latest version is available at http...

Deep eutectic solvents as performance additives in biphasic reactions

NARCIS (Netherlands)

Lan, Dongming; Wang, Xuping; Zhou, Pengfei; Hollmann, F.; Wang, Yonghua

2017-01-01

Deep eutectic solvents act as surfactants in biphasic (hydrophobic/aqueous) reaction mixtures enabling higher interfacial surface areas at lower mechanical stress as compared to simple emulsions. Exploiting this effect the rate of a chemoenzymatic epoxidation reaction was increased more than

A combination of LongSAGE with Solexa sequencing is well suited to explore the depth and the complexity of transcriptome

Directory of Open Access Journals (Sweden)

Scoté-Blachon Céline

2008-09-01

Full Text Available Abstract Background "Open" transcriptome analysis methods allow to study gene expression without a priori knowledge of the transcript sequences. As of now, SAGE (Serial Analysis of Gene Expression, LongSAGE and MPSS (Massively Parallel Signature Sequencing are the mostly used methods for "open" transcriptome analysis. Both LongSAGE and MPSS rely on the isolation of 21 pb tag sequences from each transcript. In contrast to LongSAGE, the high throughput sequencing method used in MPSS enables the rapid sequencing of very large libraries containing several millions of tags, allowing deep transcriptome analysis. However, a bias in the complexity of the transcriptome representation obtained by MPSS was recently uncovered. Results In order to make a deep analysis of mouse hypothalamus transcriptome avoiding the limitation introduced by MPSS, we combined LongSAGE with the Solexa sequencing technology and obtained a library of more than 11 millions of tags. We then compared it to a LongSAGE library of mouse hypothalamus sequenced with the Sanger method. Conclusion We found that Solexa sequencing technology combined with LongSAGE is perfectly suited for deep transcriptome analysis. In contrast to MPSS, it gives a complex representation of transcriptome as reliable as a LongSAGE library sequenced by the Sanger method.

High-speed railway real-time localization auxiliary method based on deep neural network

Science.gov (United States)

Chen, Dongjie; Zhang, Wensheng; Yang, Yang

2017-11-01

High-speed railway intelligent monitoring and management system is composed of schedule integration, geographic information, location services, and data mining technology for integration of time and space data. Assistant localization is a significant submodule of the intelligent monitoring system. In practical application, the general access is to capture the image sequences of the components by using a high-definition camera, digital image processing technique and target detection, tracking and even behavior analysis method. In this paper, we present an end-to-end character recognition method based on a deep CNN network called YOLO-toc for high-speed railway pillar plate number. Different from other deep CNNs, YOLO-toc is an end-to-end multi-target detection framework, furthermore, it exhibits a state-of-art performance on real-time detection with a nearly 50fps achieved on GPU (GTX960). Finally, we realize a real-time but high-accuracy pillar plate number recognition system and integrate natural scene OCR into a dedicated classification YOLO-toc model.

Laser Capture and Deep Sequencing Reveals the Transcriptomic Programmes Regulating the Onset of Pancreas and Liver Differentiation in Human Embryos

Directory of Open Access Journals (Sweden)

Rachel E. Jennings

2017-11-01

Full Text Available To interrogate the alternative fates of pancreas and liver in the earliest stages of human organogenesis, we developed laser capture, RNA amplification, and computational analysis of deep sequencing. Pancreas-enriched gene expression was less conserved between human and mouse than for liver. The dorsal pancreatic bud was enriched for components of Notch, Wnt, BMP, and FGF signaling, almost all genes known to cause pancreatic agenesis or hypoplasia, and over 30 unexplored transcription factors. SOX9 and RORA were imputed as key regulators in pancreas compared with EP300, HNF4A, and FOXA family members in liver. Analyses implied that current in vitro human stem cell differentiation follows a dorsal rather than a ventral pancreatic program and pointed to additional factors for hepatic differentiation. In summary, we provide the transcriptional codes regulating the start of human liver and pancreas development to facilitate stem cell research and clinical interpretation without inter-species extrapolation.

HIV-1 transmission patterns in antiretroviral therapy-naive, HIV-infected North Americans based on phylogenetic analysis by population level and ultra-deep DNA sequencing.

Directory of Open Access Journals (Sweden)

Lisa L Ross

Full Text Available Factors that contribute to the transmission of human immunodeficiency virus type 1 (HIV-1, especially drug-resistant HIV-1 variants remain a significant public health concern. In-depth phylogenetic analyses of viral sequences obtained in the screening phase from antiretroviral-naïve HIV-infected patients seeking enrollment in EPZ108859, a large open-label study in the USA, Canada and Puerto Rico (ClinicalTrials.gov NCT00440947 were examined for insights into the roles of drug resistance and epidemiological factors that could impact disease dissemination. Viral transmission clusters (VTCs were initially predicted from a phylogenetic analysis of population level HIV-1 pol sequences obtained from 690 antiretroviral-naïve subjects in 2007. Subsequently, the predicted VTCs were tested for robustness by ultra deep sequencing (UDS using pyrosequencing technology and further phylogenetic analyses. The demographic characteristics of clustered and non-clustered subjects were then compared. From 690 subjects, 69 were assigned to 1 of 30 VTCs, each containing 2 to 5 subjects. Race composition of VTCs were significantly more likely to be white (72% vs. 60%; p = 0.04. VTCs had fewer reverse transcriptase and major PI resistance mutations (9% vs. 24%; p = 0.002 than non-clustered sequences. Both men-who-have-sex-with-men (MSM (68% vs. 48%; p = 0.001 and Canadians (29% vs. 14%; p = 0.03 were significantly more frequent in VTCs than non-clustered sequences. Of the 515 subjects who initiated antiretroviral therapy, 33 experienced confirmed virologic failure through 144 weeks while only 3/33 were from VTCs. Fewer VTCs subjects (as compared to those with non-clustering virus had HIV-1 with resistance-associated mutations or experienced virologic failure during the course of the study. Our analysis shows specific geographical and drug resistance trends that correlate well with transmission clusters defined by HIV sequences of similarity

Deep sequencing of the oral microbiome reveals signatures of periodontal disease.

Directory of Open Access Journals (Sweden)

Bo Liu

Full Text Available The oral microbiome, the complex ecosystem of microbes inhabiting the human mouth, harbors several thousands of bacterial types. The proliferation of pathogenic bacteria within the mouth gives rise to periodontitis, an inflammatory disease known to also constitute a risk factor for cardiovascular disease. While much is known about individual species associated with pathogenesis, the system-level mechanisms underlying the transition from health to disease are still poorly understood. Through the sequencing of the 16S rRNA gene and of whole community DNA we provide a glimpse at the global genetic, metabolic, and ecological changes associated with periodontitis in 15 subgingival plaque samples, four from each of two periodontitis patients, and the remaining samples from three healthy individuals. We also demonstrate the power of whole-metagenome sequencing approaches in characterizing the genomes of key players in the oral microbiome, including an unculturable TM7 organism. We reveal the disease microbiome to be enriched in virulence factors, and adapted to a parasitic lifestyle that takes advantage of the disrupted host homeostasis. Furthermore, diseased samples share a common structure that was not found in completely healthy samples, suggesting that the disease state may occupy a narrow region within the space of possible configurations of the oral microbiome. Our pilot study demonstrates the power of high-throughput sequencing as a tool for understanding the role of the oral microbiome in periodontal disease. Despite a modest level of sequencing (~2 lanes Illumina 76 bp PE and high human DNA contamination (up to ~90% we were able to partially reconstruct several oral microbes and to preliminarily characterize some systems-level differences between the healthy and diseased oral microbiomes.

Oral Microbiome of Deep and Shallow Dental Pockets In Chronic Periodontitis

Science.gov (United States)

Ge, Xiuchun; Rodriguez, Rafael; Trinh, My; Gunsolley, John; Xu, Ping

2013-01-01

We examined the subgingival bacterial biodiversity in untreated chronic periodontitis patients by sequencing 16S rRNA genes. The primary purpose of the study was to compare the oral microbiome in deep (diseased) and shallow (healthy) sites. A secondary purpose was to evaluate the influences of smoking, race and dental caries on this relationship. A total of 88 subjects from two clinics were recruited. Paired subgingival plaque samples were taken from each subject, one from a probing site depth >5 mm (deep site) and the other from a probing site depth ≤3mm (shallow site). A universal primer set was designed to amplify the V4–V6 region for oral microbial 16S rRNA sequences. Differences in genera and species attributable to deep and shallow sites were determined by statistical analysis using a two-part model and false discovery rate. Fifty-one of 170 genera and 200 of 746 species were found significantly different in abundances between shallow and deep sites. Besides previously identified periodontal disease-associated bacterial species, additional species were found markedly changed in diseased sites. Cluster analysis revealed that the microbiome difference between deep and shallow sites was influenced by patient-level effects such as clinic location, race and smoking. The differences between clinic locations may be influenced by racial distribution, in that all of the African Americans subjects were seen at the same clinic. Our results suggested that there were influences from the microbiome for caries and periodontal disease and these influences are independent. PMID:23762384

Obtaining reasonable assurance on geochemical aspects of performance assessment of deep geologic repositories

International Nuclear Information System (INIS)

Van Luik, A.E.; Serne, R.J.

1986-01-01

Providing reasonable assurance that a deep geologic disposal system will perform as required by regulation involves, in part, the building of confidence by providing a sound scientific basis for the site characterization, engineered system design, and system performance modeling efforts. Geochemistry plays a role in each of these activities. Site characterization must result in a description of the in situ geochemical environment that will support the design of the engineered system and the modeling of the transport of specific radionuclides to the accessible environment. Judging the adequacy of this site characterization effort is a major aspect of providing reasonable assurance. Within site characterization, there are a number of geochemical issues that need to be addressed such as the usefulness of natural analog studies, and assessing the very long-term stability of the site geochemistry, given expected temperature and radiation conditions

Reactive Sequencing for Autonomous Navigation Evolving from Phoenix Entry, Descent, and Landing

Science.gov (United States)

Grasso, Christopher A.; Riedel, Joseph E.; Vaughan, Andrew T.

2010-01-01

guidance, navigation and control scenarios, work began three years ago on substantial upgrades to VML that are now being exercised in scenarios for lunar landing and comet/asteroid rendezvous. The advanced state-based approach includes coordinated state transition machines with distributed decision-making logic. These state machines are not merely sequences - they are reactive logic constructs capable of autonomous decision making within a well-defined domain. Combined with the JPL's AutoNav software used on Deep Space 1 and Deep Impact, the system allows spacecraft to autonomously navigate to an unmapped surface, soft-contact, and either land or ascend. The state machine architecture enabled by VML 2.1 has successfully performed sampling missions and lunar descent missions in a simulated environment, and is progressing toward flight capability. The authors are also investigating using the VML 2.1 flight director architecture to perform autonomous activities like rendezvous with a passive hypothetical Mars sample return capsule. The approach being pursued is similar to the touch-and-go sampling state machines, with the added complications associated with the search for, physical capture of, and securing of a separate spacecraft. Complications include optically finding and tracking the Orbiting Sample Capsule (OSC), keeping the OSC illuminated, making orbital adjustments, and physically capturing the OSC. Other applications could include autonomous science collection and fault compensation.

