Is the genetic landscape of the deep subsurface biosphere affected by viruses?

PubMed

Anderson, Rika E; Brazelton, William J; Baross, John A

2011-01-01

Viruses are powerful manipulators of microbial diversity, biogeochemistry, and evolution in the marine environment. Viruses can directly influence the genetic capabilities and the fitness of their hosts through the use of fitness factors and through horizontal gene transfer. However, the impact of viruses on microbial ecology and evolution is often overlooked in studies of the deep subsurface biosphere. Subsurface habitats connected to hydrothermal vent systems are characterized by constant fluid flux, dynamic environmental variability, and high microbial diversity. In such conditions, high adaptability would be an evolutionary asset, and the potential for frequent host-virus interactions would be high, increasing the likelihood that cellular hosts could acquire novel functions. Here, we review evidence supporting this hypothesis, including data indicating that microbial communities in subsurface hydrothermal fluids are exposed to a high rate of viral infection, as well as viral metagenomic data suggesting that the vent viral assemblage is particularly enriched in genes that facilitate horizontal gene transfer and host adaptability. Therefore, viruses are likely to play a crucial role in facilitating adaptability to the extreme conditions of these regions of the deep subsurface biosphere. We also discuss how these results might apply to other regions of the deep subsurface, where the nature of virus-host interactions would be altered, but possibly no less important, compared to more energetic hydrothermal systems.

Computer Viruses. Technology Update.

ERIC Educational Resources Information Center

Ponder, Tim, Comp.; Ropog, Marty, Comp.; Keating, Joseph, Comp.

This document provides general information on computer viruses, how to help protect a computer network from them, measures to take if a computer becomes infected. Highlights include the origins of computer viruses; virus contraction; a description of some common virus types (File Virus, Boot Sector/Partition Table Viruses, Trojan Horses, and…

Unexpected and novel putative viruses in the sediments of a deep-dark permanently anoxic freshwater habitat.

PubMed

Borrel, Guillaume; Colombet, Jonathan; Robin, Agnès; Lehours, Anne-Catherine; Prangishvili, David; Sime-Ngando, Télesphore

2012-11-01

Morphological diversity, abundance and community structure of viruses were examined in the deep and anoxic sediments of the volcanic Lake Pavin (France). The sediment core, encompassing 130 years of sedimentation, was subsampled every centimeter. High viral abundances were recorded and correlated to prokaryotic densities. Abundances of viruses and prokaryotes decreased with the depth, contrasting the pattern of virus-to-prokaryote ratio. According to fingerprint analyses, the community structure of viruses, bacteria and archaea gradually changed, and communities of the surface (0-10 cm) could be discriminated from those of the intermediate (11-27 cm) and deep (28-40 cm) sediment layers. Viral morphotypes similar to virions of ubiquitous dsDNA viruses of bacteria were observed. Exceptional morphotypes, previously never reported in freshwater systems, were also detected. Some of these resembled dsDNA viruses of hyperthermophilic and hyperhalophilic archaea. Moreover, unusual types of spherical and cubic virus-like particles (VLPs) were observed. Infected prokaryotic cells were detected in the whole sediment core, and their vertical distribution correlated with both viral and prokaryotic abundances. Pleomorphic ellipsoid VLPs were visible in filamentous cells tentatively identified as representatives of the archaeal genus Methanosaeta, a major group of methane producers on earth.

Unexpected and novel putative viruses in the sediments of a deep-dark permanently anoxic freshwater habitat

PubMed Central

Borrel, Guillaume; Colombet, Jonathan; Robin, Agnès; Lehours, Anne-Catherine; Prangishvili, David; Sime-Ngando, Télesphore

2012-01-01

Morphological diversity, abundance and community structure of viruses were examined in the deep and anoxic sediments of the volcanic Lake Pavin (France). The sediment core, encompassing 130 years of sedimentation, was subsampled every centimeter. High viral abundances were recorded and correlated to prokaryotic densities. Abundances of viruses and prokaryotes decreased with the depth, contrasting the pattern of virus-to-prokaryote ratio. According to fingerprint analyses, the community structure of viruses, bacteria and archaea gradually changed, and communities of the surface (0–10 cm) could be discriminated from those of the intermediate (11–27 cm) and deep (28–40 cm) sediment layers. Viral morphotypes similar to virions of ubiquitous dsDNA viruses of bacteria were observed. Exceptional morphotypes, previously never reported in freshwater systems, were also detected. Some of these resembled dsDNA viruses of hyperthermophilic and hyperhalophilic archaea. Moreover, unusual types of spherical and cubic virus-like particles (VLPs) were observed. Infected prokaryotic cells were detected in the whole sediment core, and their vertical distribution correlated with both viral and prokaryotic abundances. Pleomorphic ellipsoid VLPs were visible in filamentous cells tentatively identified as representatives of the archaeal genus Methanosaeta, a major group of methane producers on earth. PMID:22648129

Genetic diversity among pandemic 2009 influenza viruses isolated from a transmission chain

PubMed Central

2013-01-01

Background Influenza viruses such as swine-origin influenza A(H1N1) virus (A(H1N1)pdm09) generate genetic diversity due to the high error rate of their RNA polymerase, often resulting in mixed genotype populations (intra-host variants) within a single infection. This variation helps influenza to rapidly respond to selection pressures, such as those imposed by the immunological host response and antiviral therapy. We have applied deep sequencing to characterize influenza intra-host variation in a transmission chain consisting of three cases due to oseltamivir-sensitive viruses, and one derived oseltamivir-resistant case. Methods Following detection of the A(H1N1)pdm09 infections, we deep-sequenced the complete NA gene from two of the oseltamivir-sensitive virus-infected cases, and all eight gene segments of the viruses causing the remaining two cases. Results No evidence for the resistance-causing mutation (resulting in NA H275Y substitution) was observed in the oseltamivir-sensitive cases. Furthermore, deep sequencing revealed a subpopulation of oseltamivir-sensitive viruses in the case carrying resistant viruses. We detected higher levels of intra-host variation in the case carrying oseltamivir-resistant viruses than in those infected with oseltamivir-sensitive viruses. Conclusions Oseltamivir-resistance was only detected after prophylaxis with oseltamivir, suggesting that the mutation was selected for as a result of antiviral intervention. The persisting oseltamivir-sensitive virus population in the case carrying resistant viruses suggests either that a small proportion survive the treatment, or that the oseltamivir-sensitive virus rapidly re-establishes itself in the virus population after the bottleneck. Moreover, the increased intra-host variation in the oseltamivir-resistant case is consistent with the hypothesis that the population diversity of a RNA virus can increase rapidly following a population bottleneck. PMID:23587185

Is the Genetic Landscape of the Deep Subsurface Biosphere Affected by Viruses?

PubMed Central

Anderson, Rika E.; Brazelton, William J.; Baross, John A.

2011-01-01

Viruses are powerful manipulators of microbial diversity, biogeochemistry, and evolution in the marine environment. Viruses can directly influence the genetic capabilities and the fitness of their hosts through the use of fitness factors and through horizontal gene transfer. However, the impact of viruses on microbial ecology and evolution is often overlooked in studies of the deep subsurface biosphere. Subsurface habitats connected to hydrothermal vent systems are characterized by constant fluid flux, dynamic environmental variability, and high microbial diversity. In such conditions, high adaptability would be an evolutionary asset, and the potential for frequent host–virus interactions would be high, increasing the likelihood that cellular hosts could acquire novel functions. Here, we review evidence supporting this hypothesis, including data indicating that microbial communities in subsurface hydrothermal fluids are exposed to a high rate of viral infection, as well as viral metagenomic data suggesting that the vent viral assemblage is particularly enriched in genes that facilitate horizontal gene transfer and host adaptability. Therefore, viruses are likely to play a crucial role in facilitating adaptability to the extreme conditions of these regions of the deep subsurface biosphere. We also discuss how these results might apply to other regions of the deep subsurface, where the nature of virus–host interactions would be altered, but possibly no less important, compared to more energetic hydrothermal systems. PMID:22084639

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

USDA-ARS?s Scientific Manuscript database

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 RN...

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

PubMed

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.

Deep Sequencing Analysis of Apple Infecting Viruses in Korea

PubMed Central

Cho, In-Sook; Igori, Davaajargal; Lim, Seungmo; Choi, Gug-Seoun; Hammond, John; Lim, Hyoun-Sub; Moon, Jae Sun

2016-01-01

Deep sequencing has generated 52 contigs derived from five viruses; Apple chlorotic leaf spot virus (ACLSV), Apple stem grooving virus (ASGV), Apple stem pitting virus (ASPV), Apple green crinkle associated virus (AGCaV), and Apricot latent virus (ApLV) were identified from eight apple samples showing small leaves and/or growth retardation. Nucleotide (nt) sequence identity of the assembled contigs was from 68% to 99% compared to the reference sequences of the five respective viral genomes. Sequences of ASPV and ASGV were the most abundantly represented by the 52 contigs assembled. The presence of the five viruses in the samples was confirmed by RT-PCR using specific primers based on the sequences of each assembled contig. All five viruses were detected in three of the samples, whereas all samples had mixed infections with at least two viruses. The most frequently detected virus was ASPV, followed by ASGV, ApLV, ACLSV, and AGCaV which were withal found in mixed infections in the tested samples. AGCaV was identified in assembled contigs ID 1012480 and 93549, which showed 82% and 78% nt sequence identity with ORF1 of AGCaV isolate Aurora-1. ApLV was identified in three assembled contigs, ID 65587, 1802365, and 116777, which showed 77%, 78%, and 76% nt sequence identity respectively with ORF1 of ApLV isolate LA2. Deep sequencing assay was shown to be a valuable and powerful tool for detection and identification of known and unknown virome in infected apple trees, here identifying ApLV and AGCaV in commercial orchards in Korea for the first time. PMID:27721694

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

PubMed

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.

Lytic viral infection of bacterioplankton in deep waters of the western Pacific Ocean

NASA Astrophysics Data System (ADS)

Li, Y.; Luo, T.; Sun, J.; Cai, L.; Liang, Y.; Jiao, N.; Zhang, R.

2014-05-01

As the most abundant biological entities in the ocean, viruses influence host mortality and nutrient recycling mainly through lytic infection. Yet, the ecological characteristics of virioplankton and viral impacts on host mortality and biogeochemical cycling in the deep sea are largely unknown. In the present study, viral abundance and lytic infection were investigated throughout the water column in the western Pacific Ocean. Both the prokaryotic and viral abundance and production showed a significantly decreasing trend from epipelagic to meso- and bathypelagic waters. Viral abundance decreased from 0.36-1.05 × 1010 particles L-1 to 0.43-0.80 × 109 particles L-1, while the virus : prokaryote ratio varied from 7.21 to 16.23 to 2.45-23.40, at the surface and 2000 m, respectively. Lytic viral production rates in surface and 2000 m waters were, on average, 1.03 × 1010 L-1 day-1 and 5.74 × 108 L-1 day-1. Relatively high percentages of prokaryotic cells lysed by viruses at 1000 and 2000 m were observed, suggesting a significant contribution of viruses to prokaryotic mortality in the deep ocean. The carbon released by viral lysis in deep western Pacific Ocean waters was from 0.03 to 2.32 μg C L-1 day-1. Our findings demonstrated a highly dynamic and active viral population in these deep waters and suggested that virioplankton play an important role in the microbial loop and subsequently biogeochemical cycling in deep oceans.

Lytic viral infection of bacterioplankton in deep waters of the western Pacific Ocean

NASA Astrophysics Data System (ADS)

Li, Y.; Luo, T.; Sun, J.; Cai, L.; Jiao, N.; Zhang, R.

2013-12-01

As the most abundant biological entities in the ocean, viruses can influence host mortality and nutrients recycling mainly through lytic infection. Yet ecological characteristics of virioplankton and viral impacts on host mortality and biogeochemical cycling in the deep sea are largely unknown. In present study, viral abundance and lytic infection was investigated throughout the water column in the western Pacific Ocean. Both the prokaryotic and viral abundance and production showed a significantly decreasing trend from epipelagic to meso- and bathypelagic waters. Viral abundance decreased from 0.36-1.05 × 1010 particles L-1 to 0.43-0.80 × 109 particles L-1, while the virus : prokaryote ratio varied from 7.21-16.23 to 2.45-23.40, at surface and 2000 m depth, respectively. The lytic viral production rates in surface and 2000 m waters were, averagely, 1.03 × 1010 L-1 day-1 and 5.74 × 108 L-1 day-1, respectively. Relatively high percentages of prokaryotic cells lysed by virus in 1000 m and 2000 m were observed, suggesting a significant contribution of viruses to prokaryotic mortality in deep ocean. The carbon released by viral lysis in deep western Pacific Ocean waters was from 0.03 to 2.32 μg C L-1 day-1. Our findings demonstrated a highly dynamic and active viral population in the deep western Pacific Ocean and suggested that virioplankton play an important role in the microbial loop and subsequently biogeochemical cycling in deep oceans.

Fbis report. Science and technology: China, October 18, 1995

DOE Office of Scientific and Technical Information (OSTI.GOV)

NONE

1995-10-18

;Partial Contents: Nanomaterials Fabrication, Applications Research Advances Noted; CAST Announces World`s First Space-Grown Large-Diameter GaAs Monocrystal; Assay of Antiviral Activity of Antisense Phosphorothioate Oligodeoxynucleotide Against Dengue Virus; Expression and Antigenicity of Chimeric Proteins of Cholera Toxin B Subunit With Hepatitis C Virus; CNCOFIEC Signs Agreement With IBM for New Intelligent Building; Latest Reports on Optical Computing, Memory; BIDC To Introduce S3 Company`s Multimedia Accelerator Chipset; Virtual Private PCN Ring Network Based on ATM VP Cross-Connection; Beijing Gets Nation`s First Frame Relay Network; Situation of Power Industry Development and International Cooperation; Diagrams of China`s Nuclear Waste Containment Vessels; Chinese-Developed Containment Vesselmore » Material Reaches World Standards; Second Fuel Elements for Qinshan Plant Passes Inspection; and Geothermal Deep-Well Electric Pump Technology Developed.« less

Computer Viruses: Pathology and Detection.

ERIC Educational Resources Information Center

Maxwell, John R.; Lamon, William E.

1992-01-01

Explains how computer viruses were originally created, how a computer can become infected by a virus, how viruses operate, symptoms that indicate a computer is infected, how to detect and remove viruses, and how to prevent a reinfection. A sidebar lists eight antivirus resources. (four references) (LRW)

Comparison of Deep-Water Viromes from the Atlantic Ocean and the Mediterranean Sea

PubMed Central

Winter, Christian; Garcia, Juan A. L.; Weinbauer, Markus G.; DuBow, Michael S.; Herndl, Gerhard J.

2014-01-01

The aim of this study was to compare the composition of two deep-sea viral communities obtained from the Romanche Fracture Zone in the Atlantic Ocean (collected at 5200 m depth) and the southwest Mediterranean Sea (from 2400 m depth) using a pyro-sequencing approach. The results are based on 18.7% and 6.9% of the sequences obtained from the Atlantic Ocean and the Mediterranean Sea, respectively, with hits to genomes in the non-redundant viral RefSeq database. The identifiable richness and relative abundance in both viromes were dominated by archaeal and bacterial viruses accounting for 92.3% of the relative abundance in the Atlantic Ocean and for 83.6% in the Mediterranean Sea. Despite characteristic differences in hydrographic features between the sampling sites in the Atlantic Ocean and the Mediterranean Sea, 440 virus genomes were found in both viromes. An additional 431 virus genomes were identified in the Atlantic Ocean and 75 virus genomes were only found in the Mediterranean Sea. The results indicate that the rather contrasting deep-sea environments of the Atlantic Ocean and the Mediterranean Sea share a common core set of virus types constituting the majority of both virus communities in terms of relative abundance (Atlantic Ocean: 81.4%; Mediterranean Sea: 88.7%). PMID:24959907

Acute West Nile Virus Meningoencephalitis Diagnosed Via Metagenomic Deep Sequencing of Cerebrospinal Fluid in a Renal Transplant Patient.

PubMed

Wilson, M R; Zimmermann, L L; Crawford, E D; Sample, H A; Soni, P R; Baker, A N; Khan, L M; DeRisi, J L

2017-03-01

Solid organ transplant patients are vulnerable to suffering neurologic complications from a wide array of viral infections and can be sentinels in the population who are first to get serious complications from emerging infections like the recent waves of arboviruses, including West Nile virus, Chikungunya virus, Zika virus, and Dengue virus. The diverse and rapidly changing landscape of possible causes of viral encephalitis poses great challenges for traditional candidate-based infectious disease diagnostics that already fail to identify a causative pathogen in approximately 50% of encephalitis cases. We present the case of a 14-year-old girl on immunosuppression for a renal transplant who presented with acute meningoencephalitis. Traditional diagnostics failed to identify an etiology. RNA extracted from her cerebrospinal fluid was subjected to unbiased metagenomic deep sequencing, enhanced with the use of a Cas9-based technique for host depletion. This analysis identified West Nile virus (WNV). Convalescent serum serologies subsequently confirmed WNV seroconversion. These results support a clear clinical role for metagenomic deep sequencing in the setting of suspected viral encephalitis, especially in the context of the high-risk transplant patient population. © 2016 The Authors. American Journal of Transplantation published by Wiley Periodicals, Inc. on behalf of American Society of Transplant Surgeons.

Investigation of a computer virus outbreak in the pharmacy of a tertiary care teaching hospital.

PubMed

Bailey, T C; Reichley, R M

1992-10-01

A computer virus outbreak was recognized, verified, defined, investigated, and controlled using an infection control approach. The pathogenesis and epidemiology of computer virus infection are reviewed. Case-control study. Pharmacy of a tertiary care teaching institution. On October 28, 1991, 2 personal computers in the drug information center manifested symptoms consistent with the "Jerusalem" virus infection. The same day, a departmental personal computer began playing "Yankee Doodle," a sign of "Doodle" virus infection. An investigation of all departmental personal computers identified the "Stoned" virus in an additional personal computer. Controls were functioning virus-free personal computers within the department. Cases were associated with users who brought diskettes from outside the department (5/5 cases versus 5/13 controls, p = .04) and with College of Pharmacy student users (3/5 cases versus 0/13 controls, p = .012). The detection of a virus-infected diskette or personal computer was associated with the number of 5 1/4-inch diskettes in the files of personal computers, a surrogate for rate of media exchange (mean = 17.4 versus 152.5, p = .018, Wilcoxon rank sum test). After education of departmental personal computer users regarding appropriate computer hygiene and installation of virus protection software, no further spread of personal computer viruses occurred, although 2 additional Stoned-infected and 1 Jerusalem-infected diskettes were detected. We recommend that virus detection software be installed on personal computers where the interchange of diskettes among computers is necessary, that write-protect tabs be placed on all program master diskettes and data diskettes where data are being read and not written, that in the event of a computer virus outbreak, all available diskettes be quarantined and scanned by virus detection software, and to facilitate quarantine and scanning in an outbreak, that diskettes be stored in organized files.

Computer virus information update CIAC-2301

DOE Office of Scientific and Technical Information (OSTI.GOV)

Orvis, W.J.

1994-01-15

While CIAC periodically issues bulletins about specific computer viruses, these bulletins do not cover all the computer viruses that affect desktop computers. The purpose of this document is to identify most of the known viruses for the MS-DOS and Macintosh platforms and give an overview of the effects of each virus. The authors also include information on some windows, Atari, and Amiga viruses. This document is revised periodically as new virus information becomes available. This document replaces all earlier versions of the CIAC Computer virus Information Update. The date on the front cover indicates date on which the information inmore » this document was extracted from CIAC`s Virus database.« less

Sulfur oxidation genes in diverse deep-sea viruses.

PubMed

Anantharaman, Karthik; Duhaime, Melissa B; Breier, John A; Wendt, Kathleen A; Toner, Brandy M; Dick, Gregory J

2014-05-16

Viruses are the most abundant biological entities in the oceans and a pervasive cause of mortality of microorganisms that drive biogeochemical cycles. Although the ecological and evolutionary effects of viruses on marine phototrophs are well recognized, little is known about their impact on ubiquitous marine lithotrophs. Here, we report 18 genome sequences of double-stranded DNA viruses that putatively infect widespread sulfur-oxidizing bacteria. Fifteen of these viral genomes contain auxiliary metabolic genes for the α and γ subunits of reverse dissimilatory sulfite reductase (rdsr). This enzyme oxidizes elemental sulfur, which is abundant in the hydrothermal plumes studied here. Our findings implicate viruses as a key agent in the sulfur cycle and as a reservoir of genetic diversity for bacterial enzymes that underpin chemosynthesis in the deep oceans. Copyright © 2014, American Association for the Advancement of Science.

Deep-Sea Hydrothermal Vent Viruses Compensate for Microbial Metabolism in Virus-Host Interactions.

PubMed

He, Tianliang; Li, Hongyun; Zhang, Xiaobo

2017-07-11

Viruses are believed to be responsible for the mortality of host organisms. However, some recent investigations reveal that viruses may be essential for host survival. To date, it remains unclear whether viruses are beneficial or harmful to their hosts. To reveal the roles of viruses in the virus-host interactions, viromes and microbiomes of sediment samples from three deep-sea hydrothermal vents were explored in this study. To exclude the influence of exogenous DNAs on viromes, the virus particles were purified with nuclease (DNase I and RNase A) treatments and cesium chloride density gradient centrifugation. The metagenomic analysis of viromes without exogenous DNA contamination and microbiomes of vent samples indicated that viruses had compensation effects on the metabolisms of their host microorganisms. Viral genes not only participated in most of the microbial metabolic pathways but also formed branched pathways in microbial metabolisms, including pyrimidine metabolism; alanine, aspartate, and glutamate metabolism; nitrogen metabolism and assimilation pathways of the two-component system; selenocompound metabolism; aminoacyl-tRNA biosynthesis; and amino sugar and nucleotide sugar metabolism. As is well known, deep-sea hydrothermal vent ecosystems exist in relatively isolated environments which are barely influenced by other ecosystems. The metabolic compensation of hosts mediated by viruses might represent a very important aspect of virus-host interactions. IMPORTANCE Viruses are the most abundant biological entities in the oceans and have very important roles in regulating microbial community structure and biogeochemical cycles. The relationship between virus and host microbes is broadly thought to be that of predator and prey. Viruses can lyse host cells to control microbial population sizes and affect community structures of hosts by killing specific microbes. However, viruses also influence their hosts through manipulation of bacterial metabolism. We found that viral genes not only participated in most microbial metabolic pathways but also formed branched pathways in microbial metabolisms. The metabolic compensation of hosts mediated by viruses may help hosts to adapt to extreme environments and may be essential for host survival. Copyright © 2017 He et al.

An overview of computer viruses in a research environment

NASA Technical Reports Server (NTRS)

Bishop, Matt

1991-01-01

The threat of attack by computer viruses is in reality a very small part of a much more general threat, specifically threats aimed at subverting computer security. Here, computer viruses are examined as a malicious logic in a research and development environment. A relation is drawn between the viruses and various models of security and integrity. Current research techniques aimed at controlling the threats posed to computer systems by threatening viruses in particular and malicious logic in general are examined. Finally, a brief examination of the vulnerabilities of research and development systems that malicious logic and computer viruses may exploit is undertaken.

Safe Computing: An Overview of Viruses.

ERIC Educational Resources Information Center

Wodarz, Nan

2001-01-01

A computer virus is a program that replicates itself, in conjunction with an additional program that can harm a computer system. Common viruses include boot-sector, macro, companion, overwriting, and multipartite. Viruses can be fast, slow, stealthy, and polymorphic. Anti-virus products are described. (MLH)

Full genome virus detection in fecal samples using sensitive nucleic acid preparation, deep sequencing, and a novel iterative sequence classification algorithm.

PubMed

Cotten, Matthew; Oude Munnink, Bas; Canuti, Marta; Deijs, Martin; Watson, Simon J; Kellam, Paul; van der Hoek, Lia

2014-01-01

We have developed a full genome virus detection process that combines sensitive nucleic acid preparation optimised for virus identification in fecal material with Illumina MiSeq sequencing and a novel post-sequencing virus identification algorithm. Enriched viral nucleic acid was converted to double-stranded DNA and subjected to Illumina MiSeq sequencing. The resulting short reads were processed with a novel iterative Python algorithm SLIM for the identification of sequences with homology to known viruses. De novo assembly was then used to generate full viral genomes. The sensitivity of this process was demonstrated with a set of fecal samples from HIV-1 infected patients. A quantitative assessment of the mammalian, plant, and bacterial virus content of this compartment was generated and the deep sequencing data were sufficient to assembly 12 complete viral genomes from 6 virus families. The method detected high levels of enteropathic viruses that are normally controlled in healthy adults, but may be involved in the pathogenesis of HIV-1 infection and will provide a powerful tool for virus detection and for analyzing changes in the fecal virome associated with HIV-1 progression and pathogenesis.

Full Genome Virus Detection in Fecal Samples Using Sensitive Nucleic Acid Preparation, Deep Sequencing, and a Novel Iterative Sequence Classification Algorithm

PubMed Central

Cotten, Matthew; Oude Munnink, Bas; Canuti, Marta; Deijs, Martin; Watson, Simon J.; Kellam, Paul; van der Hoek, Lia

2014-01-01

We have developed a full genome virus detection process that combines sensitive nucleic acid preparation optimised for virus identification in fecal material with Illumina MiSeq sequencing and a novel post-sequencing virus identification algorithm. Enriched viral nucleic acid was converted to double-stranded DNA and subjected to Illumina MiSeq sequencing. The resulting short reads were processed with a novel iterative Python algorithm SLIM for the identification of sequences with homology to known viruses. De novo assembly was then used to generate full viral genomes. The sensitivity of this process was demonstrated with a set of fecal samples from HIV-1 infected patients. A quantitative assessment of the mammalian, plant, and bacterial virus content of this compartment was generated and the deep sequencing data were sufficient to assembly 12 complete viral genomes from 6 virus families. The method detected high levels of enteropathic viruses that are normally controlled in healthy adults, but may be involved in the pathogenesis of HIV-1 infection and will provide a powerful tool for virus detection and for analyzing changes in the fecal virome associated with HIV-1 progression and pathogenesis. PMID:24695106

viral abundance distribution in deep waters of the Northern of South China Sea

NASA Astrophysics Data System (ADS)

He, Lei; Yin, Kedong

2017-04-01

Little is known about the vertical distribution and interaction of viruses and bacteria in the deep ocean water column. The vertical distribution of viral-like particles and bacterial abundance was investigated in the deep water column in the South China Sea during September 2005 along with salinity, temperature and dissolved oxygen. There were double maxima in the ratio of viral to bacterial abundance (VBR) in the water column: the subsurface maximum located at 50-100 m near the pycnocline layer, and the deep maximum at 800-1000 m. At the subsurface maximum of VBR, both viral and bacterial abundance were maximal in the water column, and at the deep maximum of VBR, both viral and bacterial abundance were low, but bacterial abundance was relatively lower than viral abundance. The subsurface VBR maximum coincided with the subsurface chlorophyll maximum while the deep VBR maximum coincided with the minimum in dissolved oxygen (2.91mg L-1). Therefore, we hypothesize that the two maxima were formed by different mechanisms. The subsurface VBR maximum was formed due to an increase in bacterial abundance resulting from the stimulation of abundant organic supply at the subsurface chlorophyll maximum, whereas the deep VBR maximum was formed due to a decrease in bacterial abundance caused by more limitation of organic matter at the oxygen minimum. The evidence suggests that viruses play an important role in controlling bacterial abundance in the deep water column due to the limitation of organic matter supply. In turn, this slows down the formation of the oxygen minimum in which oxygen may be otherwise lower. The mechanism has a great implication that viruses could control bacterial decomposition of organic matter, oxygen consumption and nutrient remineralization in the deep oceans.

Protecting Your Computer from Viruses

ERIC Educational Resources Information Center

Descy, Don E.

2006-01-01

A computer virus is defined as a software program capable of reproducing itself and usually capable of causing great harm to files or other programs on the same computer. The existence of computer viruses--or the necessity of avoiding viruses--is part of using a computer. With the advent of the Internet, the door was opened wide for these…

Optimization of conditions to sequence long cDNAs from viruses

USDA-ARS?s Scientific Manuscript database

Fourth generation sequencing with the Minion nanopore sequencer provides opportunity to obtain deep coverage and long read for single molecules. This will benefit studies on RNA viruses. In the past, Sanger, Illumina, and Ion Torrent sequencing have been utilized to study RNA viruses. Both technique...

Detection and characterization of the first North American mastrevirus in Switchgrass

USDA-ARS?s Scientific Manuscript database

Virus infections have the potential to reduce biomass yields in energy crops, including Panicum virgatum (switchgrass). As a first step towards managing virus-induced biomass reductions, deep sequencing was used to identify viruses associated with mosaic symptoms in switch grass, which detected thre...

Computer Bytes, Viruses and Vaccines.

ERIC Educational Resources Information Center

Palmore, Teddy B.

1989-01-01

Presents a history of computer viruses, explains various types of viruses and how they affect software or computer operating systems, and describes examples of specific viruses. Available vaccines are explained, and precautions for protecting programs and disks are given. (nine references) (LRW)

Research on computer virus database management system

NASA Astrophysics Data System (ADS)

Qi, Guoquan

2011-12-01

The growing proliferation of computer viruses becomes the lethal threat and research focus of the security of network information. While new virus is emerging, the number of viruses is growing, virus classification increasing complex. Virus naming because of agencies' capture time differences can not be unified. Although each agency has its own virus database, the communication between each other lacks, or virus information is incomplete, or a small number of sample information. This paper introduces the current construction status of the virus database at home and abroad, analyzes how to standardize and complete description of virus characteristics, and then gives the information integrity, storage security and manageable computer virus database design scheme.

Identification and Characterization of Two Novel RNA Viruses from Anopheles gambiae Species Complex Mosquitoes

PubMed Central

Carissimo, Guillaume; Eiglmeier, Karin; Reveillaud, Julie; Holm, Inge; Diallo, Mawlouth; Diallo, Diawo; Vantaux, Amélie; Kim, Saorin; Ménard, Didier; Siv, Sovannaroth; Belda, Eugeni; Bischoff, Emmanuel; Antoniewski, Christophe; Vernick, Kenneth D.

2016-01-01

Mosquitoes of the Anopheles gambiae complex display strong preference for human bloodmeals and are major malaria vectors in Africa. However, their interaction with viruses or role in arbovirus transmission during epidemics has been little examined, with the exception of O’nyong-nyong virus, closely related to Chikungunya virus. Deep-sequencing has revealed different RNA viruses in natural insect viromes, but none have been previously described in the Anopheles gambiae species complex. Here, we describe two novel insect RNA viruses, a Dicistrovirus and a Cypovirus, found in laboratory colonies of An. gambiae taxa using small-RNA deep sequencing. Sequence analysis was done with Metavisitor, an open-source bioinformatic pipeline for virus discovery and de novo genome assembly. Wild-collected Anopheles from Senegal and Cambodia were positive for the Dicistrovirus and Cypovirus, displaying high sequence identity to the laboratory-derived virus. Thus, the Dicistrovirus (Anopheles C virus, AnCV) and Cypovirus (Anopheles Cypovirus, AnCPV) are components of the natural virome of at least some anopheline species. Their possible influence on mosquito immunity or transmission of other pathogens is unknown. These natural viruses could be developed as models for the study of Anopheles-RNA virus interactions in low security laboratory settings, in an analogous manner to the use of rodent malaria parasites for studies of mosquito anti-parasite immunity. PMID:27138938

Gating mass cytometry data by deep learning.