Calibration and performance measurements for the nasa deep space network aperture enhancement project (daep)

Science.gov (United States)

LaBelle, Remi C.; Rochblatt, David J.

2018-06-01

The NASA Deep Space Network (DSN) has recently constructed two new 34-m antennas at the Canberra Deep Space Communications Complex (CDSCC). These new antennas are part of the larger DAEP project to add six new 34-m antennas to the DSN, including two in Madrid, three in Canberra and one in Goldstone (California). The DAEP project included development and implementation of several new technologies for the X, and Ka (32 GHz) -band uplink and downlink electronics. The electronics upgrades were driven by several different considerations, including parts obsolescence, cost reduction, improved reliability and maintainability, and capability to meet future performance requirements. The new antennas are required to support TT&C links for all of the NASA deep-space spacecraft, as well as for several international partners. Some of these missions, such as Voyager 1 and 2, have very limited link budgets, which results in demanding requirements for system G/T performance. These antennas are also required to support radio science missions with several spacecraft, which dictate some demanding requirements for spectral purity, amplitude stability and phase stability for both the uplink and downlink electronics. After completion of these upgrades, a comprehensive campaign of tests and measurements took place to characterize the electronics and calibrate the antennas. Radiometric measurement techniques were applied to characterize, calibrate, and optimize the performance of the antenna parameters. These included optical and RF high-resolution holographic and total power radiometry techniques. The methodology and techniques utilized for the measurement and calibration of the antennas is described in this paper. Lessons learned (not all discussed in this paper) from the commissioning of the first antenna (DSS-35) were applied to the commissioning of the second antenna (DSS-36). These resulted in achieving antenna aperture efficiency of 66% (for DSS-36), at Ka-Band (32-Ghz), which is

Effect of QW thickness and numbers on performance characteristics of deep violet InGaN MQW lasers

Science.gov (United States)

Alahyarizadeh, Gh.; Amirhoseiny, M.; Hassan, Z.

2015-03-01

The performance characteristics of deep violet indium gallium nitride (InGaN) multiquantum well (MQW) laser diodes (LDs) with an emission wavelength of around 390 nm have been investigated using the integrated system engineering technical computer aided design (ISE-TCAD) software. A comparative study on the effect of quantum well (QW) thickness and number on electrical and optical performance of deep violet In0.082Ga0.918N/GaN MQW LDs have been carried out. The simulation results showed that the highest slope efficiency and external differential quantum efficiency (DQE), as well as the lowest threshold current are obtained when the number of wells is two. The different QW thickness values of 2.2, 2.5, 2.8, 3 and 3.2 nm were compared and the best results were achieved for 2.5 nm QW thickness. The radiative recombination rate decreases with increasing QW thickness because of decreasing electron and hole carrier densities in wells. By increasing QW thickness, output power decreases and threshold current increases.

Deep Predictive Models in Interactive Music

OpenAIRE

Martin, Charles P.; Ellefsen, Kai Olav; Torresen, Jim

2018-01-01

Automatic music generation is a compelling task where much recent progress has been made with deep learning models. In this paper, we ask how these models can be integrated into interactive music systems; how can they encourage or enhance the music making of human users? Musical performance requires prediction to operate instruments, and perform in groups. We argue that predictive models could help interactive systems to understand their temporal context, and ensemble behaviour. Deep learning...

Time and space resolved deep metagenomics to investigate selection pressures on low abundant species in complex environments

DEFF Research Database (Denmark)

Albertsen, Mads; Saunders, Aaron Marc; Nielsen, Kåre Lehmann

and between EBPR plants we sequenced a total of 10 samples from 3 different plants over a 3 year period at a depth of 25 Gb each. In addition, one time point was selected for deep sequencing, generating 200 Gb of sequence divided between replicates. Quantitative FISH analysis using >30 oligonucleotide probes...

A deep learning framework for causal shape transformation.

Science.gov (United States)

Lore, Kin Gwn; Stoecklein, Daniel; Davies, Michael; Ganapathysubramanian, Baskar; Sarkar, Soumik

2018-02-01

Recurrent neural network (RNN) and Long Short-term Memory (LSTM) networks are the common go-to architecture for exploiting sequential information where the output is dependent on a sequence of inputs. However, in most considered problems, the dependencies typically lie in the latent domain which may not be suitable for applications involving the prediction of a step-wise transformation sequence that is dependent on the previous states only in the visible domain with a known terminal state. We propose a hybrid architecture of convolution neural networks (CNN) and stacked autoencoders (SAE) to learn a sequence of causal actions that nonlinearly transform an input visual pattern or distribution into a target visual pattern or distribution with the same support and demonstrated its practicality in a real-world engineering problem involving the physics of fluids. We solved a high-dimensional one-to-many inverse mapping problem concerning microfluidic flow sculpting, where the use of deep learning methods as an inverse map is very seldom explored. This work serves as a fruitful use-case to applied scientists and engineers in how deep learning can be beneficial as a solution for high-dimensional physical problems, and potentially opening doors to impactful advance in fields such as material sciences and medical biology where multistep topological transformations is a key element. Copyright © 2017 Elsevier Ltd. All rights reserved.

Bacterial diversity and biogeography in deep-sea sediments of the South Atlantic Ocean

DEFF Research Database (Denmark)

Schauer, Regina; Bienhold, Christina; Ramette, Alban

2010-01-01

in 1051 sequences. Phylotypes affiliated with Gammaproteobacteria, Deltaproteobacteria and Acidobacteria were present in all three basins. The distribution of these shared phylotypes seemed to be influenced neither by the Walvis Ridge nor by different deep water masses, suggesting a high dispersal......Microbial biogeographic patterns in the deep sea depend on the ability of microorganisms to disperse. One possible limitation to microbial dispersal may be the Walvis Ridge that separates the Antarctic Lower Circumpolar Deep Water from the North Atlantic Deep Water. We examined bacterial...... communities in three basins of the eastern South Atlantic Ocean to determine diversity and biogeography of bacterial communities in deep-sea surface sediments. The analysis of 16S ribosomal RNA (rRNA) gene clone libraries in each basin revealed a high diversity, representing 521 phylotypes with 98% identity...

Gene Prediction in Metagenomic Fragments with Deep Learning

Directory of Open Access Journals (Sweden)

Shao-Wu Zhang

2017-01-01

Full Text Available Next generation sequencing technologies used in metagenomics yield numerous sequencing fragments which come from thousands of different species. Accurately identifying genes from metagenomics fragments is one of the most fundamental issues in metagenomics. In this article, by fusing multifeatures (i.e., monocodon usage, monoamino acid usage, ORF length coverage, and Z-curve features and using deep stacking networks learning model, we present a novel method (called Meta-MFDL to predict the metagenomic genes. The results with 10 CV and independent tests show that Meta-MFDL is a powerful tool for identifying genes from metagenomic fragments.

Relationships among head posture, pain intensity, disability and deep cervical flexor muscle performance in subjects with postural neck pain

Directory of Open Access Journals (Sweden)

Arun V. Subbarayalu, PhD

2017-12-01

Full Text Available Objectives: Information Technology (IT professionals working with computers gradually develop forward head posture and, as a result, these professionals are susceptible to several neck disorders. This study intended to reveal the relationships between pain intensity, disability, head posture and deep cervical flexor (DCF muscle performance in patients with postural neck pain. Methods: A cross-sectional study was conducted on 84 IT professionals who were diagnosed with postural neck pain. The participants were recruited with a random sampling approach. A Visual Analogue Scale (VAS, the Northwick Park Neck Pain Questionnaire (NPQ, the Modified Head Posture Spinal Curvature Instrument (MHPSCI, and the Stabilizer Pressure Biofeedback Unit were used to measure neck pain intensity, neck disability, head posture, and DCF muscle performance, respectively. Results: The Pearson correlation coefficient revealed a significantly strong positive relationship between the VAS and the NPQ (r = 0.734. The cranio-vertebral (CV angle was found to have a significantly negative correlation with the VAS (r = −0.536 and a weak negative correlation with the NPQ (r = −0.389. Conclusion: This study concluded that a smaller CV angle corresponded to greater neck pain intensity and disability. Furthermore, there is no significant relationship between CV angle and DCF muscle performance, indicating that head posture re-education through postural correction exercises would not completely correct the motor control deficits in DCF muscles. In addition, a suitable exercise regimen that exclusively targets the deep cervical flexor muscle to improve its endurance is warranted. Keywords: Craniovertebral angle, Disability deep cervical flexors muscle performance, Head posture, Postural neck pain

Performance of the second Deep Inelastic Neutron Scatering spectrometer at the Bariloche electron LINAC

International Nuclear Information System (INIS)

Palomino, L A Rodríguez; Blostein, J J; Dawidowski, J

2013-01-01

We report on the new Deep Inelastic Neutron Scattering detector bank recently implemented at the Bariloche electron LINAC. We show the characterization and calibration process carried out, which comprises the determinarion of the detector bank efficiency, and the evaluation of the performance of the filter difference technique. As part of the benchmarking process, polyethylene spectra were measured and analyzed, and the scattering cross sections for carbon and hydrogen were determined in the process. With the addition of this new detector bank to the existing one, we evaluate the combined capacity of the two banks

Use of deep whole-genome sequencing data to identify structure risk variants in breast cancer susceptibility genes.

Science.gov (United States)

Guo, Xingyi; Shi, Jiajun; Cai, Qiuyin; Shu, Xiao-Ou; He, Jing; Wen, Wanqing; Allen, Jamie; Pharoah, Paul; Dunning, Alison; Hunter, David J; Kraft, Peter; Easton, Douglas F; Zheng, Wei; Long, Jirong

2018-03-01

Functional disruptions of susceptibility genes by large genomic structure variant (SV) deletions in germlines are known to be associated with cancer risk. However, few studies have been conducted to systematically search for SV deletions in breast cancer susceptibility genes. We analysed deep (> 30x) whole-genome sequencing (WGS) data generated in blood samples from 128 breast cancer patients of Asian and European descent with either a strong family history of breast cancer or early cancer onset disease. To identify SV deletions in known or suspected breast cancer susceptibility genes, we used multiple SV calling tools including Genome STRiP, Delly, Manta, BreakDancer and Pindel. SV deletions were detected by at least three of these bioinformatics tools in five genes. Specifically, we identified heterozygous deletions covering a fraction of the coding regions of BRCA1 (with approximately 80kb in two patients), and TP53 genes (with ∼1.6 kb in two patients), and of intronic regions (∼1 kb) of the PALB2 (one patient), PTEN (three patients) and RAD51C genes (one patient). We confirmed the presence of these deletions using real-time quantitative PCR (qPCR). Our study identified novel SV deletions in breast cancer susceptibility genes and the identification of such SV deletions may improve clinical testing.

Optical Performance of Breadboard Amon-Ra Imaging Channel Instrument for Deep Space Albedo Measurement

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Won Hyun Park

2007-03-01

Full Text Available The AmonRa instrument, the primary payload of the international EARTHSHINE mission, is designed for measurement of deep space albedo from L1 halo orbit. We report the optical design, tolerance analysis and the optical performance of the breadborad AmonRa imaging channel instrument optimized for the mission science requirements. In particular, an advanced wavefront feedback process control technique was used for the instrumentation process including part fabrication, system alignment and integration. The measured performances for the complete breadboard system are the RMS 0.091 wave(test wavelength: 632.8 nm in wavefront error, the ensquared energy of 61.7%(in 14 μ m and the MTF of 35.3%(Nyquist frequency: 35.7 mm^{-1} at the center field. These resulting optical system performances prove that the breadboard AmonRa instrument, as built, satisfies the science requirements of the EARTHSHINE mission.

Neuronal pathology in deep grey matter structures: a multimodal imaging analysis combining PET and MRI

Energy Technology Data Exchange (ETDEWEB)

Bosque-Freeman, L.; Leroy, C.; Galanaud, D.; Sureau, F.; Assouad, R.; Tourbah, A.; Papeix, C.; Comtat, C.; Trebossen, R.; Lubetzki, C.; Delforge, J.; Bottlaender, M.; Stankoff, B. [Serv. Hosp. Frederic Joliot, Orsay (France)

2009-07-01

Objective: To assess neuronal damage in deep gray matter structures by positron emission tomography (PET) using [{sup 11}C]-flumazenil (FMZ), a specific central benzodiazepine receptor antagonist, and [{sup 18}F]-fluorodeoxyglucose (FDG), which reflects neuronal metabolism. To compare results obtained by PET and those with multimodal magnetic resonance imaging (MRI). Background: It is now accepted that neuronal injury plays a crucial role in the occurrence and progression of neurological disability in multiple sclerosis (MS). To date, available MRI techniques do not specifically assess neuronal damage, but early abnormalities, such as iron deposition or atrophy, have been described in deep gray matter structures. Whether those MRI modifications correspond to neuronal damage remains to be further investigated. Materials and methods: Nine healthy volunteers were compared to 10 progressive and 9 relapsing remitting (RR) MS patients. Each subject performed two PET examinations with [{sup 11}C]-FMZ and [{sup 18}F]-FDG, on a high resolution research tomograph dedicated to brain imaging (Siemens Medical Solution, spatial resolution of 2.5 mm). Deep gray matter regions were manually segmented on T1-weighted MR images with the mutual information algorithm (www.brainvisa.info), and co-registered with PET images. A multimodal MRI including T1 pre and post gadolinium, T2-proton density sequences, magnetization transfer, diffusion tensor, and protonic spectroscopy was also performed for each subject. Results: On PET with [{sup 11}C]-FMZ, there was a pronounced decrease in receptor density for RR patients in all deep gray matter structures investigated, whereas the density was unchanged or even increased in the same regions for progressive patients. Whether the different patterns between RR and progressive patients reflect distinct pathogenic mechanisms is currently investigated by comparing PET and multimodal MRI results. Conclusion: Combination of PET and multimodal MR imaging

Bacterial community diversity of the deep-sea octocoral Paramuricea placomus

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Christina A. Kellogg

2016-09-01

Full Text Available Compared to tropical corals, much less is known about deep-sea coral biology and ecology. Although the microbial communities of some deep-sea corals have been described, this is the first study to characterize the bacterial community associated with the deep-sea octocoral, Paramuricea placomus. Samples from five colonies of P. placomus were collected from Baltimore Canyon (379–382 m depth in the Atlantic Ocean off the east coast of the United States of America. DNA was extracted from the coral samples and 16S rRNA gene amplicons were pyrosequenced using V4-V5 primers. Three samples sequenced deeply (>4,000 sequences each and were further analyzed. The dominant microbial phylum was Proteobacteria, but other major phyla included Firmicutes and Planctomycetes. A conserved community of bacterial taxa held in common across the three P. placomus colonies was identified, comprising 68–90% of the total bacterial community depending on the coral individual. The bacterial community of P. placomus does not appear to include the genus Endozoicomonas, which has been found previously to be the dominant bacterial associate in several temperate and tropical gorgonians. Inferred functionality suggests the possibility of nitrogen cycling by the core bacterial community.

Supraspinatus tendon tears at 3.0 T shoulder MR arthrography: diagnosis with 3D isotropic turbo spin-echo SPACE sequence versus 2D conventional sequences

Energy Technology Data Exchange (ETDEWEB)

Jung, Joon-Yong; Jee, Won-Hee; Park, Michael Y.; Lee, So-Yeon [Seoul St. Mary' s Hospital, The Catholic University of Korea, Department of Radiology, Seoul (Korea, Republic of); Kim, Yang-Soo [Seoul St. Mary' s Hospital, The Catholic University of Korea, Department of Orthopedic Surgery, Seoul (Korea, Republic of)

2012-11-15

To assess the diagnostic performance of shoulder MR arthrography with 3D isotropic fat-suppressed (FS) turbo spin-echo sequence (TSE-SPACE) for supraspinatus tendon tears in comparison with 2D conventional sequences at 3.0 T. The study was HIPAA-compliant and approved by the institutional review board with a waiver of informed consent. Eighty-seven arthroscopically confirmed patients who underwent 3.0 T shoulder MR arthrography with 2D sequences and 3D TSE-SPACE were included in a consecutive fashion from March 2009 to February 2010. Two reviewers independently analyzed 2D sequences and 3D TSE-SPACE. Sensitivity, specificity, accuracy, and interobserver agreement ({kappa}) were compared between 2D sequences and 3D TSE-SPACE for full-thickness and partial-thickness supraspinatus tendon tears together and for partial-thickness supraspinatus tendon tears alone. There were 33 full-thickness tears and 28 partial-thickness tears of supraspinatus tendons. For full-thickness and partial-thickness supraspinatus tendon tears together, the mean sensitivity, specificity, and accuracy of both readers were 96, 92, and 94% on 2D sequences and 91, 84, and 89% on 3D TSE-SPACE. For partial-thickness supraspinatus tendon tears alone, the mean sensitivity, specificity, and accuracy were 95, 92, and 94% on 2D sequences and 84, 85, and 84% on 3D TSE-SPACE. There was no statistical difference between 2D sequences and 3D TSE-SPACE. Interobserver agreements were almost perfect on 2D conventional sequences and substantial on 3D TSE-SPACE. Compared with 2D conventional sequences, MR arthrography using 3D TSE-SPACE was comparable for diagnosing supraspinatus tendon tears despite limitations in detecting small partial-thickness tears and in discriminating between full-thickness and deep partial-thickness tears. (orig.)

Supraspinatus tendon tears at 3.0 T shoulder MR arthrography: diagnosis with 3D isotropic turbo spin-echo SPACE sequence versus 2D conventional sequences

International Nuclear Information System (INIS)

Jung, Joon-Yong; Jee, Won-Hee; Park, Michael Y.; Lee, So-Yeon; Kim, Yang-Soo

2012-01-01

To assess the diagnostic performance of shoulder MR arthrography with 3D isotropic fat-suppressed (FS) turbo spin-echo sequence (TSE-SPACE) for supraspinatus tendon tears in comparison with 2D conventional sequences at 3.0 T. The study was HIPAA-compliant and approved by the institutional review board with a waiver of informed consent. Eighty-seven arthroscopically confirmed patients who underwent 3.0 T shoulder MR arthrography with 2D sequences and 3D TSE-SPACE were included in a consecutive fashion from March 2009 to February 2010. Two reviewers independently analyzed 2D sequences and 3D TSE-SPACE. Sensitivity, specificity, accuracy, and interobserver agreement (κ) were compared between 2D sequences and 3D TSE-SPACE for full-thickness and partial-thickness supraspinatus tendon tears together and for partial-thickness supraspinatus tendon tears alone. There were 33 full-thickness tears and 28 partial-thickness tears of supraspinatus tendons. For full-thickness and partial-thickness supraspinatus tendon tears together, the mean sensitivity, specificity, and accuracy of both readers were 96, 92, and 94% on 2D sequences and 91, 84, and 89% on 3D TSE-SPACE. For partial-thickness supraspinatus tendon tears alone, the mean sensitivity, specificity, and accuracy were 95, 92, and 94% on 2D sequences and 84, 85, and 84% on 3D TSE-SPACE. There was no statistical difference between 2D sequences and 3D TSE-SPACE. Interobserver agreements were almost perfect on 2D conventional sequences and substantial on 3D TSE-SPACE. Compared with 2D conventional sequences, MR arthrography using 3D TSE-SPACE was comparable for diagnosing supraspinatus tendon tears despite limitations in detecting small partial-thickness tears and in discriminating between full-thickness and deep partial-thickness tears. (orig.)

Three-dimensional fluid-attenuated inversion recovery sequence for visualisation of subthalamic nucleus for deep brain stimulation in Parkinson's disease

Energy Technology Data Exchange (ETDEWEB)

Heo, Young Jin [University of Ulsan College of Medicine, Asan Medical Center, Department of Radiology, Research Institute of Radiology, Seoul (Korea, Republic of); Inje University, Department of Radiology, Busan Paik Hospital, Busan (Korea, Republic of); Kim, Sang Joon; Kim, Ho Sung; Choi, Choong Gon; Jung, Seung Chai [University of Ulsan College of Medicine, Asan Medical Center, Department of Radiology, Research Institute of Radiology, Seoul (Korea, Republic of); Lee, Jung Kyo [University of Ulsan College of Medicine, Asan Medical Center, Department of Neurosurgery, Seoul (Korea, Republic of); Lee, Chong Sik; Chung, Sun J. [University of Ulsan College of Medicine, Asan Medical Center, Department of Neurology, Seoul (Korea, Republic of); Cho, So Hyun [Department of Radiology, Busan (Korea, Republic of); Lee, Gyoung Ro [Philips HealthCare Korea, Seoul (Korea, Republic of)

2015-09-15

Deep brain stimulation (DBS) of the subthalamic nucleus (STN) is an accepted treatment for advanced Parkinson's disease (PD). However, targeting the STN is difficult due to its relatively small size and variable location. The purpose of this study was to assess which of the following sequences obtained with the 3.0 T MR system can accurately delineate the STN: coronal 3D fluid-attenuated inversion recovery (FLAIR), 2D T2*-weighted fast-field echo (T2*-FFE) and 2D T2-weighted turbo spin-echo (TSE) sequences. We included 20 consecutive patients with PD who underwent 3.0 T MR for DBS targeting. 3D FLAIR, 2D T2*-FFE and T2-TSE images were obtained for all study patients. Image quality and demarcation of the STN were analysed using 4-point scales, and contrast ratio (CR) of the STN and normal white matter was calculated. The Friedman test was used to compare the three sequences. In qualitative analysis, the 2D T2*-FFE image showed more artefacts than 3D FLAIR or 2D T2-TSE, but the difference did not reach statistical significance. 3D FLAIR images showed significantly superior demarcation of the STN compared with 2D T2*-FFE and T2-TSE images (P < 0.001, respectively). The CR of 3D FLAIR was significantly higher than that of 2D T2*-FFE or T2-TSE images in multiple comparison correction (P < 0.001), but there was no significant difference in the CR between 2D T2*-FFE and T2-TSE images. Coronal 3D FLAIR images showed the most accurate demarcation of the STN for DBS targeting among coronal 3D FLAIR, 2D T2*-FFE and T2-TSE images. (orig.)