PubMed

Li, Huamin; Shaham, Uri; Stanton, Kelly P; Yao, Yi; Montgomery, Ruth R; Kluger, Yuval

2017-11-01

Mass cytometry or CyTOF is an emerging technology for high-dimensional multiparameter single cell analysis that overcomes many limitations of fluorescence-based flow cytometry. New methods for analyzing CyTOF data attempt to improve automation, scalability, performance and interpretation of data generated in large studies. Assigning individual cells into discrete groups of cell types (gating) involves time-consuming sequential manual steps, untenable for larger studies. We introduce DeepCyTOF, a standardization approach for gating, based on deep learning techniques. DeepCyTOF requires labeled cells from only a single sample. It is based on domain adaptation principles and is a generalization of previous work that allows us to calibrate between a target distribution and a source distribution in an unsupervised manner. We show that DeepCyTOF is highly concordant (98%) with cell classification obtained by individual manual gating of each sample when applied to a collection of 16 biological replicates of primary immune blood cells, even when measured across several instruments. Further, DeepCyTOF achieves very high accuracy on the semi-automated gating challenge of the FlowCAP-I competition as well as two CyTOF datasets generated from primary immune blood cells: (i) 14 subjects with a history of infection with West Nile virus (WNV), (ii) 34 healthy subjects of different ages. We conclude that deep learning in general, and DeepCyTOF specifically, offers a powerful computational approach for semi-automated gating of CyTOF and flow cytometry data. Our codes and data are publicly available at https://github.com/KlugerLab/deepcytof.git. yuval.kluger@yale.edu. Supplementary data are available at Bioinformatics online. © The Author 2017. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com

Deep cerebral venous thrombosis mimicking influenza-associated acute necrotizing encephalopathy: a case report.

PubMed

Taniguchi, Daisuke; Nakajima, Sho; Hayashida, Arisa; Kuroki, Takuma; Eguchi, Hiroto; Machida, Yutaka; Hattori, Nobutaka; Miwa, Hideto

2017-09-26

Acute necrotizing encephalopathy is one of the most devastating neurological complications of influenza virus infection. Acute necrotizing encephalopathy preferentially affects the thalamus bilaterally, as does deep cerebral venous thrombosis, which can lead to misdiagnosis. A 52-year-old Japanese woman infected with seasonal influenza B virus presented to the emergency care unit in our hospital with progressive alteration of her level of consciousness. Bilateral thalamic lesions were demonstrated by magnetic resonance imaging, leading to a tentative diagnosis of acute necrotizing encephalopathy. However, she had deep cerebral venous thrombosis, and the presence of diminished signal and enlargement of deep cerebral veins on T2*-weighted imaging contributed to a revised diagnosis of deep cerebral venous thrombosis. Anticoagulant therapy was initiated, leading to her gradual recovery, with recanalization of the deep venous system and straight sinus. To the best of our knowledge, these results represent the first report of deep cerebral venous thrombosis associated with influenza infection. It is clinically important to recognize that deep cerebral venous thrombosis, although rare, might be one of the neurological complications of influenza infection. In the presence of bilateral thalamic lesions in patients with influenza infection, deep cerebral venous thrombosis should be considered in addition to acute necrotizing encephalopathy. Delays in diagnosis and commencement of anticoagulant therapy can lead to unfavorable outcomes.

Email networks and the spread of computer viruses

NASA Astrophysics Data System (ADS)

Newman, M. E.; Forrest, Stephanie; Balthrop, Justin

2002-09-01

Many computer viruses spread via electronic mail, making use of computer users' email address books as a source for email addresses of new victims. These address books form a directed social network of connections between individuals over which the virus spreads. Here we investigate empirically the structure of this network using data drawn from a large computer installation, and discuss the implications of this structure for the understanding and prevention of computer virus epidemics.

METHODOLOGICAL NOTES: Computer viruses and methods of combatting them

NASA Astrophysics Data System (ADS)

Landsberg, G. L.

1991-02-01

This article examines the current virus situation for personal computers and time-sharing computers. Basic methods of combatting viruses are presented. Specific recommendations are given to eliminate the most widespread viruses. A short description is given of a universal antiviral system, PHENIX, which has been developed.

[Birth and death process of computer viruses].

PubMed

Segawa, Katsunori; Nakano, Tatsuya; Nakata, Kotoko; Hayashi, Yuzuru

2006-01-01

The daily variations in the number of computer viruses found attaching to e-mails and the number of accesses to the home page of a national institute in Japan are examined. The power spectral densities (PSD) of the variation in the computer viruses show a time-correlation characteristic of Markov process, but the daily access number does not (identified as white noise). Like biological viruses, the variation in the computer viruses can be described by the birth-and-death model known as a Markov process.

Deep Sequencing Analysis of RNAs from Citrus Plants Grown in a Citrus Sudden Death-Affected Area Reveals Diverse Known and Putative Novel Viruses.

PubMed

Matsumura, Emilyn E; Coletta-Filho, Helvecio D; Nouri, Shahideh; Falk, Bryce W; Nerva, Luca; Oliveira, Tiago S; Dorta, Silvia O; Machado, Marcos A

2017-04-24

Citrus sudden death (CSD) has caused the death of approximately four million orange trees in a very important citrus region in Brazil. Although its etiology is still not completely clear, symptoms and distribution of affected plants indicate a viral disease. In a search for viruses associated with CSD, we have performed a comparative high-throughput sequencing analysis of the transcriptome and small RNAs from CSD-symptomatic and -asymptomatic plants using the Illumina platform. The data revealed mixed infections that included Citrus tristeza virus (CTV) as the most predominant virus, followed by the Citrus sudden death-associated virus (CSDaV), Citrus endogenous pararetrovirus (CitPRV) and two putative novel viruses tentatively named Citrus jingmen-like virus (CJLV), and Citrus virga-like virus (CVLV). The deep sequencing analyses were sensitive enough to differentiate two genotypes of both viruses previously associated with CSD-affected plants: CTV and CSDaV. Our data also showed a putative association of the CSD-symptomatic plants with a specific CSDaV genotype and a likely association with CitPRV as well, whereas the two putative novel viruses showed to be more associated with CSD-asymptomatic plants. This is the first high-throughput sequencing-based study of the viral sequences present in CSD-affected citrus plants, and generated valuable information for further CSD studies.

Ecogenomics and potential biogeochemical impacts of globally abundant ocean viruses.

PubMed

Roux, Simon; Brum, Jennifer R; Dutilh, Bas E; Sunagawa, Shinichi; Duhaime, Melissa B; Loy, Alexander; Poulos, Bonnie T; Solonenko, Natalie; Lara, Elena; Poulain, Julie; Pesant, Stéphane; Kandels-Lewis, Stefanie; Dimier, Céline; Picheral, Marc; Searson, Sarah; Cruaud, Corinne; Alberti, Adriana; Duarte, Carlos M; Gasol, Josep M; Vaqué, Dolors; Bork, Peer; Acinas, Silvia G; Wincker, Patrick; Sullivan, Matthew B

2016-09-29

Ocean microbes drive biogeochemical cycling on a global scale. However, this cycling is constrained by viruses that affect community composition, metabolic activity, and evolutionary trajectories. Owing to challenges with the sampling and cultivation of viruses, genome-level viral diversity remains poorly described and grossly understudied, with less than 1% of observed surface-ocean viruses known. Here we assemble complete genomes and large genomic fragments from both surface- and deep-ocean viruses sampled during the Tara Oceans and Malaspina research expeditions, and analyse the resulting 'global ocean virome' dataset to present a global map of abundant, double-stranded DNA viruses complete with genomic and ecological contexts. A total of 15,222 epipelagic and mesopelagic viral populations were identified, comprising 867 viral clusters (defined as approximately genus-level groups). This roughly triples the number of known ocean viral populations and doubles the number of candidate bacterial and archaeal virus genera, providing a near-complete sampling of epipelagic communities at both the population and viral-cluster level. We found that 38 of the 867 viral clusters were locally or globally abundant, together accounting for nearly half of the viral populations in any global ocean virome sample. While two-thirds of these clusters represent newly described viruses lacking any cultivated representative, most could be computationally linked to dominant, ecologically relevant microbial hosts. Moreover, we identified 243 viral-encoded auxiliary metabolic genes, of which only 95 were previously known. Deeper analyses of four of these auxiliary metabolic genes (dsrC, soxYZ, P-II (also known as glnB) and amoC) revealed that abundant viruses may directly manipulate sulfur and nitrogen cycling throughout the epipelagic ocean. This viral catalog and functional analyses provide a necessary foundation for the meaningful integration of viruses into ecosystem models where they act as key players in nutrient cycling and trophic networks.

Ecogenomics and potential biogeochemical impacts of globally abundant ocean viruses

NASA Astrophysics Data System (ADS)

2016-09-01

Ocean microbes drive biogeochemical cycling on a global scale. However, this cycling is constrained by viruses that affect community composition, metabolic activity, and evolutionary trajectories. Owing to challenges with the sampling and cultivation of viruses, genome-level viral diversity remains poorly described and grossly understudied, with less than 1% of observed surface-ocean viruses known. Here we assemble complete genomes and large genomic fragments from both surface- and deep-ocean viruses sampled during the Tara Oceans and Malaspina research expeditions, and analyse the resulting ‘global ocean virome’ dataset to present a global map of abundant, double-stranded DNA viruses complete with genomic and ecological contexts. A total of 15,222 epipelagic and mesopelagic viral populations were identified, comprising 867 viral clusters (defined as approximately genus-level groups). This roughly triples the number of known ocean viral populations and doubles the number of candidate bacterial and archaeal virus genera, providing a near-complete sampling of epipelagic communities at both the population and viral-cluster level. We found that 38 of the 867 viral clusters were locally or globally abundant, together accounting for nearly half of the viral populations in any global ocean virome sample. While two-thirds of these clusters represent newly described viruses lacking any cultivated representative, most could be computationally linked to dominant, ecologically relevant microbial hosts. Moreover, we identified 243 viral-encoded auxiliary metabolic genes, of which only 95 were previously known. Deeper analyses of four of these auxiliary metabolic genes (dsrC, soxYZ, P-II (also known as glnB) and amoC) revealed that abundant viruses may directly manipulate sulfur and nitrogen cycling throughout the epipelagic ocean. This viral catalog and functional analyses provide a necessary foundation for the meaningful integration of viruses into ecosystem models where they act as key players in nutrient cycling and trophic networks.

Thermal nanostructure: An order parameter multiscale ensemble approach

NASA Astrophysics Data System (ADS)

Cheluvaraja, S.; Ortoleva, P.

2010-02-01

Deductive all-atom multiscale techniques imply that many nanosystems can be understood in terms of the slow dynamics of order parameters that coevolve with the quasiequilibrium probability density for rapidly fluctuating atomic configurations. The result of this multiscale analysis is a set of stochastic equations for the order parameters whose dynamics is driven by thermal-average forces. We present an efficient algorithm for sampling atomistic configurations in viruses and other supramillion atom nanosystems. This algorithm allows for sampling of a wide range of configurations without creating an excess of high-energy, improbable ones. It is implemented and used to calculate thermal-average forces. These forces are then used to search the free-energy landscape of a nanosystem for deep minima. The methodology is applied to thermal structures of Cowpea chlorotic mottle virus capsid. The method has wide applicability to other nanosystems whose properties are described by the CHARMM or other interatomic force field. Our implementation, denoted SIMNANOWORLD™, achieves calibration-free nanosystem modeling. Essential atomic-scale detail is preserved via a quasiequilibrium probability density while overall character is provided via predicted values of order parameters. Applications from virology to the computer-aided design of nanocapsules for delivery of therapeutic agents and of vaccines for nonenveloped viruses are envisioned.

Environmental surveillance of viruses by tangential flow filtration and metagenomic reconstruction.

PubMed

Furtak, Vyacheslav; Roivainen, Merja; Mirochnichenko, Olga; Zagorodnyaya, Tatiana; Laassri, Majid; Zaidi, Sohail Z; Rehman, Lubna; Alam, Muhammad M; Chizhikov, Vladimir; Chumakov, Konstantin

2016-04-14

An approach is proposed for environmental surveillance of poliovirus by concentrating sewage samples with tangential flow filtration (TFF) followed by deep sequencing of viral RNA. Subsequent to testing the method with samples from Finland, samples from Pakistan, a country endemic for poliovirus, were investigated. Genomic sequencing was either performed directly, for unbiased identification of viruses regardless of their ability to grow in cell cultures, or after virus enrichment by cell culture or immunoprecipitation. Bioinformatics enabled separation and determination of individual consensus sequences. Overall, deep sequencing of the entire viral population identified polioviruses, non-polio enteroviruses, and other viruses. In Pakistani sewage samples, adeno-associated virus, unable to replicate autonomously in cell cultures, was the most abundant human virus. The presence of recombinants of wild polioviruses of serotype 1 (WPV1) was also inferred, whereby currently circulating WPV1 of south-Asian (SOAS) lineage comprised two sub-lineages depending on their non-capsid region origin. Complete genome analyses additionally identified point mutants and intertypic recombinants between attenuated Sabin strains in the Pakistani samples, and in one Finnish sample. The approach could allow rapid environmental surveillance of viruses causing human infections. It creates a permanent digital repository of the entire virome potentially useful for retrospective screening of future discovered viruses.

Quantitative Viral Community DNA Analysis Reveals the Dominance of Single-Stranded DNA Viruses in Offshore Upper Bathyal Sediment from Tohoku, Japan

PubMed Central

Yoshida, Mitsuhiro; Mochizuki, Tomohiro; Urayama, Syun-Ichi; Yoshida-Takashima, Yukari; Nishi, Shinro; Hirai, Miho; Nomaki, Hidetaka; Takaki, Yoshihiro; Nunoura, Takuro; Takai, Ken

2018-01-01

Previous studies on marine environmental virology have primarily focused on double-stranded DNA (dsDNA) viruses; however, it has recently been suggested that single-stranded DNA (ssDNA) viruses are more abundant in marine ecosystems. In this study, we performed a quantitative viral community DNA analysis to estimate the relative abundance and composition of both ssDNA and dsDNA viruses in offshore upper bathyal sediment from Tohoku, Japan (water depth = 500 m). The estimated dsDNA viral abundance ranged from 3 × 106 to 5 × 106 genome copies per cm3 sediment, showing values similar to the range of fluorescence-based direct virus counts. In contrast, the estimated ssDNA viral abundance ranged from 1 × 108 to 3 × 109 genome copies per cm3 sediment, thus providing an estimation that the ssDNA viral populations represent 96.3–99.8% of the benthic total DNA viral assemblages. In the ssDNA viral metagenome, most of the identified viral sequences were associated with ssDNA viral families such as Circoviridae and Microviridae. The principle components analysis of the ssDNA viral sequence components from the sedimentary ssDNA viral metagenomic libraries found that the different depth viral communities at the study site all exhibited similar profiles compared with deep-sea sediment ones at other reference sites. Our results suggested that deep-sea benthic ssDNA viruses have been significantly underestimated by conventional direct virus counts and that their contributions to deep-sea benthic microbial mortality and geochemical cycles should be further addressed by such a new quantitative approach. PMID:29467725

The Computer Virus Threat and What You Can Do about It.

ERIC Educational Resources Information Center

Lateulere, John

1992-01-01

Discussion of computer viruses describes two types of viruses and how they work; suggests ways to prevent or minimize virus risk; and explains how to recognize a virus and limit damage once a virus attacks. A sidebar lists several antivirus software products. (two references) (NRP)

Computer Virus Protection

ERIC Educational Resources Information Center

Rajala, Judith B.

2004-01-01

A computer virus is a program--a piece of executable code--that has the unique ability to replicate. Like biological viruses, computer viruses can spread quickly and are often difficult to eradicate. They can attach themselves to just about any type of file, and are spread by replicating and being sent from one individual to another. Simply having…

Deep Sequencing to Identify the Causes of Viral Encephalitis

PubMed Central

Chan, Benjamin K.; Wilson, Theodore; Fischer, Kael F.; Kriesel, John D.

2014-01-01

Deep sequencing allows for a rapid, accurate characterization of microbial DNA and RNA sequences in many types of samples. Deep sequencing (also called next generation sequencing or NGS) is being developed to assist with the diagnosis of a wide variety of infectious diseases. In this study, seven frozen brain samples from deceased subjects with recent encephalitis were investigated. RNA from each sample was extracted, randomly reverse transcribed and sequenced. The sequence analysis was performed in a blinded fashion and confirmed with pathogen-specific PCR. This analysis successfully identified measles virus sequences in two brain samples and herpes simplex virus type-1 sequences in three brain samples. No pathogen was identified in the other two brain specimens. These results were concordant with pathogen-specific PCR and partially concordant with prior neuropathological examinations, demonstrating that deep sequencing can accurately identify viral infections in frozen brain tissue. PMID:24699691

Survey of potato viruses and viroids in Heilongjiang province of China by sRNA deep sequencing and VirusDetect

USDA-ARS?s Scientific Manuscript database

Heilongjiang province is one of the most important potato production areas in China. Frequent outbreaks of virus and viroid diseases in production fields have significantly decreased potato yield and quality. However, we still do not have a clear understanding on the composition and genetic diversit...

Complete genome sequence of southern tomato virus identified from China using next generation sequencing

USDA-ARS?s Scientific Manuscript database

Complete genome sequence of a double-stranded RNA (dsRNA) virus, southern tomato virus (STV), on tomatoes in China, was elucidated using small RNAs deep sequencing. The identified STV_CN12 shares 99% sequence identity to other isolates from Mexico, France, Spain, and U.S. This is the first report ...

Computer Virus Bibliography, 1988-1989.

ERIC Educational Resources Information Center

Bologna, Jack, Comp.

This bibliography lists 14 books, 154 journal articles, 34 newspaper articles, and 3 research papers published during 1988-1989 on the subject of computer viruses, software protection and 'cures', virus hackers, and other related issues. Some of the sources listed include Computers and Security, Computer Security Digest, PC Week, Time, the New…

ViDiT-CACTUS: an inexpensive and versatile library preparation and sequence analysis method for virus discovery and other microbiology applications.

PubMed

Verhoeven, Joost Theo Petra; Canuti, Marta; Munro, Hannah J; Dufour, Suzanne C; Lang, Andrew S

2018-04-19

High-throughput sequencing (HTS) technologies are becoming increasingly important within microbiology research, but aspects of library preparation, such as high cost per sample or strict input requirements, make HTS difficult to implement in some niche applications and for research groups on a budget. To answer these necessities, we developed ViDiT, a customizable, PCR-based, extremely low-cost (<5 US dollars per sample) and versatile library preparation method, and CACTUS, an analysis pipeline designed to rely on cloud computing power to generate high-quality data from ViDiT-based experiments without the need of expensive servers. We demonstrate here the versatility and utility of these methods within three fields of microbiology: virus discovery, amplicon-based viral genome sequencing and microbiome profiling. ViDiT-CACTUS allowed the identification of viral fragments from 25 different viral families from 36 oropharyngeal-cloacal swabs collected from wild birds, the sequencing of three almost complete genomes of avian influenza A viruses (>90% coverage), and the characterization and functional profiling of the complete microbial diversity (bacteria, archaea, viruses) within a deep-sea carnivorous sponge. ViDiT-CACTUS demonstrated its validity in a wide range of microbiology applications and its simplicity and modularity make it easily implementable in any molecular biology laboratory, towards various research goals.

Coping with Computer Viruses: General Discussion and Review of Symantec Anti-Virus for the Macintosh.

ERIC Educational Resources Information Center

Primich, Tracy

1992-01-01

Discusses computer viruses that attack the Macintosh and describes Symantec AntiVirus for Macintosh (SAM), a commercial program designed to detect and eliminate viruses; sample screen displays are included. SAM is recommended for use in library settings as well as two public domain virus protection programs. (four references) (MES)

Time-Sampled Population Sequencing Reveals the Interplay of Selection and Genetic Drift in Experimental Evolution of Potato Virus Y

PubMed Central

2017-01-01

ABSTRACT RNA viruses are one of the fastest-evolving biological entities. Within their hosts, they exist as genetically diverse populations (i.e., viral mutant swarms), which are sculpted by different evolutionary mechanisms, such as mutation, natural selection, and genetic drift, and also the interactions between genetic variants within the mutant swarms. To elucidate the mechanisms that modulate the population diversity of an important plant-pathogenic virus, we performed evolution experiments with Potato virus Y (PVY) in potato genotypes that differ in their defense response against the virus. Using deep sequencing of small RNAs, we followed the temporal dynamics of standing and newly generated variations in the evolving viral lineages. A time-sampled approach allowed us to (i) reconstruct theoretical haplotypes in the starting population by using clustering of single nucleotide polymorphisms' trajectories and (ii) use quantitative population genetics approaches to estimate the contribution of selection and genetic drift, and their interplay, to the evolution of the virus. We detected imprints of strong selective sweeps and narrow genetic bottlenecks, followed by the shift in frequency of selected haplotypes. Comparison of patterns of viral evolution in differently susceptible host genotypes indicated possible diversifying evolution of PVY in the less-susceptible host (efficient in the accumulation of salicylic acid). IMPORTANCE High diversity of within-host populations of RNA viruses is an important aspect of their biology, since they represent a reservoir of genetic variants, which can enable quick adaptation of viruses to a changing environment. This study focuses on an important plant virus, Potato virus Y, and describes, at high resolution, temporal changes in the structure of viral populations within different potato genotypes. A novel and easy-to-implement computational approach was established to cluster single nucleotide polymorphisms into viral haplotypes from very short sequencing reads. During the experiment, a shift in the frequency of selected viral haplotypes was observed after a narrow genetic bottleneck, indicating an important role of the genetic drift in the evolution of the virus. On the other hand, a possible case of diversifying selection of the virus was observed in less susceptible host genotypes. PMID:28592544

Temporal Analysis of the Honey Bee Microbiome Reveals Four Novel Viruses and Seasonal Prevalence of Known Viruses, Nosema, and Crithidia

PubMed Central

Engel, Juan C.; Ruby, J. Graham; Ganem, Donald; Andino, Raul; DeRisi, Joseph L.

2011-01-01

Honey bees (Apis mellifera) play a critical role in global food production as pollinators of numerous crops. Recently, honey bee populations in the United States, Canada, and Europe have suffered an unexplained increase in annual losses due to a phenomenon known as Colony Collapse Disorder (CCD). Epidemiological analysis of CCD is confounded by a relative dearth of bee pathogen field studies. To identify what constitutes an abnormal pathophysiological condition in a honey bee colony, it is critical to have characterized the spectrum of exogenous infectious agents in healthy hives over time. We conducted a prospective study of a large scale migratory bee keeping operation using high-frequency sampling paired with comprehensive molecular detection methods, including a custom microarray, qPCR, and ultra deep sequencing. We established seasonal incidence and abundance of known viruses, Nosema sp., Crithidia mellificae, and bacteria. Ultra deep sequence analysis further identified four novel RNA viruses, two of which were the most abundant observed components of the honey bee microbiome (∼1011 viruses per honey bee). Our results demonstrate episodic viral incidence and distinct pathogen patterns between summer and winter time-points. Peak infection of common honey bee viruses and Nosema occurred in the summer, whereas levels of the trypanosomatid Crithidia mellificae and Lake Sinai virus 2, a novel virus, peaked in January. PMID:21687739

Human enteric viruses in groundwater from a confined bedrock aquifer

USGS Publications Warehouse

Borchardt, M. A.; Bradbury, K.R.; Gotkowitz, M.B.; Cherry, J.A.; Parker, B.L.

2007-01-01

Confined aquifers are overlain by low-permeability aquitards that are commonly assumed to protect underlying aquifers from microbial contaminants. However, empirical data on microbial contamination beneath aquitards is limited. This study determined the occurrence of human pathogenic viruses in well water from a deep sandstone aquifer confined by a regionally extensive shale aquitard. Three public water-supply wells were each sampled 10 times over 15 months. Samples were analyzed by reverse transcription-polymerase chain reaction (RT-PCR) for several virus groups and by cell culture for infectious enteroviruses. Seven of 30 samples were positive by RT-PCR for enteroviruses; one of these was positive for infectious echovirus 18. The virus-positive samples were collected from two wells cased through the aquitard, indicating the viruses were present in the confined aquifer. Samples from the same wells showed atmospheric tritium, indicating water recharged within the past few decades. Hydrogeologic conditions support rapid porous media transport of viruses through the upper sandstone aquifer to the top of the aquitard 61 m below ground surface. Natural fractures in the shale aquitard are one possible virus transport pathway through the aquitard; however, windows, cross-connecting well bores, or imperfect grout seals along well casings also may be involved. Deep confined aquifers can be more vulnerable to contamination by human viruses than commonly believed. ?? 2007 American Chemical Society.

Temporal analysis of the honey bee microbiome reveals four novel viruses and seasonal prevalence of known viruses, Nosema, and Crithidia.

PubMed

Runckel, Charles; Flenniken, Michelle L; Engel, Juan C; Ruby, J Graham; Ganem, Donald; Andino, Raul; DeRisi, Joseph L

2011-01-01

Honey bees (Apis mellifera) play a critical role in global food production as pollinators of numerous crops. Recently, honey bee populations in the United States, Canada, and Europe have suffered an unexplained increase in annual losses due to a phenomenon known as Colony Collapse Disorder (CCD). Epidemiological analysis of CCD is confounded by a relative dearth of bee pathogen field studies. To identify what constitutes an abnormal pathophysiological condition in a honey bee colony, it is critical to have characterized the spectrum of exogenous infectious agents in healthy hives over time. We conducted a prospective study of a large scale migratory bee keeping operation using high-frequency sampling paired with comprehensive molecular detection methods, including a custom microarray, qPCR, and ultra deep sequencing. We established seasonal incidence and abundance of known viruses, Nosema sp., Crithidia mellificae, and bacteria. Ultra deep sequence analysis further identified four novel RNA viruses, two of which were the most abundant observed components of the honey bee microbiome (∼10(11) viruses per honey bee). Our results demonstrate episodic viral incidence and distinct pathogen patterns between summer and winter time-points. Peak infection of common honey bee viruses and Nosema occurred in the summer, whereas levels of the trypanosomatid Crithidia mellificae and Lake Sinai virus 2, a novel virus, peaked in January.

Cyberethics: Identifying the Moral, Legal and Social Issues of Cybertechnology in K-12 Classrooms

ERIC Educational Resources Information Center

Brown, David S.; Wang, Tao

2008-01-01

Two computer viruses that have caused hundreds of millions of dollars in damage over the past four years are the Melissa and the Sasser virus. In March of 1999, the Melissa virus first appeared on the Internet and spread rapidly throughout computer systems in the United States and Europe. The virus made its way through 1.2 million computers in the…

Deep Learning for Computer Vision: A Brief Review

PubMed Central

Doulamis, Nikolaos; Doulamis, Anastasios; Protopapadakis, Eftychios

2018-01-01

Over the last years deep learning methods have been shown to outperform previous state-of-the-art machine learning techniques in several fields, with computer vision being one of the most prominent cases. This review paper provides a brief overview of some of the most significant deep learning schemes used in computer vision problems, that is, Convolutional Neural Networks, Deep Boltzmann Machines and Deep Belief Networks, and Stacked Denoising Autoencoders. A brief account of their history, structure, advantages, and limitations is given, followed by a description of their applications in various computer vision tasks, such as object detection, face recognition, action and activity recognition, and human pose estimation. Finally, a brief overview is given of future directions in designing deep learning schemes for computer vision problems and the challenges involved therein. PMID:29487619

Computer Viruses: An Epidemic Real or Imagined?

ERIC Educational Resources Information Center

Chaffin, Ellen

1989-01-01

Discusses the current problems associated with computer viruses that affect system and software users and the frequency with which they occur. System security is discussed, steps to take to safeguard against damage by computer viruses are suggested, and anti-viral programs, or vaccines, are described. (LRW)

Small-RNA Deep Sequencing Reveals Arctium tomentosum as a Natural Host of Alstroemeria virus X and a New Putative Emaravirus

PubMed Central

Bi, Yaqi; Tugume, Arthur K.; Valkonen, Jari P. T.

2012-01-01

Background Arctium species (Asteraceae) are distributed worldwide and are used as food and rich sources of secondary metabolites for the pharmaceutical industry, e.g., against avian influenza virus. RNA silencing is an antiviral defense mechanism that detects and destroys virus-derived double-stranded RNA, resulting in accumulation of virus-derived small RNAs (21–24 nucleotides) that can be used for generic detection of viruses by small-RNA deep sequencing (SRDS). Methodology/Principal Findings SRDS was used to detect viruses in the biennial wild plant species Arctium tomentosum (woolly burdock; family Asteraceae) displaying virus-like symptoms of vein yellowing and leaf mosaic in southern Finland. Assembly of the small-RNA reads resulted in contigs homologous to Alstroemeria virus X (AlsVX), a positive/single-stranded RNA virus of genus Potexvirus (family Alphaflexiviridae), or related to negative/single-stranded RNA viruses of the genus Emaravirus. The coat protein gene of AlsVX was 81% and 89% identical to the two AlsVX isolates from Japan and Norway, respectively. The deduced, partial nucleocapsid protein amino acid sequence of the emara-like virus was only 78% or less identical to reported emaraviruses and showed no variability among the virus isolates characterized. This virus—tentatively named as Woolly burdock yellow vein virus—was exclusively associated with yellow vein and leaf mosaic symptoms in woolly burdock, whereas AlsVX was detected in only one of the 52 plants tested. Conclusions/Significance These results provide novel information about natural virus infections in Acrtium species and reveal woolly burdock as the first natural host of AlsVX besides Alstroemeria (family Alstroemeriaceae). Results also revealed a new virus related to the recently emerged Emaravirus genus and demonstrated applicability of SRDS to detect negative-strand RNA viruses. SRDS potentiates virus surveys of wild plants, a research area underrepresented in plant virology, and helps reveal natural reservoirs of viruses that cause yield losses in cultivated plants. PMID:22912734

Enumeration of viruses and prokaryotes in deep-sea sediments and cold seeps of the Gulf of Mexico

USGS Publications Warehouse

Kellogg, Christina A.

2010-01-01

Little is known about the distribution and abundance of viruses in deep-sea cold-seep environments. Like hydrothermal vents, seeps support communities of macrofauna that are sustained by chemosynthetic bacteria. Sediments close to these communities are hypothesized to be more microbiologically active and therefore to host higher numbers of viruses than non-seep areas. Push cores were taken at five types of Gulf of Mexico habitats at water depths below 1000 m using a remotely operated vehicle (ROV). The habitats included non-seep reference sediment, brine seeps, a microbial mat, an urchin field, and a pogonophoran worm community. Samples were processed immediately for enumeration of viruses and prokaryotes without the addition of a preservative. Prokaryote counts were an order of magnitude lower in sediments directly in contact with macrofauna (urchins, pogonophorans) compared to all other samples (107 vs. 108 cells g-1 dry weight) and were highest in areas of elevated salinity (brine seeps). Viral-Like Particle (VLP) counts were lowest in the reference sediments and pogonophoran cores (108 VLP g-1 dry wt), higher in brine seeps (109 VLP g-1 dry wt), and highest in the microbial mats (1010 VLP g-1 dry wt). Virus-prokaryote ratios (VPR) ranged from <5 in the reference sediment to >30 in the microbial mats and >60 in the urchin field. VLP counts and VPR were all significantly greater than those reported from sediments in the deep Mediterranean Sea and in most cases were higher than recent data from a cold-seep site near Japan. The high VPR suggest that greater microbial activity in or near cold-seep environments results in greater viral production and therefore higher numbers of viruses.

Enumeration of viruses and prokaryotes in deep-sea sediments and cold seeps of the Gulf of Mexico

NASA Astrophysics Data System (ADS)

Kellogg, Christina A.

2010-11-01

Little is known about the distribution and abundance of viruses in deep-sea cold-seep environments. Like hydrothermal vents, seeps support communities of macrofauna that are sustained by chemosynthetic bacteria. Sediments close to these communities are hypothesized to be more microbiologically active and therefore to host higher numbers of viruses than non-seep areas. Push cores were taken at five types of Gulf of Mexico habitats at water depths below 1000 m using a remotely operated vehicle (ROV). The habitats included non-seep reference sediment, brine seeps, a microbial mat, an urchin field, and a pogonophoran worm community. Samples were processed immediately for enumeration of viruses and prokaryotes without the addition of a preservative. Prokaryote counts were an order of magnitude lower in sediments directly in contact with macrofauna (urchins, pogonophorans) compared to all other samples (107 vs. 108 cells g-1 dry weight) and were highest in areas of elevated salinity (brine seeps). Viral-Like Particle (VLP) counts were lowest in the reference sediments and pogonophoran cores (108 VLP g-1 dry wt), higher in brine seeps (109 VLP g-1 dry wt), and highest in the microbial mats (1010 VLP g-1 dry wt). Virus-prokaryote ratios (VPR) ranged from <5 in the reference sediment to >30 in the microbial mats and >60 in the urchin field. VLP counts and VPR were all significantly greater than those reported from sediments in the deep Mediterranean Sea and in most cases were higher than recent data from a cold-seep site near Japan. The high VPR suggest that greater microbial activity in or near cold-seep environments results in greater viral production and therefore higher numbers of viruses.

Archaeal Viruses Contribute to the Novel Viral Assemblage Inhabiting Oceanic, Basalt-Hosted Deep Subsurface Crustal Fluids

NASA Astrophysics Data System (ADS)

Nigro, O. D.; Rappe, M. S.; Jungbluth, S.; Lin, H. T.; Steward, G.

2015-12-01

Fluids contained in the basalt-hosted deep subsurface of the world's oceans represent one of the most inaccessible and understudied biospheres on earth. Recent improvements in sampling infrastructure have allowed us to collect large volumes of crustal fluids (~104 L) from Circulation Obviation Retrofit Kits (CORKs) placed in boreholes located on the eastern flank of the Juan de Fuca Ridge (JdFR). We detected viruses within these fluids by TEM and epifluorescence microscopy in samples collected from 2010 to 2014. Viral abundance, determined by epifluorescence counts, indicated that concentrations of viruses in subsurface basement fluids (~105 ml-1) are lower than the overlying seawater, but are higher in abundance than microbial cells in the same samples. Analysis of TEM images revealed distinct viral morphologies (rod and spindle-shaped) that resemble the morphologies of viral families infecting archaea. There are very few, if any, direct observations of these viral morphologies in marine samples, although they have been observed in enrichment cultures and their signature genes detected in metagenomic studies from hydrothermal vents and marine sediments. Analysis of metagenomes from the JdFR crustal fluids revealed sequences with homology to archaeal viruses from the rudiviridae, bicaudaviridae and fuselloviridae. Prokaryotic communities in fluids percolating through the basaltic basement rock of the JdFR flank are distinct from those inhabiting the overlying sediments and seawater. Similarly, our data support the idea that the viral assemblage in these fluids is distinct from viral assemblages in other marine and terrestrial aquatic environments. Our data also suggest that viruses contribute to the mortality of deep subsurface prokaryotes through cell lysis, and viruses may alter the genetic potential of their hosts through the processes of lysogenic conversion and horizontal gene transfer.

Use of sequence-independent-single-primer-amplification (SISPA) for whole genome sequencing using illumina MiSeq platform for avian influenza virus, Newcastle disease virus, and infectious bronchitis virus

USDA-ARS?s Scientific Manuscript database

Over the past decade, Next Generation Sequencing (NGS) technologies, also called deep sequencing, have continued to evolve, increasing capacity and lower the cost necessary for large genome sequencing projects. The one of the advantage of NGS platforms is the possibility to sequence the samples with...

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

PubMed

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.

Virus Alert: Ten Steps to Safe Computing.

ERIC Educational Resources Information Center

Gunter, Glenda A.

1997-01-01

Discusses computer viruses and explains how to detect them; discusses virus protection and the need to update antivirus software; and offers 10 safe computing tips, including scanning floppy disks and commercial software, how to safely download files from the Internet, avoiding pirated software copies, and backing up files. (LRW)

An Efficient Strategy of Screening for Pathogens in Wild-Caught Ticks and Mosquitoes by Reusing Small RNA Deep Sequencing Data

PubMed Central

An, Xiaoping; Fan, Hang; Ma, Maijuan; Anderson, Benjamin D.; Jiang, Jiafu; Liu, Wei; Cao, Wuchun; Tong, Yigang

2014-01-01

This paper explored our hypothesis that sRNA (18∼30 bp) deep sequencing technique can be used as an efficient strategy to identify microorganisms other than viruses, such as prokaryotic and eukaryotic pathogens. In the study, the clean reads derived from the sRNA deep sequencing data of wild-caught ticks and mosquitoes were compared against the NCBI nucleotide collection (non-redundant nt database) using Blastn. The blast results were then analyzed with in-house Python scripts. An empirical formula was proposed to identify the putative pathogens. Results showed that not only viruses but also prokaryotic and eukaryotic species of interest can be screened out and were subsequently confirmed with experiments. Specially, a novel Rickettsia spp. was indicated to exist in Haemaphysalis longicornis ticks collected in Beijing. Our study demonstrated the reuse of sRNA deep sequencing data would have the potential to trace the origin of pathogens or discover novel agents of emerging/re-emerging infectious diseases. PMID:24618575

The impact of countermeasure propagation on the prevalence of computer viruses.

PubMed

Chen, Li-Chiou; Carley, Kathleen M

2004-04-01

Countermeasures such as software patches or warnings can be effective in helping organizations avert virus infection problems. However, current strategies for disseminating such countermeasures have limited their effectiveness. We propose a new approach, called the Countermeasure Competing (CMC) strategy, and use computer simulation to formally compare its relative effectiveness with three antivirus strategies currently under consideration. CMC is based on the idea that computer viruses and countermeasures spread through two separate but interlinked complex networks-the virus-spreading network and the countermeasure-propagation network, in which a countermeasure acts as a competing species against the computer virus. Our results show that CMC is more effective than other strategies based on the empirical virus data. The proposed CMC reduces the size of virus infection significantly when the countermeasure-propagation network has properties that favor countermeasures over viruses, or when the countermeasure-propagation rate is higher than the virus-spreading rate. In addition, our work reveals that CMC can be flexibly adapted to different uncertainties in the real world, enabling it to be tuned to a greater variety of situations than other strategies.

Identification and characterization of Citrus tristeza virus isolates breaking resistance in trifoliate orange in California

USDA-ARS?s Scientific Manuscript database

Most Citrus tristeza virus (CTV) isolates in California are biologically mild and symptomless in commercial cultivars on CTV tolerant rootstocks. However, to better define California CTV isolates showing divergent serological and genetic profiles, selected isolates were subjected to deep sequencing ...

Deep learning for computational chemistry

DOE Office of Scientific and Technical Information (OSTI.GOV)

Goh, Garrett B.; Hodas, Nathan O.; Vishnu, Abhinav

The rise and fall of artificial neural networks is well documented in the scientific literature of both the fields of computer science and computational chemistry. Yet almost two decades later, we are now seeing a resurgence of interest in deep learning, a machine learning algorithm based on “deep” neural networks. Within the last few years, we have seen the transformative impact of deep learning the computer science domain, notably in speech recognition and computer vision, to the extent that the majority of practitioners in those field are now regularly eschewing prior established models in favor of deep learning models. Inmore » this review, we provide an introductory overview into the theory of deep neural networks and their unique properties as compared to traditional machine learning algorithms used in cheminformatics. By providing an overview of the variety of emerging applications of deep neural networks, we highlight its ubiquity and broad applicability to a wide range of challenges in the field, including QSAR, virtual screening, protein structure modeling, QM calculations, materials synthesis and property prediction. In reviewing the performance of deep neural networks, we observed a consistent outperformance against non neural networks state-of-the-art models across disparate research topics, and deep neural network based models often exceeded the “glass ceiling” expectations of their respective tasks. Coupled with the maturity of GPU-accelerated computing for training deep neural networks and the exponential growth of chemical data on which to train these networks on, we anticipate that deep learning algorithms will be a useful tool and may grow into a pivotal role for various challenges in the computational chemistry field.« less

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

PubMed Central

Villacreses, Javier; Rojas-Herrera, Marcelo; Sánchez, Carolina; Hewstone, Nicole; Undurraga, Soledad F.; Alzate, Juan F.; Manque, Patricio; Maracaja-Coutinho, Vinicius; Polanco, Victor

2015-01-01

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. PMID:25855242

Discovery of a new genotype of Squash mosaic virus through deep sequencing of small RNAs and development of a qRT-PCR for broad spectrum detection

USDA-ARS?s Scientific Manuscript database

Squash mosaic virus (SqMV), a seed-borne virus belonging to the genus Commovirus in the family Comoviridae, could cause a serious yield loss on cucurbit crops worldwide. SqMV has a bipartite single-stranded ribonucleic acid (RNA) genome (RNA-1 and RNA-2) encapsidated separately with two capsid prote...

Scalable metagenomic taxonomy classification using a reference genome database

PubMed Central

Ames, Sasha K.; Hysom, David A.; Gardner, Shea N.; Lloyd, G. Scott; Gokhale, Maya B.; Allen, Jonathan E.

2013-01-01

Motivation: Deep metagenomic sequencing of biological samples has the potential to recover otherwise difficult-to-detect microorganisms and accurately characterize biological samples with limited prior knowledge of sample contents. Existing metagenomic taxonomic classification algorithms, however, do not scale well to analyze large metagenomic datasets, and balancing classification accuracy with computational efficiency presents a fundamental challenge. Results: A method is presented to shift computational costs to an off-line computation by creating a taxonomy/genome index that supports scalable metagenomic classification. Scalable performance is demonstrated on real and simulated data to show accurate classification in the presence of novel organisms on samples that include viruses, prokaryotes, fungi and protists. Taxonomic classification of the previously published 150 giga-base Tyrolean Iceman dataset was found to take <20 h on a single node 40 core large memory machine and provide new insights on the metagenomic contents of the sample. Availability: Software was implemented in C++ and is freely available at http://sourceforge.net/projects/lmat Contact: allen99@llnl.gov Supplementary information: Supplementary data are available at Bioinformatics online. PMID:23828782

Viral discovery and diversity in trypanosomatid protozoa with a focus on relatives of the human parasite Leishmania.

PubMed

Grybchuk, Danyil; Akopyants, Natalia S; Kostygov, Alexei Y; Konovalovas, Aleksandras; Lye, Lon-Fye; Dobson, Deborah E; Zangger, Haroun; Fasel, Nicolas; Butenko, Anzhelika; Frolov, Alexander O; Votýpka, Jan; d'Avila-Levy, Claudia M; Kulich, Pavel; Moravcová, Jana; Plevka, Pavel; Rogozin, Igor B; Serva, Saulius; Lukeš, Julius; Beverley, Stephen M; Yurchenko, Vyacheslav

2018-01-16

Knowledge of viral diversity is expanding greatly, but many lineages remain underexplored. We surveyed RNA viruses in 52 cultured monoxenous relatives of the human parasite Leishmania ( Crithidia and Leptomonas ), as well as plant-infecting Phytomonas Leptomonas pyrrhocoris was a hotbed for viral discovery, carrying a virus (Leptomonas pyrrhocoris ostravirus 1) with a highly divergent RNA-dependent RNA polymerase missed by conventional BLAST searches, an emergent clade of tombus-like viruses, and an example of viral endogenization. A deep-branching clade of trypanosomatid narnaviruses was found, notable as Leptomonas seymouri bearing Narna-like virus 1 (LepseyNLV1) have been reported in cultures recovered from patients with visceral leishmaniasis. A deep-branching trypanosomatid viral lineage showing strong affinities to bunyaviruses was termed " Leishbunyavirus " (LBV) and judged sufficiently distinct to warrant assignment within a proposed family termed " Leishbunyaviridae " Numerous relatives of trypanosomatid viruses were found in insect metatranscriptomic surveys, which likely arise from trypanosomatid microbiota. Despite extensive sampling we found no relatives of the totivirus Leishmaniavirus (LRV1/2), implying that it was acquired at about the same time the Leishmania became able to parasitize vertebrates. As viruses were found in over a quarter of isolates tested, many more are likely to be found in the >600 unsurveyed trypanosomatid species. Viral loss was occasionally observed in culture, providing potentially isogenic virus-free lines enabling studies probing the biological role of trypanosomatid viruses. These data shed important insights on the emergence of viruses within an important trypanosomatid clade relevant to human disease.

Overreaction to External Attacks on Computer Systems Could Be More Harmful than the Viruses Themselves.

ERIC Educational Resources Information Center

King, Kenneth M.

1988-01-01

Discussion of the recent computer virus attacks on computers with vulnerable operating systems focuses on the values of educational computer networks. The need for computer security procedures is emphasized, and the ethical use of computer hardware and software is discussed. (LRW)

Viral infections as controlling factors for the deep biosphere? (Invited)

NASA Astrophysics Data System (ADS)

Engelen, B.; Engelhardt, T.; Sahlberg, M.; Cypionka, H.

2009-12-01

The marine deep biosphere represents the largest biotope on Earth. Throughout the last years, we have obtained interesting insights into its microbial community composition. However, one component that was completely overlooked so far is the viral inventory of deep-subsurface sediments. While viral infections were identified to have a major impact on the benthic microflora of deep-sea surface sediments (Danavaro et al. 2008), no studies were performed on deep-biosphere samples, so far. As grazers probably play only a minor role in anoxic and highly compressed deep sediments, viruses might be the main “predators” for indigenous microorganisms. Furthermore, the release of cell components, called “the viral shunt”, could have a major impact on the deep biosphere in providing labile organic compounds to non-infected microorganisms in these generally nutrient depleted sediments. However, direct counting of viruses in sediments is highly challenging due to the small size of viruses and the high background of small particles. Even molecular surveys using “universal” PCR primers that target phage-specific genes fail due to the vast phage diversity. One solution for this problem is the lysogenic viral life cycle as many bacteriophages integrate their DNA into the host genome. It is estimated that up to 70% of cultivated bacteria contain prophages within their genome. Therefore, culture collections (Batzke et al. 2007) represent an archive of the viral composition within the respective habitat. These prophages can be induced to become free phage particles in stimulation experiments in which the host cells are set under certain stress situations such as a treatment with UV exposure or DNA-damaging antibiotics. The study of the viral component within the deep biosphere offers to answer the following questions: To which extent are deep-biosphere populations controlled by viral infections? What is the inter- and intra-specific diversity and the host-specific viral biogeography? Can viral infections tell us something about the physiological state of indigenous microorganisms? Finally, we will obtain estimates for the viral shunt as an important factor for sustaining the deep biosphere. References: Batzke A, Engelen B, Sass H, Cypionka H (2007) Phylogenetic and physiological diversity of cultured deep-biosphere bacteria from Equatorial Pacific Ocean and Peru Margin sediments. Geomicrobiology J 24:261-273 Danovaro R, Dell'Anno A, Corinaldesi C, Magagnini M, Noble R, Tamburini C, Weinbauer M (2008) Major viral impact on the functioning of benthic deep-sea ecosystems. Nature 454: 1084-U1027.

A prospective study of primary Epstein-Barr virus infections among university students in Hong Kong.

PubMed

Dan, R; Chang, R S

1990-04-01

In 1988 at the Chinese University of Hong Kong 1,039 entering freshmen were screened for antibody against the Epstein-Barr virus and 71 (6.8%) were negative. One year later, 16 (25.8%) of 62 negatives converted to positive with asymptomatic infections. There was seroconversion in 14 subjects who did not give histories of deep kissing. There was an association between antibody status of Epstein-Barr and cytomegaloviruses, but not between Epstein-Barr virus and human herpes virus type 6.

Layered virus protection for the operations and administrative messaging system

NASA Technical Reports Server (NTRS)

Cortez, R. H.

2002-01-01

NASA's Deep Space Network (DSN) is critical in supporting the wide variety of operating and plannedunmanned flight projects. For day-to-day operations it relies on email communication between the three Deep Space Communication Complexes (Canberra, Goldstone, Madrid) and NASA's Jet Propulsion Laboratory. The Operations & Administrative Messaging system, based on the Microsoft Windows NTand Exchange platform, provides the infrastructure that is required for reliable, mission-critical messaging. The reliability of this system, however, is threatened by the proliferation of email viruses that continue to spread at alarming rates. A layered approach to email security has been implemented across the DSN to protect against this threat.

A novel pathogenic aviadenovirus from red-bellied parrots (Poicephalus rufiventris) unveils deep recombination events among avian host lineages

DOE Office of Scientific and Technical Information (OSTI.GOV)

Das, Shubhagata, E-mail: sdas@csu.edu.au

Competing roles of coevolution, selective pressure and recombination are an emerging interest in virus evolution. We report a novel aviadenovirus from captive red-bellied parrots (Poicephalus rufiventris) that uncovers evidence of deep recombination among aviadenoviruses. The sequence identity of the virus was most closely related to Turkey adenovirus D (42% similarity) and other adenoviruses in chickens, turkeys and pigeons. Sequencing and comparative analysis showed that the genome comprised 40,930 nucleotides containing 42 predicted open reading frames (ORFs) 19 of which had strong similarity with genes from other adenovirus species. The new genome unveiled a lineage that likely participated in deep recombinationmore » events across the genus Aviadenovirus accounting for an ancient evolutionary relationship. We hypothesize frequent host switch events and recombination among adenovirus progenitors in Galloanserae hosts caused the radiation of extant aviadenoviruses and the newly assembled Poicephalus adenovirus genome points to a potentially broader host range of these viruses among birds. - Highlights: •Shows how a single new genome can change overall phylogeny. •Reveals host switch events among adenovirus progenitors in Galloanserae hosts. •Points to a potentially broader host range of adenoviruses among birds and wildlife .« less

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

PubMed

Sachsenröder, Jana; Twardziok, Sven; Hammerl, Jens A; Janczyk, Pawel; Wrede, Paul; Hertwig, Stefan; Johne, Reimar

2012-01-01

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. 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. 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 comparability of the method and may be used for further method optimization.

DANoC: An Efficient Algorithm and Hardware Codesign of Deep Neural Networks on Chip.

PubMed

Zhou, Xichuan; Li, Shengli; Tang, Fang; Hu, Shengdong; Lin, Zhi; Zhang, Lei

2017-07-18

Deep neural networks (NNs) are the state-of-the-art models for understanding the content of images and videos. However, implementing deep NNs in embedded systems is a challenging task, e.g., a typical deep belief network could exhaust gigabytes of memory and result in bandwidth and computational bottlenecks. To address this challenge, this paper presents an algorithm and hardware codesign for efficient deep neural computation. A hardware-oriented deep learning algorithm, named the deep adaptive network, is proposed to explore the sparsity of neural connections. By adaptively removing the majority of neural connections and robustly representing the reserved connections using binary integers, the proposed algorithm could save up to 99.9% memory utility and computational resources without undermining classification accuracy. An efficient sparse-mapping-memory-based hardware architecture is proposed to fully take advantage of the algorithmic optimization. Different from traditional Von Neumann architecture, the deep-adaptive network on chip (DANoC) brings communication and computation in close proximity to avoid power-hungry parameter transfers between on-board memory and on-chip computational units. Experiments over different image classification benchmarks show that the DANoC system achieves competitively high accuracy and efficiency comparing with the state-of-the-art approaches.

[Surgical Treatment of Right Atrial Malignant Tumor Thrombus Under Deep Hypothermia with Intermittent Circulatory Arrest;Report of Two Cases].

PubMed

Fujita, Akira; Kobayashi, Toshiro; Hironaka, Hideharu; Jinbou, Mitsutaka; Uesugi, Naomasa; Saito, Satoshi; Takahashi, Tsuyoshi; Gohra, Hidenori

2016-12-01

Right atrial tumor thrombus is rare in patients with visceral malignant tumors and can cause right heart failure or sudden death. We present 2 cases of right atrial tumor thrombus treated under deep hypothermic intermittent circulatory arrest (DHICA). A 45-year-old man with right heart failure was diagnosed with right renal cancer extending to the right atrium. Computed tomography revealed no metastasis. He underwent right nephrectomy and tumor thrombus resection under DHICA. He was discharged on postoperative day 11 in good clinical course. A 67-year-old woman with hepatitis C virus liver cirrhosis( Child-Pugh A) was diagnosed with hepatocellular carcinoma and right atrial tumor. She underwent S8 and tumor thrombus resection under DHICA. Hemorrhagic diathesis was controlled using fresh frozen plasma transfusion. She was discharged on postoperative day 24 without liver failure. In cases of atrial tumor thrombus resection, DIHCA may be useful to achieve a bloodless operation field because the procedure is relatively simple and the primary disease need not be considered.

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

PubMed

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.

Deep learning for computational chemistry.

PubMed

Goh, Garrett B; Hodas, Nathan O; Vishnu, Abhinav

2017-06-15

The rise and fall of artificial neural networks is well documented in the scientific literature of both computer science and computational chemistry. Yet almost two decades later, we are now seeing a resurgence of interest in deep learning, a machine learning algorithm based on multilayer neural networks. Within the last few years, we have seen the transformative impact of deep learning in many domains, particularly in speech recognition and computer vision, to the extent that the majority of expert practitioners in those field are now regularly eschewing prior established models in favor of deep learning models. In this review, we provide an introductory overview into the theory of deep neural networks and their unique properties that distinguish them from traditional machine learning algorithms used in cheminformatics. By providing an overview of the variety of emerging applications of deep neural networks, we highlight its ubiquity and broad applicability to a wide range of challenges in the field, including quantitative structure activity relationship, virtual screening, protein structure prediction, quantum chemistry, materials design, and property prediction. In reviewing the performance of deep neural networks, we observed a consistent outperformance against non-neural networks state-of-the-art models across disparate research topics, and deep neural network-based models often exceeded the "glass ceiling" expectations of their respective tasks. Coupled with the maturity of GPU-accelerated computing for training deep neural networks and the exponential growth of chemical data on which to train these networks on, we anticipate that deep learning algorithms will be a valuable tool for computational chemistry. © 2017 Wiley Periodicals, Inc. © 2017 Wiley Periodicals, Inc.

Comparative study viruses with computer-aided phase microscope AIRYSCAN

NASA Astrophysics Data System (ADS)

Tychinsky, Vladimir P.; Koufal, Georgy E.; Perevedentseva, Elena V.; Vyshenskaia, Tatiana V.

1996-12-01

Traditionally viruses are studied with scanning electron microscopy (SEM) after complicated procedure of sample preparation without the possibility to study it under natural conditions. We obtained images of viruses (Vaccinia virus, Rotavirus) and rickettsias (Rickettsia provazekii, Coxiella burnetti) in native state with computer-aided phase microscope airyscan -- the interference microscope of Linnik layout with phase modulation of the reference wave with dissector image tube as coordinate-sensitive photodetector and computer processing of phase image. A light source was the He-Ne laser. The main result is coincidence of dimensions and shape of phase images with available information concerning their morphology obtained with SEM and other methods. The fine structure of surface and nuclei is observed. This method may be applied for virus recognition and express identification, investigation of virus structure and the analysis of cell-virus interaction.

Evaluation of mechanisms that might control transport of wastewater contaminants in bedrock multi-aquifer systems

USDA-ARS?s Scientific Manuscript database

Ongoing research has identified infectious human enteric viruses in the Madison, Wisconsin, public supply wells that draw water from a deep, confined sandstone aquifer. These viruses most likely originate from leaking sanitary sewers and are a potential human health risk. Due to a relatively short (...

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

USDA-ARS?s Scientific Manuscript database

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...

Computer Intrusions and Attacks.

ERIC Educational Resources Information Center

Falk, Howard

1999-01-01

Examines some frequently encountered unsolicited computer intrusions, including computer viruses, worms, Java applications, trojan horses or vandals, e-mail spamming, hoaxes, and cookies. Also discusses virus-protection software, both for networks and for individual users. (LRW)

Task 1.5 Genomic Shift and Drift Trends of Emerging Pathogens

DOE Office of Scientific and Technical Information (OSTI.GOV)

Borucki, M

2010-01-05

The Lawrence Livermore National Laboratory (LLNL) Bioinformatics group has recently taken on a role in DTRA's Transformation Medical Technologies Initiative (TMTI). The high-level goal of TMTI is to accelerate the development of broad-spectrum countermeasures. To achieve those goals, TMTI has a near term need to conduct analyses of genomic shift and drift trends of emerging pathogens, with a focused eye on select agent pathogens, as well as antibiotic and virulence markers. Most emerging human pathogens are zoonotic viruses with a genome composed of RNA. The high mutation rate of the replication enzymes of RNA viruses contributes to sequence drift andmore » provides one mechanism for these viruses to adapt to diverse hosts (interspecies transmission events) and cause new human and zoonotic diseases. Additionally, new viral pathogens frequently emerge due to genetic shift (recombination and segment reassortment) which allows for dramatic genotypic and phenotypic changes to occur rapidly. Bacterial pathogens also evolve via genetic drift and shift, although sequence drift generally occurs at a much slower rate for bacteria as compared to RNA viruses. However, genetic shift such as lateral gene transfer and inter- and intragenomic recombination enables bacteria to rapidly acquire new mechanisms of survival and antibiotic resistance. New technologies such as rapid whole genome sequencing of bacterial genomes, ultra-deep sequencing of RNA virus populations, metagenomic studies of environments rich in antibiotic resistance genes, and the use of microarrays for the detection and characterization of emerging pathogens provide mechanisms to address the challenges posed by the rapid emergence of pathogens. Bioinformatic algorithms that enable efficient analysis of the massive amounts of data generated by these technologies as well computational modeling of protein structures and evolutionary processes need to be developed to allow the technology to fulfill its potential.« less

Genetic characterization of novel putative rhabdovirus and dsRNA virus from Japanese persimmon.

PubMed

Ito, Takao; Suzaki, Koichi; Nakano, Masaaki

2013-08-01

Deep-sequencing analysis of nucleic acids from leaf tissue of Japanese persimmon trees exhibiting fruit apex disorder in some fruits detected two molecules that were graft transmitted to healthy seedlings. One of the complete genomes consisted of 13 467 nt and encoded six genes similar to those of plant rhabdoviruses. The virus formed a distinct cluster in the genus Cytorhabdovirus with lettuce necrotic yellows virus, lettuce yellow mottle virus and strawberry crinkle virus in a phylogenetic tree based on the L protein (RNA-dependent RNA polymerase, RdRp). The other consisted of 7475 nt and shared a genome organization similar to those of some insect and fungal viruses having dsRNA genomes. In a phylogenetic tree using the RdRp sequence of several unassigned dsRNA viruses, the virus formed a possible new genus cluster with two insect viruses, Circulifer tenellus virus 1 and Spissistilus festinus virus 1, and one plant virus, cucurbit yellows-associated virus.

Deep sequencing of H7N8 avian influenza viruses from surveillance zone supports H7N8 high pathogenicity avian influenza was limited to a single outbreak farm in Indiana during 2016

USDA-ARS?s Scientific Manuscript database

In mid-January 2016, an outbreak of H7N8 high pathogenicity avian influenza (HPAI) virus in commercial turkeys occurred in Indiana. The outbreak was first detected by an increase in mortality followed by laboratory confirmation of H7N8 HPAI virus. Surveillance within the 10 km Control Zone detected...

The complete genome sequence of a new polerovirus in strawberry plants from eastern Canada showing strawberry decline symptoms.

PubMed

Xiang, Yu; Bernardy, Mike; Bhagwat, Basdeo; Wiersma, Paul A; DeYoung, Robyn; Bouthillier, Michel

2015-02-01

Strawberry decline disease, probably caused by synergistic reactions of mixed virus infections, threatens the North American strawberry industry. Deep sequencing of strawberry plant samples from eastern Canada resulted in the identification of a new virus genome resembling poleroviruses in sequence and genome structure. Phylogenetic analysis suggests that it is a new member of the genus Polerovirus, family Luteoviridae. The virus is tentatively named "strawberry polerovirus 1" (SPV1).

Spatial Distribution of Viruses Associated with Planktonic and Attached Microbial Communities in Hydrothermal Environments

PubMed Central

Nunoura, Takuro; Kazama, Hiromi; Noguchi, Takuroh; Inoue, Kazuhiro; Akashi, Hironori; Yamanaka, Toshiro; Toki, Tomohiro; Yamamoto, Masahiro; Furushima, Yasuo; Ueno, Yuichiro; Yamamoto, Hiroyuki; Takai, Ken

2012-01-01

Viruses play important roles in marine surface ecosystems, but little is known about viral ecology and virus-mediated processes in deep-sea hydrothermal microbial communities. In this study, we examined virus-like particle (VLP) abundances in planktonic and attached microbial communities, which occur in physical and chemical gradients in both deep and shallow submarine hydrothermal environments (mixing waters between hydrothermal fluids and ambient seawater and dense microbial communities attached to chimney surface areas or macrofaunal bodies and colonies). We found that viruses were widely distributed in a variety of hydrothermal microbial habitats, with the exception of the interior parts of hydrothermal chimney structures. The VLP abundance and VLP-to-prokaryote ratio (VPR) in the planktonic habitats increased as the ratio of hydrothermal fluid to mixing water increased. On the other hand, the VLP abundance in attached microbial communities was significantly and positively correlated with the whole prokaryotic abundance; however, the VPRs were always much lower than those for the surrounding hydrothermal waters. This is the first report to show VLP abundance in the attached microbial communities of submarine hydrothermal environments, which presented VPR values significantly lower than those in planktonic microbial communities reported before. These results suggested that viral lifestyles (e.g., lysogenic prevalence) and virus interactions with prokaryotes are significantly different among the planktonic and attached microbial communities that are developing in the submarine hydrothermal environments. PMID:22210205

Detection of bovine viral diarrhoea virus nucleic acid, but not infectious virus, in bovine serum used for human vaccine manufacture.

PubMed

Laassri, Majid; Mee, Edward T; Connaughton, Sarah M; Manukyan, Hasmik; Gruber, Marion; Rodriguez-Hernandez, Carmen; Minor, Philip D; Schepelmann, Silke; Chumakov, Konstantin; Wood, David J

2018-06-22

Bovine viral diarrhoea virus (BVDV) is a cattle pathogen that has previously been reported to be present in bovine raw materials used in the manufacture of biological products for human use. Seven lots of trivalent measles, mumps and rubella (MMR) vaccine and 1 lot of measles vaccine from the same manufacturer, together with 17 lots of foetal bovine serum (FBS) from different vendors, 4 lots of horse serum, 2 lots of bovine trypsin and 5 lots of porcine trypsin were analysed for BVDV using recently developed techniques, including PCR assays for BVDV detection, a qRT-PCR and immunofluorescence-based virus replication assays, and deep sequencing to identify and genotype BVDV genomes. All FBS lots and one lot of bovine-derived trypsin were PCR-positive for the presence of BVDV genome; in contrast all vaccine lots and the other samples were negative. qRT-PCR based virus replication assay and immunofluorescence-based infection assay detected no infectious BVDV in the PCR-positive samples. Complete BVDV genomes were generated from FBS samples by deep sequencing, and all were BVDV type 1. These data confirmed that BVDV nucleic acid may be present in bovine-derived raw materials, but no infectious virus or genomic RNA was detected in the final vaccine products. Copyright © 2018 International Alliance for Biological Standardization. All rights reserved.

Computer viruses

NASA Technical Reports Server (NTRS)

Denning, Peter J.

1988-01-01

The worm, Trojan horse, bacterium, and virus are destructive programs that attack information stored in a computer's memory. Virus programs, which propagate by incorporating copies of themselves into other programs, are a growing menace in the late-1980s world of unprotected, networked workstations and personal computers. Limited immunity is offered by memory protection hardware, digitally authenticated object programs,and antibody programs that kill specific viruses. Additional immunity can be gained from the practice of digital hygiene, primarily the refusal to use software from untrusted sources. Full immunity requires attention in a social dimension, the accountability of programmers.

Geomicrobiology and Metagenomics of Terrestrial Deep Subsurface Microbiomes.

PubMed

Itävaara, M; Salavirta, H; Marjamaa, K; Ruskeeniemi, T

2016-01-01

Fractures in the deep subsurface of Earth's crust are inhabited by diverse microbial communities that participate in biogeochemical cycles of the Earth. Life on Earth, which arose c. 3.5-4.0 billion years ago, reaches down at least 5 km in the crust. Deep mines, caves, and boreholes have provided scientists with opportunities to sample deep subsurface microbiomes and to obtain information on the species diversity and functions. A wide variety of bacteria, archaea, eukaryotes, and viruses are now known to reside in the crust, but their functions are still largely unknown. The crust at different depths has varying geological composition and hosts endemic microbiomes accordingly. The diversity is driven by geological formations and gases evolving from deeper depths. Cooperation among different species is still mostly unexplored, but viruses are known to restrict density of bacterial and archaeal populations. Due to the complex growth requirements of the deep subsurface microbiomes, the new knowledge about their diversity and functions is mostly obtained by molecular methods, eg, meta'omics'. Geomicrobiology is a multidisciplinary research area combining disciplines from geology, mineralogy, geochemistry, and microbiology. Geomicrobiology is concerned with the interaction of microorganisms and geological processes. At the surface of mineralogical or rock surfaces, geomicrobial processes occur mainly under aerobic conditions. In the deep subsurface, however, the environmental conditions are reducing and anaerobic. The present chapter describes the world of microbiomes in deep terrestrial geological environments as well as metagenomic and metatranscriptomic methods suitable for studies of these enigmatic communities. Copyright © 2016 Elsevier Inc. All rights reserved.