Three-dimensional fluid-attenuated inversion recovery sequence for visualisation of subthalamic nucleus for deep brain stimulation in Parkinson's disease

International Nuclear Information System (INIS)

Heo, Young Jin; Kim, Sang Joon; Kim, Ho Sung; Choi, Choong Gon; Jung, Seung Chai; Lee, Jung Kyo; Lee, Chong Sik; Chung, Sun J.; Cho, So Hyun; Lee, Gyoung Ro

2015-01-01

Deep brain stimulation (DBS) of the subthalamic nucleus (STN) is an accepted treatment for advanced Parkinson's disease (PD). However, targeting the STN is difficult due to its relatively small size and variable location. The purpose of this study was to assess which of the following sequences obtained with the 3.0 T MR system can accurately delineate the STN: coronal 3D fluid-attenuated inversion recovery (FLAIR), 2D T2*-weighted fast-field echo (T2*-FFE) and 2D T2-weighted turbo spin-echo (TSE) sequences. We included 20 consecutive patients with PD who underwent 3.0 T MR for DBS targeting. 3D FLAIR, 2D T2*-FFE and T2-TSE images were obtained for all study patients. Image quality and demarcation of the STN were analysed using 4-point scales, and contrast ratio (CR) of the STN and normal white matter was calculated. The Friedman test was used to compare the three sequences. In qualitative analysis, the 2D T2*-FFE image showed more artefacts than 3D FLAIR or 2D T2-TSE, but the difference did not reach statistical significance. 3D FLAIR images showed significantly superior demarcation of the STN compared with 2D T2*-FFE and T2-TSE images (P < 0.001, respectively). The CR of 3D FLAIR was significantly higher than that of 2D T2*-FFE or T2-TSE images in multiple comparison correction (P < 0.001), but there was no significant difference in the CR between 2D T2*-FFE and T2-TSE images. Coronal 3D FLAIR images showed the most accurate demarcation of the STN for DBS targeting among coronal 3D FLAIR, 2D T2*-FFE and T2-TSE images. (orig.)

Deep sequencing-based transcriptome profiling analysis of bacteria-challenged Lateolabrax japonicus reveals insight into the immune-relevant genes in marine fish

Directory of Open Access Journals (Sweden)

Xiang Li-xin

2010-08-01

Full Text Available Abstract Background Systematic research on fish immunogenetics is indispensable in understanding the origin and evolution of immune systems. This has long been a challenging task because of the limited number of deep sequencing technologies and genome backgrounds of non-model fish available. The newly developed Solexa/Illumina RNA-seq and Digital gene expression (DGE are high-throughput sequencing approaches and are powerful tools for genomic studies at the transcriptome level. This study reports the transcriptome profiling analysis of bacteria-challenged Lateolabrax japonicus using RNA-seq and DGE in an attempt to gain insights into the immunogenetics of marine fish. Results RNA-seq analysis generated 169,950 non-redundant consensus sequences, among which 48,987 functional transcripts with complete or various length encoding regions were identified. More than 52% of these transcripts are possibly involved in approximately 219 known metabolic or signalling pathways, while 2,673 transcripts were associated with immune-relevant genes. In addition, approximately 8% of the transcripts appeared to be fish-specific genes that have never been described before. DGE analysis revealed that the host transcriptome profile of Vibrio harveyi-challenged L. japonicus is considerably altered, as indicated by the significant up- or down-regulation of 1,224 strong infection-responsive transcripts. Results indicated an overall conservation of the components and transcriptome alterations underlying innate and adaptive immunity in fish and other vertebrate models. Analysis suggested the acquisition of numerous fish-specific immune system components during early vertebrate evolution. Conclusion This study provided a global survey of host defence gene activities against bacterial challenge in a non-model marine fish. Results can contribute to the in-depth study of candidate genes in marine fish immunity, and help improve current understanding of host

Long-term survival of indirect pulp treatment performed in primary and permanent teeth with clinically diagnosed deep carious lesions

NARCIS (Netherlands)

Gruythuysen, R.; van Strijp, G.; Wu, M.K.

2010-01-01

Introduction: This retrospective study examined clinically and radiographically the 3-year survival of teeth treated with indirect pulp treatment (IPT) performed between 2000 and 2004. Methods: Sixty-six uncooperative children (4-18 years old) with at least one tooth with clinically diagnosed deep

Deep Sea Coral voucher sequence dataset - Identification of deep-sea corals collected during the 2009 - 2014 West Coast Groundfish Bottom Trawl Survey

Data.gov (United States)

National Oceanic and Atmospheric Administration, Department of Commerce — Data for this project resides in the West Coast Groundfish Bottom Trawl Survey Database. Deep-sea corals are often components of trawling bycatch, though their...

Unveiling the Biodiversity of Deep-Sea Nematodes through Metabarcoding: Are We Ready to Bypass the Classical Taxonomy?

Science.gov (United States)

Dell'Anno, Antonio; Carugati, Laura; Corinaldesi, Cinzia; Riccioni, Giulia; Danovaro, Roberto

2015-01-01

Nematodes inhabiting benthic deep-sea ecosystems account for >90% of the total metazoan abundances and they have been hypothesised to be hyper-diverse, but their biodiversity is still largely unknown. Metabarcoding could facilitate the census of biodiversity, especially for those tiny metazoans for which morphological identification is difficult. We compared, for the first time, different DNA extraction procedures based on the use of two commercial kits and a previously published laboratory protocol and tested their suitability for sequencing analyses of 18S rDNA of marine nematodes. We also investigated the reliability of Roche 454 sequencing analyses for assessing the biodiversity of deep-sea nematode assemblages previously morphologically identified. Finally, intra-genomic variation in 18S rRNA gene repeats was investigated by Illumina MiSeq in different deep-sea nematode morphospecies to assess the influence of polymorphisms on nematode biodiversity estimates. Our results indicate that the two commercial kits should be preferred for the molecular analysis of biodiversity of deep-sea nematodes since they consistently provide amplifiable DNA suitable for sequencing. We report that the morphological identification of deep-sea nematodes matches the results obtained by metabarcoding analysis only at the order-family level and that a large portion of Operational Clustered Taxonomic Units (OCTUs) was not assigned. We also show that independently from the cut-off criteria and bioinformatic pipelines used, the number of OCTUs largely exceeds the number of individuals and that 18S rRNA gene of different morpho-species of nematodes displayed intra-genomic polymorphisms. Our results indicate that metabarcoding is an important tool to explore the diversity of deep-sea nematodes, but still fails in identifying most of the species due to limited number of sequences deposited in the public databases, and in providing quantitative data on the species encountered. These aspects

Advances in Planetary Protection at the Deep Space Gateway

Science.gov (United States)

Spry, J. A.; Siegel, B.; Race, M.; Rummel, J. D.; Pugel, D. E.; Groen, F. J.; Kminek, G.; Conley, C. A.; Carosso, N. J.

2018-02-01

Planetary protection knowledge gaps that can be addressed by science performed at the Deep Space Gateway in the areas of human health and performance, space biology, and planetary sciences that enable future exploration in deep space, at Mars, and other targets.

High-throughput verification of transcriptional starting sites by Deep-RACE

DEFF Research Database (Denmark)

Olivarius, Signe; Plessy, Charles; Carninci, Piero

2009-01-01

We present a high-throughput method for investigating the transcriptional starting sites of genes of interest, which we named Deep-RACE (Deep–rapid amplification of cDNA ends). Taking advantage of the latest sequencing technology, it allows the parallel analysis of multiple genes and is free...

Sequencing Effects of Balance and Plyometric Training on Physical Performance in Youth Soccer Athletes.

Science.gov (United States)

Hammami, Raouf; Granacher, Urs; Makhlouf, Issam; Behm, David G; Chaouachi, Anis

2016-12-01

Hammami, R, Granacher, U, Makhlouf, I, Behm, DG, and Chaouachi, A. Sequencing effects of balance and plyometric training on physical performance in youth soccer athletes. J Strength Cond Res 30(12): 3278-3289, 2016-Balance training may have a preconditioning effect on subsequent power training with youth. There are no studies examining whether the sequencing of balance and plyometric training has additional training benefits. The objective was to examine the effect of sequencing balance and plyometric training on the performance of 12- to 13-year-old athletes. Twenty-four young elite soccer players trained twice per week for 8 weeks either with an initial 4 weeks of balance training followed by 4 weeks of plyometric training (BPT) or 4 weeks of plyometric training proceeded by 4 weeks of balance training (PBT). Testing was conducted pre- and posttraining and included medicine ball throw; horizontal and vertical jumps; reactive strength; leg stiffness; agility; 10-, 20-, and 30-m sprints; Standing Stork balance test; and Y-Balance test. Results indicated that BPT provided significantly greater improvements with reactive strength index, absolute and relative leg stiffness, triple hop test, and a trend for the Y-Balance test (p = 0.054) compared with PBT. Although all other measures had similar changes for both groups, the average relative improvement for the BPT was 22.4% (d = 1.5) vs. 15.0% (d = 1.1) for the PBT. BPT effect sizes were greater with 8 of 13 measures. In conclusion, although either sequence of BPT or PBT improved jumping, hopping, sprint acceleration, and Standing Stork and Y-Balance, BPT initiated greater training improvements in reactive strength index, absolute and relative leg stiffness, triple hop test, and the Y-Balance test. BPT may provide either similar or superior performance enhancements compared with PBT.

Human fatigue expression recognition through image-based dynamic multi-information and bimodal deep learning

Science.gov (United States)

Zhao, Lei; Wang, Zengcai; Wang, Xiaojin; Qi, Yazhou; Liu, Qing; Zhang, Guoxin

2016-09-01

Human fatigue is an important cause of traffic accidents. To improve the safety of transportation, we propose, in this paper, a framework for fatigue expression recognition using image-based facial dynamic multi-information and a bimodal deep neural network. First, the landmark of face region and the texture of eye region, which complement each other in fatigue expression recognition, are extracted from facial image sequences captured by a single camera. Then, two stacked autoencoder neural networks are trained for landmark and texture, respectively. Finally, the two trained neural networks are combined by learning a joint layer on top of them to construct a bimodal deep neural network. The model can be used to extract a unified representation that fuses landmark and texture modalities together and classify fatigue expressions accurately. The proposed system is tested on a human fatigue dataset obtained from an actual driving environment. The experimental results demonstrate that the proposed method performs stably and robustly, and that the average accuracy achieves 96.2%.

Deep sequencing-based analysis of the Cymbidium ensifolium floral transcriptome.