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

PubMed

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 study of packaging signals in other RNA viruses. Improved understanding of RNA packaging may lead to novel vaccine approaches or targets for antiviral drugs with broad spectrum activity. Copyright © 2017 Logan et al.

Is There Still Room for Novel Viral Pathogens in Pediatric Respiratory Tract Infections?

PubMed Central

Taboada, Blanca; Espinoza, Marco A.; Isa, Pavel; Aponte, Fernando E.; Arias-Ortiz, María A.; Monge-Martínez, Jesús; Rodríguez-Vázquez, Rubén; Díaz-Hernández, Fidel; Zárate-Vidal, Fernando; Wong-Chew, Rosa María; Firo-Reyes, Verónica; del Río-Almendárez, Carlos N.; Gaitán-Meza, Jesús; Villaseñor-Sierra, Alberto; Martínez-Aguilar, Gerardo; Salas-Mier, Ma. del Carmen; Noyola, Daniel E.; Pérez-Gónzalez, Luis F.; López, Susana; Santos-Preciado, José I.; Arias, Carlos F.

2014-01-01

Viruses are the most frequent cause of respiratory disease in children. However, despite the advanced diagnostic methods currently in use, in 20 to 50% of respiratory samples a specific pathogen cannot be detected. In this work, we used a metagenomic approach and deep sequencing to examine respiratory samples from children with lower and upper respiratory tract infections that had been previously found negative for 6 bacteria and 15 respiratory viruses by PCR. Nasal washings from 25 children (out of 250) hospitalized with a diagnosis of pneumonia and nasopharyngeal swabs from 46 outpatient children (out of 526) were studied. DNA reads for at least one virus commonly associated to respiratory infections was found in 20 of 25 hospitalized patients, while reads for pathogenic respiratory bacteria were detected in the remaining 5 children. For outpatients, all the samples were pooled into 25 DNA libraries for sequencing. In this case, in 22 of the 25 sequenced libraries at least one respiratory virus was identified, while in all other, but one, pathogenic bacteria were detected. In both patient groups reads for respiratory syncytial virus, coronavirus-OC43, and rhinovirus were identified. In addition, viruses less frequently associated to respiratory infections were also found. Saffold virus was detected in outpatient but not in hospitalized children. Anellovirus, rotavirus, and astrovirus, as well as several animal and plant viruses were detected in both groups. No novel viruses were identified. Adding up the deep sequencing results to the PCR data, 79.2% of 250 hospitalized and 76.6% of 526 ambulatory patients were positive for viruses, and all other children, but one, had pathogenic respiratory bacteria identified. These results suggest that at least in the type of populations studied and with the sampling methods used the odds of finding novel, clinically relevant viruses, in pediatric respiratory infections are low. PMID:25412469

Emergence of the virulence-associated PB2 E627K substitution in a fatal human case of highly pathogenic avian influenza virus A(H7N7) infection as determined by Illumina ultra-deep sequencing.

PubMed

Jonges, Marcel; Welkers, Matthijs R A; Jeeninga, Rienk E; Meijer, Adam; Schneeberger, Peter; Fouchier, Ron A M; de Jong, Menno D; Koopmans, Marion

2014-02-01

Avian influenza viruses are capable of crossing the species barrier and infecting humans. Although evidence of human-to-human transmission of avian influenza viruses to date is limited, evolution of variants toward more-efficient human-to-human transmission could result in a new influenza virus pandemic. In both the avian influenza A(H5N1) and the recently emerging avian influenza A(H7N9) viruses, the polymerase basic 2 protein (PB2) E627K mutation appears to be of key importance for human adaptation. During a large influenza A(H7N7) virus outbreak in the Netherlands in 2003, the A(H7N7) virus isolated from a fatal human case contained the PB2 E627K mutation as well as a hemagglutinin (HA) K416R mutation. In this study, we aimed to investigate whether these mutations occurred in the avian or the human host by Illumina Ultra-Deep sequencing of three previously uninvestigated clinical samples obtained from the fatal case. In addition, we investigated three chicken samples, two of which were obtained from the source farm. Results showed that the PB2 E627K mutation was not present in any of the chicken samples tested. Surprisingly, the avian samples were characterized by the presence of influenza virus defective RNA segments, suggestive for the synthesis of defective interfering viruses during infection in poultry. In the human samples, the PB2 E627K mutation was identified with increasing frequency during infection. Our results strongly suggest that human adaptation marker PB2 E627K has emerged during virus infection of a single human host, emphasizing the importance of reducing human exposure to avian influenza viruses to reduce the likelihood of viral adaptation to humans.

Characterization of preferential flow pathways in a siliciclastic aquifer system using human enteric viruses and groundwater geochemistry

USDA-ARS?s Scientific Manuscript database

Human enteric viruses have been recognized as an emerging groundwater contaminant and are found only in human waste. In urban environments the most likely source of human waste is from sanitary sewers. Determining the travel time for near-surface contaminants to reach deep public supply wells is i...

Complete genome sequence of Southern tomato virus naturally infecting tomatoes in Bangladesh using small RNA deep sequencing

USDA-ARS?s Scientific Manuscript database

The complete genome sequence of a Southern tomato virus (STV) isolate on tomato plants in a seed production field in Bangladesh was obtained for the first time using next generation sequencing. The identified isolate STV_BD-13 shares high degree of sequence identity (99%) with several known STV isol...

Complete genome sequence of a novel genotype of squash mosaic virus

USDA-ARS?s Scientific Manuscript database

Complete genome sequence of a novel genotype of Squash mosaic virus (SqMV) infecting squash plants in Spain was obtained using deep sequencing of small ribonucleic acids and assembly. The low nucleotide sequence identities, with 87-88% on RNA1 and 84-86% on RNA2 to known SqMV isolates, suggest a new...

Diversity of viruses detected by deep sequencing in pigs from a common background

USDA-ARS?s Scientific Manuscript database

The trial was successful in identifying a number of viruses in the feces of the pigs demonstrating the application of this technology to determine the background noise in the animals. The findings in this study are similar to the fecal virome in pigs from a typical commercial swine farm in the Unite...

First complete genome sequence of an emerging cucumber green mottle mosaic virus isolate in North America

USDA-ARS?s Scientific Manuscript database

The complete genome sequence (6,423 nt) of an emerging Cucumber green mottle mosaic virus (CGMMV) isolate on cucumber in North America was determined through deep sequencing of sRNA and rapid amplification of cDNA ends. It shares 99% nucleotide sequence identity to the Asian genotype, but only 90% t...

Deep Sequencing Reveals the Complete Genome Sequence of Sweet potato virus G from East Timor

PubMed Central

Maina, Solomon; Edwards, Owain R.; Barbetti, Martin J.; de Almeida, Luis; Ximenes, Abel

2016-01-01

We present the first complete Sweet potato virus G (SPVG) genome from sweet potato in East Timor and compare it with seven complete SPVG genomes from South Korea (three), Taiwan (two), Argentina (one), and the United States (one). It most resembles the genomes from the United States and South Korea. PMID:27609925

Using small RNA deep sequencing data to detect siRNA duplexes induced by plant viruses

USDA-ARS?s Scientific Manuscript database

Small interfering RNA (siRNA) duplexes are produced in plants during virus infection, which are short (usually 21 to 24-base pair) double-stranded RNAs (dsRNAs) with several overhanging nucleotides on the 5' end and 3' end. The investigation of the siRNA duplexes is useful to better understand the R...

Computer Viruses. Legal and Policy Issues Facing Colleges and Universities.

ERIC Educational Resources Information Center

Johnson, David R.; And Others

Compiled by various members of the higher educational community together with risk managers, computer center managers, and computer industry experts, this report recommends establishing policies on an institutional level to protect colleges and universities from computer viruses and the accompanying liability. Various aspects of the topic are…

Identifying pathways for sanitary sewer pathogens to reach deep water supply wells in Madison, Wisconsin

USDA-ARS?s Scientific Manuscript database

Previous work conducted by the Wisconsin Geological and Natural History Survey indicated that human enteric viruses from leaking sewers are present in several municipal wells in Madison, WI. These wells are the drinking water source for the City of Madison, are typically 700 to 900 feet deep, and pe...

Deep vein thrombosis associated with dengue fever.

PubMed

Roy, Amrita; Chaudhuri, Jasodhara; Chakraborty, Swapna

2013-11-08

Hemorrhagic manifestations are common with Dengue but thrombotic events are uncommonly reported. 11-year-old boy who presented with ileo-femoral deep vein thrombosis associated with serologically confirmed infection with DEN1 dengue virus. There was no other history or investigation suggestive of a procoagulant state. Successfully treated with enoxaparin and warfarin. Thrombotic complications are possible with dengue infection.

Within-Host Evolution of Human Influenza Virus.

PubMed

Xue, Katherine S; Moncla, Louise H; Bedford, Trevor; Bloom, Jesse D

2018-03-10

The rapid global evolution of influenza virus begins with mutations that arise de novo in individual infections, but little is known about how evolution occurs within hosts. We review recent progress in understanding how and why influenza viruses evolve within human hosts. Advances in deep sequencing make it possible to measure within-host genetic diversity in both acute and chronic influenza infections. Factors like antigenic selection, antiviral treatment, tissue specificity, spatial structure, and multiplicity of infection may affect how influenza viruses evolve within human hosts. Studies of within-host evolution can contribute to our understanding of the evolutionary and epidemiological factors that shape influenza virus's global evolution. Copyright © 2018 Elsevier Ltd. All rights reserved.

Stopping computer crimes

NASA Technical Reports Server (NTRS)

Denning, Peter J.

1989-01-01

Two new books about intrusions and computer viruses remind us that attacks against our computers on networks are the actions of human beings. Cliff Stoll's book about the hacker who spent a year, beginning in Aug. 1986, attempting to use the Lawrence Berkeley Computer as a stepping-stone for access to military secrets is a spy thriller that illustrates the weaknesses of our password systems and the difficulties in compiling evidence against a hacker engaged in espionage. Pamela Kane's book about viruses that attack IBM PC's shows that viruses are the modern version of the old problem of a Trojan horse attack. It discusses the most famous viruses and their countermeasures, and it comes with a floppy disk of utility programs that will disinfect your PC and thwart future attack.

Application of viromics: a new approach to the understanding of viral infections in humans.

PubMed

Ramamurthy, Mageshbabu; Sankar, Sathish; Kannangai, Rajesh; Nandagopal, Balaji; Sridharan, Gopalan

2017-12-01

This review is focused at exploring the strengths of modern technology driven data compiled in the areas of virus gene sequencing, virus protein structures and their implication to viral diagnosis and therapy. The information for virome analysis (viromics) is generated by the study of viral genomes (entire nucleotide sequence) and viral genes (coding for protein). Presently, the study of viral infectious diseases in terms of etiopathogenesis and development of newer therapeutics is undergoing rapid changes. Currently, viromics relies on deep sequencing, next generation sequencing (NGS) data and public domain databases like GenBank and unique virus specific databases. Two commonly used NGS platforms: Illumina and Ion Torrent, recommend maximum fragment lengths of about 300 and 400 nucleotides for analysis respectively. Direct detection of viruses in clinical samples is now evolving using these methods. Presently, there are a considerable number of good treatment options for HBV/HIV/HCV. These viruses however show development of drug resistance. The drug susceptibility regions of the genomes are sequenced and the prediction of drug resistance is now possible from 3 public domains available on the web. This has been made possible through advances in the technology with the advent of high throughput sequencing and meta-analysis through sophisticated and easy to use software and the use of high speed computers for bioinformatics. More recently NGS technology has been improved with single-molecule real-time sequencing. Here complete long reads can be obtained with less error overcoming a limitation of the NGS which is inherently prone to software anomalies that arise in the hands of personnel without adequate training. The development in understanding the viruses in terms of their genome, pathobiology, transcriptomics and molecular epidemiology constitutes viromics. It could be stated that these developments will bring about radical changes and advancement especially in the field of antiviral therapy and diagnostic virology.

Finding a needle in the virus metagenome haystack--micro-metagenome analysis captures a snapshot of the diversity of a bacteriophage armoire.

PubMed

Ray, Jessica; Dondrup, Michael; Modha, Sejal; Steen, Ida Helene; Sandaa, Ruth-Anne; Clokie, Martha

2012-01-01

Viruses are ubiquitous in the oceans and critical components of marine microbial communities, regulating nutrient transfer to higher trophic levels or to the dissolved organic pool through lysis of host cells. Hydrothermal vent systems are oases of biological activity in the deep oceans, for which knowledge of biodiversity and its impact on global ocean biogeochemical cycling is still in its infancy. In order to gain biological insight into viral communities present in hydrothermal vent systems, we developed a method based on deep-sequencing of pulsed field gel electrophoretic bands representing key viral fractions present in seawater within and surrounding a hydrothermal plume derived from Loki's Castle vent field at the Arctic Mid-Ocean Ridge. The reduction in virus community complexity afforded by this novel approach enabled the near-complete reconstruction of a lambda-like phage genome from the virus fraction of the plume. Phylogenetic examination of distinct gene regions in this lambdoid phage genome unveiled diversity at loci encoding superinfection exclusion- and integrase-like proteins. This suggests the importance of fine-tuning lyosgenic conversion as a viral survival strategy, and provides insights into the nature of host-virus and virus-virus interactions, within hydrothermal plumes. By reducing the complexity of the viral community through targeted sequencing of prominent dsDNA viral fractions, this method has selectively mimicked virus dominance approaching that hitherto achieved only through culturing, thus enabling bioinformatic analysis to locate a lambdoid viral "needle" within the greater viral community "haystack". Such targeted analyses have great potential for accelerating the extraction of biological knowledge from diverse and poorly understood environmental viral communities.

Viral Infection Induces Expression of Novel Phased MicroRNAs from Conserved Cellular MicroRNA Precursors

PubMed Central

Zhang, Jiayao; Zhao, Shuqi; Zheng, Hong; Gao, Ge; Wei, Liping; Li, Yi

2011-01-01

RNA silencing, mediated by small RNAs including microRNAs (miRNAs) and small interfering RNAs (siRNAs), is a potent antiviral or antibacterial mechanism, besides regulating normal cellular gene expression critical for development and physiology. To gain insights into host small RNA metabolism under infections by different viruses, we used Solexa/Illumina deep sequencing to characterize the small RNA profiles of rice plants infected by two distinct viruses, Rice dwarf virus (RDV, dsRNA virus) and Rice stripe virus (RSV, a negative sense and ambisense RNA virus), respectively, as compared with those from non-infected plants. Our analyses showed that RSV infection enhanced the accumulation of some rice miRNA*s, but not their corresponding miRNAs, as well as accumulation of phased siRNAs from a particular precursor. Furthermore, RSV infection also induced the expression of novel miRNAs in a phased pattern from several conserved miRNA precursors. In comparison, no such changes in host small RNA expression was observed in RDV-infected rice plants. Significantly RSV infection elevated the expression levels of selective OsDCLs and OsAGOs, whereas RDV infection only affected the expression of certain OsRDRs. Our results provide a comparative analysis, via deep sequencing, of changes in the small RNA profiles and in the genes of RNA silencing machinery induced by different viruses in a natural and economically important crop host plant. They uncover new mechanisms and complexity of virus-host interactions that may have important implications for further studies on the evolution of cellular small RNA biogenesis that impact pathogen infection, pathogenesis, as well as organismal development. PMID:21901091

Deep sequencing of viral small-RNAs of citrus tristeza virus (CTV) reveals genomic differences between two Italian isolates of CTV

USDA-ARS?s Scientific Manuscript database

A recent Citrus tristeza virus (CTV) epidemic of quick decline (QD) killed many sweet orange trees grafted on sour orange rootstock in Sicily but left some asymptomatic trees in the same field. Recent reports indicated cross-protection involves exclusion of a severe CTV strain by a mild strain of th...

Deep Sequencing Reveals a Divergent Ugandan cassava brown streak virus Isolate from Malawi

PubMed Central

Winter, Stephan; Mukasa, Settumba; Tairo, Fred; Sseruwagi, Peter; Ndunguru, Joseph; Duffy, Siobain

2017-01-01

ABSTRACT Illumina sequencing of RNA from a cassava cutting from northern Malawi produced a genome of Ugandan cassava brown streak virus (UCBSV-MW-NB7_2013). Sequence comparisons revealed stronger similarity to an isolate from nearby Tanzania (93.4% pairwise nucleotide identity) than to those previously reported from Malawi (86.9 to 87.0%). PMID:28818908

Whole-Genome Characterization of Prunus necrotic ringspot virus Infecting Sweet Cherry in China

PubMed Central

2018-01-01

ABSTRACT Prunus necrotic ringspot virus (PNRSV) causes yield loss in most cultivated stone fruits, including sweet cherry. Using a small RNA deep-sequencing approach combined with end-genome sequence cloning, we identified the complete genomes of all three PNRSV strands from PNRSV-infected sweet cherry trees and compared them with those of two previously reported isolates. PMID:29496825

Deep sequencing of H7N8 avian influenza viruses from surveillance zone supports H7N8 high pathogenicity avian influenza was limited to a single outbreak farm in Indiana during 2016.

PubMed

Lee, Dong-Hun; Torchetti, Mia Kim; Killian, Mary Lea; Swayne, David E

2017-07-01

In mid-January 2016, an outbreak of H7N8 high-pathogenicity avian influenza virus (HPAIV) in commercial turkeys occurred in Indiana. Surveillance within the 10km control zone identified H7N8 low-pathogenicity avian influenza virus (LPAIV) in nine surrounding turkey flocks but no other HPAIV-affected premises. We sequenced four of the H7N8 HPAIV isolated from the single farm and nine LPAIV identified during control zone surveillance. Evaluation included phylogenetic network analysis indicating close relatedness across the HPAIV and LPAIV, and that the progenitor H7N8 LPAIV spread among the affected turkey farms in Indiana, followed by spontaneous mutation to HPAIV on a single premise through acquisition of three basic amino acids at the hemagglutinin cleavage site. Deep sequencing of the available viruses failed to identify subpopulations in either the HPAIV or LPAIV suggesting mutation to HPAIV likely occurred on a single farm and the HPAIV did not spread to epidemiologically linked LPAIV-affected farms. Published by Elsevier Inc.

Permeability Surface of Deep Middle Cerebral Artery Territory on Computed Tomographic Perfusion Predicts Hemorrhagic Transformation After Stroke.

PubMed

Li, Qiao; Gao, Xinyi; Yao, Zhenwei; Feng, Xiaoyuan; He, Huijin; Xue, Jing; Gao, Peiyi; Yang, Lumeng; Cheng, Xin; Chen, Weijian; Yang, Yunjun

2017-09-01

Permeability surface (PS) on computed tomographic perfusion reflects blood-brain barrier permeability and is related to hemorrhagic transformation (HT). HT of deep middle cerebral artery (MCA) territory can occur after recanalization of proximal large-vessel occlusion. We aimed to determine the relationship between HT and PS of deep MCA territory. We retrospectively reviewed 70 consecutive acute ischemic stroke patients presenting with occlusion of the distal internal carotid artery or M1 segment of the MCA. All patients underwent computed tomographic perfusion within 6 hours after symptom onset. Computed tomographic perfusion data were postprocessed to generate maps of different perfusion parameters. Risk factors were identified for increased deep MCA territory PS. Receiver operating characteristic curve analysis was performed to calculate the optimal PS threshold to predict HT of deep MCA territory. Increased PS was associated with HT of deep MCA territory. After adjustments for age, sex, onset time to computed tomographic perfusion, and baseline National Institutes of Health Stroke Scale, poor collateral status (odds ratio, 7.8; 95% confidence interval, 1.67-37.14; P =0.009) and proximal MCA-M1 occlusion (odds ratio, 4.12; 95% confidence interval, 1.03-16.52; P =0.045) were independently associated with increased deep MCA territory PS. Relative PS most accurately predicted HT of deep MCA territory (area under curve, 0.94; optimal threshold, 2.89). Increased PS can predict HT of deep MCA territory after recanalization therapy for cerebral proximal large-vessel occlusion. Proximal MCA-M1 complete occlusion and distal internal carotid artery occlusion in conjunction with poor collaterals elevate deep MCA territory PS. © 2017 American Heart Association, Inc.

Doxycycline Inducible Melanogenic Vaccinia Virus as Theranostic Anti-Cancer Agent.

PubMed

Kirscher, Lorenz; Deán-Ben, Xosé Luis; Scadeng, Miriam; Zaremba, Angelika; Zhang, Qian; Kober, Christina; Fehm, Thomas Felix; Razansky, Daniel; Ntziachristos, Vasilis; Stritzker, Jochen; Szalay, Aladar A

2015-01-01

We reported earlier the diagnostic potential of a melanogenic vaccinia virus based system in magnetic resonance (MRI) and optoacoustic deep tissue imaging (MSOT). Since melanin overproduction lead to attenuated virus replication, we constructed a novel recombinant vaccinia virus strain (rVACV), GLV-1h462, which expressed the key enzyme of melanogenesis (tyrosinase) under the control of an inducible promoter-system. In this study melanin production was detected after exogenous addition of doxycycline in two different tumor xenograft mouse models. Furthermore, it was confirmed that this novel vaccinia virus strain still facilitated signal enhancement as detected by MRI and optoacoustic tomography. At the same time we demonstrated an enhanced oncolytic potential compared to the constitutively melanin synthesizing rVACV system.

Discovery of replicating circular RNAs by RNA-seq and computational algorithms.

PubMed

Zhang, Zhixiang; Qi, Shuishui; Tang, Nan; Zhang, Xinxin; Chen, Shanshan; Zhu, Pengfei; Ma, Lin; Cheng, Jinping; Xu, Yun; Lu, Meiguang; Wang, Hongqing; Ding, Shou-Wei; Li, Shifang; Wu, Qingfa

2014-12-01

Replicating circular RNAs are independent plant pathogens known as viroids, or act to modulate the pathogenesis of plant and animal viruses as their satellite RNAs. The rate of discovery of these subviral pathogens was low over the past 40 years because the classical approaches are technical demanding and time-consuming. We previously described an approach for homology-independent discovery of replicating circular RNAs by analysing the total small RNA populations from samples of diseased tissues with a computational program known as progressive filtering of overlapping small RNAs (PFOR). However, PFOR written in PERL language is extremely slow and is unable to discover those subviral pathogens that do not trigger in vivo accumulation of extensively overlapping small RNAs. Moreover, PFOR is yet to identify a new viroid capable of initiating independent infection. Here we report the development of PFOR2 that adopted parallel programming in the C++ language and was 3 to 8 times faster than PFOR. A new computational program was further developed and incorporated into PFOR2 to allow the identification of circular RNAs by deep sequencing of long RNAs instead of small RNAs. PFOR2 analysis of the small RNA libraries from grapevine and apple plants led to the discovery of Grapevine latent viroid (GLVd) and Apple hammerhead viroid-like RNA (AHVd-like RNA), respectively. GLVd was proposed as a new species in the genus Apscaviroid, because it contained the typical structural elements found in this group of viroids and initiated independent infection in grapevine seedlings. AHVd-like RNA encoded a biologically active hammerhead ribozyme in both polarities, and was not specifically associated with any of the viruses found in apple plants. We propose that these computational algorithms have the potential to discover novel circular RNAs in plants, invertebrates and vertebrates regardless of whether they replicate and/or induce the in vivo accumulation of small RNAs.

Discovery of Replicating Circular RNAs by RNA-Seq and Computational Algorithms

PubMed Central

Tang, Nan; Zhang, Xinxin; Chen, Shanshan; Zhu, Pengfei; Ma, Lin; Cheng, Jinping; Xu, Yun; Lu, Meiguang; Wang, Hongqing; Ding, Shou-Wei; Li, Shifang; Wu, Qingfa

2014-01-01

Replicating circular RNAs are independent plant pathogens known as viroids, or act to modulate the pathogenesis of plant and animal viruses as their satellite RNAs. The rate of discovery of these subviral pathogens was low over the past 40 years because the classical approaches are technical demanding and time-consuming. We previously described an approach for homology-independent discovery of replicating circular RNAs by analysing the total small RNA populations from samples of diseased tissues with a computational program known as progressive filtering of overlapping small RNAs (PFOR). However, PFOR written in PERL language is extremely slow and is unable to discover those subviral pathogens that do not trigger in vivo accumulation of extensively overlapping small RNAs. Moreover, PFOR is yet to identify a new viroid capable of initiating independent infection. Here we report the development of PFOR2 that adopted parallel programming in the C++ language and was 3 to 8 times faster than PFOR. A new computational program was further developed and incorporated into PFOR2 to allow the identification of circular RNAs by deep sequencing of long RNAs instead of small RNAs. PFOR2 analysis of the small RNA libraries from grapevine and apple plants led to the discovery of Grapevine latent viroid (GLVd) and Apple hammerhead viroid-like RNA (AHVd-like RNA), respectively. GLVd was proposed as a new species in the genus Apscaviroid, because it contained the typical structural elements found in this group of viroids and initiated independent infection in grapevine seedlings. AHVd-like RNA encoded a biologically active hammerhead ribozyme in both polarities, and was not specifically associated with any of the viruses found in apple plants. We propose that these computational algorithms have the potential to discover novel circular RNAs in plants, invertebrates and vertebrates regardless of whether they replicate and/or induce the in vivo accumulation of small RNAs. PMID:25503469

Computational approach for elucidating interactions of cross-species miRNAs and their targets in Flaviviruses.

PubMed

Shinde, Santosh P; Banerjee, Amit Kumar; Arora, Neelima; Murty, U S N; Sripathi, Venkateswara Rao; Pal-Bhadra, Manika; Bhadra, Utpal

2015-03-01

Combating viral diseases has been a challenging task since time immemorial. Available molecular approaches are limited and not much effective for this daunting task. MicroRNA based therapies have shown promise in recent times. MicroRNAs are tiny non-coding RNAs that regulate translational repression of target mRNA in highly specific manner. In this study, we have determined the target regions for human and viral microRNAs in the conserved genomic regions of selected viruses of Flaviviridae family using miRanda and performed a comparative target selectivity analysis among them. Specific target regions were determined and they were compared extensively among themselves by exploring their position to determine the vicinity. Based on the multiplicity and cooperativity analysis, interaction maps were developed manually to represent the interactions between top-ranking miRNAs and genomes of the viruses considered in this study. Self-organizing map (SOM) was used to cluster the best-ranked microRNAs based on the vital physicochemical properties. This study will provide deep insight into the interrelation of the viral and human microRNAs interactions with the selected Flaviviridae genomes and will help to identify cross-species microRNA targets on the viral genome.

Stability and Hopf bifurcation for a delayed SLBRS computer virus model.

PubMed

Zhang, Zizhen; Yang, Huizhong

2014-01-01

By incorporating the time delay due to the period that computers use antivirus software to clean the virus into the SLBRS model a delayed SLBRS computer virus model is proposed in this paper. The dynamical behaviors which include local stability and Hopf bifurcation are investigated by regarding the delay as bifurcating parameter. Specially, direction and stability of the Hopf bifurcation are derived by applying the normal form method and center manifold theory. Finally, an illustrative example is also presented to testify our analytical results.

Ebola virus: A gap in drug design and discovery - experimental and computational perspective.

PubMed

Balmith, Marissa; Faya, Mbuso; Soliman, Mahmoud E S

2017-03-01

The Ebola virus, formally known as the Ebola hemorrhagic fever, is an acute viral syndrome causing sporadic outbreaks that have ravaged West Africa. Due to its extreme virulence and highly transmissible nature, Ebola has been classified as a category A bioweapon organism. Only recently have vaccine or drug regimens for the Ebola virus been developed, including Zmapp and peptides. In addition, existing drugs which have been repurposed toward anti-Ebola virus activity have been re-examined and are seen to be promising candidates toward combating Ebola. Drug development involving computational tools has been widely employed toward target-based drug design. Screening large libraries have greatly stimulated research toward effective anti-Ebola virus drug regimens. Current emphasis has been placed on the investigation of host proteins and druggable viral targets. There is a huge gap in the literature regarding guidelines in the discovery of Ebola virus inhibitors, which may be due to the lack of information on the Ebola drug targets, binding sites, and mechanism of action of the virus. This review focuses on Ebola virus inhibitors, drugs which could be repurposed to combat the Ebola virus, computational methods which study drug-target interactions as well as providing further insight into the mode of action of the Ebola virus. © 2016 John Wiley & Sons A/S.

Computer Viruses and Related Threats: A Management Guide.

ERIC Educational Resources Information Center

Wack, John P.; Carnahan, Lisa J.

This document contains guidance for managing the threats of computer viruses, Trojan horses, network worms, etc. and related software along with unauthorized use. It is geared towards managers of end-user groups, managers dealing with multi-user systems, personal computers, and networks. The guidance is general and addresses the vulnerabilities…

Whole-Genome Characterization of Prunus necrotic ringspot virus Infecting Sweet Cherry in China.

PubMed

Wang, Jiawei; Zhai, Ying; Zhu, Dongzi; Liu, Weizhen; Pappu, Hanu R; Liu, Qingzhong

2018-03-01

Prunus necrotic ringspot virus (PNRSV) causes yield loss in most cultivated stone fruits, including sweet cherry. Using a small RNA deep-sequencing approach combined with end-genome sequence cloning, we identified the complete genomes of all three PNRSV strands from PNRSV-infected sweet cherry trees and compared them with those of two previously reported isolates. Copyright © 2018 Wang et al.

De Novo Generation and Characterization of New Zika Virus Isolate Using Sequence Data from a Microcephaly Case.

PubMed

Setoh, Yin Xiang; Prow, Natalie A; Peng, Nias; Hugo, Leon E; Devine, Gregor; Hazlewood, Jessamine E; Suhrbier, Andreas; Khromykh, Alexander A

2017-01-01

Zika virus (ZIKV) has recently emerged and is the etiological agent of congenital Zika syndrome (CZS), a spectrum of congenital abnormalities arising from neural tissue infections in utero . Herein, we describe the de novo generation of a new ZIKV isolate, ZIKV Natal , using a modified circular polymerase extension reaction protocol and sequence data obtained from a ZIKV-infected fetus with microcephaly. ZIKV Natal thus has no laboratory passage history and is unequivocally associated with CZS. ZIKV Natal could be used to establish a fetal brain infection model in IFNAR -/- mice (including intrauterine growth restriction) without causing symptomatic infections in dams. ZIKV Natal was also able to be transmitted by Aedes aegypti mosquitoes. ZIKV Natal thus retains key aspects of circulating pathogenic ZIKVs and illustrates a novel methodology for obtaining an authentic functional viral isolate by using data from deep sequencing of infected tissues. IMPORTANCE The major complications of an ongoing Zika virus outbreak in the Americas and Asia are congenital defects caused by the virus's ability to cross the placenta and infect the fetal brain. The ability to generate molecular tools to analyze viral isolates from the current outbreak is essential for furthering our understanding of how these viruses cause congenital defects. The majority of existing viral isolates and infectious cDNA clones generated from them have undergone various numbers of passages in cell culture and/or suckling mice, which is likely to result in the accumulation of adaptive mutations that may affect viral properties. The approach described herein allows rapid generation of new, fully functional Zika virus isolates directly from deep sequencing data from virus-infected tissues without the need for prior virus passaging and for the generation and propagation of full-length cDNA clones. The approach should be applicable to other medically important flaviviruses and perhaps other positive-strand RNA viruses.

Computer-aided classification of lung nodules on computed tomography images via deep learning technique

PubMed Central

Hua, Kai-Lung; Hsu, Che-Hao; Hidayati, Shintami Chusnul; Cheng, Wen-Huang; Chen, Yu-Jen

2015-01-01

Lung cancer has a poor prognosis when not diagnosed early and unresectable lesions are present. The management of small lung nodules noted on computed tomography scan is controversial due to uncertain tumor characteristics. A conventional computer-aided diagnosis (CAD) scheme requires several image processing and pattern recognition steps to accomplish a quantitative tumor differentiation result. In such an ad hoc image analysis pipeline, every step depends heavily on the performance of the previous step. Accordingly, tuning of classification performance in a conventional CAD scheme is very complicated and arduous. Deep learning techniques, on the other hand, have the intrinsic advantage of an automatic exploitation feature and tuning of performance in a seamless fashion. In this study, we attempted to simplify the image analysis pipeline of conventional CAD with deep learning techniques. Specifically, we introduced models of a deep belief network and a convolutional neural network in the context of nodule classification in computed tomography images. Two baseline methods with feature computing steps were implemented for comparison. The experimental results suggest that deep learning methods could achieve better discriminative results and hold promise in the CAD application domain. PMID:26346558

Computer-aided classification of lung nodules on computed tomography images via deep learning technique.

PubMed

Hua, Kai-Lung; Hsu, Che-Hao; Hidayati, Shintami Chusnul; Cheng, Wen-Huang; Chen, Yu-Jen

2015-01-01

Lung cancer has a poor prognosis when not diagnosed early and unresectable lesions are present. The management of small lung nodules noted on computed tomography scan is controversial due to uncertain tumor characteristics. A conventional computer-aided diagnosis (CAD) scheme requires several image processing and pattern recognition steps to accomplish a quantitative tumor differentiation result. In such an ad hoc image analysis pipeline, every step depends heavily on the performance of the previous step. Accordingly, tuning of classification performance in a conventional CAD scheme is very complicated and arduous. Deep learning techniques, on the other hand, have the intrinsic advantage of an automatic exploitation feature and tuning of performance in a seamless fashion. In this study, we attempted to simplify the image analysis pipeline of conventional CAD with deep learning techniques. Specifically, we introduced models of a deep belief network and a convolutional neural network in the context of nodule classification in computed tomography images. Two baseline methods with feature computing steps were implemented for comparison. The experimental results suggest that deep learning methods could achieve better discriminative results and hold promise in the CAD application domain.

Fracture zones in the Mid Atlantic Ridge lead to alterations in prokaryotic and viral parameters in deep-water masses

PubMed Central

Muck, Simone; Griessler, Thomas; Köstner, Nicole; Klimiuk, Adam; Winter, Christian; Herndl, Gerhard J.

2014-01-01

We hypothesized that mixing zones of deep-water masses act as ecotones leading to alterations in microbial diversity and activity due to changes in the biogeochemical characteristics of these boundary systems. We determined the changes in prokaryotic and viral abundance and production in the Vema Fracture Zone (VFZ) of the subtropical North Atlantic Ocean, where North Atlantic Deep Water (NADW) and Antarctic Bottom Water (AABW) are funneled through this narrow canyon and therefore, are subjected to intense vertical mixing. Consequently, salinity, potential temperature, oxygen, PO4, SiO4, NO3 were altered in the NADW inside the VFZ as compared to the NADW outside of the VFZ. Also, viral abundance, lytic viral production (VP) and the virus-to-prokaryote ratio (VPR) were elevated in the NADW in the VFZ as compared to the NADW outside the VFZ. In contrast to lytic VP, lysogenic VP and both the frequency of lytically (FIC) and lysogenically infected cells (FLC) did not significantly differ between in- and outside the VFZ. Generally, FIC was higher than FLC throughout the water column. Prokaryotic (determined by T-RFLP) and viral (determined by RAPD-PCR) community composition was depth-stratified inside and outside the VFZ. The viral community was more modified both with depth and over distance inside the VFZ as compared to the northern section and to the prokaryotic communities. However, no clusters of prokaryotic and viral communities characteristic for the VFZ were identified. Based on our observations, we conclude that turbulent mixing of the deep water masses impacts not only the physico-chemical parameters of the mixing zone but also the interaction between viruses and prokaryotes due to a stimulation of the overall activity. However, only minor effects of deep water mixing were observed on the community composition of the dominant prokaryotes and viruses. PMID:24917857

A Study of Complex Deep Learning Networks on High Performance, Neuromorphic, and Quantum Computers

DOE Office of Scientific and Technical Information (OSTI.GOV)

Potok, Thomas E; Schuman, Catherine D; Young, Steven R

Current Deep Learning models use highly optimized convolutional neural networks (CNN) trained on large graphical processing units (GPU)-based computers with a fairly simple layered network topology, i.e., highly connected layers, without intra-layer connections. Complex topologies have been proposed, but are intractable to train on current systems. Building the topologies of the deep learning network requires hand tuning, and implementing the network in hardware is expensive in both cost and power. In this paper, we evaluate deep learning models using three different computing architectures to address these problems: quantum computing to train complex topologies, high performance computing (HPC) to automatically determinemore » network topology, and neuromorphic computing for a low-power hardware implementation. Due to input size limitations of current quantum computers we use the MNIST dataset for our evaluation. The results show the possibility of using the three architectures in tandem to explore complex deep learning networks that are untrainable using a von Neumann architecture. We show that a quantum computer can find high quality values of intra-layer connections and weights, while yielding a tractable time result as the complexity of the network increases; a high performance computer can find optimal layer-based topologies; and a neuromorphic computer can represent the complex topology and weights derived from the other architectures in low power memristive hardware. This represents a new capability that is not feasible with current von Neumann architecture. It potentially enables the ability to solve very complicated problems unsolvable with current computing technologies.« less

Viral Predation and Host Immunity Structure Microbial Communities in a Terrestrial Deep Subsurface, Hydraulically Fractured Shale System

NASA Astrophysics Data System (ADS)

Daly, R. A.; Mouser, P. J.; Trexler, R.; Wrighton, K. C.

2014-12-01

Despite a growing appreciation for the ecological role of viruses in marine and gut systems, little is known about their role in the terrestrial deep (> 2000 m) subsurface. We used assembly-based metagenomics to examine the viral component in fluids from hydraulically fractured Marcellus shale gas wells. Here we reconstructed microbial and viral genomes from samples collected 7, 82, and 328 days post fracturing. Viruses accounted for 4.14%, 0.92% and 0.59% of the sample reads that mapped to the assembly. We identified 6 complete, circularized viral genomes and an additional 92 viral contigs > 5 kb with a maximum contig size of 73.6 kb. A BLAST comparison to NCBI viral genomes revealed that 85% of viral contigs had significant hits to the viral order Caudovirales, with 43% of sequences belonging to the family Siphoviridae, 38% to Myoviridae, and 12% to Podoviridae. Enrichment of Caudovirales viruses was supported by a large number of predicted proteins characteristic of tailed viruses including terminases (TerL), tape measure, tail formation, and baseplate related proteins. The viral contigs included evidence of lytic and temperate lifestyles, with the 7 day sample having the greatest number of detected lytic viruses. Notably in this sample, the most abundant virus was lytic and its inferred host, a member of the Vibrionaceae, was not detected at later time points. Analyses of CRISPR sequences (a viral and foreign DNA immune system in bacteria and archaea), linked 18 viral contigs to hosts. CRISPR linkages increased through time and all bacterial and archaeal genomes recovered in the final time point had genes for CRISPR-mediated viral defense. The majority of CRISPR sequences linked phage genomes to several Halanaerobium strains, which are the dominant and persisting members of the community inferred to be responsible for carbon and sulfur cycling in these shales. Network analysis revealed that several viruses were present in the 82 and 328 day samples; this viral persistence is consistent with concomitant temporal stability in geochemistry and microbial community composition. Our findings suggest that after a disturbance (hydraulic fracturing) viral predation and host immunity is an important controller of microbial community structure, metabolism, and thus biogeochemical cycling in the deep subsurface.

Modeling the state dependent impulse control for computer virus propagation under media coverage

NASA Astrophysics Data System (ADS)

Liang, Xiyin; Pei, Yongzhen; Lv, Yunfei

2018-02-01

A state dependent impulsive control model is proposed to model the spread of computer virus incorporating media coverage. By the successor function, the sufficient conditions for the existence and uniqueness of order-1 periodic solution are presented first. Secondly, for two classes of periodic solutions, the geometric property of successor function and the analogue of the Poincaré criterion are employed to obtain the stability results. These results show that the number of the infective computers is under the threshold all the time. Finally, the theoretic and numerical analysis show that media coverage can delay the spread of computer virus.

Machine Learning, deep learning and optimization in computer vision

NASA Astrophysics Data System (ADS)

Canu, Stéphane

2017-03-01

As quoted in the Large Scale Computer Vision Systems NIPS workshop, computer vision is a mature field with a long tradition of research, but recent advances in machine learning, deep learning, representation learning and optimization have provided models with new capabilities to better understand visual content. The presentation will go through these new developments in machine learning covering basic motivations, ideas, models and optimization in deep learning for computer vision, identifying challenges and opportunities. It will focus on issues related with large scale learning that is: high dimensional features, large variety of visual classes, and large number of examples.

Deep sequencing of evolving pathogen populations: applications, errors, and bioinformatic solutions

PubMed Central

2014-01-01

Deep sequencing harnesses the high throughput nature of next generation sequencing technologies to generate population samples, treating information contained in individual reads as meaningful. Here, we review applications of deep sequencing to pathogen evolution. Pioneering deep sequencing studies from the virology literature are discussed, such as whole genome Roche-454 sequencing analyses of the dynamics of the rapidly mutating pathogens hepatitis C virus and HIV. Extension of the deep sequencing approach to bacterial populations is then discussed, including the impacts of emerging sequencing technologies. While it is clear that deep sequencing has unprecedented potential for assessing the genetic structure and evolutionary history of pathogen populations, bioinformatic challenges remain. We summarise current approaches to overcoming these challenges, in particular methods for detecting low frequency variants in the context of sequencing error and reconstructing individual haplotypes from short reads. PMID:24428920

Computational clustering for viral reference proteomes

PubMed Central

Chen, Chuming; Huang, Hongzhan; Mazumder, Raja; Natale, Darren A.; McGarvey, Peter B.; Zhang, Jian; Polson, Shawn W.; Wang, Yuqi; Wu, Cathy H.

2016-01-01

Motivation: The enormous number of redundant sequenced genomes has hindered efforts to analyze and functionally annotate proteins. As the taxonomy of viruses is not uniformly defined, viral proteomes pose special challenges in this regard. Grouping viruses based on the similarity of their proteins at proteome scale can normalize against potential taxonomic nomenclature anomalies. Results: We present Viral Reference Proteomes (Viral RPs), which are computed from complete virus proteomes within UniProtKB. Viral RPs based on 95, 75, 55, 35 and 15% co-membership in proteome similarity based clusters are provided. Comparison of our computational Viral RPs with UniProt’s curator-selected Reference Proteomes indicates that the two sets are consistent and complementary. Furthermore, each Viral RP represents a cluster of virus proteomes that was consistent with virus or host taxonomy. We provide BLASTP search and FTP download of Viral RP protein sequences, and a browser to facilitate the visualization of Viral RPs. Availability and implementation: http://proteininformationresource.org/rps/viruses/ Contact: chenc@udel.edu Supplementary information: Supplementary data are available at Bioinformatics online. PMID:27153712

Complete genome sequence of a new enamovirus from Argentina infecting alfalfa plants showing dwarfism symptoms.

PubMed

Bejerman, Nicolás; Giolitti, Fabián; Trucco, Verónica; de Breuil, Soledad; Dietzgen, Ralf G; Lenardon, Sergio

2016-07-01

Alfalfa dwarf disease, probably caused by synergistic interactions of mixed virus infections, is a major and emergent disease that threatens alfalfa production in Argentina. Deep sequencing of diseased alfalfa plant samples from the central region of Argentina resulted in the identification of a new virus genome resembling enamoviruses in sequence and genome structure. Phylogenetic analysis suggests that it is a new member of the genus Enamovirus, family Luteoviridae. The virus is tentatively named "alfalfa enamovirus 1" (AEV-1). The availability of the AEV-1 genome sequence will make it possible to assess the genetic variability of this virus and to construct an infectious clone to investigate its role in alfalfa dwarfism disease.

Stability and Hopf Bifurcation for a Delayed SLBRS Computer Virus Model

PubMed Central

Yang, Huizhong

2014-01-01

By incorporating the time delay due to the period that computers use antivirus software to clean the virus into the SLBRS model a delayed SLBRS computer virus model is proposed in this paper. The dynamical behaviors which include local stability and Hopf bifurcation are investigated by regarding the delay as bifurcating parameter. Specially, direction and stability of the Hopf bifurcation are derived by applying the normal form method and center manifold theory. Finally, an illustrative example is also presented to testify our analytical results. PMID:25202722

DEEP: a general computational framework for predicting enhancers

PubMed Central

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

2015-01-01

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/. PMID:25378307

Characterization by Deep Sequencing of Prunus virus T, a Novel Tepovirus Infecting Prunus Species.

PubMed

Marais, Armelle; Faure, Chantal; Mustafayev, Eldar; Barone, Maria; Alioto, Daniela; Candresse, Thierry

2015-01-01

Double-stranded RNAs purified from a cherry tree collected in Italy and a plum tree collected in Azerbaijan were submitted to deep sequencing. Contigs showing weak but significant identity with various members of the family Betaflexiviridae were reconstructed. Sequence comparisons led to the conclusion that the viral isolates identified in the analyzed Prunus plants belong to the same viral species. Their genome organization is similar to that of some members of the family Betaflexiviridae, with three overlapping open reading frames (RNA polymerase, movement protein, and capsid protein). Phylogenetic analyses of the deduced encoded proteins showed a clustering with the sole member of the genus Tepovirus, Potato virus T (PVT). Given these results, the name Prunus virus T (PrVT) is proposed for the new virus. It should be considered as a new member of the genus Tepovirus, even if the level of nucleotide identity with PVT is borderline with the genus demarcation criteria for the family Betaflexiviridae. A reverse-transcription polymerase chain reaction detection assay was developed and allowed the identification of two other PrVT isolates and an estimate of 1% prevalence in the large Prunus collection screened. Due to the mixed infection status of all hosts identified to date, it was not possible to correlate the presence of PrVT with specific symptoms.

Viral activities and life cycles in deep subseafloor sediments.

PubMed

Engelhardt, Tim; Orsi, William D; Jørgensen, Bo Barker

2015-12-01

Viruses are highly abundant in marine subsurface sediments and can even exceed the number of prokaryotes. However, their activity and quantitative impact on microbial populations are still poorly understood. Here, we use gene expression data from published continental margin subseafloor metatranscriptomes to qualitatively assess viral diversity and activity in sediments up to 159 metres below seafloor (mbsf). Mining of the metatranscriptomic data revealed 4651 representative viral homologues (RVHs), representing 2.2% of all metatranscriptome sequence reads, which have close translated homology (average 77%, range 60-97% amino acid identity) to viral proteins. Archaea-infecting RVHs are exclusively detected in the upper 30 mbsf, whereas RVHs for filamentous inoviruses predominate in the deepest sediment layers. RVHs indicative of lysogenic phage-host interactions and lytic activity, notably cell lysis, are detected at all analysed depths and suggest a dynamic virus-host association in the marine deep biosphere studied here. Ongoing lytic viral activity is further indicated by the expression of clustered, regularly interspaced, short palindromic repeat-associated cascade genes involved in cellular defence against viral attacks. The data indicate the activity of viruses in subsurface sediment of the Peruvian margin and suggest that viruses indeed cause cell mortality and may play an important role in the turnover of subseafloor microbial biomass. © 2015 Society for Applied Microbiology and John Wiley & Sons Ltd.

The FELICIA bulletin board system and the IRBIS anonymous FTP server: Computer security information sources for the DOE community. CIAC-2302

DOE Office of Scientific and Technical Information (OSTI.GOV)

Orvis, W.J.

1993-11-03

The Computer Incident Advisory Capability (CIAC) operates two information servers for the DOE community, FELICIA (formerly FELIX) and IRBIS. FELICIA is a computer Bulletin Board System (BBS) that can be accessed by telephone with a modem. IRBIS is an anonymous ftp server that can be accessed on the Internet. Both of these servers contain all of the publicly available CIAC, CERT, NIST, and DDN bulletins, virus descriptions, the VIRUS-L moderated virus bulletin board, copies of public domain and shareware virus- detection/protection software, and copies of useful public domain and shareware utility programs. This guide describes how to connect these systemsmore » and obtain files from them.« less

Viruses in diarrhoeic dogs include novel kobuviruses and sapoviruses.

PubMed

Li, Linlin; Pesavento, Patricia A; Shan, Tongling; Leutenegger, Christian M; Wang, Chunlin; Delwart, Eric

2011-11-01

The close interactions of dogs with humans and surrounding wildlife provide frequent opportunities for cross-species virus transmissions. In order to initiate an unbiased characterization of the eukaryotic viruses in the gut of dogs, this study used deep sequencing of partially purified viral capsid-protected nucleic acids from the faeces of 18 diarrhoeic dogs. Known canine parvoviruses, coronaviruses and rotaviruses were identified, and the genomes of the first reported canine kobuvirus and sapovirus were characterized. Canine kobuvirus, the first sequenced canine picornavirus and the closest genetic relative of the diarrhoea-causing human Aichi virus, was detected at high frequency in the faeces of both healthy and diarrhoeic dogs. Canine sapovirus constituted a novel genogroup within the genus Sapovirus, a group of viruses also associated with human and animal diarrhoea. These results highlight the high frequency of new virus detection possible even in extensively studied animal species using metagenomics approaches, and provide viral genomes for further disease-association studies.

De Novo Generation and Characterization of New Zika Virus Isolate Using Sequence Data from a Microcephaly Case

PubMed Central

Setoh, Yin Xiang; Prow, Natalie A.; Peng, Nias; Hugo, Leon E.; Devine, Gregor; Hazlewood, Jessamine E.

2017-01-01

ABSTRACT Zika virus (ZIKV) has recently emerged and is the etiological agent of congenital Zika syndrome (CZS), a spectrum of congenital abnormalities arising from neural tissue infections in utero. Herein, we describe the de novo generation of a new ZIKV isolate, ZIKVNatal, using a modified circular polymerase extension reaction protocol and sequence data obtained from a ZIKV-infected fetus with microcephaly. ZIKVNatal thus has no laboratory passage history and is unequivocally associated with CZS. ZIKVNatal could be used to establish a fetal brain infection model in IFNAR−/− mice (including intrauterine growth restriction) without causing symptomatic infections in dams. ZIKVNatal was also able to be transmitted by Aedes aegypti mosquitoes. ZIKVNatal thus retains key aspects of circulating pathogenic ZIKVs and illustrates a novel methodology for obtaining an authentic functional viral isolate by using data from deep sequencing of infected tissues. IMPORTANCE The major complications of an ongoing Zika virus outbreak in the Americas and Asia are congenital defects caused by the virus’s ability to cross the placenta and infect the fetal brain. The ability to generate molecular tools to analyze viral isolates from the current outbreak is essential for furthering our understanding of how these viruses cause congenital defects. The majority of existing viral isolates and infectious cDNA clones generated from them have undergone various numbers of passages in cell culture and/or suckling mice, which is likely to result in the accumulation of adaptive mutations that may affect viral properties. The approach described herein allows rapid generation of new, fully functional Zika virus isolates directly from deep sequencing data from virus-infected tissues without the need for prior virus passaging and for the generation and propagation of full-length cDNA clones. The approach should be applicable to other medically important flaviviruses and perhaps other positive-strand RNA viruses. PMID:28529976

Unraveling the Web of Viroinformatics: Computational Tools and Databases in Virus Research

PubMed Central

Priyadarshini, Pragya; Vrati, Sudhanshu

2014-01-01

The beginning of the second century of research in the field of virology (the first virus was discovered in 1898) was marked by its amalgamation with bioinformatics, resulting in the birth of a new domain—viroinformatics. The availability of more than 100 Web servers and databases embracing all or specific viruses (for example, dengue virus, influenza virus, hepatitis virus, human immunodeficiency virus [HIV], hemorrhagic fever virus [HFV], human papillomavirus [HPV], West Nile virus, etc.) as well as distinct applications (comparative/diversity analysis, viral recombination, small interfering RNA [siRNA]/short hairpin RNA [shRNA]/microRNA [miRNA] studies, RNA folding, protein-protein interaction, structural analysis, and phylotyping and genotyping) will definitely aid the development of effective drugs and vaccines. However, information about their access and utility is not available at any single source or on any single platform. Therefore, a compendium of various computational tools and resources dedicated specifically to virology is presented in this article. PMID:25428870

Unraveling the web of viroinformatics: computational tools and databases in virus research.

PubMed

Sharma, Deepak; Priyadarshini, Pragya; Vrati, Sudhanshu

2015-02-01

The beginning of the second century of research in the field of virology (the first virus was discovered in 1898) was marked by its amalgamation with bioinformatics, resulting in the birth of a new domain--viroinformatics. The availability of more than 100 Web servers and databases embracing all or specific viruses (for example, dengue virus, influenza virus, hepatitis virus, human immunodeficiency virus [HIV], hemorrhagic fever virus [HFV], human papillomavirus [HPV], West Nile virus, etc.) as well as distinct applications (comparative/diversity analysis, viral recombination, small interfering RNA [siRNA]/short hairpin RNA [shRNA]/microRNA [miRNA] studies, RNA folding, protein-protein interaction, structural analysis, and phylotyping and genotyping) will definitely aid the development of effective drugs and vaccines. However, information about their access and utility is not available at any single source or on any single platform. Therefore, a compendium of various computational tools and resources dedicated specifically to virology is presented in this article. Copyright © 2015, American Society for Microbiology. All Rights Reserved.

ISCB Ebola Award for Important Future Research on the Computational Biology of Ebola Virus

PubMed Central

Karp, Peter D.; Berger, Bonnie; Kovats, Diane; Lengauer, Thomas; Linial, Michal; Sabeti, Pardis; Hide, Winston; Rost, Burkhard

2015-01-01

Speed is of the essence in combating Ebola; thus, computational approaches should form a significant component of Ebola research. As for the development of any modern drug, computational biology is uniquely positioned to contribute through comparative analysis of the genome sequences of Ebola strains as well as 3-D protein modeling. Other computational approaches to Ebola may include large-scale docking studies of Ebola proteins with human proteins and with small-molecule libraries, computational modeling of the spread of the virus, computational mining of the Ebola literature, and creation of a curated Ebola database. Taken together, such computational efforts could significantly accelerate traditional scientific approaches. In recognition of the need for important and immediate solutions from the field of computational biology against Ebola, the International Society for Computational Biology (ISCB) announces a prize for an important computational advance in fighting the Ebola virus. ISCB will confer the ISCB Fight against Ebola Award, along with a prize of US$2,000, at its July 2016 annual meeting (ISCB Intelligent Systems for Molecular Biology (ISMB) 2016, Orlando, Florida). PMID:26097686

ISCB Ebola Award for Important Future Research on the Computational Biology of Ebola Virus.

PubMed

Karp, Peter D; Berger, Bonnie; Kovats, Diane; Lengauer, Thomas; Linial, Michal; Sabeti, Pardis; Hide, Winston; Rost, Burkhard

2015-01-01

Speed is of the essence in combating Ebola; thus, computational approaches should form a significant component of Ebola research. As for the development of any modern drug, computational biology is uniquely positioned to contribute through comparative analysis of the genome sequences of Ebola strains as well as 3-D protein modeling. Other computational approaches to Ebola may include large-scale docking studies of Ebola proteins with human proteins and with small-molecule libraries, computational modeling of the spread of the virus, computational mining of the Ebola literature, and creation of a curated Ebola database. Taken together, such computational efforts could significantly accelerate traditional scientific approaches. In recognition of the need for important and immediate solutions from the field of computational biology against Ebola, the International Society for Computational Biology (ISCB) announces a prize for an important computational advance in fighting the Ebola virus. ISCB will confer the ISCB Fight against Ebola Award, along with a prize of US$2,000, at its July 2016 annual meeting (ISCB Intelligent Systems for Molecular Biology (ISMB) 2016, Orlando, Florida).

Computational approaches to predict bacteriophage–host relationships

PubMed Central

Edwards, Robert A.; McNair, Katelyn; Faust, Karoline; Raes, Jeroen; Dutilh, Bas E.

2015-01-01

Metagenomics has changed the face of virus discovery by enabling the accurate identification of viral genome sequences without requiring isolation of the viruses. As a result, metagenomic virus discovery leaves the first and most fundamental question about any novel virus unanswered: What host does the virus infect? The diversity of the global virosphere and the volumes of data obtained in metagenomic sequencing projects demand computational tools for virus–host prediction. We focus on bacteriophages (phages, viruses that infect bacteria), the most abundant and diverse group of viruses found in environmental metagenomes. By analyzing 820 phages with annotated hosts, we review and assess the predictive power of in silico phage–host signals. Sequence homology approaches are the most effective at identifying known phage–host pairs. Compositional and abundance-based methods contain significant signal for phage–host classification, providing opportunities for analyzing the unknowns in viral metagenomes. Together, these computational approaches further our knowledge of the interactions between phages and their hosts. Importantly, we find that all reviewed signals significantly link phages to their hosts, illustrating how current knowledge and insights about the interaction mechanisms and ecology of coevolving phages and bacteria can be exploited to predict phage–host relationships, with potential relevance for medical and industrial applications. PMID:26657537

Selection on hemagglutinin imposes a bottleneck during mammalian transmission of reassortant H5N1 influenza viruses

PubMed Central

Wilker, Peter R.; Dinis, Jorge M.; Starrett, Gabriel; Imai, Masaki; Hatta, Masato; Nelson, Chase W.; O’Connor, David H.; Hughes, Austin L.; Neumann, Gabriele; Kawaoka, Yoshihiro; Friedrich, Thomas C.

2013-01-01

The emergence of human-transmissible H5N1 avian influenza viruses poses a major pandemic threat. H5N1 viruses are thought to be highly genetically diverse both among and within hosts, but the effects of this diversity on viral replication and transmission are poorly understood. Here we use deep sequencing to investigate the impact of within-host viral variation on adaptation and transmission of H5N1 viruses in ferrets. We show that although within-host genetic diversity in hemagglutinin (HA) increases during replication in inoculated ferrets, HA diversity is dramatically reduced upon respiratory droplet transmission, where infection is established by only 1–2 distinct HA segments from a diverse source virus population in transmitting animals. Moreover, minor HA variants present in as little as 5.9% of viruses within the source animal become dominant in ferrets infected via respiratory droplets. These findings demonstrate that selective pressures acting during influenza virus transmission among mammals impose a significant bottleneck. PMID:24149915

Optimizing Targeting of Intrusion Detection Systems in Social Networks

NASA Astrophysics Data System (ADS)

Puzis, Rami; Tubi, Meytal; Elovici, Yuval

Internet users communicate with each other in various ways: by Emails, instant messaging, social networking, accessing Web sites, etc. In the course of communicating, users may unintentionally copy files contaminated with computer viruses and worms [1, 2] to their computers and spread them to other users [3]. (Hereafter we will use the term "threats", rather than computer viruses and computer worms). The Internet is the chief source of these threats [4].

Parallel Distributed Processing Theory in the Age of Deep Networks.

PubMed

Bowers, Jeffrey S

2017-12-01

Parallel distributed processing (PDP) models in psychology are the precursors of deep networks used in computer science. However, only PDP models are associated with two core psychological claims, namely that all knowledge is coded in a distributed format and cognition is mediated by non-symbolic computations. These claims have long been debated in cognitive science, and recent work with deep networks speaks to this debate. Specifically, single-unit recordings show that deep networks learn units that respond selectively to meaningful categories, and researchers are finding that deep networks need to be supplemented with symbolic systems to perform some tasks. Given the close links between PDP and deep networks, it is surprising that research with deep networks is challenging PDP theory. Copyright © 2017. Published by Elsevier Ltd.

Superficial herpes simplex virus wound infection following lung transplantation.

PubMed

Karolak, Wojtek; Wojarski, Jacek; Zegleń, Sławomir; Ochman, Marek; Urlik, Maciej; Hudzik, Bartosz; Wozniak-Grygiel, Elzbieta; Maruszewski, Marcin

2017-08-01

Surgical site infections (SSIs) are infections of tissues, organs, or spaces exposed by surgeons during performance of an invasive procedure. SSIs are classified into superficial, which are limited to skin and subcutaneous tissues, and deep. The incidence of deep SSIs in lung transplant (LTx) patients is estimated at 5%. No reports have been published as to the incidence of superficial SSIs specifically in LTx patients. Common sense would dictate that the majority of superficial SSIs would be bacterial. Uncommonly, fungal SSIs may occur, and we believe that no reports exist as to the incidence of viral wound infections in LTx patients, or in any solid organ transplant patients. We report a de novo superficial wound infection with herpes simplex virus following lung transplantation, its possible source, treatment, and resolution. © 2017 John Wiley & Sons A/S. Published by John Wiley & Sons Ltd.

Metatranscriptomic analyses of honey bee colonies.

PubMed

Tozkar, Cansu Ö; Kence, Meral; Kence, Aykut; Huang, Qiang; Evans, Jay D

2015-01-01

Honey bees face numerous biotic threats from viruses to bacteria, fungi, protists, and mites. Here we describe a thorough analysis of microbes harbored by worker honey bees collected from field colonies in geographically distinct regions of Turkey. Turkey is one of the World's most important centers of apiculture, harboring five subspecies of Apis mellifera L., approximately 20% of the honey bee subspecies in the world. We use deep ILLUMINA-based RNA sequencing to capture RNA species for the honey bee and a sampling of all non-endogenous species carried by bees. After trimming and mapping these reads to the honey bee genome, approximately 10% of the sequences (9-10 million reads per library) remained. These were then mapped to a curated set of public sequences containing ca. Sixty megabase-pairs of sequence representing known microbial species associated with honey bees. Levels of key honey bee pathogens were confirmed using quantitative PCR screens. We contrast microbial matches across different sites in Turkey, showing new country recordings of Lake Sinai virus, two Spiroplasma bacterium species, symbionts Candidatus Schmidhempelia bombi, Frischella perrara, Snodgrassella alvi, Gilliamella apicola, Lactobacillus spp.), neogregarines, and a trypanosome species. By using metagenomic analysis, this study also reveals deep molecular evidence for the presence of bacterial pathogens (Melissococcus plutonius, Paenibacillus larvae), Varroa destructor-1 virus, Sacbrood virus, and fungi. Despite this effort we did not detect KBV, SBPV, Tobacco ringspot virus, VdMLV (Varroa Macula like virus), Acarapis spp., Tropilaeleps spp. and Apocephalus (phorid fly). We discuss possible impacts of management practices and honey bee subspecies on microbial retinues. The described workflow and curated microbial database will be generally useful for microbial surveys of healthy and declining honey bees.

Metatranscriptomic analyses of honey bee colonies

PubMed Central

Tozkar, Cansu Ö.; Kence, Meral; Kence, Aykut; Huang, Qiang; Evans, Jay D.

2015-01-01

Honey bees face numerous biotic threats from viruses to bacteria, fungi, protists, and mites. Here we describe a thorough analysis of microbes harbored by worker honey bees collected from field colonies in geographically distinct regions of Turkey. Turkey is one of the World's most important centers of apiculture, harboring five subspecies of Apis mellifera L., approximately 20% of the honey bee subspecies in the world. We use deep ILLUMINA-based RNA sequencing to capture RNA species for the honey bee and a sampling of all non-endogenous species carried by bees. After trimming and mapping these reads to the honey bee genome, approximately 10% of the sequences (9–10 million reads per library) remained. These were then mapped to a curated set of public sequences containing ca. Sixty megabase-pairs of sequence representing known microbial species associated with honey bees. Levels of key honey bee pathogens were confirmed using quantitative PCR screens. We contrast microbial matches across different sites in Turkey, showing new country recordings of Lake Sinai virus, two Spiroplasma bacterium species, symbionts Candidatus Schmidhempelia bombi, Frischella perrara, Snodgrassella alvi, Gilliamella apicola, Lactobacillus spp.), neogregarines, and a trypanosome species. By using metagenomic analysis, this study also reveals deep molecular evidence for the presence of bacterial pathogens (Melissococcus plutonius, Paenibacillus larvae), Varroa destructor-1 virus, Sacbrood virus, and fungi. Despite this effort we did not detect KBV, SBPV, Tobacco ringspot virus, VdMLV (Varroa Macula like virus), Acarapis spp., Tropilaeleps spp. and Apocephalus (phorid fly). We discuss possible impacts of management practices and honey bee subspecies on microbial retinues. The described workflow and curated microbial database will be generally useful for microbial surveys of healthy and declining honey bees. PMID:25852743

World Reference Center for Arboviruses.