Directory of Open Access Journals (Sweden)

Xiaobai Li

Full Text Available Cymbidium ensifolium is a Chinese Cymbidium with an elegant shape, beautiful appearance, and a fragrant aroma. C. ensifolium has a long history of cultivation in China and it has excellent commercial value as a potted plant and cut flower. The development of C. ensifolium genomic resources has been delayed because of its large genome size. Taking advantage of technical and cost improvement of RNA-Seq, we extracted total mRNA from flower buds and mature flowers and obtained a total of 9.52 Gb of filtered nucleotides comprising 98,819,349 filtered reads. The filtered reads were assembled into 101,423 isotigs, representing 51,696 genes. Of the 101,423 isotigs, 41,873 were putative homologs of annotated sequences in the public databases, of which 158 were associated with floral development and 119 were associated with flowering. The isotigs were categorized according to their putative functions. In total, 10,212 of the isotigs were assigned into 25 eukaryotic orthologous groups (KOGs, 41,690 into 58 gene ontology (GO terms, and 9,830 into 126 Arabidopsis Kyoto Encyclopedia of Genes and Genomes (KEGG pathways, and 9,539 isotigs into 123 rice pathways. Comparison of the isotigs with those of the two related orchid species P. equestris and C. sinense showed that 17,906 isotigs are unique to C. ensifolium. In addition, a total of 7,936 SSRs and 16,676 putative SNPs were identified. To our knowledge, this transcriptome database is the first major genomic resource for C. ensifolium and the most comprehensive transcriptomic resource for genus Cymbidium. These sequences provide valuable information for understanding the molecular mechanisms of floral development and flowering. Sequences predicted to be unique to C. ensifolium would provide more insights into C. ensifolium gene diversity. The numerous SNPs and SSRs identified in the present study will contribute to marker development for C. ensifolium.

Auditory, visual and auditory-visual memory and sequencing performance in typically developing children.

Science.gov (United States)

Pillai, Roshni; Yathiraj, Asha

2017-09-01

The study evaluated whether there exists a difference/relation in the way four different memory skills (memory score, sequencing score, memory span, & sequencing span) are processed through the auditory modality, visual modality and combined modalities. Four memory skills were evaluated on 30 typically developing children aged 7 years and 8 years across three modality conditions (auditory, visual, & auditory-visual). Analogous auditory and visual stimuli were presented to evaluate the three modality conditions across the two age groups. The children obtained significantly higher memory scores through the auditory modality compared to the visual modality. Likewise, their memory scores were significantly higher through the auditory-visual modality condition than through the visual modality. However, no effect of modality was observed on the sequencing scores as well as for the memory and the sequencing span. A good agreement was seen between the different modality conditions that were studied (auditory, visual, & auditory-visual) for the different memory skills measures (memory scores, sequencing scores, memory span, & sequencing span). A relatively lower agreement was noted only between the auditory and visual modalities as well as between the visual and auditory-visual modality conditions for the memory scores, measured using Bland-Altman plots. The study highlights the efficacy of using analogous stimuli to assess the auditory, visual as well as combined modalities. The study supports the view that the performance of children on different memory skills was better through the auditory modality compared to the visual modality. Copyright © 2017 Elsevier B.V. All rights reserved.

Performance of an Artificial Multi-observer Deep Neural Network for Fully Automated Segmentation of Polycystic Kidneys.

Science.gov (United States)

Kline, Timothy L; Korfiatis, Panagiotis; Edwards, Marie E; Blais, Jaime D; Czerwiec, Frank S; Harris, Peter C; King, Bernard F; Torres, Vicente E; Erickson, Bradley J

2017-08-01

Deep learning techniques are being rapidly applied to medical imaging tasks-from organ and lesion segmentation to tissue and tumor classification. These techniques are becoming the leading algorithmic approaches to solve inherently difficult image processing tasks. Currently, the most critical requirement for successful implementation lies in the need for relatively large datasets that can be used for training the deep learning networks. Based on our initial studies of MR imaging examinations of the kidneys of patients affected by polycystic kidney disease (PKD), we have generated a unique database of imaging data and corresponding reference standard segmentations of polycystic kidneys. In the study of PKD, segmentation of the kidneys is needed in order to measure total kidney volume (TKV). Automated methods to segment the kidneys and measure TKV are needed to increase measurement throughput and alleviate the inherent variability of human-derived measurements. We hypothesize that deep learning techniques can be leveraged to perform fast, accurate, reproducible, and fully automated segmentation of polycystic kidneys. Here, we describe a fully automated approach for segmenting PKD kidneys within MR images that simulates a multi-observer approach in order to create an accurate and robust method for the task of segmentation and computation of TKV for PKD patients. A total of 2000 cases were used for training and validation, and 400 cases were used for testing. The multi-observer ensemble method had mean ± SD percent volume difference of 0.68 ± 2.2% compared with the reference standard segmentations. The complete framework performs fully automated segmentation at a level comparable with interobserver variability and could be considered as a replacement for the task of segmentation of PKD kidneys by a human.

Recalcitrant deep and shallow nodes in Aristolochia (Aristolochiaceae) illuminated using anchored hybrid enrichment.

Science.gov (United States)

Wanke, Stefan; Granados Mendoza, Carolina; Müller, Sebastian; Paizanni Guillén, Anna; Neinhuis, Christoph; Lemmon, Alan R; Lemmon, Emily Moriarty; Samain, Marie-Stéphanie

2017-12-01

Recalcitrant relationships are characterized by very short internodes that can be found among shallow and deep phylogenetic scales all over the tree of life. Adding large amounts of presumably informative sequences, while decreasing systematic error, has been suggested as a possible approach to increase phylogenetic resolution. The development of enrichment strategies, coupled with next generation sequencing, resulted in a cost-effective way to facilitate the reconstruction of recalcitrant relationships. By applying the anchored hybrid enrichment (AHE) genome partitioning strategy to Aristolochia using an universal angiosperm probe set, we obtained 231-233 out of 517 single or low copy nuclear loci originally contained in the enrichment kit, resulting in a total alignment length of 154,756bp to 160,150bp. Since Aristolochia (Piperales; magnoliids) is distantly related to any angiosperm species whose genome has been used for the plant AHE probe design (Amborella trichopoda being the closest), it serves as a proof of universality for this probe set. Aristolochia comprises approximately 500 species grouped in several clades (OTUs), whose relationships to each other are partially unknown. Previous phylogenetic studies have shown that these lineages branched deep in time and in quick succession, seen as short-deep internodes. Short-shallow internodes are also characteristic of some Aristolochia lineages such as Aristolochia subsection Pentandrae, a clade of presumably recent diversification. This subsection is here included to test the performance of AHE at species level. Filtering and subsampling loci using the phylogenetic informativeness method resolves several recalcitrant phylogenetic relationships within Aristolochia. By assuming different ploidy levels during bioinformatics processing of raw data, first hints are obtained that polyploidization contributed to the evolution of Aristolochia. Phylogenetic results are discussed in the light of current systematics and

Use of whole genome deep sequencing to define emerging minority variants in virus envelope genes in herpesvirus treated with novel antimicrobial K21.

Science.gov (United States)

Tweedy, Joshua G; Prusty, Bhupesh K; Gompels, Ursula A

2017-10-01

New antivirals are required to prevent rising antimicrobial resistance from replication inhibitors. The aim of this study was to analyse the range of emerging mutations in herpesvirus by whole genome deep sequencing. We tested human herpesvirus 6 treatment with novel antiviral K21, where evidence indicated distinct effects on virus envelope proteins. We treated BACmid cloned virus in order to analyse mechanisms and candidate targets for resistance. Illumina based next generation sequencing technology enabled analyses of mutations in 85 genes to depths of 10,000 per base detecting low prevalent minority variants (<1%). After four passages in tissue culture the untreated virus accumulated mutations in infected cells giving an emerging mixed population (45-73%) of non-synonymous SNPs in six genes including two envelope glycoproteins. Strikingly, treatment with K21 did not accumulate the passage mutations; instead a high frequency mutation was selected in envelope protein gQ2, part of the gH/gL complex essential for herpesvirus infection. This introduced a stop codon encoding a truncation mutation previously observed in increased virion production. There was reduced detection of the glycoprotein complex in infected cells. This supports a novel pathway for K21 targeting virion envelopes distinct from replication inhibition. Copyright © 2017 The Authors. Published by Elsevier B.V. All rights reserved.

The 40Ar-39Ar dating of the metasomatites in the deep-fault zones of margin suture system of Siberian platform

International Nuclear Information System (INIS)

Savel'eva, V.B.; Travin, A.V.; Zyryanov, A.S.

2003-01-01

For clarifying the time sequence of metasomatites formation of diverse geochemical types in deep-fault zones of margin suture system of Siberian platform the 40 Ar- 39 Ar-isotope dating of their rock-forming minerals was performed. It was ascertained that formation of major metasomatic formations in the Baikal and Sayan branches of the margin suture system of Siberian platform was asynchronous, the time lag being in excess of 100 bl. years [ru

In-depth performance evaluation of PFP and ESG sequence-based function prediction methods in CAFA 2011 experiment

Directory of Open Access Journals (Sweden)

Chitale Meghana

2013-02-01

Full Text Available Abstract Background Many Automatic Function Prediction (AFP methods were developed to cope with an increasing growth of the number of gene sequences that are available from high throughput sequencing experiments. To support the development of AFP methods, it is essential to have community wide experiments for evaluating performance of existing AFP methods. Critical Assessment of Function Annotation (CAFA is one such community experiment. The meeting of CAFA was held as a Special Interest Group (SIG meeting at the Intelligent Systems in Molecular Biology (ISMB conference in 2011. Here, we perform a detailed analysis of two sequence-based function prediction methods, PFP and ESG, which were developed in our lab, using the predictions submitted to CAFA. Results We evaluate PFP and ESG using four different measures in comparison with BLAST, Prior, and GOtcha. In addition to the predictions submitted to CAFA, we further investigate performance of a different scoring function to rank order predictions by PFP as well as PFP/ESG predictions enriched with Priors that simply adds frequently occurring Gene Ontology terms as a part of predictions. Prediction accuracies of each method were also evaluated separately for different functional categories. Successful and unsuccessful predictions by PFP and ESG are also discussed in comparison with BLAST. Conclusion The in-depth analysis discussed here will complement the overall assessment by the CAFA organizers. Since PFP and ESG are based on sequence database search results, our analyses are not only useful for PFP and ESG users but will also shed light on the relationship of the sequence similarity space and functions that can be inferred from the sequences.

Deep Microbial Ecosystems in the U.S. Great Basin: A Second Home for Desulforudis audaxviator?

Science.gov (United States)

Moser, D. P.

2012-12-01

Deep subsurface microbial ecosystems have attracted scientific and public interest in recent years. Of deep habitats so far investigated, continental hard rock environments may be the least understood. Our Census of Deep Life (CoDL) project targets deep microbial ecosystems of three little explored (for microbiology), North American geological provinces: the Basin and Range, Black Hills, and Canadian Shield. Here we focus on the Basin and Range, specifically radioactive fluids from nuclear device test cavities (U12N.10 tunnel and ER-EC-11) at the Nevada National Security Site (NNSS) and non-radioactive samples from a deep dolomite aquifer associated with Death Valley, CA (BLM-1 and Nevares Deep Well 2). Six pyrotag sequencing runs were attempted at the Marine Biology Lab (MBL) (bacterial v6v4 amplification for all sites and archaeal v6v4 amplification for BLM-1 and Nevares DW2). Of these, DNA extracts from five samples (all but Nevares DW2 Arch) successfully amplified. Bacterial libraries were generally dominated by Proteobacteria, Firmicutes, and Nitrospirae (ER-EC-11: Proteobacteria (45%), Deinococcus-Thermus (35%), Firmicutes (15%); U12N.10: Proteobacteria (37%), Firmicutes (32%), Nitrospirae (15%), Bacteroidetes (11%); BLM-1 (Bact): Firmicutes (93%); and Nevares DW2: Firmicutes (51%), Proteobacteria (16%), Nitrospirae (15%)). The BLM-1 (Arch) library contained >99% Euryarchaeota, with 98% of sequences represented by a single uncharacterized species of Methanothermobacter. Alpha diversity was calculated using the MBL VAMPS (Visualization and Analysis of Microbial Population Structures) system; showing the highest richness at both the phylum and genus levels in U12N.10 (Sp = 42; Sg = 341), and the lowest (Sp = 3; Sg = 11) in the BLM-1(Arch) library. Diversity was covered well at this depth of sequencing (~20,000 reads per sample) based on rarefaction analysis. One Firmicute lineage, candidatus D. audaxviator, has been shown to dominate microbial communities from

Probabilistic Motor Sequence Yields Greater Offline and Less Online Learning than Fixed Sequence.