DTIC Science & Technology

1997-07-01

reagent bank, to identify emerging viruses by antigenic and genetic methods, and (d) ordering and cataloguing the virus collection into a computer...encephalitis viruses in mosquitoes collected in Rhode Island and Connecticut. Eastern equine encephalitis (EKE) virus was isolated from 93 of 1800...Results of ri state mosquito- virus isolation studies Viruses identified Date of first isolation Number of isolations Date of last isolation Other

Viruses in the Oceanic Basement.

PubMed

Nigro, Olivia D; Jungbluth, Sean P; Lin, Huei-Ting; Hsieh, Chih-Chiang; Miranda, Jaclyn A; Schvarcz, Christopher R; Rappé, Michael S; Steward, Grieg F

2017-03-07

Microbial life has been detected well into the igneous crust of the seafloor (i.e., the oceanic basement), but there have been no reports confirming the presence of viruses in this habitat. To detect and characterize an ocean basement virome, geothermally heated fluid samples (ca. 60 to 65°C) were collected from 117 to 292 m deep into the ocean basement using seafloor observatories installed in two boreholes (Integrated Ocean Drilling Program [IODP] U1362A and U1362B) drilled in the eastern sediment-covered flank of the Juan de Fuca Ridge. Concentrations of virus-like particles in the fluid samples were on the order of 0.2 × 10 5 to 2 × 10 5  ml -1 ( n = 8), higher than prokaryote-like cells in the same samples by a factor of 9 on average (range, 1.5 to 27). Electron microscopy revealed diverse viral morphotypes similar to those of viruses known to infect bacteria and thermophilic archaea. An analysis of virus-like sequences in basement microbial metagenomes suggests that those from archaeon-infecting viruses were the most common (63 to 80%). Complete genomes of a putative archaeon-infecting virus and a prophage within an archaeal scaffold were identified among the assembled sequences, and sequence analysis suggests that they represent lineages divergent from known thermophilic viruses. Of the clustered regularly interspaced short palindromic repeat (CRISPR)-containing scaffolds in the metagenomes for which a taxonomy could be inferred (163 out of 737), 51 to 55% appeared to be archaeal and 45 to 49% appeared to be bacterial. These results imply that the warmed, highly altered fluids in deeply buried ocean basement harbor a distinct assemblage of novel viruses, including many that infect archaea, and that these viruses are active participants in the ecology of the basement microbiome. IMPORTANCE The hydrothermally active ocean basement is voluminous and likely provided conditions critical to the origins of life, but the microbiology of this vast habitat is not well understood. Viruses in particular, although integral to the origins, evolution, and ecology of all life on earth, have never been documented in basement fluids. This report provides the first estimate of free virus particles (virions) within fluids circulating through the extrusive basalt of the seafloor and describes the morphological and genetic signatures of basement viruses. These data push the known geographical limits of the virosphere deep into the ocean basement and point to a wealth of novel viral diversity, exploration of which could shed light on the early evolution of viruses. Copyright © 2017 Nigro et al.

NGS of Virus-Derived Small RNAs as a Diagnostic Method Used to Determine Viromes of Hungarian Vineyards

PubMed Central

Czotter, Nikoletta; Molnar, Janos; Szabó, Emese; Demian, Emese; Kontra, Levente; Baksa, Ivett; Szittya, Gyorgy; Kocsis, Laszlo; Deak, Tamas; Bisztray, Gyorgy; Tusnady, Gabor E.; Burgyan, Jozsef; Varallyay, Eva

2018-01-01

As virus diseases cannot be controlled by traditional plant protection methods, the risk of their spread have to be minimized on vegetatively propagated plants, such as grapevine. Metagenomic approaches used for virus diagnostics offer a unique opportunity to reveal the presence of all viral pathogens in the investigated plant, which is why their application can reduce the risk of using infected material for a new plantation. Here we used a special branch, deep sequencing of virus-derived small RNAs, of this high-throughput method for virus diagnostics, and determined viromes of vineyards in Hungary. With NGS of virus-derived small RNAs we could detect not only the viruses tested routinely, but also new ones, which had never been described in Hungary before. Virus presence did not correlate with the age of the plantation, moreover phylogenetic analysis of the identified virus isolates suggests that infections are mostly caused by the use of infected propagating material. Our results, validated by other molecular methods, raised further questions to be answered before this method can be introduced as a routine, reliable test for grapevine virus diagnostics. PMID:25741336

Convolutional Deep Belief Networks for Single-Cell/Object Tracking in Computational Biology and Computer Vision.

PubMed

Zhong, Bineng; Pan, Shengnan; Zhang, Hongbo; Wang, Tian; Du, Jixiang; Chen, Duansheng; Cao, Liujuan

2016-01-01

In this paper, we propose deep architecture to dynamically learn the most discriminative features from data for both single-cell and object tracking in computational biology and computer vision. Firstly, the discriminative features are automatically learned via a convolutional deep belief network (CDBN). Secondly, we design a simple yet effective method to transfer features learned from CDBNs on the source tasks for generic purpose to the object tracking tasks using only limited amount of training data. Finally, to alleviate the tracker drifting problem caused by model updating, we jointly consider three different types of positive samples. Extensive experiments validate the robustness and effectiveness of the proposed method.

Convolutional Deep Belief Networks for Single-Cell/Object Tracking in Computational Biology and Computer Vision

PubMed Central

Pan, Shengnan; Zhang, Hongbo; Wang, Tian; Du, Jixiang; Chen, Duansheng; Cao, Liujuan

2016-01-01

In this paper, we propose deep architecture to dynamically learn the most discriminative features from data for both single-cell and object tracking in computational biology and computer vision. Firstly, the discriminative features are automatically learned via a convolutional deep belief network (CDBN). Secondly, we design a simple yet effective method to transfer features learned from CDBNs on the source tasks for generic purpose to the object tracking tasks using only limited amount of training data. Finally, to alleviate the tracker drifting problem caused by model updating, we jointly consider three different types of positive samples. Extensive experiments validate the robustness and effectiveness of the proposed method. PMID:27847827

On the impact of approximate computation in an analog DeSTIN architecture.

PubMed

Young, Steven; Lu, Junjie; Holleman, Jeremy; Arel, Itamar

2014-05-01

Deep machine learning (DML) holds the potential to revolutionize machine learning by automating rich feature extraction, which has become the primary bottleneck of human engineering in pattern recognition systems. However, the heavy computational burden renders DML systems implemented on conventional digital processors impractical for large-scale problems. The highly parallel computations required to implement large-scale deep learning systems are well suited to custom hardware. Analog computation has demonstrated power efficiency advantages of multiple orders of magnitude relative to digital systems while performing nonideal computations. In this paper, we investigate typical error sources introduced by analog computational elements and their impact on system-level performance in DeSTIN--a compositional deep learning architecture. These inaccuracies are evaluated on a pattern classification benchmark, clearly demonstrating the robustness of the underlying algorithm to the errors introduced by analog computational elements. A clear understanding of the impacts of nonideal computations is necessary to fully exploit the efficiency of analog circuits.

Interactions and Survival of Enteric Viruses in Soil Materials

PubMed Central

Sobsey, Mark D.; Dean, Cheryl H.; Knuckles, Maurice E.; Wagner, Ray A.

1980-01-01

There were marked differences in the abilities of eight different soil materials to remove and retain viruses from settled sewage, but for each soil material the behavior of two different viruses, poliovirus type 1 and reovirus type 3, was often similar. Virus adsorption to soil materials was rapid, the majority occurring within 15 min. Clayey materials efficiently adsorbed both viruses from wastewater over a range of pH and total dissolved solids levels. Sands and organic soil materials were comparatively poor adsorbents, but in some cases their ability to adsorb viruses increased at low pH and with the addition of total dissolved solids or divalent cations. Viruses in suspensions of soil material in settled sewage survived for considerable time periods, despite microbial activity. In some cases virus survival was prolonged in suspensions of soil materials compared to soil-free controls. Although sandy and organic soil materials were poor virus adsorbents when suspended in wastewater, they gave ≥95% virus removal from intermittently applied wastewater as unsaturated, 10-cm-deep columns. However, considerable quantities of the retained viruses were washed from the columns by simulated rainfall. Under the same conditions, clayey soil material removed ≥99.9995% of the viruses from applied wastewater, and none were washed from the columns by simulated rainfall. PMID:6250478

Linear-array based full-view high-resolution photoacoustic computed tomography of whole mouse brain functions in vivo

NASA Astrophysics Data System (ADS)

Li, Lei; Zhang, Pengfei; Wang, Lihong V.

2018-02-01

Photoacoustic computed tomography (PACT) is a non-invasive imaging technique offering high contrast, high resolution, and deep penetration in biological tissues. We report a photoacoustic computed tomography (PACT) system equipped with a high frequency linear array for anatomical and functional imaging of the mouse whole brain. The linear array was rotationally scanned in the coronal plane to achieve the full-view coverage. We investigated spontaneous neural activities in the deep brain by monitoring the hemodynamics and observed strong interhemispherical correlations between contralateral regions, both in the cortical layer and in the deep regions.

Optimal control strategy for a novel computer virus propagation model on scale-free networks

NASA Astrophysics Data System (ADS)

Zhang, Chunming; Huang, Haitao

2016-06-01

This paper aims to study the combined impact of reinstalling system and network topology on the spread of computer viruses over the Internet. Based on scale-free network, this paper proposes a novel computer viruses propagation model-SLBOSmodel. A systematic analysis of this new model shows that the virus-free equilibrium is globally asymptotically stable when its spreading threshold is less than one; nevertheless, it is proved that the viral equilibrium is permanent if the spreading threshold is greater than one. Then, the impacts of different model parameters on spreading threshold are analyzed. Next, an optimally controlled SLBOS epidemic model on complex networks is also studied. We prove that there is an optimal control existing for the control problem. Some numerical simulations are finally given to illustrate the main results.

Two novel Alphaflexiviridae members revealed by deep sequencing of the Vanilla (Orchidaceae) virome.

PubMed

Grisoni, Michel; Marais, Armelle; Filloux, Denis; Saison, Anne; Faure, Chantal; Julian, Charlotte; Theil, Sébastien; Contreras, Sandy; Teycheney, Pierre-Yves; Roumagnac, Philippe; Candresse, Thierry

2017-12-01

The genomes of two novel viruses were assembled from 454 pyrosequencing data obtained from vanilla leaves from La Réunion. Based on genome organization and homologies, one agent was unambiguously classified as a member of the genus Potexvirus and named vanilla virus X (VVX). The second one, vanilla latent virus (VLV), is phylogenetically close to three unclassified members of the family Alphaflexiviridae with similarity to allexiviruses, and despite the presence of an additional 8-kDa open reading frame, we propose to include VLV as a new member of the genus Allexivirus. Both VVX and VLV were mechanically transmitted to vanilla plants, resulting in asymptomatic infections.

Effects of maximum node degree on computer virus spreading in scale-free networks

NASA Astrophysics Data System (ADS)

Bamaarouf, O.; Ould Baba, A.; Lamzabi, S.; Rachadi, A.; Ez-Zahraouy, H.

2017-10-01

The increase of the use of the Internet networks favors the spread of viruses. In this paper, we studied the spread of viruses in the scale-free network with different topologies based on the Susceptible-Infected-External (SIE) model. It is found that the network structure influences the virus spreading. We have shown also that the nodes of high degree are more susceptible to infection than others. Furthermore, we have determined a critical maximum value of node degree (Kc), below which the network is more resistible and the computer virus cannot expand into the whole network. The influence of network size is also studied. We found that the network with low size is more effective to reduce the proportion of infected nodes.

Viral Diagnostics in Plants Using Next Generation Sequencing: Computational Analysis in Practice.

PubMed

Jones, Susan; Baizan-Edge, Amanda; MacFarlane, Stuart; Torrance, Lesley

2017-01-01

Viruses cause significant yield and quality losses in a wide variety of cultivated crops. Hence, the detection and identification of viruses is a crucial facet of successful crop production and of great significance in terms of world food security. Whilst the adoption of molecular techniques such as RT-PCR has increased the speed and accuracy of viral diagnostics, such techniques only allow the detection of known viruses, i.e., each test is specific to one or a small number of related viruses. Therefore, unknown viruses can be missed and testing can be slow and expensive if molecular tests are unavailable. Methods for simultaneous detection of multiple viruses have been developed, and (NGS) is now a principal focus of this area, as it enables unbiased and hypothesis-free testing of plant samples. The development of NGS protocols capable of detecting multiple known and emergent viruses present in infected material is proving to be a major advance for crops, nuclear stocks or imported plants and germplasm, in which disease symptoms are absent, unspecific or only triggered by multiple viruses. Researchers want to answer the question "how many different viruses are present in this crop plant?" without knowing what they are looking for: RNA-sequencing (RNA-seq) of plant material allows this question to be addressed. As well as needing efficient nucleic acid extraction and enrichment protocols, virus detection using RNA-seq requires fast and robust bioinformatics methods to enable host sequence removal and virus classification. In this review recent studies that use RNA-seq for virus detection in a variety of crop plants are discussed with specific emphasis on the computational methods implemented. The main features of a number of specific bioinformatics workflows developed for virus detection from NGS data are also outlined and possible reasons why these have not yet been widely adopted are discussed. The review concludes by discussing the future directions of this field, including the use of bioinformatics tools for virus detection deployed in analytical environments using cloud computing.

mRNA deep sequencing reveals 75 new genes and a complex transcriptional landscape in Mimivirus.

PubMed

Legendre, Matthieu; Audic, Stéphane; Poirot, Olivier; Hingamp, Pascal; Seltzer, Virginie; Byrne, Deborah; Lartigue, Audrey; Lescot, Magali; Bernadac, Alain; Poulain, Julie; Abergel, Chantal; Claverie, Jean-Michel

2010-05-01

Mimivirus, a virus infecting Acanthamoeba, is the prototype of the Mimiviridae, the latest addition to the nucleocytoplasmic large DNA viruses. The Mimivirus genome encodes close to 1000 proteins, many of them never before encountered in a virus, such as four amino-acyl tRNA synthetases. To explore the physiology of this exceptional virus and identify the genes involved in the building of its characteristic intracytoplasmic "virion factory," we coupled electron microscopy observations with the massively parallel pyrosequencing of the polyadenylated RNA fractions of Acanthamoeba castellanii cells at various time post-infection. We generated 633,346 reads, of which 322,904 correspond to Mimivirus transcripts. This first application of deep mRNA sequencing (454 Life Sciences [Roche] FLX) to a large DNA virus allowed the precise delineation of the 5' and 3' extremities of Mimivirus mRNAs and revealed 75 new transcripts including several noncoding RNAs. Mimivirus genes are expressed across a wide dynamic range, in a finely regulated manner broadly described by three main temporal classes: early, intermediate, and late. This RNA-seq study confirmed the AAAATTGA sequence as an early promoter element, as well as the presence of palindromes at most of the polyadenylation sites. It also revealed a new promoter element correlating with late gene expression, which is also prominent in Sputnik, the recently described Mimivirus "virophage." These results-validated genome-wide by the hybridization of total RNA extracted from infected Acanthamoeba cells on a tiling array (Agilent)--will constitute the foundation on which to build subsequent functional studies of the Mimivirus/Acanthamoeba system.

Choline and Geranate Deep Eutectic Solvent as a Broad-Spectrum Antiseptic Agent for Preventive and Therapeutic Applications.

PubMed

Zakrewsky, Michael; Banerjee, Amrita; Apte, Sanjana; Kern, Theresa L; Jones, Mattie R; Sesto, Rico E Del; Koppisch, Andrew T; Fox, David T; Mitragotri, Samir

2016-06-01

Antiseptic agents are the primary arsenal to disinfect skin and prevent pathogens spreading within the host as well as into the surroundings; however the Food and Drug Administration published a report in 2015 requiring additional validation of nearly all current antiseptic agents before their continued use can be allowed. This vulnerable position calls for urgent identification of novel antiseptic agents. Recently, the ability of a deep eutectic, Choline And Geranate (CAGE), to treat biofilms of Pseudomonas aeruginosa and Salmonella enterica was demonstrated. Here it is reported that CAGE exhibits broad-spectrum antimicrobial activity against a number of drug-resistant bacteria, fungi, and viruses including clinical isolates of Mycobacterium tuberculosis, Staphylococcus aureus, and Candida albicans as well as laboratory strains of Herpes Simplex Virus. Studies in human keratinocytes and mice show that CAGE affords negligible local or systemic toxicity, and an ≈180-14 000-fold improved efficacy/toxicity ratio over currently used antiseptic agents. Further, CAGE penetrates deep into the dermis and treats pathogens located in deep skin layers as confirmed by the ability of CAGE in vivo to treat Propionibacterium acnes infection. In combination, the results clearly demonstrate CAGE holds promise as a transformative platform antiseptic agent for preventive as well as therapeutic applications. © 2016 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Characterization of apple stem grooving virus and apple chlorotic leaf spot virus identified in a crab apple tree.

PubMed

Li, Yongqiang; Deng, Congliang; Bian, Yong; Zhao, Xiaoli; Zhou, Qi

2017-04-01

Apple stem grooving virus (ASGV), apple chlorotic leaf spot virus (ACLSV), and prunus necrotic ringspot virus (PNRSV) were identified in a crab apple tree by small RNA deep sequencing. The complete genome sequence of ACLSV isolate BJ (ACLSV-BJ) was 7554 nucleotides and shared 67.0%-83.0% nucleotide sequence identity with other ACLSV isolates. A phylogenetic tree based on the complete genome sequence of all available ACLSV isolates showed that ACLSV-BJ clustered with the isolates SY01 from hawthorn, MO5 from apple, and JB, KMS and YH from pear. The complete nucleotide sequence of ASGV-BJ was 6509 nucleotides (nt) long and shared 78.2%-80.7% nucleotide sequence identity with other isolates. ASGV-BJ and the isolate ASGV_kfp clustered together in the phylogenetic tree as an independent clade. Recombination analysis showed that isolate ASGV-BJ was a naturally occurring recombinant.

Deep learning aided decision support for pulmonary nodules diagnosing: a review.

PubMed

Yang, Yixin; Feng, Xiaoyi; Chi, Wenhao; Li, Zhengyang; Duan, Wenzhe; Liu, Haiping; Liang, Wenhua; Wang, Wei; Chen, Ping; He, Jianxing; Liu, Bo

2018-04-01

Deep learning techniques have recently emerged as promising decision supporting approaches to automatically analyze medical images for different clinical diagnosing purposes. Diagnosing of pulmonary nodules by using computer-assisted diagnosing has received considerable theoretical, computational, and empirical research work, and considerable methods have been developed for detection and classification of pulmonary nodules on different formats of images including chest radiographs, computed tomography (CT), and positron emission tomography in the past five decades. The recent remarkable and significant progress in deep learning for pulmonary nodules achieved in both academia and the industry has demonstrated that deep learning techniques seem to be promising alternative decision support schemes to effectively tackle the central issues in pulmonary nodules diagnosing, including feature extraction, nodule detection, false-positive reduction, and benign-malignant classification for the huge volume of chest scan data. The main goal of this investigation is to provide a comprehensive state-of-the-art review of the deep learning aided decision support for pulmonary nodules diagnosing. As far as the authors know, this is the first time that a review is devoted exclusively to deep learning techniques for pulmonary nodules diagnosing.

Using barcoded Zika virus to assess virus population structure in vitro and in Aedes aegypti mosquitoes.

PubMed

Weger-Lucarelli, James; Garcia, Selene M; Rückert, Claudia; Byas, Alex; O'Connor, Shelby L; Aliota, Matthew T; Friedrich, Thomas C; O'Connor, David H; Ebel, Gregory D

2018-06-20

Arboviruses such as Zika virus (ZIKV, Flaviviridae; Flavivirus) must replicate in both mammalian and insect hosts possessing strong immune defenses. Accordingly, transmission between and replication within hosts involves genetic bottlenecks, during which viral population size and genetic diversity may be significantly reduced. To help quantify these bottlenecks and their effects, we constructed 4 "barcoded" ZIKV populations that theoretically contain thousands of barcodes each. After identifying the most diverse barcoded virus, we passaged this virus 3 times in 2 mammalian and mosquito cell lines and characterized the population using deep sequencing of the barcoded region of the genome. C6/36 maintain higher barcode diversity, even after 3 passages, than Vero. Additionally, field-caught mosquitoes exposed to the virus to assess bottlenecks in a natural host. A progressive reduction in barcode diversity occurred throughout systemic infection of these mosquitoes. Differences in bottlenecks during systemic spread were observed between different populations of Aedes aegypti. Copyright © 2018. Published by Elsevier Inc.

Novel Virus Discovery and Genome Reconstruction from Field RNA Samples Reveals Highly Divergent Viruses in Dipteran Hosts

PubMed Central

Bass, David; Moureau, Gregory; Tang, Shuoya; McAlister, Erica; Culverwell, C. Lorna; Glücksman, Edvard; Wang, Hui; Brown, T. David K.; Gould, Ernest A.; Harbach, Ralph E.; de Lamballerie, Xavier; Firth, Andrew E.

2013-01-01

We investigated whether small RNA (sRNA) sequenced from field-collected mosquitoes and chironomids (Diptera) can be used as a proxy signature of viral prevalence within a range of species and viral groups, using sRNAs sequenced from wild-caught specimens, to inform total RNA deep sequencing of samples of particular interest. Using this strategy, we sequenced from adult Anopheles maculipennis s.l. mosquitoes the apparently nearly complete genome of one previously undescribed virus related to chronic bee paralysis virus, and, from a pool of Ochlerotatus caspius and Oc. detritus mosquitoes, a nearly complete entomobirnavirus genome. We also reconstructed long sequences (1503-6557 nt) related to at least nine other viruses. Crucially, several of the sequences detected were reconstructed from host organisms highly divergent from those in which related viruses have been previously isolated or discovered. It is clear that viral transmission and maintenance cycles in nature are likely to be significantly more complex and taxonomically diverse than previously expected. PMID:24260463

PREDICTING ATTENUATION OF VIRUSES DURING PERCOLATION IN SOILS: 2. USER'S GUIDE TO THE VIRULO 1.0 COMPUTER MODEL

EPA Science Inventory

In the EPA document Predicting Attenuation of Viruses During Percolation in Soils 1. Probabilistic Model the conceptual, theoretical, and mathematical foundations for a predictive screening model were presented. In this current volume we present a User's Guide for the computer mo...

Evaluation of a Biometric Keystroke Typing Dynamics Computer Security System

DTIC Science & Technology

1992-03-01

intrusions, numerous computer systems have been threatened or destroyed by virus attacks. A recent example was the virus called " Michelangelo ," which...threatened to destroy all data on infected hard disks on the birthday of the artist Michelangelo , 6 March, in 1992. During the 1991 Persian Gulf War

Swept-Source Optical Coherence Tomography Angiography in West Nile Virus Chorioretinitis and Associated Occlusive Retinal Vasculitis.

PubMed

Khairallah, Moncef; Kahloun, Rim; Gargouri, Salma; Jelliti, Bechir; Sellami, Dorra; Ben Yahia, Salim; Feki, Jamel

2017-08-01

A 65-year-old man with diabetes and a history of fever of unknown origin 2 weeks earlier complained of sudden decreased vision in the left eye. The patient was diagnosed with bilateral West Nile virus (WNV) chorioretinitis associated with occlusive retinal vasculitis in the left eye. Swept-source optical coherence tomography angiography (SS-OCTA) of the left eye showed extensive, well-delineated, hypointense non-perfusion areas and perifoveal capillary arcade disruption in the superficial capillary plexus, as well as larger non-perfusion areas, capillary rarefaction, and diffuse capillary network attenuation and disorganization in the deep capillary plexus. OCTA may be a valuable tool for noninvasively assessing occlusive retinal vasculitis associated with WNV infection. It allows an accurate detection and precise delineation of areas of retinal capillary nonperfusion in both the superficial and deep capillary plexuses. [Ophthalmic Surg Lasers Imaging Retina. 2017;48:672-675.]. Copyright 2017, SLACK Incorporated.

A Deep Learning Approach to on-Node Sensor Data Analytics for Mobile or Wearable Devices.

PubMed

Ravi, Daniele; Wong, Charence; Lo, Benny; Yang, Guang-Zhong

2017-01-01

The increasing popularity of wearable devices in recent years means that a diverse range of physiological and functional data can now be captured continuously for applications in sports, wellbeing, and healthcare. This wealth of information requires efficient methods of classification and analysis where deep learning is a promising technique for large-scale data analytics. While deep learning has been successful in implementations that utilize high-performance computing platforms, its use on low-power wearable devices is limited by resource constraints. In this paper, we propose a deep learning methodology, which combines features learned from inertial sensor data together with complementary information from a set of shallow features to enable accurate and real-time activity classification. The design of this combined method aims to overcome some of the limitations present in a typical deep learning framework where on-node computation is required. To optimize the proposed method for real-time on-node computation, spectral domain preprocessing is used before the data are passed onto the deep learning framework. The classification accuracy of our proposed deep learning approach is evaluated against state-of-the-art methods using both laboratory and real world activity datasets. Our results show the validity of the approach on different human activity datasets, outperforming other methods, including the two methods used within our combined pipeline. We also demonstrate that the computation times for the proposed method are consistent with the constraints of real-time on-node processing on smartphones and a wearable sensor platform.

Top-down and bottom-up control on bacterial diversity in a western Norwegian deep-silled fjord.

PubMed

Storesund, Julia E; Erga, Svein Rune; Ray, Jessica L; Thingstad, T Frede; Sandaa, Ruth-Anne

2015-07-01

We investigated the relationship between viruses and co-occurring bacterial communities in the Sognefjord, a deep-silled fjord in Western Norway. A combination of flow cytometry and automated ribosomal intergenic spacer analysis (ARISA) was used to assess prokaryote and viral abundances, and bacterial diversity and community composition, respectively, in depth profiles and at two different sampling seasons (November and May). With one exception, bacterial diversity did not vary between samples regardless of depth or season. The virus and prokaryote abundances as well as bacterial community composition, however, varied significantly with season and depth, suggesting a link between the Sognefjord viral community and potential bacterial host community diversity. To our knowledge, these findings provide the first description of microbial communities in the unique Sognefjord ecosystem, and in addition are in agreement with the simple model version of the 'Killing the Winner' theory (KtW), which postulates that microbial community diversity is a feature that is essentially top-down controlled by viruses, while community composition is bottom-up controlled by competition for limiting growth substrates. © FEMS 2015. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.

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

PubMed

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.

Convolutional networks for fast, energy-efficient neuromorphic computing

PubMed Central

Esser, Steven K.; Merolla, Paul A.; Arthur, John V.; Cassidy, Andrew S.; Appuswamy, Rathinakumar; Andreopoulos, Alexander; Berg, David J.; McKinstry, Jeffrey L.; Melano, Timothy; Barch, Davis R.; di Nolfo, Carmelo; Datta, Pallab; Amir, Arnon; Taba, Brian; Flickner, Myron D.; Modha, Dharmendra S.

2016-01-01

Deep networks are now able to achieve human-level performance on a broad spectrum of recognition tasks. Independently, neuromorphic computing has now demonstrated unprecedented energy-efficiency through a new chip architecture based on spiking neurons, low precision synapses, and a scalable communication network. Here, we demonstrate that neuromorphic computing, despite its novel architectural primitives, can implement deep convolution networks that (i) approach state-of-the-art classification accuracy across eight standard datasets encompassing vision and speech, (ii) perform inference while preserving the hardware’s underlying energy-efficiency and high throughput, running on the aforementioned datasets at between 1,200 and 2,600 frames/s and using between 25 and 275 mW (effectively >6,000 frames/s per Watt), and (iii) can be specified and trained using backpropagation with the same ease-of-use as contemporary deep learning. This approach allows the algorithmic power of deep learning to be merged with the efficiency of neuromorphic processors, bringing the promise of embedded, intelligent, brain-inspired computing one step closer. PMID:27651489

Convolutional networks for fast, energy-efficient neuromorphic computing.

PubMed

Esser, Steven K; Merolla, Paul A; Arthur, John V; Cassidy, Andrew S; Appuswamy, Rathinakumar; Andreopoulos, Alexander; Berg, David J; McKinstry, Jeffrey L; Melano, Timothy; Barch, Davis R; di Nolfo, Carmelo; Datta, Pallab; Amir, Arnon; Taba, Brian; Flickner, Myron D; Modha, Dharmendra S

2016-10-11

Deep networks are now able to achieve human-level performance on a broad spectrum of recognition tasks. Independently, neuromorphic computing has now demonstrated unprecedented energy-efficiency through a new chip architecture based on spiking neurons, low precision synapses, and a scalable communication network. Here, we demonstrate that neuromorphic computing, despite its novel architectural primitives, can implement deep convolution networks that (i) approach state-of-the-art classification accuracy across eight standard datasets encompassing vision and speech, (ii) perform inference while preserving the hardware's underlying energy-efficiency and high throughput, running on the aforementioned datasets at between 1,200 and 2,600 frames/s and using between 25 and 275 mW (effectively >6,000 frames/s per Watt), and (iii) can be specified and trained using backpropagation with the same ease-of-use as contemporary deep learning. This approach allows the algorithmic power of deep learning to be merged with the efficiency of neuromorphic processors, bringing the promise of embedded, intelligent, brain-inspired computing one step closer.

[The Triumph of "Stupidity" : Deep Blue`s Victory over Garri Kasparov. The Controversy about its Impact on Artficial Intelligence Research].

PubMed

Heßler, Martina

2017-03-01

The competition between the chess computer Deep Blue and the former chess world champion Garri Kasparov in 1997 was a spectacle staged for the media. However, the chess game, like other games, was also a test field for artificial intelligence research. On the one hand Deep Blue's victory was called a "milestone" for AI research, on the other hand, a dead end, since the superiority of the chess computer was based on pure computing power and had nothing to do with "real" AI.The article questions the premises of these different interpretations and maps Deep Blue and its way of playing chess into the history of AI. This also requires an analysis of the underlying concepts of thinking. Finally, the essay calls for assuming different "ways of thinking" for man and computer. Instead of fundamental discussions of concepts of thinking, we should ask about the consequences of the human-machine division of labor.

Metagenomic Analysis of Viral Communities in (Hado)Pelagic Sediments

PubMed Central

Yoshida, Mitsuhiro; Takaki, Yoshihiro; Eitoku, Masamitsu; Nunoura, Takuro; Takai, Ken

2013-01-01

In this study, we analyzed viral metagenomes (viromes) in the sedimentary habitats of three geographically and geologically distinct (hado)pelagic environments in the northwest Pacific; the Izu-Ogasawara Trench (water depth = 9,760 m) (OG), the Challenger Deep in the Mariana Trench (10,325 m) (MA), and the forearc basin off the Shimokita Peninsula (1,181 m) (SH). Virus abundance ranged from 106 to 1011 viruses/cm3 of sediments (down to 30 cm below the seafloor [cmbsf]). We recovered viral DNA assemblages (viromes) from the (hado)pelagic sediment samples and obtained a total of 37,458, 39,882, and 70,882 sequence reads by 454 GS FLX Titanium pyrosequencing from the virome libraries of the OG, MA, and SH (hado)pelagic sediments, respectively. Only 24−30% of the sequence reads from each virome library exhibited significant similarities to the sequences deposited in the public nr protein database (E-value <10−3 in BLAST). Among the sequences identified as potential viral genes based on the BLAST search, 95−99% of the sequence reads in each library were related to genes from single-stranded DNA (ssDNA) viral families, including Microviridae, Circoviridae, and Geminiviridae. A relatively high abundance of sequences related to the genetic markers (major capsid protein [VP1] and replication protein [Rep]) of two ssDNA viral groups were also detected in these libraries, thereby revealing a high genotypic diversity of their viruses (833 genotypes for VP1 and 2,551 genotypes for Rep). A majority of the viral genes predicted from each library were classified into three ssDNA viral protein categories: Rep, VP1, and minor capsid protein. The deep-sea sedimentary viromes were distinct from the viromes obtained from the oceanic and fresh waters and marine eukaryotes, and thus, deep-sea sediments harbor novel viromes, including previously unidentified ssDNA viruses. PMID:23468952

Metagenomic analysis of viral communities in (hado)pelagic sediments.