Science.gov (United States)

Du, Yue; Prashad, Shikha; Schoenbrun, Ilana; Clark, Jane E

2016-01-01

It is well acknowledged that motor sequences can be learned quickly through online learning. Subsequently, the initial acquisition of a motor sequence is boosted or consolidated by offline learning. However, little is known whether offline learning can drive the fast learning of motor sequences (i.e., initial sequence learning in the first training session). To examine offline learning in the fast learning stage, we asked four groups of young adults to perform the serial reaction time (SRT) task with either a fixed or probabilistic sequence and with or without preliminary knowledge (PK) of the presence of a sequence. The sequence and PK were manipulated to emphasize either procedural (probabilistic sequence; no preliminary knowledge (NPK)) or declarative (fixed sequence; with PK) memory that were found to either facilitate or inhibit offline learning. In the SRT task, there were six learning blocks with a 2 min break between each consecutive block. Throughout the session, stimuli followed the same fixed or probabilistic pattern except in Block 5, in which stimuli appeared in a random order. We found that PK facilitated the learning of a fixed sequence, but not a probabilistic sequence. In addition to overall learning measured by the mean reaction time (RT), we examined the progressive changes in RT within and between blocks (i.e., online and offline learning, respectively). It was found that the two groups who performed the fixed sequence, regardless of PK, showed greater online learning than the other two groups who performed the probabilistic sequence. The groups who performed the probabilistic sequence, regardless of PK, did not display online learning, as indicated by a decline in performance within the learning blocks. However, they did demonstrate remarkably greater offline improvement in RT, which suggests that they are learning the probabilistic sequence offline. These results suggest that in the SRT task, the fast acquisition of a motor sequence is driven

Implicit motor sequence learning and working memory performance changes across the adult life span

Directory of Open Access Journals (Sweden)

Sarah Nadine Meissner

2016-04-01

Full Text Available Although implicit motor sequence learning is rather well understood in young adults, effects of aging on this kind of learning are controversial. There is first evidence that working memory (WM might play a role in implicit motor sequence learning in young adults as well as in adults above the age of 65. However the knowledge about the development of these processes across the adult life span is rather limited. As the average age of our population continues to rise, a better understanding of age-related changes in motor sequence learning and potentially mediating cognitive processes takes on increasing significance. Therefore, we investigated aging effects on implicit motor sequence learning and WM. Sixty adults (18-71 years completed verbal and visuospatial n-back tasks and were trained on a serial reaction time task. Randomly varying trials served as control condition. To further assess consolidation indicated by off-line improvement and reduced susceptibility to interference, reaction times (RTs were determined 1 h after initial learning. Young and older but not middle-aged adults showed motor sequence learning. Nine out of 20 older adults (compared to one young/one middle-aged exhibited some evidence of sequence awareness. After 1 h, young and middle-aged adults showed off-line improvement. However, RT facilitation was not specific to sequence trials. Importantly, susceptibility to interference was reduced in young and older adults indicating the occurrence of consolidation. Although WM performance declined in older participants when load was high, it was not significantly related to sequence learning. The data reveal a decline in motor sequence learning in middle-aged but not in older adults. The use of explicit learning strategies in older adults might account for the latter result.

deepBase v2.0: identification, expression, evolution and function of small RNAs, LncRNAs and circular RNAs from deep-sequencing data.

Science.gov (United States)

Zheng, Ling-Ling; Li, Jun-Hao; Wu, Jie; Sun, Wen-Ju; Liu, Shun; Wang, Ze-Lin; Zhou, Hui; Yang, Jian-Hua; Qu, Liang-Hu

2016-01-04

Small non-coding RNAs (e.g. miRNAs) and long non-coding RNAs (e.g. lincRNAs and circRNAs) are emerging as key regulators of various cellular processes. However, only a very small fraction of these enigmatic RNAs have been well functionally characterized. In this study, we describe deepBase v2.0 (http://biocenter.sysu.edu.cn/deepBase/), an updated platform, to decode evolution, expression patterns and functions of diverse ncRNAs across 19 species. deepBase v2.0 has been updated to provide the most comprehensive collection of ncRNA-derived small RNAs generated from 588 sRNA-Seq datasets. Moreover, we developed a pipeline named lncSeeker to identify 176 680 high-confidence lncRNAs from 14 species. Temporal and spatial expression patterns of various ncRNAs were profiled. We identified approximately 24 280 primate-specific, 5193 rodent-specific lncRNAs, and 55 highly conserved lncRNA orthologs between human and zebrafish. We annotated 14 867 human circRNAs, 1260 of which are orthologous to mouse circRNAs. By combining expression profiles and functional genomic annotations, we developed lncFunction web-server to predict the function of lncRNAs based on protein-lncRNA co-expression networks. This study is expected to provide considerable resources to facilitate future experimental studies and to uncover ncRNA functions. © The Author(s) 2015. Published by Oxford University Press on behalf of Nucleic Acids Research.

Single-Cell Whole-Genome Amplification and Sequencing: Methodology and Applications.

Science.gov (United States)

Huang, Lei; Ma, Fei; Chapman, Alec; Lu, Sijia; Xie, Xiaoliang Sunney

2015-01-01

We present a survey of single-cell whole-genome amplification (WGA) methods, including degenerate oligonucleotide-primed polymerase chain reaction (DOP-PCR), multiple displacement amplification (MDA), and multiple annealing and looping-based amplification cycles (MALBAC). The key parameters to characterize the performance of these methods are defined, including genome coverage, uniformity, reproducibility, unmappable rates, chimera rates, allele dropout rates, false positive rates for calling single-nucleotide variations, and ability to call copy-number variations. Using these parameters, we compare five commercial WGA kits by performing deep sequencing of multiple single cells. We also discuss several major applications of single-cell genomics, including studies of whole-genome de novo mutation rates, the early evolution of cancer genomes, circulating tumor cells (CTCs), meiotic recombination of germ cells, preimplantation genetic diagnosis (PGD), and preimplantation genomic screening (PGS) for in vitro-fertilized embryos.

Maximum entropy methods for extracting the learned features of deep neural networks.

Science.gov (United States)

Finnegan, Alex; Song, Jun S

2017-10-01

New architectures of multilayer artificial neural networks and new methods for training them are rapidly revolutionizing the application of machine learning in diverse fields, including business, social science, physical sciences, and biology. Interpreting deep neural networks, however, currently remains elusive, and a critical challenge lies in understanding which meaningful features a network is actually learning. We present a general method for interpreting deep neural networks and extracting network-learned features from input data. We describe our algorithm in the context of biological sequence analysis. Our approach, based on ideas from statistical physics, samples from the maximum entropy distribution over possible sequences, anchored at an input sequence and subject to constraints implied by the empirical function learned by a network. Using our framework, we demonstrate that local transcription factor binding motifs can be identified from a network trained on ChIP-seq data and that nucleosome positioning signals are indeed learned by a network trained on chemical cleavage nucleosome maps. Imposing a further constraint on the maximum entropy distribution also allows us to probe whether a network is learning global sequence features, such as the high GC content in nucleosome-rich regions. This work thus provides valuable mathematical tools for interpreting and extracting learned features from feed-forward neural networks.

Deep Borehole Disposal as an Alternative Concept to Deep Geological Disposal

Energy Technology Data Exchange (ETDEWEB)

Lee, Jongyoul; Lee, Minsoo; Choi, Heuijoo; Kim, Kyungsu [Korea Atomic Energy Research Institute, Daejeon (Korea, Republic of)

2016-10-15

In this paper, the general concept and key technologies for deep borehole disposal of spent fuels or HLW, as an alternative method to the mined geological disposal method, were reviewed. After then an analysis on the distance between boreholes for the disposal of HLW was carried out. Based on the results, a disposal area were calculated approximately and compared with that of mined geological disposal. These results will be used as an input for the analyses of applicability for DBD in Korea. The disposal safety of this system has been demonstrated with underground research laboratory and some advanced countries such as Finland and Sweden are implementing their disposal project on commercial stage. However, if the spent fuels or the high-level radioactive wastes can be disposed of in the depth of 3-5 km and more stable rock formation, it has several advantages. Therefore, as an alternative disposal concept to the mined deep geological disposal concept (DGD), very deep borehole disposal (DBD) technology is under consideration in number of countries in terms of its outstanding safety and cost effectiveness. In this paper, the general concept of deep borehole disposal for spent fuels or high level radioactive wastes was reviewed. And the key technologies, such as drilling technology of large diameter borehole, packaging and emplacement technology, sealing technology and performance/safety analyses technologies, and their challenges in development of deep borehole disposal system were analyzed. Also, very preliminary deep borehole disposal concept including disposal canister concept was developed according to the nuclear environment in Korea.

Deep Borehole Disposal as an Alternative Concept to Deep Geological Disposal

International Nuclear Information System (INIS)

Lee, Jongyoul; Lee, Minsoo; Choi, Heuijoo; Kim, Kyungsu

2016-01-01

In this paper, the general concept and key technologies for deep borehole disposal of spent fuels or HLW, as an alternative method to the mined geological disposal method, were reviewed. After then an analysis on the distance between boreholes for the disposal of HLW was carried out. Based on the results, a disposal area were calculated approximately and compared with that of mined geological disposal. These results will be used as an input for the analyses of applicability for DBD in Korea. The disposal safety of this system has been demonstrated with underground research laboratory and some advanced countries such as Finland and Sweden are implementing their disposal project on commercial stage. However, if the spent fuels or the high-level radioactive wastes can be disposed of in the depth of 3-5 km and more stable rock formation, it has several advantages. Therefore, as an alternative disposal concept to the mined deep geological disposal concept (DGD), very deep borehole disposal (DBD) technology is under consideration in number of countries in terms of its outstanding safety and cost effectiveness. In this paper, the general concept of deep borehole disposal for spent fuels or high level radioactive wastes was reviewed. And the key technologies, such as drilling technology of large diameter borehole, packaging and emplacement technology, sealing technology and performance/safety analyses technologies, and their challenges in development of deep borehole disposal system were analyzed. Also, very preliminary deep borehole disposal concept including disposal canister concept was developed according to the nuclear environment in Korea

Distributed deep learning networks among institutions for medical imaging.