PubMed

Yoshida, Mitsuhiro; Takaki, Yoshihiro; Eitoku, Masamitsu; Nunoura, Takuro; Takai, Ken

2013-01-01

In this study, we analyzed viral metagenomes (viromes) in the sedimentary habitats of three geographically and geologically distinct (hado)pelagic environments in the northwest Pacific; the Izu-Ogasawara Trench (water depth = 9,760 m) (OG), the Challenger Deep in the Mariana Trench (10,325 m) (MA), and the forearc basin off the Shimokita Peninsula (1,181 m) (SH). Virus abundance ranged from 10(6) to 10(11) viruses/cm(3) of sediments (down to 30 cm below the seafloor [cmbsf]). We recovered viral DNA assemblages (viromes) from the (hado)pelagic sediment samples and obtained a total of 37,458, 39,882, and 70,882 sequence reads by 454 GS FLX Titanium pyrosequencing from the virome libraries of the OG, MA, and SH (hado)pelagic sediments, respectively. Only 24-30% of the sequence reads from each virome library exhibited significant similarities to the sequences deposited in the public nr protein database (E-value <10(-3) in BLAST). Among the sequences identified as potential viral genes based on the BLAST search, 95-99% of the sequence reads in each library were related to genes from single-stranded DNA (ssDNA) viral families, including Microviridae, Circoviridae, and Geminiviridae. A relatively high abundance of sequences related to the genetic markers (major capsid protein [VP1] and replication protein [Rep]) of two ssDNA viral groups were also detected in these libraries, thereby revealing a high genotypic diversity of their viruses (833 genotypes for VP1 and 2,551 genotypes for Rep). A majority of the viral genes predicted from each library were classified into three ssDNA viral protein categories: Rep, VP1, and minor capsid protein. The deep-sea sedimentary viromes were distinct from the viromes obtained from the oceanic and fresh waters and marine eukaryotes, and thus, deep-sea sediments harbor novel viromes, including previously unidentified ssDNA viruses.

Deep sequencing analysis of viral short RNAs from an infected Pinot Noir grapevine.

PubMed

Pantaleo, Vitantonio; Saldarelli, Pasquale; Miozzi, Laura; Giampetruzzi, Annalisa; Gisel, Andreas; Moxon, Simon; Dalmay, Tamas; Bisztray, György; Burgyan, Jozsef

2010-12-05

Virus-derived short interfering RNAs (vsiRNAs) isolated from grapevine V. vinifera Pinot Noir clone ENTAV 115 were analyzed by high-throughput sequencing using the Illumina Solexa platform. We identified and characterized vsiRNAs derived from grapevine field plants naturally infected with different viruses belonging to the genera Foveavirus, Maculavirus, Marafivirus and Nepovirus. These vsiRNAs were mainly of 21 and 22 nucleotides (nt) in size and were discontinuously distributed throughout Grapevine rupestris stem-pitting associated virus (GRSPaV) and Grapevine fleck virus (GFkV) genomic RNAs. Among the studied viruses, GRSPaV and GFkV vsiRNAs had a 5' terminal nucleotide bias, which differed from that described for experimental viral infections in Arabidopsis thaliana. VsiRNAs were found to originate from both genomic and antigenomic GRSPaV RNA strands, whereas with the grapevine tymoviruses GFkV and Grapevine Red Globe associated virus (GRGV), the large majority derived from the antigenomic viral strand, a feature never observed in other plant-virus interactions. Copyright © 2010 Elsevier Inc. All rights reserved.

Dynamic properties of epidemic spreading on finite size complex networks

NASA Astrophysics Data System (ADS)

Li, Ying; Liu, Yang; Shan, Xiu-Ming; Ren, Yong; Jiao, Jian; Qiu, Ben

2005-11-01

The Internet presents a complex topological structure, on which computer viruses can easily spread. By using theoretical analysis and computer simulation methods, the dynamic process of disease spreading on finite size networks with complex topological structure is investigated. On the finite size networks, the spreading process of SIS (susceptible-infected-susceptible) model is a finite Markov chain with an absorbing state. Two parameters, the survival probability and the conditional infecting probability, are introduced to describe the dynamic properties of disease spreading on finite size networks. Our results can help understanding computer virus epidemics and other spreading phenomena on communication and social networks. Also, knowledge about the dynamic character of virus spreading is helpful for adopting immunity policy.

Large-scale Labeled Datasets to Fuel Earth Science Deep Learning Applications

NASA Astrophysics Data System (ADS)

Maskey, M.; Ramachandran, R.; Miller, J.

2017-12-01

Deep learning has revolutionized computer vision and natural language processing with various algorithms scaled using high-performance computing. However, generic large-scale labeled datasets such as the ImageNet are the fuel that drives the impressive accuracy of deep learning results. Large-scale labeled datasets already exist in domains such as medical science, but creating them in the Earth science domain is a challenge. While there are ways to apply deep learning using limited labeled datasets, there is a need in the Earth sciences for creating large-scale labeled datasets for benchmarking and scaling deep learning applications. At the NASA Marshall Space Flight Center, we are using deep learning for a variety of Earth science applications where we have encountered the need for large-scale labeled datasets. We will discuss our approaches for creating such datasets and why these datasets are just as valuable as deep learning algorithms. We will also describe successful usage of these large-scale labeled datasets with our deep learning based applications.

A tradeoff between the losses caused by computer viruses and the risk of the manpower shortage

PubMed Central

Bi, Jichao; Yang, Lu-Xing; Wu, Yingbo; Tang, Yuan Yan

2018-01-01

This article addresses the tradeoff between the losses caused by a new virus and the size of the team for developing an antivirus against the virus. First, an individual-level virus spreading model is proposed to capture the spreading process of the virus before the appearance of its natural enemy. On this basis, the tradeoff problem is modeled as a discrete optimization problem. Next, the influences of different factors, including the infection force, the infection function, the available manpower, the alarm threshold, the antivirus development effort and the network topology, on the optimal team size are examined through computer simulations. This work takes the first step toward the tradeoff problem, and the findings are instructive to the decision makers of network security companies. PMID:29370222

A tradeoff between the losses caused by computer viruses and the risk of the manpower shortage.

PubMed

Bi, Jichao; Yang, Lu-Xing; Yang, Xiaofan; Wu, Yingbo; Tang, Yuan Yan

2018-01-01

This article addresses the tradeoff between the losses caused by a new virus and the size of the team for developing an antivirus against the virus. First, an individual-level virus spreading model is proposed to capture the spreading process of the virus before the appearance of its natural enemy. On this basis, the tradeoff problem is modeled as a discrete optimization problem. Next, the influences of different factors, including the infection force, the infection function, the available manpower, the alarm threshold, the antivirus development effort and the network topology, on the optimal team size are examined through computer simulations. This work takes the first step toward the tradeoff problem, and the findings are instructive to the decision makers of network security companies.

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

PubMed Central

2013-01-01

Background 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. Results Coverage depth was generally lower for IAPV than DWV, and marked gaps in coverage occurred in several narrow regions (< 50 bp) of IAPV. These coverage gaps occurred across sequencing runs and were virtually unchanged when reads were re-mapped with greater permissiveness (up to 8% divergence), suggesting a recurrent sequencing artifact rather than strain divergence. Consensus sequences of DWV for each sample showed little phylogenetic divergence, low nucleotide diversity, and strongly negative values of Fu and Li’s D statistic, suggesting a recent population bottleneck and/or purifying 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. Conclusions 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. PMID:23497218

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

PubMed

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 (< 50 bp) of IAPV. These coverage gaps occurred across sequencing runs and were virtually unchanged when reads were re-mapped with greater permissiveness (up to 8% divergence), suggesting a recurrent sequencing artifact rather than strain divergence. Consensus sequences of DWV for each sample showed little phylogenetic divergence, low nucleotide diversity, and strongly negative values of Fu and Li's D statistic, suggesting a recent population bottleneck and/or purifying 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.

Deep hierarchies in the primate visual cortex: what can we learn for computer vision?

PubMed

Krüger, Norbert; Janssen, Peter; Kalkan, Sinan; Lappe, Markus; Leonardis, Ales; Piater, Justus; Rodríguez-Sánchez, Antonio J; Wiskott, Laurenz

2013-08-01

Computational modeling of the primate visual system yields insights of potential relevance to some of the challenges that computer vision is facing, such as object recognition and categorization, motion detection and activity recognition, or vision-based navigation and manipulation. This paper reviews some functional principles and structures that are generally thought to underlie the primate visual cortex, and attempts to extract biological principles that could further advance computer vision research. Organized for a computer vision audience, we present functional principles of the processing hierarchies present in the primate visual system considering recent discoveries in neurophysiology. The hierarchical processing in the primate visual system is characterized by a sequence of different levels of processing (on the order of 10) that constitute a deep hierarchy in contrast to the flat vision architectures predominantly used in today's mainstream computer vision. We hope that the functional description of the deep hierarchies realized in the primate visual system provides valuable insights for the design of computer vision algorithms, fostering increasingly productive interaction between biological and computer vision research.

3D image reconstruction algorithms for cryo-electron-microscopy images of virus particles

NASA Astrophysics Data System (ADS)

Doerschuk, Peter C.; Johnson, John E.

2000-11-01

A statistical model for the object and the complete image formation process in cryo electron microscopy of viruses is presented. Using this model, maximum likelihood reconstructions of the 3D structure of viruses are computed using the expectation maximization algorithm and an example based on Cowpea mosaic virus is provided.

Zika Virus-Associated Cognitive Impairment in Adolescent, 2016.

PubMed

Zucker, Jason; Neu, Natalie; Chiriboga, Claudia A; Hinton, Veronica J; Leonardo, Marc; Sheikh, Arif; Thakur, Kiran

2017-06-01

Incidence of neurologic manifestations associated with Zika virus infection has been increasing. In 2016, neuropsychological and cognitive changes developed in an adolescent after travel to a Zika virus-endemic area. Single-photon emission computed tomography and neuropsychological testing raised the possibility that Zika virus infection may lead to neuropsychiatric and cognitive symptoms.

Computations in the deep vs superficial layers of the cerebral cortex.

PubMed

Rolls, Edmund T; Mills, W Patrick C

2017-11-01

A fundamental question is how the cerebral neocortex operates functionally, computationally. The cerebral neocortex with its superficial and deep layers and highly developed recurrent collateral systems that provide a basis for memory-related processing might perform somewhat different computations in the superficial and deep layers. Here we take into account the quantitative connectivity within and between laminae. Using integrate-and-fire neuronal network simulations that incorporate this connectivity, we first show that attractor networks implemented in the deep layers that are activated by the superficial layers could be partly independent in that the deep layers might have a different time course, which might because of adaptation be more transient and useful for outputs from the neocortex. In contrast the superficial layers could implement more prolonged firing, useful for slow learning and for short-term memory. Second, we show that a different type of computation could in principle be performed in the superficial and deep layers, by showing that the superficial layers could operate as a discrete attractor network useful for categorisation and feeding information forward up a cortical hierarchy, whereas the deep layers could operate as a continuous attractor network useful for providing a spatially and temporally smooth output to output systems in the brain. A key advance is that we draw attention to the functions of the recurrent collateral connections between cortical pyramidal cells, often omitted in canonical models of the neocortex, and address principles of operation of the neocortex by which the superficial and deep layers might be specialized for different types of attractor-related memory functions implemented by the recurrent collaterals. Copyright © 2017 Elsevier Inc. All rights reserved.

Computational 3D structures of drug-targeting proteins in the 2009-H1N1 influenza A virus

NASA Astrophysics Data System (ADS)

Du, Qi-Shi; Wang, Shu-Qing; Huang, Ri-Bo; Chou, Kuo-Chen

2010-01-01

The neuraminidase (NA) and M2 proton channel of influenza virus are the drug-targeting proteins, based on which several drugs were developed. However these once powerful drugs encountered drug-resistant problem to the H5N1 and H1N1 flu. To address this problem, the computational 3D structures of NA and M2 proteins of 2009-H1N1 influenza virus were built using the molecular modeling technique and computational chemistry method. Based on the models the structure features of NA and M2 proteins were analyzed, the docking structures of drug-protein complexes were computed, and the residue mutations were annotated. The results may help to solve the drug-resistant problem and stimulate designing more effective drugs against 2009-H1N1 influenza pandemic.

Crispen's Five Antivirus Rules.

ERIC Educational Resources Information Center

Crispen, Patrick Douglas

2000-01-01

Provides rules for protecting computers from viruses, Trojan horses, or worms. Topics include purchasing commercial antivirus programs and keeping them updated; updating virus definitions weekly; precautions before opening attached files; macro virus protection in Microsoft Word; and precautions with executable files. (LRW)

Computer aided lung cancer diagnosis with deep learning algorithms

NASA Astrophysics Data System (ADS)

Sun, Wenqing; Zheng, Bin; Qian, Wei

2016-03-01

Deep learning is considered as a popular and powerful method in pattern recognition and classification. However, there are not many deep structured applications used in medical imaging diagnosis area, because large dataset is not always available for medical images. In this study we tested the feasibility of using deep learning algorithms for lung cancer diagnosis with the cases from Lung Image Database Consortium (LIDC) database. The nodules on each computed tomography (CT) slice were segmented according to marks provided by the radiologists. After down sampling and rotating we acquired 174412 samples with 52 by 52 pixel each and the corresponding truth files. Three deep learning algorithms were designed and implemented, including Convolutional Neural Network (CNN), Deep Belief Networks (DBNs), Stacked Denoising Autoencoder (SDAE). To compare the performance of deep learning algorithms with traditional computer aided diagnosis (CADx) system, we designed a scheme with 28 image features and support vector machine. The accuracies of CNN, DBNs, and SDAE are 0.7976, 0.8119, and 0.7929, respectively; the accuracy of our designed traditional CADx is 0.7940, which is slightly lower than CNN and DBNs. We also noticed that the mislabeled nodules using DBNs are 4% larger than using traditional CADx, this might be resulting from down sampling process lost some size information of the nodules.

Sensitivity of Small RNA-Based Detection of Plant Viruses.

PubMed

Santala, Johanna; Valkonen, Jari P T

2018-01-01

Plants recognize unrelated viruses by the antiviral defense system called RNA interference (RNAi). RNAi processes double-stranded viral RNA into small RNAs (sRNAs) of 21-24 nucleotides, the reassembly of which into longer strands in silico allows virus identification by comparison with the sequences available in databases. The aim of this study was to compare the virus detection sensitivity of sRNA-based virus diagnosis with the established virus species-specific polymerase chain reaction (PCR) approach. Viruses propagated in tobacco plants included three engineered, infectious clones of Potato virus A (PVA), each carrying a different marker gene, and an infectious clone of Potato virus Y (PVY). Total RNA (containing sRNA) was isolated and subjected to reverse-transcription real-time PCR (RT-RT-PCR) and sRNA deep-sequencing at different concentrations. RNA extracted from various crop plants was included in the reactions to normalize RNA concentrations. Targeted detection of selected viruses showed a similar threshold for the sRNA and reverse-transcription quantitative PCR (RT-qPCR) analyses. The detection limit for PVY and PVA by RT-qPCR in this study was 3 and 1.5 fg of viral RNA, respectively, in 50 ng of total RNA per PCR reaction. When knowledge was available about the viruses likely present in the samples, sRNA-based virus detection was 10 times more sensitive than RT-RT-PCR. The advantage of sRNA analysis is the detection of all tested viruses without the need for virus-specific primers or probes.

Message from the ISCB: ISCB Ebola award for important future research on the computational biology of Ebola virus.

PubMed

Karp, Peter D; Berger, Bonnie; Kovats, Diane; Lengauer, Thomas; Linial, Michal; Sabeti, Pardis; Hide, Winston; Rost, Burkhard

2015-02-15

Speed is of the essence in combating Ebola; thus, computational approaches should form a significant component of Ebola research. As for the development of any modern drug, computational biology is uniquely positioned to contribute through comparative analysis of the genome sequences of Ebola strains and three-dimensional protein modeling. Other computational approaches to Ebola may include large-scale docking studies of Ebola proteins with human proteins and with small-molecule libraries, computational modeling of the spread of the virus, computational mining of the Ebola literature and creation of a curated Ebola database. Taken together, such computational efforts could significantly accelerate traditional scientific approaches. In recognition of the need for important and immediate solutions from the field of computational biology against Ebola, the International Society for Computational Biology (ISCB) announces a prize for an important computational advance in fighting the Ebola virus. ISCB will confer the ISCB Fight against Ebola Award, along with a prize of US$2000, at its July 2016 annual meeting (ISCB Intelligent Systems for Molecular Biology 2016, Orlando, FL). dkovats@iscb.org or rost@in.tum.de. © The Author 2015. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com.

Deep Learning in Gastrointestinal Endoscopy.

PubMed

Patel, Vivek; Armstrong, David; Ganguli, Malika; Roopra, Sandeep; Kantipudi, Neha; Albashir, Siwar; Kamath, Markad V

2016-01-01

Gastrointestinal (GI) endoscopy is used to inspect the lumen or interior of the GI tract for several purposes, including, (1) making a clinical diagnosis, in real time, based on the visual appearances; (2) taking targeted tissue samples for subsequent histopathological examination; and (3) in some cases, performing therapeutic interventions targeted at specific lesions. GI endoscopy is therefore predicated on the assumption that the operator-the endoscopist-is able to identify and characterize abnormalities or lesions accurately and reproducibly. However, as in other areas of clinical medicine, such as histopathology and radiology, many studies have documented marked interobserver and intraobserver variability in lesion recognition. Thus, there is a clear need and opportunity for techniques or methodologies that will enhance the quality of lesion recognition and diagnosis and improve the outcomes of GI endoscopy. Deep learning models provide a basis to make better clinical decisions in medical image analysis. Biomedical image segmentation, classification, and registration can be improved with deep learning. Recent evidence suggests that the application of deep learning methods to medical image analysis can contribute significantly to computer-aided diagnosis. Deep learning models are usually considered to be more flexible and provide reliable solutions for image analysis problems compared to conventional computer vision models. The use of fast computers offers the possibility of real-time support that is important for endoscopic diagnosis, which has to be made in real time. Advanced graphics processing units and cloud computing have also favored the use of machine learning, and more particularly, deep learning for patient care. This paper reviews the rapidly evolving literature on the feasibility of applying deep learning algorithms to endoscopic imaging.

From clinical sample to complete genome: Comparing methods for the extraction of HIV-1 RNA for high-throughput deep sequencing.

PubMed

Cornelissen, Marion; Gall, Astrid; Vink, Monique; Zorgdrager, Fokla; Binter, Špela; Edwards, Stephanie; Jurriaans, Suzanne; Bakker, Margreet; Ong, Swee Hoe; Gras, Luuk; van Sighem, Ard; Bezemer, Daniela; de Wolf, Frank; Reiss, Peter; Kellam, Paul; Berkhout, Ben; Fraser, Christophe; van der Kuyl, Antoinette C

2017-07-15

The BEEHIVE (Bridging the Evolution and Epidemiology of HIV in Europe) project aims to analyse nearly-complete viral genomes from >3000 HIV-1 infected Europeans using high-throughput deep sequencing techniques to investigate the virus genetic contribution to virulence. Following the development of a computational pipeline, including a new de novo assembler for RNA virus genomes, to generate larger contiguous sequences (contigs) from the abundance of short sequence reads that characterise the data, another area that determines genome sequencing success is the quality and quantity of the input RNA. A pilot experiment with 125 patient plasma samples was performed to investigate the optimal method for isolation of HIV-1 viral RNA for long amplicon genome sequencing. Manual isolation with the QIAamp Viral RNA Mini Kit (Qiagen) was superior over robotically extracted RNA using either the QIAcube robotic system, the mSample Preparation Systems RNA kit with automated extraction by the m2000sp system (Abbott Molecular), or the MagNA Pure 96 System in combination with the MagNA Pure 96 Instrument (Roche Diagnostics). We scored amplification of a set of four HIV-1 amplicons of ∼1.9, 3.6, 3.0 and 3.5kb, and subsequent recovery of near-complete viral genomes. Subsequently, 616 BEEHIVE patient samples were analysed to determine factors that influence successful amplification of the genome in four overlapping amplicons using the QIAamp Viral RNA Kit for viral RNA isolation. Both low plasma viral load and high sample age (stored before 1999) negatively influenced the amplification of viral amplicons >3kb. A plasma viral load of >100,000 copies/ml resulted in successful amplification of all four amplicons for 86% of the samples, this value dropped to only 46% for samples with viral loads of <20,000 copies/ml. Copyright © 2016 The Authors. Published by Elsevier B.V. All rights reserved.

Development and applications of an acoustic package for deep-sea sub-bottom profiling and detailed seafloor imaging

NASA Astrophysics Data System (ADS)

Nishimura, Kiyokazu; Kisimoto, Kiyoyuki; Joshima, Masato; Arai, Kohsaku

In the deep-sea geological survey, good survey results are difficult to obtain by a conventional surface-towed acoustic survey system, because the horizontal resolution is limited due to the long distance between the sensor and the target (seafloor). In order to improve the horizontal resolution, a deep-tow system, which tows the sensor in the vicinity of seafloor, is most practical, and many such systems have been developed and used until today. It is not easy, however, to carry out a high-density survey in a small area by maneuvering the towing body altitude sufficiently close to the seafloor with rugged topography. A ROV (Remotely Operated Vehicle) can be used to solve this problem. The ROV makes a high-density 2D survey feasible because of its maneuverability, although a long-distance survey is difficult with it. Accordingly, we have developed an acoustic survey system installed on a ROV. The system named DAIPACK (Deep-sea Acoustic Imaging Package) consists of (1) a deep-sea sub-bottom profiler and (2) a deep-sea sidescan sonar. (1) Deep-sea sub-bottom profiler A light-weight and compact sub-bottom profiler for shallow water was chosen to improve and repackage for the deep sea usage. The system is composed of three units; a transducer, an electronic unit and a notebook computer for system control and data acquisition. The source frequency is 10kHz. To convert the system for the deep sea, the transducer was exchanged for the deep sea model, and the electronic unit was improved accordingly. The electronic unit and the notebook computer were installed in a spherical pressure vessel. (2) Deep-sea sidescan sonar We remodeled a compact shallow sea sidescan sonar(water depth limitation is 30m ) into a deep sea one. This sidescan sonar is composed of a sonar towfish (transducers and an electronic unit ), a cable and a notebook computer (data processor). To accommodate in the deep water, the transducers were remodeled into a high pressure resistance type, and the electronic unit and the computer unit were stored in a spherical pressure vessel. The frequency output of the sidescan sonar is 330kHz, and the ranging distance is variable from 15m to 120m (one side).

Alternatives to Retroorbital Blood Collection in Hispid Cotton Rats (Sigmodon hispidus)

PubMed Central

Ayers, Jessica D; Rota, Paul A; Collins, Marcus L; Drew, Clifton P

2012-01-01

Cotton rats (Sigmodon hispidus) are a valuable animal model for many human viral diseases, including polio virus, measles virus, respiratory syncytial virus, and herpes simplex virus. Although cotton rats have been used in research since 1939, few publications address handling and sampling techniques for this species, and the retroorbital sinus remains the recommended blood sampling site. Here we assessed blood sampling methods that are currently used in other species and a novel subzygomatic sampling site for their use in S. hispidus. The subzygomatic approach accesses a venous sinus that possibly is unique to this species and that lies just below the zygomatic arch of the maxilla and deep to the masseter muscle. We report that both the novel subzygomatic approach and the sublingual vein method can be used effectively in cotton rats. PMID:22776125

NASA's 3D Flight Computer for Space Applications

NASA Technical Reports Server (NTRS)

Alkalai, Leon

2000-01-01

The New Millennium Program (NMP) Integrated Product Development Team (IPDT) for Microelectronics Systems was planning to validate a newly developed 3D Flight Computer system on its first deep-space flight, DS1, launched in October 1998. This computer, developed in the 1995-97 time frame, contains many new computer technologies previously never used in deep-space systems. They include: advanced 3D packaging architecture for future low-mass and low-volume avionics systems; high-density 3D packaged chip-stacks for both volatile and non-volatile mass memory: 400 Mbytes of local DRAM memory, and 128 Mbytes of Flash memory; high-bandwidth Peripheral Component Interface (Per) local-bus with a bridge to VME; high-bandwidth (20 Mbps) fiber-optic serial bus; and other attributes, such as standard support for Design for Testability (DFT). Even though this computer system did not complete on time for delivery to the DS1 project, it was an important development along a technology roadmap towards highly integrated and highly miniaturized avionics systems for deep-space applications. This continued technology development is now being performed by NASA's Deep Space System Development Program (also known as X2000) and within JPL's Center for Integrated Space Microsystems (CISM).

Deep Learning: A Primer for Radiologists.

PubMed

Chartrand, Gabriel; Cheng, Phillip M; Vorontsov, Eugene; Drozdzal, Michal; Turcotte, Simon; Pal, Christopher J; Kadoury, Samuel; Tang, An

2017-01-01

Deep learning is a class of machine learning methods that are gaining success and attracting interest in many domains, including computer vision, speech recognition, natural language processing, and playing games. Deep learning methods produce a mapping from raw inputs to desired outputs (eg, image classes). Unlike traditional machine learning methods, which require hand-engineered feature extraction from inputs, deep learning methods learn these features directly from data. With the advent of large datasets and increased computing power, these methods can produce models with exceptional performance. These models are multilayer artificial neural networks, loosely inspired by biologic neural systems. Weighted connections between nodes (neurons) in the network are iteratively adjusted based on example pairs of inputs and target outputs by back-propagating a corrective error signal through the network. For computer vision tasks, convolutional neural networks (CNNs) have proven to be effective. Recently, several clinical applications of CNNs have been proposed and studied in radiology for classification, detection, and segmentation tasks. This article reviews the key concepts of deep learning for clinical radiologists, discusses technical requirements, describes emerging applications in clinical radiology, and outlines limitations and future directions in this field. Radiologists should become familiar with the principles and potential applications of deep learning in medical imaging. © RSNA, 2017.

Deep learning aided decision support for pulmonary nodules diagnosing: a review

PubMed Central

Yang, Yixin; Feng, Xiaoyi; Chi, Wenhao; Li, Zhengyang; Duan, Wenzhe; Liu, Haiping; Liang, Wenhua; Wang, Wei; Chen, Ping

2018-01-01

Deep learning techniques have recently emerged as promising decision supporting approaches to automatically analyze medical images for different clinical diagnosing purposes. Diagnosing of pulmonary nodules by using computer-assisted diagnosing has received considerable theoretical, computational, and empirical research work, and considerable methods have been developed for detection and classification of pulmonary nodules on different formats of images including chest radiographs, computed tomography (CT), and positron emission tomography in the past five decades. The recent remarkable and significant progress in deep learning for pulmonary nodules achieved in both academia and the industry has demonstrated that deep learning techniques seem to be promising alternative decision support schemes to effectively tackle the central issues in pulmonary nodules diagnosing, including feature extraction, nodule detection, false-positive reduction, and benign-malignant classification for the huge volume of chest scan data. The main goal of this investigation is to provide a comprehensive state-of-the-art review of the deep learning aided decision support for pulmonary nodules diagnosing. As far as the authors know, this is the first time that a review is devoted exclusively to deep learning techniques for pulmonary nodules diagnosing. PMID:29780633

A comparative study of approaches to compute the field distribution of deep brain stimulation in the Hemiparkinson rat model.

PubMed

Bohme, Andrea; van Rienen, Ursula

2016-08-01

Computational modeling of the stimulating field distribution during Deep Brain Stimulation provides an opportunity to advance our knowledge of this neurosurgical therapy for Parkinson's disease. There exist several approaches to model the target region for Deep Brain Stimulation in Hemi-parkinson Rats with volume conductor models. We have described and compared the normalized mapping approach as well as the modeling with three-dimensional structures, which include curvilinear coordinates to assure an anatomically realistic conductivity tensor orientation.

The Ebola virus VP35 protein binds viral immunostimulatory and host RNAs identified through deep sequencing.

PubMed

Dilley, Kari A; Voorhies, Alexander A; Luthra, Priya; Puri, Vinita; Stockwell, Timothy B; Lorenzi, Hernan; Basler, Christopher F; Shabman, Reed S

2017-01-01

Ebola virus and Marburg virus are members of the Filovirdae family and causative agents of hemorrhagic fever with high fatality rates in humans. Filovirus virulence is partially attributed to the VP35 protein, a well-characterized inhibitor of the RIG-I-like receptor pathway that triggers the antiviral interferon (IFN) response. Prior work demonstrates the ability of VP35 to block potent RIG-I activators, such as Sendai virus (SeV), and this IFN-antagonist activity is directly correlated with its ability to bind RNA. Several structural studies demonstrate that VP35 binds short synthetic dsRNAs; yet, there are no data that identify viral immunostimulatory RNAs (isRNA) or host RNAs bound to VP35 in cells. Utilizing a SeV infection model, we demonstrate that both viral isRNA and host RNAs are bound to Ebola and Marburg VP35s in cells. By deep sequencing the purified VP35-bound RNA, we identified the SeV copy-back defective interfering (DI) RNA, previously identified as a robust RIG-I activator, as the isRNA bound by multiple filovirus VP35 proteins, including the VP35 protein from the West African outbreak strain (Makona EBOV). Moreover, RNAs isolated from a VP35 RNA-binding mutant were not immunostimulatory and did not include the SeV DI RNA. Strikingly, an analysis of host RNAs bound by wild-type, but not mutant, VP35 revealed that select host RNAs are preferentially bound by VP35 in cell culture. Taken together, these data support a model in which VP35 sequesters isRNA in virus-infected cells to avert RIG-I like receptor (RLR) activation.

mRNA deep sequencing reveals 75 new genes and a complex transcriptional landscape in Mimivirus

PubMed Central

Legendre, Matthieu; Audic, Stéphane; Poirot, Olivier; Hingamp, Pascal; Seltzer, Virginie; Byrne, Deborah; Lartigue, Audrey; Lescot, Magali; Bernadac, Alain; Poulain, Julie; Abergel, Chantal; Claverie, Jean-Michel

2010-01-01

Mimivirus, a virus infecting Acanthamoeba, is the prototype of the Mimiviridae, the latest addition to the nucleocytoplasmic large DNA viruses. The Mimivirus genome encodes close to 1000 proteins, many of them never before encountered in a virus, such as four amino-acyl tRNA synthetases. To explore the physiology of this exceptional virus and identify the genes involved in the building of its characteristic intracytoplasmic “virion factory,” we coupled electron microscopy observations with the massively parallel pyrosequencing of the polyadenylated RNA fractions of Acanthamoeba castellanii cells at various time post-infection. We generated 633,346 reads, of which 322,904 correspond to Mimivirus transcripts. This first application of deep mRNA sequencing (454 Life Sciences [Roche] FLX) to a large DNA virus allowed the precise delineation of the 5′ and 3′ extremities of Mimivirus mRNAs and revealed 75 new transcripts including several noncoding RNAs. Mimivirus genes are expressed across a wide dynamic range, in a finely regulated manner broadly described by three main temporal classes: early, intermediate, and late. This RNA-seq study confirmed the AAAATTGA sequence as an early promoter element, as well as the presence of palindromes at most of the polyadenylation sites. It also revealed a new promoter element correlating with late gene expression, which is also prominent in Sputnik, the recently described Mimivirus “virophage.” These results—validated genome-wide by the hybridization of total RNA extracted from infected Acanthamoeba cells on a tiling array (Agilent)—will constitute the foundation on which to build subsequent functional studies of the Mimivirus/Acanthamoeba system. PMID:20360389

Small RNA Analysis in Sindbis Virus Infected Human HEK293 Cells

PubMed Central

Dalmay, Tamas; Powell, Penny P.