Science.gov (United States)

Chang, Ken; Balachandar, Niranjan; Lam, Carson; Yi, Darvin; Brown, James; Beers, Andrew; Rosen, Bruce; Rubin, Daniel L; Kalpathy-Cramer, Jayashree

2018-03-29

Deep learning has become a promising approach for automated support for clinical diagnosis. When medical data samples are limited, collaboration among multiple institutions is necessary to achieve high algorithm performance. However, sharing patient data often has limitations due to technical, legal, or ethical concerns. In this study, we propose methods of distributing deep learning models as an attractive alternative to sharing patient data. We simulate the distribution of deep learning models across 4 institutions using various training heuristics and compare the results with a deep learning model trained on centrally hosted patient data. The training heuristics investigated include ensembling single institution models, single weight transfer, and cyclical weight transfer. We evaluated these approaches for image classification in 3 independent image collections (retinal fundus photos, mammography, and ImageNet). We find that cyclical weight transfer resulted in a performance that was comparable to that of centrally hosted patient data. We also found that there is an improvement in the performance of cyclical weight transfer heuristic with a high frequency of weight transfer. We show that distributing deep learning models is an effective alternative to sharing patient data. This finding has implications for any collaborative deep learning study.

Integrated analysis of gene expression, CpG island methylation, and gene copy number in breast cancer cells by deep sequencing.

Directory of Open Access Journals (Sweden)

Zhifu Sun

Full Text Available We used deep sequencing technology to profile the transcriptome, gene copy number, and CpG island methylation status simultaneously in eight commonly used breast cell lines to develop a model for how these genomic features are integrated in estrogen receptor positive (ER+ and negative breast cancer. Total mRNA sequence, gene copy number, and genomic CpG island methylation were carried out using the Illumina Genome Analyzer. Sequences were mapped to the human genome to obtain digitized gene expression data, DNA copy number in reference to the non-tumor cell line (MCF10A, and methylation status of 21,570 CpG islands to identify differentially expressed genes that were correlated with methylation or copy number changes. These were evaluated in a dataset from 129 primary breast tumors. Gene expression in cell lines was dominated by ER-associated genes. ER+ and ER- cell lines formed two distinct, stable clusters, and 1,873 genes were differentially expressed in the two groups. Part of chromosome 8 was deleted in all ER- cells and part of chromosome 17 amplified in all ER+ cells. These loci encoded 30 genes that were overexpressed in ER+ cells; 9 of these genes were overexpressed in ER+ tumors. We identified 149 differentially expressed genes that exhibited differential methylation of one or more CpG islands within 5 kb of the 5' end of the gene and for which mRNA abundance was inversely correlated with CpG island methylation status. In primary tumors we identified 84 genes that appear to be robust components of the methylation signature that we identified in ER+ cell lines. Our analyses reveal a global pattern of differential CpG island methylation that contributes to the transcriptome landscape of ER+ and ER- breast cancer cells and tumors. The role of gene amplification/deletion appears to more modest, although several potentially significant genes appear to be regulated by copy number aberrations.

Comparing humans and deep learning performance for grading AMD: A study in using universal deep features and transfer learning for automated AMD analysis.

Science.gov (United States)

Burlina, Philippe; Pacheco, Katia D; Joshi, Neil; Freund, David E; Bressler, Neil M

2017-03-01

When left untreated, age-related macular degeneration (AMD) is the leading cause of vision loss in people over fifty in the US. Currently it is estimated that about eight million US individuals have the intermediate stage of AMD that is often asymptomatic with regard to visual deficit. These individuals are at high risk for progressing to the advanced stage where the often treatable choroidal neovascular form of AMD can occur. Careful monitoring to detect the onset and prompt treatment of the neovascular form as well as dietary supplementation can reduce the risk of vision loss from AMD, therefore, preferred practice patterns recommend identifying individuals with the intermediate stage in a timely manner. Past automated retinal image analysis (ARIA) methods applied on fundus imagery have relied on engineered and hand-designed visual features. We instead detail the novel application of a machine learning approach using deep learning for the problem of ARIA and AMD analysis. We use transfer learning and universal features derived from deep convolutional neural networks (DCNN). We address clinically relevant 4-class, 3-class, and 2-class AMD severity classification problems. Using 5664 color fundus images from the NIH AREDS dataset and DCNN universal features, we obtain values for accuracy for the (4-, 3-, 2-) class classification problem of (79.4%, 81.5%, 93.4%) for machine vs. (75.8%, 85.0%, 95.2%) for physician grading. This study demonstrates the efficacy of machine grading based on deep universal features/transfer learning when applied to ARIA and is a promising step in providing a pre-screener to identify individuals with intermediate AMD and also as a tool that can facilitate identifying such individuals for clinical studies aimed at developing improved therapies. It also demonstrates comparable performance between computer and physician grading. Copyright © 2017 Elsevier Ltd. All rights reserved.

Hyaline articular cartilage: relaxation times, pulse-sequence parameters and MR appearance at 1.5 T

Energy Technology Data Exchange (ETDEWEB)

Chalkias, S.M. [Dept. of Radiology, A.H.E.P.A. General Hospital of the Aristotelian Univ., Thessaloniki (Greece); Pozzi-Mucelli, R.S. [Dept. of Radiology, Univ. of Trieste (Italy); Pozzi-Mucelli, M. [Orthopaedic Clinic, Univ. of Trieste (Italy); Frezza, F. [Dept. of Radiology, Univ. of Trieste (Italy); Longo, R. [Dept. of Radiology, Univ. of Trieste (Italy)

1994-08-01

In order to optimize the parameters for the best visualization of the internal architecture of the hyaline articular cartilage a study both ex vivo and in vivo was performed. Accurate T1 and T2 relaxation times of articular cartilage were obtained with a particular mixed sequence and then used for the creation of isocontrast intensity graphs. These graphs subsequently allowed in all pulse sequences (spin echo, SE and gradient echo, GRE) the best combination of repetition time (TR), echo time (TE) and flip angle (FA) for optimization of signal differences between MR cartilage zones. For SE sequences maximum contrast between cartilage zones can be obtained by using a long TR (> 1,500 ms) with a short TE (< 30 ms), whereas for GRE sequences maximum contrast is obtained with the shortest TE (< 15 ms) combined with a relatively long TR (> 400 ms) and an FA greater than 40 . A trilaminar appearance was demonstrated with a superficial and deep hypointense zone in all sequences and an intermediate zone that was moderately hyperintense on SE T1-weighted images, slightly more hyperintense on proton density Rho and SE T2-weighted images and even more hyperintense on GRE images. (orig.)

DeepFlavour in CMS

CERN Multimedia

CERN. Geneva

2017-01-01

Flavour-tagging of jets is an important task in collider based high energy physics and a field where machine learning tools are applied by all major experiments. A new tagger (DeepFlavour) was developed and commissioned in CMS that is based on an advanced machine learning procedure. A deep neural network is used to do multi-classification of jets that origin from a b-quark, two b-quarks, a c-quark, two c-quarks or light colored particles (u, d, s-quark or gluon). The performance was measured in both, data and simulation. The talk will also include the measured performance of all taggers in CMS. The different taggers and results will be discussed and compared with some focus on details of the newest tagger.

Deep sequencing of the mitochondrial genome reveals common heteroplasmic sites in NADH dehydrogenase genes.

Science.gov (United States)

Liu, Chunyu; Fetterman, Jessica L; Liu, Poching; Luo, Yan; Larson, Martin G; Vasan, Ramachandran S; Zhu, Jun; Levy, Daniel

2018-03-01

Increasing evidence implicates mitochondrial dysfunction in aging and age-related conditions. But little is known about the molecular basis for this connection. A possible cause may be mutations in the mitochondrial DNA (mtDNA), which are often heteroplasmic-the joint presence of different alleles at a single locus in the same individual. However, the involvement of mtDNA heteroplasmy in aging and age-related conditions has not been investigated thoroughly. We deep-sequenced the complete mtDNA genomes of 356 Framingham Heart Study participants (52% women, mean age 43, mean coverage 4570-fold), identified 2880 unique mutations and comprehensively annotated them by MITOMAP and PolyPhen-2. We discovered 11 heteroplasmic "hot" spots [NADH dehydrogenase (ND) subunit 1, 4, 5 and 6 genes, n = 7; cytochrome c oxidase I (COI), n = 2; 16S rRNA, n = 1; D-loop, n = 1] for which the alternative-to-reference allele ratios significantly increased with advancing age (Bonferroni correction p < 0.001). Four of these heteroplasmic mutations in ND and COI genes were predicted to be deleterious nonsynonymous mutations which may have direct impact on ATP production. We confirmed previous findings that healthy individuals carry many low-frequency heteroplasmy mutations with potentially deleterious effects. We hypothesize that the effect of a single deleterious heteroplasmy may be minimal due to a low mutant-to-wildtype allele ratio, whereas the aggregate effects of many deleterious mutations may cause changes in mitochondrial function and contribute to age-related diseases. The identification of age-related mtDNA mutations is an important step to understand the genetic architecture of age-related diseases and may uncover novel therapeutic targets for such diseases.

The predictable nature of the Paleozoic sedimentary sequence beneath the Bruce nuclear site in Southern Ontario, Canada

International Nuclear Information System (INIS)

Parmenter, Andrew; Jensen, Mark; Crowe, Richard

2012-01-01

Document available in extended abstract form only. A key aspect of a Deep Geologic Repository (DGR) safety case is the ability to develop and communicate an understanding of the geologic stability and resilience to change at time frames relevant to demonstrating repository performance. As part of an on-going Environmental Assessment, Ontario Power Generation (OPG) recently completed site-specific investigations within an 850 m thick Paleozoic sedimentary sequence beneath the Bruce nuclear site for the proposed development of a DGR for Low and Intermediate Level Waste (L and ILW). As envisioned, the shaft-accessed DGR would be excavated at a nominal depth of 680 m within the low permeability Ordovician argillaceous limestone of the Cobourg Formation, which is overlain by more than 200 m of low permeability Ordovician shale. The geo-scientific investigations revealed a relatively undeformed and laterally continuous architecture within the sedimentary sequence at the repository scale (1.5 km 2 ) and beyond. This paper explores the predictable nature of the sedimentary sequence that has contributed to increasing confidence in an understanding of the spatial distribution of groundwater system properties, deep groundwater system evolution and natural barrier performance. Multi-disciplinary geo-scientific investigations of the Bruce nuclear site were completed in 3 phases between 2006 and 2010. The sub-surface investigations included a deep drilling, coring and in-situ testing program and, the completion of a 19.7 km (9 lines) 2-D seismic reflection survey. The drilling program involved 6 (150 mm dia.) deep boreholes (4-vertical; 2 inclined) that were extended through the sedimentary sequence from 4 drill sites, arranged around the 0.3 km 2 footprint of the proposed repository. The more than 3.8 km of rock core (77 mm dia.) retrieved have provided, in part, a strong basis to understand bedrock lithology and mineralogy, facies assemblages, structure, and oil and gas

Deconstructing the genetic basis of spent sulphite liquor tolerance using deep sequencing of genome-shuffled yeast.

Science.gov (United States)

Pinel, Dominic; Colatriano, David; Jiang, Heng; Lee, Hung; Martin, Vincent Jj

2015-01-01

Identifying the genetic basis of complex microbial phenotypes is currently a major barrier to our understanding of multigenic traits and our ability to rationally design biocatalysts with highly specific attributes for the biotechnology industry. Here, we demonstrate that strain evolution by meiotic recombination-based genome shuffling coupled with deep sequencing can be used to deconstruct complex phenotypes and explore the nature of multigenic traits, while providing concrete targets for strain development. We determined genomic variations found within Saccharomyces cerevisiae previously evolved in our laboratory by genome shuffling for tolerance to spent sulphite liquor. The representation of these variations was backtracked through parental mutant pools and cross-referenced with RNA-seq gene expression analysis to elucidate the importance of single mutations and key biological processes that play a role in our trait of interest. Our findings pinpoint novel genes and biological determinants of lignocellulosic hydrolysate inhibitor tolerance in yeast. These include the following: protein homeostasis constituents, including Ubp7p and Art5p, related to ubiquitin-mediated proteolysis; stress response transcriptional repressor, Nrg1p; and NADPH-dependent glutamate dehydrogenase, Gdh1p. Reverse engineering a prominent mutation in ubiquitin-specific protease gene UBP7 in a laboratory S. cerevisiae strain effectively increased spent sulphite liquor tolerance. This study advances understanding of yeast tolerance mechanisms to inhibitory substrates and biocatalyst design for a biomass-to-biofuel/biochemical industry, while providing insights into the process of mutation accumulation that occurs during genome shuffling.

Application of SCALE 6.1 MAVRIC Sequence for Activation Calculation in Reactor Primary Shield Concrete

International Nuclear Information System (INIS)

Kim, Yong IL

2014-01-01

Activation calculation requires flux information at desired location and reaction cross sections for the constituent elements to obtain production rate of activation products. Generally it is not an easy task to obtain fluxes or reaction rates with low uncertainties in a reasonable time for deep penetration problems by using standard Monte Carlo methods. The MAVRIC (Monaco with Automated Variance Reduction using Importance Calculations) sequence in SCALE 6.1 code package is intended to perform radiation transport on problems that are too challenging for standard, unbiased Monte Carlo methods. And the SCALE code system provides plenty of ENDF reaction types enough to consider almost all activation reactions in the nuclear reactor materials. To evaluate the activation of the important isotopes in primary shield, SCALE 6.1 MAVRIC sequence has been utilized for the KSNP reactor model and the calculated results are compared to the isotopic activity concentration of related standard. Related to the planning for decommission, the activation products in concrete primary shield such as Fe-55, Co-60, Ba-133, Eu-152, and Eu-154 are identified as important elements according to the comparisons with related standard for exemption. In this study, reference data are used for the concrete compositions in the activation calculation to see the applicability of MAVRIC code to the evaluation of activation inventory in the concrete primary shield. The composition data of trace elements as shown in Table 1 are obtained from various US power plant sites and accordingly they have large variations in quantity due to the characteristics of concrete composition. In practical estimation of activation radioactivity for a specific plant related to decommissioning, rigorous chemical analysis of concrete samples of the plant would first have to be performed to get exact information for compositions of concrete. Considering the capability of solving deep penetration transport problems and richness

Whole-genome sequencing identifies EN1 as a determinant of bone density and fracture

DEFF Research Database (Denmark)

Zheng, Hou-Feng; Forgetta, Vincenzo; Hsu, Yi-Hsiang

2015-01-01

. Associations for BMD were derived from whole-genome sequencing (n = 2,882 from UK10K (ref. 10); a population-based genome sequencing consortium), whole-exome sequencing (n = 3,549), deep imputation of genotyped samples using a combined UK10K/1000 Genomes reference panel (n = 26,534), and de novo replication...

Deep inelastic scattering

International Nuclear Information System (INIS)

Aubert, J.J.

1982-01-01

Deep inelastic lepton-nucleon interaction experiments are renewed. Singlet and non-singlet structure functions are measured and the consistency of the different results is checked. A detailed analysis of the scaling violation is performed in terms of the quantum chromodynamics predictions [fr

Development of Hydro-Mechanical Deep Drawing

DEFF Research Database (Denmark)

Zhang, Shi-Hong; Danckert, Joachim

1998-01-01

The hydro-mechanical deep-drawing process is reviewed in this article. The process principles and features are introduced and the developments of the hydro-mechanical deep-drawing process in process performances, in theory and in numerical simulation are described. The applications are summarized....... Some other related hydraulic forming processes are also dealt with as a comparison....

Evaluation of automatic time gain compensated in-vivo ultrasound sequences

DEFF Research Database (Denmark)

Axelsen, Martin Christian; Røeboe, Kristian Frostholm; Hemmsen, Martin Christian

2010-01-01

algorithm for automatic time gain compensation (TGC) on in-vivo ultrasound sequences. Forty ultrasound sequences were recorded from the abdomen of two healthy volunteers. Each sequence of 5 sec was recorded with 40 frames/sec. Post processing each frame, a mask is created wherein anechoic and hyper echoic...... regions are mapped. Near field hyper intensity and deep areas with low signal strength are also included in the mask. The algorithm uses this mask to create a parallel image where anechoic and hyper echoic regions are eliminated. From this, the mean power is calculated as a function of depth. The power...

Bacterial Diversity in Bentonites, Engineered Barrier for Deep Geological Disposal of Radioactive Wastes.

Science.gov (United States)

Lopez-Fernandez, Margarita; Cherkouk, Andrea; Vilchez-Vargas, Ramiro; Jauregui, Ruy; Pieper, Dietmar; Boon, Nico; Sanchez-Castro, Ivan; Merroun, Mohamed L

2015-11-01

The long-term disposal of radioactive wastes in a deep geological repository is the accepted international solution for the treatment and management of these special residues. The microbial community of the selected host rocks and engineered barriers for the deep geological repository may affect the performance and the safety of the radioactive waste disposal. In this work, the bacterial population of bentonite formations of Almeria (Spain), selected as a reference material for bentonite-engineered barriers in the disposal of radioactive wastes, was studied. 16S ribosomal RNA (rRNA) gene-based approaches were used to study the bacterial community of the bentonite samples by traditional clone libraries and Illumina sequencing. Using both techniques, the bacterial diversity analysis revealed similar results, with phylotypes belonging to 14 different bacterial phyla: Acidobacteria, Actinobacteria, Armatimonadetes, Bacteroidetes, Chloroflexi, Cyanobacteria, Deinococcus-Thermus, Firmicutes, Gemmatimonadetes, Planctomycetes, Proteobacteria, Nitrospirae, Verrucomicrobia and an unknown phylum. The dominant groups of the community were represented by Proteobacteria and Bacteroidetes. A high diversity was found in three of the studied samples. However, two samples were less diverse and dominated by Betaproteobacteria.

A study on the effect of varying sequence of lab performance skills on lab performance of high school physics students

Science.gov (United States)

Bournia-Petrou, Ethel A.

The main goal of this investigation was to study how student rank in class, student gender and skill sequence affect high school students' performance on the lab skills involved in a laboratory-based inquiry task in physics. The focus of the investigation was the effect of skill sequence as determined by the particular task. The skills considered were: Hypothesis, Procedure, Planning, Data, Graph, Calculations and Conclusion. Three physics lab tasks based on the simple pendulum concept were administered to 282 Regents physics high school students. The reliability of the designed tasks was high. Student performance was evaluated on individual student written responses and a scoring rubric. The tasks had high discrimination power and were of moderate difficulty (65%). It was found that, student performance was weak on Conclusion (42%), Hypothesis (48%), and Procedure (51%), where the numbers in parentheses represent the mean as a percentage of the maximum possible score. Student performance was strong on Calculations (91%), Data (82%), Graph (74%) and Plan (68%). Out of all seven skills, Procedure had the strongest correlation (.73) with the overall task performance. Correlation analysis revealed some strong relationships among the seven skills which were grouped in two distinct clusters: Hypothesis, Procedure and Plan belong to one, and Data, Graph, Calculations, and Conclusion belong to the other. This distinction may indicate different mental processes at play within each skill cluster. The effect of student rank was not statistically significant according to the MANOVA results due to the large variation of rank levels among the participating schools. The effect of gender was significant on the entire test because of performance differences on Calculations and Graph, where male students performed better than female students. Skill sequence had a significant effect on the skills of Procedure, Plan, Data and Conclusion. Students are rather weak in proposing a

Taxonomic research on deep-sea macrofauna in the South China Sea using the Chinese deep-sea submersible Jiaolong.

Science.gov (United States)

Li, Xinzheng

2017-07-01

This paper reviews the taxonomic and biodiversity studies of deep-sea invertebrates in the South China Sea based on the samples collected by the Chinese manned deep-sea submersible Jiaolong. To date, 6 new species have been described, including the sponges Lophophysema eversa, Saccocalyx microhexactin and Semperella jiaolongae as well as the crustaceans Uroptychus jiaolongae, Uroptychus spinulosus and Globospongicola jiaolongi; some newly recorded species from the South China Sea have also been reported. The Bathymodiolus platifrons-Shinkaia crosnieri deep-sea cold seep community has been reported by Li (2015), as has the mitochondrial genome of the glass sponge L. eversa by Zhang et al. (2016). The population structures of two dominant species, the shrimp Shinkaia crosnieri and the mussel Bathymodiolus platifrons, from the cold seep Bathymodiolus platifrons-Shinkaia crosnieri community in the South China Sea and the hydrothermal vents in the Okinawa Trough, were compared using molecular analysis. The systematic position of the shrimp genus Globospongicola was discussed based on 16S rRNA gene sequences. © 2017 International Society of Zoological Sciences, Institute of Zoology/Chinese Academy of Sciences and John Wiley & Sons Australia, Ltd.

Transmission Bottleneck Size Estimation from Pathogen Deep-Sequencing Data, with an Application to Human Influenza A Virus.

Science.gov (United States)

Sobel Leonard, Ashley; Weissman, Daniel B; Greenbaum, Benjamin; Ghedin, Elodie; Koelle, Katia

2017-07-15

The bottleneck governing infectious disease transmission describes the size of the pathogen population transferred from the donor to the recipient host. Accurate quantification of the bottleneck size is particularly important for rapidly evolving pathogens such as influenza virus, as narrow bottlenecks reduce the amount of transferred viral genetic diversity and, thus, may decrease the rate of viral adaptation. Previous studies have estimated bottleneck sizes governing viral transmission by using statistical analyses of variants identified in pathogen sequencing data. These analyses, however, did not account for variant calling thresholds and stochastic viral replication dynamics within recipient hosts. Because these factors can skew bottleneck size estimates, we introduce a new method for inferring bottleneck sizes that accounts for these factors. Through the use of a simulated data set, we first show that our method, based on beta-binomial sampling, accurately recovers transmission bottleneck sizes, whereas other methods fail to do so. We then apply our method to a data set of influenza A virus (IAV) infections for which viral deep-sequencing data from transmission pairs are available. We find that the IAV transmission bottleneck size estimates in this study are highly variable across transmission pairs, while the mean bottleneck size of 196 virions is consistent with a previous estimate for this data set. Furthermore, regression analysis shows a positive association between estimated bottleneck size and donor infection severity, as measured by temperature. These results support findings from experimental transmission studies showing that bottleneck sizes across transmission events can be variable and influenced in part by epidemiological factors. IMPORTANCE The transmission bottleneck size describes the size of the pathogen population transferred from the donor to the recipient host and may affect the rate of pathogen adaptation within host populations. Recent