2013-01-01

Introduction In contrast to the defence mechanism of RNA interference (RNAi) in plants and invertebrates, its role in the innate response to virus infection of mammals is a matter of debate. Since RNAi has a well-established role in controlling infection of the alphavirus Sindbis virus (SINV) in insects, we have used this virus to investigate the role of RNAi in SINV infection of human cells. Results SINV AR339 and TR339-GFP were adapted to grow in HEK293 cells. Deep sequencing of small RNAs (sRNAs) early in SINV infection (4 and 6 hpi) showed low abundance (0.8%) of viral sRNAs (vsRNAs), with no size, sequence or location specific patterns characteristic of Dicer products nor did they possess any discernible pattern to ascribe to a specific RNAi biogenesis pathway. This was supported by multiple variants for each sequence, and lack of hot spots along the viral genome sequence. The abundance of the best defined vsRNAs was below the limit of Northern blot detection. The adaptation of the virus to HEK293 cells showed little sequence changes compared to the reference; however, a SNP in E1 gene with a preference from G to C was found. Deep sequencing results showed little variation of expression of cellular microRNAs (miRNAs) at 4 and 6 hpi compared to uninfected cells. Twelve miRNAs exhibiting some minor differential expression by sequencing, showed no difference in expression by Northern blot analysis. Conclusions We show that, unlike SINV infection of invertebrates, generation of Dicer-dependent svRNAs and change in expression of cellular miRNAs were not detected as part of the Human response to SINV. PMID:24391886

The Ebola virus VP35 protein binds viral immunostimulatory and host RNAs identified through deep sequencing

PubMed Central

Voorhies, Alexander A.; Luthra, Priya; Puri, Vinita; Stockwell, Timothy B.; Lorenzi, Hernan; Basler, Christopher F.; Shabman, Reed S.

2017-01-01

Ebola virus and Marburg virus are members of the Filovirdae family and causative agents of hemorrhagic fever with high fatality rates in humans. Filovirus virulence is partially attributed to the VP35 protein, a well-characterized inhibitor of the RIG-I-like receptor pathway that triggers the antiviral interferon (IFN) response. Prior work demonstrates the ability of VP35 to block potent RIG-I activators, such as Sendai virus (SeV), and this IFN-antagonist activity is directly correlated with its ability to bind RNA. Several structural studies demonstrate that VP35 binds short synthetic dsRNAs; yet, there are no data that identify viral immunostimulatory RNAs (isRNA) or host RNAs bound to VP35 in cells. Utilizing a SeV infection model, we demonstrate that both viral isRNA and host RNAs are bound to Ebola and Marburg VP35s in cells. By deep sequencing the purified VP35-bound RNA, we identified the SeV copy-back defective interfering (DI) RNA, previously identified as a robust RIG-I activator, as the isRNA bound by multiple filovirus VP35 proteins, including the VP35 protein from the West African outbreak strain (Makona EBOV). Moreover, RNAs isolated from a VP35 RNA-binding mutant were not immunostimulatory and did not include the SeV DI RNA. Strikingly, an analysis of host RNAs bound by wild-type, but not mutant, VP35 revealed that select host RNAs are preferentially bound by VP35 in cell culture. Taken together, these data support a model in which VP35 sequesters isRNA in virus-infected cells to avert RIG-I like receptor (RLR) activation. PMID:28636653

Deep mutational scanning identifies sites in influenza nucleoprotein that affect viral inhibition by MxA

PubMed Central

Ashenberg, Orr; Padmakumar, Jai

2017-01-01

The innate-immune restriction factor MxA inhibits influenza replication by targeting the viral nucleoprotein (NP). Human influenza virus is more resistant than avian influenza virus to inhibition by human MxA, and prior work has compared human and avian viral strains to identify amino-acid differences in NP that affect sensitivity to MxA. However, this strategy is limited to identifying sites in NP where mutations that affect MxA sensitivity have fixed during the small number of documented zoonotic transmissions of influenza to humans. Here we use an unbiased deep mutational scanning approach to quantify how all single amino-acid mutations to NP affect MxA sensitivity in the context of replication-competent virus. We both identify new sites in NP where mutations affect MxA resistance and re-identify mutations known to have increased MxA resistance during historical adaptations of influenza to humans. Most of the sites where mutations have the greatest effect are almost completely conserved across all influenza A viruses, and the amino acids at these sites confer relatively high resistance to MxA. These sites cluster in regions of NP that appear to be important for its recognition by MxA. Overall, our work systematically identifies the sites in influenza nucleoprotein where mutations affect sensitivity to MxA. We also demonstrate a powerful new strategy for identifying regions of viral proteins that affect inhibition by host factors. PMID:28346537

Viral Diagnostics in Plants Using Next Generation Sequencing: Computational Analysis in Practice

PubMed Central

Jones, Susan; Baizan-Edge, Amanda; MacFarlane, Stuart; Torrance, Lesley

2017-01-01

Viruses cause significant yield and quality losses in a wide variety of cultivated crops. Hence, the detection and identification of viruses is a crucial facet of successful crop production and of great significance in terms of world food security. Whilst the adoption of molecular techniques such as RT-PCR has increased the speed and accuracy of viral diagnostics, such techniques only allow the detection of known viruses, i.e., each test is specific to one or a small number of related viruses. Therefore, unknown viruses can be missed and testing can be slow and expensive if molecular tests are unavailable. Methods for simultaneous detection of multiple viruses have been developed, and (NGS) is now a principal focus of this area, as it enables unbiased and hypothesis-free testing of plant samples. The development of NGS protocols capable of detecting multiple known and emergent viruses present in infected material is proving to be a major advance for crops, nuclear stocks or imported plants and germplasm, in which disease symptoms are absent, unspecific or only triggered by multiple viruses. Researchers want to answer the question “how many different viruses are present in this crop plant?” without knowing what they are looking for: RNA-sequencing (RNA-seq) of plant material allows this question to be addressed. As well as needing efficient nucleic acid extraction and enrichment protocols, virus detection using RNA-seq requires fast and robust bioinformatics methods to enable host sequence removal and virus classification. In this review recent studies that use RNA-seq for virus detection in a variety of crop plants are discussed with specific emphasis on the computational methods implemented. The main features of a number of specific bioinformatics workflows developed for virus detection from NGS data are also outlined and possible reasons why these have not yet been widely adopted are discussed. The review concludes by discussing the future directions of this field, including the use of bioinformatics tools for virus detection deployed in analytical environments using cloud computing. PMID:29123534

Deep Sequencing of Influenza A Virus from a Human Challenge Study Reveals a Selective Bottleneck and Only Limited Intrahost Genetic Diversification.

PubMed

Sobel Leonard, Ashley; McClain, Micah T; Smith, Gavin J D; Wentworth, David E; Halpin, Rebecca A; Lin, Xudong; Ransier, Amy; Stockwell, Timothy B; Das, Suman R; Gilbert, Anthony S; Lambkin-Williams, Robert; Ginsburg, Geoffrey S; Woods, Christopher W; Koelle, Katia

2016-12-15

Knowledge of influenza virus evolution at the point of transmission and at the intrahost level remains limited, particularly for human hosts. Here, we analyze a unique viral data set of next-generation sequencing (NGS) samples generated from a human influenza challenge study wherein 17 healthy subjects were inoculated with cell- and egg-passaged virus. Nasal wash samples collected from 7 of these subjects were successfully deep sequenced. From these, we characterized changes in the subjects' viral populations during infection and identified differences between the virus in these samples and the viral stock used to inoculate the subjects. We first calculated pairwise genetic distances between the subjects' nasal wash samples, the viral stock, and the influenza virus A/Wisconsin/67/2005 (H3N2) reference strain used to generate the stock virus. These distances revealed that considerable viral evolution occurred at various points in the human challenge study. Further quantitative analyses indicated that (i) the viral stock contained genetic variants that originated and likely were selected for during the passaging process, (ii) direct intranasal inoculation with the viral stock resulted in a selective bottleneck that reduced nonsynonymous genetic diversity in the viral hemagglutinin and nucleoprotein, and (iii) intrahost viral evolution continued over the course of infection. These intrahost evolutionary dynamics were dominated by purifying selection. Our findings indicate that rapid viral evolution can occur during acute influenza infection in otherwise healthy human hosts when the founding population size of the virus is large, as is the case with direct intranasal inoculation. Influenza viruses circulating among humans are known to rapidly evolve over time. However, little is known about how influenza virus evolves across single transmission events and over the course of a single infection. To address these issues, we analyze influenza virus sequences from a human challenge experiment that initiated infection with a cell- and egg-passaged viral stock, which appeared to have adapted during its preparation. We find that the subjects' viral populations differ genetically from the viral stock, with subjects' viral populations having lower representation of the amino-acid-changing variants that arose during viral preparation. We also find that most of the viral evolution occurring over single infections is characterized by further decreases in the frequencies of these amino-acid-changing variants and that only limited intrahost genetic diversification through new mutations is apparent. Our findings indicate that influenza virus populations can undergo rapid genetic changes during acute human infections. Copyright © 2016 Sobel Leonard et al.

Deep Learning in Medical Imaging: General Overview

PubMed Central

Lee, June-Goo; Jun, Sanghoon; Cho, Young-Won; Lee, Hyunna; Kim, Guk Bae

2017-01-01

The artificial neural network (ANN)–a machine learning technique inspired by the human neuronal synapse system–was introduced in the 1950s. However, the ANN was previously limited in its ability to solve actual problems, due to the vanishing gradient and overfitting problems with training of deep architecture, lack of computing power, and primarily the absence of sufficient data to train the computer system. Interest in this concept has lately resurfaced, due to the availability of big data, enhanced computing power with the current graphics processing units, and novel algorithms to train the deep neural network. Recent studies on this technology suggest its potentially to perform better than humans in some visual and auditory recognition tasks, which may portend its applications in medicine and healthcare, especially in medical imaging, in the foreseeable future. This review article offers perspectives on the history, development, and applications of deep learning technology, particularly regarding its applications in medical imaging. PMID:28670152

Deep Learning in Medical Imaging: General Overview.

PubMed

Lee, June-Goo; Jun, Sanghoon; Cho, Young-Won; Lee, Hyunna; Kim, Guk Bae; Seo, Joon Beom; Kim, Namkug

2017-01-01

The artificial neural network (ANN)-a machine learning technique inspired by the human neuronal synapse system-was introduced in the 1950s. However, the ANN was previously limited in its ability to solve actual problems, due to the vanishing gradient and overfitting problems with training of deep architecture, lack of computing power, and primarily the absence of sufficient data to train the computer system. Interest in this concept has lately resurfaced, due to the availability of big data, enhanced computing power with the current graphics processing units, and novel algorithms to train the deep neural network. Recent studies on this technology suggest its potentially to perform better than humans in some visual and auditory recognition tasks, which may portend its applications in medicine and healthcare, especially in medical imaging, in the foreseeable future. This review article offers perspectives on the history, development, and applications of deep learning technology, particularly regarding its applications in medical imaging.

Understanding Virtual Epidemics: Children's Folk Conceptions of a Computer Virus

ERIC Educational Resources Information Center

Kafai, Yasmin B.

2008-01-01

Our work investigates the annual outbreak of Whypox, a virtual epidemic in Whyville.net, a virtual world with over 1.2 million registered players ages 8-16. We examined online and classroom participants' understanding of a computer virus using surveys and design activities. Our analyses reveal that students have a mostly naive understanding of a…

An Historical Analysis of Factors Contributing to the Emergence of the Intrusion Detection Discipline and its Role in Information Assurance

DTIC Science & Technology

2005-03-01

computing equipment, the idea of computer security has also become embedded in our society. Ever since the Michelangelo virus of 1992, when...Bibliography TheWorldwide Michelangelo Virus Scare of 1992. Retrieved February 2, 2004 from http://www.vmyths.com/fas/fas_inc/inc1.cfm Allen, J

Fifty years of computer analysis in chest imaging: rule-based, machine learning, deep learning.

PubMed

van Ginneken, Bram

2017-03-01

Half a century ago, the term "computer-aided diagnosis" (CAD) was introduced in the scientific literature. Pulmonary imaging, with chest radiography and computed tomography, has always been one of the focus areas in this field. In this study, I describe how machine learning became the dominant technology for tackling CAD in the lungs, generally producing better results than do classical rule-based approaches, and how the field is now rapidly changing: in the last few years, we have seen how even better results can be obtained with deep learning. The key differences among rule-based processing, machine learning, and deep learning are summarized and illustrated for various applications of CAD in the chest.

Digging deeper on "deep" learning: A computational ecology approach.

PubMed

Buscema, Massimo; Sacco, Pier Luigi

2017-01-01

We propose an alternative approach to "deep" learning that is based on computational ecologies of structurally diverse artificial neural networks, and on dynamic associative memory responses to stimuli. Rather than focusing on massive computation of many different examples of a single situation, we opt for model-based learning and adaptive flexibility. Cross-fertilization of learning processes across multiple domains is the fundamental feature of human intelligence that must inform "new" artificial intelligence.

NiftyNet: a deep-learning platform for medical imaging.

PubMed

Gibson, Eli; Li, Wenqi; Sudre, Carole; Fidon, Lucas; Shakir, Dzhoshkun I; Wang, Guotai; Eaton-Rosen, Zach; Gray, Robert; Doel, Tom; Hu, Yipeng; Whyntie, Tom; Nachev, Parashkev; Modat, Marc; Barratt, Dean C; Ourselin, Sébastien; Cardoso, M Jorge; Vercauteren, Tom

2018-05-01

Medical image analysis and computer-assisted intervention problems are increasingly being addressed with deep-learning-based solutions. Established deep-learning platforms are flexible but do not provide specific functionality for medical image analysis and adapting them for this domain of application requires substantial implementation effort. Consequently, there has been substantial duplication of effort and incompatible infrastructure developed across many research groups. This work presents the open-source NiftyNet platform for deep learning in medical imaging. The ambition of NiftyNet is to accelerate and simplify the development of these solutions, and to provide a common mechanism for disseminating research outputs for the community to use, adapt and build upon. The NiftyNet infrastructure provides a modular deep-learning pipeline for a range of medical imaging applications including segmentation, regression, image generation and representation learning applications. Components of the NiftyNet pipeline including data loading, data augmentation, network architectures, loss functions and evaluation metrics are tailored to, and take advantage of, the idiosyncracies of medical image analysis and computer-assisted intervention. NiftyNet is built on the TensorFlow framework and supports features such as TensorBoard visualization of 2D and 3D images and computational graphs by default. We present three illustrative medical image analysis applications built using NiftyNet infrastructure: (1) segmentation of multiple abdominal organs from computed tomography; (2) image regression to predict computed tomography attenuation maps from brain magnetic resonance images; and (3) generation of simulated ultrasound images for specified anatomical poses. The NiftyNet infrastructure enables researchers to rapidly develop and distribute deep learning solutions for segmentation, regression, image generation and representation learning applications, or extend the platform to new applications. Copyright © 2018 The Authors. Published by Elsevier B.V. All rights reserved.

Research Advances: DNA Computing Targets West Nile Virus, Other Deadly Diseases, and Tic-Tac-Toe; Marijuana Component May Offer Hope for Alzheimer's Disease Treatment; New Wound Dressing May Lead to Maggot Therapy--Without the Maggots

ERIC Educational Resources Information Center

King, Angela G.

2007-01-01

This article presents three reports of research advances. The first report describes a deoxyribonucleic acid (DNA)-based computer that could lead to faster, more accurate tests for diagnosing West Nile Virus and bird flu. Representing the first "medium-scale integrated molecular circuit," it is the most powerful computing device of its type to…

Enhanced Experience Replay for Deep Reinforcement Learning

DTIC Science & Technology

2015-11-01

ARL-TR-7538 ● NOV 2015 US Army Research Laboratory Enhanced Experience Replay for Deep Reinforcement Learning by David Doria...Experience Replay for Deep Reinforcement Learning by David Doria, Bryan Dawson, and Manuel Vindiola Computational and Information Sciences Directorate...

Antigenic Patterns and Evolution of the Human Influenza A (H1N1) Virus

PubMed Central

Liu, Mi; Zhao, Xiang; Hua, Sha; Du, Xiangjun; Peng, Yousong; Li, Xiyan; Lan, Yu; Wang, Dayan; Wu, Aiping; Shu, Yuelong; Jiang, Taijiao

2015-01-01

The influenza A (H1N1) virus causes seasonal epidemics that result in severe illnesses and deaths almost every year. A deep understanding of the antigenic patterns and evolution of human influenza A (H1N1) virus is extremely important for its effective surveillance and prevention. Through development of antigenicity inference method for human influenza A (H1N1), named PREDAC-H1, we systematically mapped the antigenic patterns and evolution of the human influenza A (H1N1) virus. Eight dominant antigenic clusters have been inferred for seasonal H1N1 viruses since 1977, which demonstrated sequential replacements over time with a similar pattern in Asia, Europe and North America. Among them, six clusters emerged first in Asia. As for China, three of the eight antigenic clusters were detected in South China earlier than in North China, indicating the leading role of South China in H1N1 transmission. The comprehensive view of the antigenic evolution of human influenza A (H1N1) virus can help formulate better strategy for its prevention and control. PMID:26412348

Antigenic Patterns and Evolution of the Human Influenza A (H1N1) Virus.

PubMed

Liu, Mi; Zhao, Xiang; Hua, Sha; Du, Xiangjun; Peng, Yousong; Li, Xiyan; Lan, Yu; Wang, Dayan; Wu, Aiping; Shu, Yuelong; Jiang, Taijiao

2015-09-28

The influenza A (H1N1) virus causes seasonal epidemics that result in severe illnesses and deaths almost every year. A deep understanding of the antigenic patterns and evolution of human influenza A (H1N1) virus is extremely important for its effective surveillance and prevention. Through development of antigenicity inference method for human influenza A (H1N1), named PREDAC-H1, we systematically mapped the antigenic patterns and evolution of the human influenza A (H1N1) virus. Eight dominant antigenic clusters have been inferred for seasonal H1N1 viruses since 1977, which demonstrated sequential replacements over time with a similar pattern in Asia, Europe and North America. Among them, six clusters emerged first in Asia. As for China, three of the eight antigenic clusters were detected in South China earlier than in North China, indicating the leading role of South China in H1N1 transmission. The comprehensive view of the antigenic evolution of human influenza A (H1N1) virus can help formulate better strategy for its prevention and control.

Characterization of viral siRNA populations in honey bee colony collapse disorder.

PubMed

Chejanovsky, Nor; Ophir, Ron; Schwager, Michal Sharabi; Slabezki, Yossi; Grossman, Smadar; Cox-Foster, Diana

2014-04-01

Colony Collapse Disorder (CCD), a special case of collapse of honey bee colonies, has resulted in significant losses for beekeepers. CCD-colonies show abundance of pathogens which suggests that they have a weakened immune system. Since honey bee viruses are major players in colony collapse and given the important role of viral RNA interference (RNAi) in combating viral infections we investigated if CCD-colonies elicit an RNAi response. Deep-sequencing analysis of samples from CCD-colonies from US and Israel revealed abundant small interfering RNAs (siRNA) of 21-22 nucleotides perfectly matching the Israeli acute paralysis virus (IAPV), Kashmir virus and Deformed wing virus genomes. Israeli colonies showed high titers of IAPV and a conserved RNAi-pattern of matching the viral genome. That was also observed in sample analysis from colonies experimentally infected with IAPV. Our results suggest that CCD-colonies set out a siRNA response that is specific against predominant viruses associated with colony losses. Copyright © 2014 Elsevier Inc. All rights reserved.

Commensal Viruses of Mosquitoes: Host Restriction, Transmission, and Interaction with Arboviral Pathogens

PubMed Central

Hall, Roy A.; Bielefeldt-Ohmann, Helle; McLean, Breeanna J.; O’Brien, Caitlin A.; Colmant, Agathe M.G.; Piyasena, Thisun B.H.; Harrison, Jessica J.; Newton, Natalee D.; Barnard, Ross T.; Prow, Natalie A.; Deerain, Joshua M.; Mah, Marcus G.K.Y.; Hobson-Peters, Jody

2016-01-01

Recent advances in virus detection strategies and deep sequencing technologies have enabled the identification of a multitude of new viruses that persistently infect mosquitoes but do not infect vertebrates. These are usually referred to as insect-specific viruses (ISVs). These novel viruses have generated considerable interest in their modes of transmission, persistence in mosquito populations, the mechanisms that restrict their host range to mosquitoes, and their interactions with pathogens transmissible by the same mosquito. In this article, we discuss studies in our laboratory and others that demonstrate that many ISVs are efficiently transmitted directly from the female mosquito to their progeny via infected eggs, and, moreover, that persistent infection of mosquito cell cultures or whole mosquitoes with ISVs can restrict subsequent infection, replication, and transmission of some mosquito-borne viral pathogens. This suggests that some ISVs may act as natural regulators of arboviral transmission. We also discuss viral and host factors that may be responsible for their host restriction. PMID:28096646

From orphan virus to pathogen: the path to the clinical lab.

PubMed

Li, Linlin; Delwart, Eric

2011-10-01

Viral metagenomics has recently yielded numerous previously uncharacterized viral genomes from human and animal samples. We review some of the metagenomics tools and strategies to determine which orphan viruses are likely pathogens. Disease association studies compare viral prevalence in patients with unexplained symptoms versus healthy individuals but require these case and control groups to be closely matched epidemiologically. The development of an antibody response in convalescent serum can temporarily link symptoms with a recent infection. Neutralizing antibody detection require often difficult cell culture virus amplification. Antibody binding assays require proper antigen synthesis and positive control sera to set assay thresholds. High levels of viral genetic diversity within orphan viral groups, frequent co-infections, low or rare pathogenicity, and chronic virus shedding, can all complicate disease association studies. The limited availability of matched cases and controls sample sets from different age groups and geographic origins is a major block for estimating the pathogenic potential of recently characterized orphan viruses. Current limitations on the practical use of deep sequencing for viral diagnostics are listed.

The duck genome and transcriptome provide insight into an avian influenza virus reservoir species

PubMed Central

Chen, Hualan; Zhang, Yong; Qian, Wubin; Kim, Heebal; Gan, Shangquan; Zhao, Yiqiang; Li, Jianwen; Yi, Kang; Feng, Huapeng; Zhu, Pengyang; Li, Bo; Liu, Qiuyue; Fairley, Suan; Magor, Katharine E; Du, Zhenlin; Hu, Xiaoxiang; Goodman, Laurie; Tafer, Hakim; Vignal, Alain; Lee, Taeheon; Kim, Kyu-Won; Sheng, Zheya; An, Yang; Searle, Steve; Herrero, Javier; Groenen, Martien A M; Crooijmans, Richard P M A; Faraut, Thomas; Cai, Qingle; Webster, Robert G; Aldridge, Jerry R; Warren, Wesley C; Bartschat, Sebastian; Kehr, Stephanie; Marz, Manja; Stadler, Peter F; Smith, Jacqueline; Kraus, Robert H S; Zhao, Yaofeng; Ren, Liming; Fei, Jing; Morisson, Mireille; Kaiser, Pete; Griffin, Darren K; Rao, Man; Pitel, Frederique; Wang, Jun; Li, Ning

2014-01-01

The duck (Anas platyrhynchos) is one of the principal natural hosts of influenza A viruses. We present the duck genome sequence and perform deep transcriptome analyses to investigate immune-related genes. Our data indicate that the duck possesses a contractive immune gene repertoire, as in chicken and zebra finch, and this repertoire has been shaped through lineage-specific duplications. We identify genes that are responsive to influenza A viruses using the lung transcriptomes of control ducks and ones that were infected with either a highly pathogenic (A/duck/Hubei/49/05) or a weakly pathogenic (A/goose/Hubei/65/05) H5N1 virus. Further, we show how the duck’s defense mechanisms against influenza infection have been optimized through the diversification of its β-defensin and butyrophilin-like repertoires. These analyses, in combination with the genomic and transcriptomic data, provide a resource for characterizing the interaction between host and influenza viruses. PMID:23749191

Prediction of common epitopes on hemagglutinin of the influenza A virus (H1 subtype).

PubMed

Guo, Chunyan; Xie, Xin; Li, Huijin; Zhao, Penghua; Zhao, Xiangrong; Sun, Jingying; Wang, Haifang; Liu, Yang; Li, Yan; Hu, Qiaoxia; Hu, Jun; Li, Yuan

2015-02-01

Influenza A virus infection is a persistent threat to public health worldwide due to hemagglutinin (HA) variation. Current vaccines against influenza A virus provide immunity to viral isolates similar to vaccine strains. Antibodies against common epitopes provide immunity to diverse influenza virus strains and protect against future pandemic influenza. Therefore, it is vital to analyze common HA antigenic epitopes of influenza virus. In this study, 14 strains of monoclonal antibodies with high sensitivity to common epitopes of influenza virus antigens identified in our previous study were selected as the tool to predict common HA epitopes. The common HA antigenic epitopes were divided into four categories by ELISA blocking experiments, and separately, into three categories according to the preliminary results of computer simulation. Comparison between the results of computer simulations and ELISA blocking experiments indicated that at least two classes of common epitopes are present in influenza virus HA. This study provides experimental data for improving the prediction of HA epitopes of influenza virus (H1 subtype) and the development of a potential universal vaccine as well as a novel approach for the prediction of epitopes on other pathogenic microorganisms. Copyright © 2014 Elsevier Inc. All rights reserved.

A Recently Discovered Pathogenic Paramyxovirus, Sosuga Virus, is Present in Rousettus aegyptiacus Fruit Bats at Multiple Locations in Uganda.

PubMed

Amman, Brian R; Albariño, Cesar G; Bird, Brian H; Nyakarahuka, Luke; Sealy, Tara K; Balinandi, Stephen; Schuh, Amy J; Campbell, Shelly M; Ströher, Ute; Jones, Megan E B; Vodzack, Megan E; Reeder, DeeAnn M; Kaboyo, Winyi; Nichol, Stuart T; Towner, Jonathan S

2015-07-01

In August 2012, a wildlife biologist became ill immediately following a 6-wk field trip to collect bats and rodents in South Sudan and Uganda. After returning to the US, the biologist was admitted to the hospital with multiple symptoms including fever, malaise, headache, generalized myalgia and arthralgia, stiffness in the neck, and sore throat. Soon after admission, the patient developed a maculopapular rash and oropharynx ulcerations. The patient remained hospitalized for 14 d. Several suspect pathogens, including viral hemorrhagic fever viruses such as Ebola viruses and Marburg viruses, were ruled out through standard diagnostic testing. However, deep sequencing and metagenomic analyses identified a novel paramyxovirus, later named Sosuga virus, in the patient's blood. To determine the potential source, bat tissues collected during the 3-wk period just prior to the onset of symptoms were tested for Sosuga virus, and several Egyptian rousette bats (Rousettus aegyptiacus) were found to be positive. Further analysis of archived Egyptian rousette tissues collected at other localities in Uganda found additional Sosuga virus-positive bats, suggesting this species could be a potential natural reservoir for this novel paramyxovirus.

Breaking the 1000-gene barrier for Mimivirus using ultra-deep genome and transcriptome sequencing.

PubMed

Legendre, Matthieu; Santini, Sébastien; Rico, Alain; Abergel, Chantal; Claverie, Jean-Michel

2011-03-04

Mimivirus, a giant dsDNA virus infecting Acanthamoeba, is the prototype of the mimiviridae family, the latest addition to the family of the nucleocytoplasmic large DNA viruses (NCLDVs). Its 1.2 Mb-genome was initially predicted to encode 917 genes. A subsequent RNA-Seq analysis precisely mapped many transcript boundaries and identified 75 new genes. We now report a much deeper analysis using the SOLiD™ technology combining RNA-Seq of the Mimivirus transcriptome during the infectious cycle (202.4 Million reads), and a complete genome re-sequencing (45.3 Million reads). This study corrected the genome sequence and identified several single nucleotide polymorphisms. Our results also provided clear evidence of previously overlooked transcription units, including an important RNA polymerase subunit distantly related to Euryarchea homologues. The total Mimivirus gene count is now 1018, 11% greater than the original annotation. This study highlights the huge progress brought about by ultra-deep sequencing for the comprehensive annotation of virus genomes, opening the door to a complete one-nucleotide resolution level description of their transcriptional activity, and to the realistic modeling of the viral genome expression at the ultimate molecular level. This work also illustrates the need to go beyond bioinformatics-only approaches for the annotation of short protein and non-coding genes in viral genomes.

Deep learning

NASA Astrophysics Data System (ADS)

Lecun, Yann; Bengio, Yoshua; Hinton, Geoffrey

2015-05-01

Deep learning allows computational models that are composed of multiple processing layers to learn representations of data with multiple levels of abstraction. These methods have dramatically improved the state-of-the-art in speech recognition, visual object recognition, object detection and many other domains such as drug discovery and genomics. Deep learning discovers intricate structure in large data sets by using the backpropagation algorithm to indicate how a machine should change its internal parameters that are used to compute the representation in each layer from the representation in the previous layer. Deep convolutional nets have brought about breakthroughs in processing images, video, speech and audio, whereas recurrent nets have shone light on sequential data such as text and speech.

Deep learning.

PubMed

LeCun, Yann; Bengio, Yoshua; Hinton, Geoffrey

2015-05-28

Deep learning allows computational models that are composed of multiple processing layers to learn representations of data with multiple levels of abstraction. These methods have dramatically improved the state-of-the-art in speech recognition, visual object recognition, object detection and many other domains such as drug discovery and genomics. Deep learning discovers intricate structure in large data sets by using the backpropagation algorithm to indicate how a machine should change its internal parameters that are used to compute the representation in each layer from the representation in the previous layer. Deep convolutional nets have brought about breakthroughs in processing images, video, speech and audio, whereas recurrent nets have shone light on sequential data such as text and speech.

Overview of deep learning in medical imaging.

PubMed

Suzuki, Kenji

2017-09-01

The use of machine learning (ML) has been increasing rapidly in the medical imaging field, including computer-aided diagnosis (CAD), radiomics, and medical image analysis. Recently, an ML area called deep learning emerged in the computer vision field and became very popular in many fields. It started from an event in late 2012, when a deep-learning approach based on a convolutional neural network (CNN) won an overwhelming victory in the best-known worldwide computer vision competition, ImageNet Classification. Since then, researchers in virtually all fields, including medical imaging, have started actively participating in the explosively growing field of deep learning. In this paper, the area of deep learning in medical imaging is overviewed, including (1) what was changed in machine learning before and after the introduction of deep learning, (2) what is the source of the power of deep learning, (3) two major deep-learning models: a massive-training artificial neural network (MTANN) and a convolutional neural network (CNN), (4) similarities and differences between the two models, and (5) their applications to medical imaging. This review shows that ML with feature input (or feature-based ML) was dominant before the introduction of deep learning, and that the major and essential difference between ML before and after deep learning is the learning of image data directly without object segmentation or feature extraction; thus, it is the source of the power of deep learning, although the depth of the model is an important attribute. The class of ML with image input (or image-based ML) including deep learning has a long history, but recently gained popularity due to the use of the new terminology, deep learning. There are two major models in this class of ML in medical imaging, MTANN and CNN, which have similarities as well as several differences. In our experience, MTANNs were substantially more efficient in their development, had a higher performance, and required a lesser number of training cases than did CNNs. "Deep learning", or ML with image input, in medical imaging is an explosively growing, promising field. It is expected that ML with image input will be the mainstream area in the field of medical imaging in the next few decades.

Highly Pathogenic Avian Influenza Virus (H5N1) in Frozen Duck Carcasses, Germany, 2007

PubMed Central

Harder, Timm C.; Teuffert, Jürgen; Starick, Elke; Gethmann, Jörn; Grund, Christian; Fereidouni, Sasan; Durban, Markus; Bogner, Karl-Heinz; Neubauer-Juric, Antonie; Repper, Reinhard; Hlinak, Andreas; Engelhardt, Andreas; Nöckler, Axel; Smietanka, Krzysztof; Minta, Zenon; Kramer, Matthias; Globig, Anja; Mettenleiter, Thomas C.; Conraths, Franz J.

2009-01-01

We conducted phylogenetic and epidemiologic analyses to determine sources of outbreaks of highly pathogenic avian influenza virus (HPAIV), subtype H5N1, in poultry holdings in 2007 in Germany, and a suspected incursion of HPAIV into the food chain through contaminated deep-frozen duck carcasses. In summer 2007, HPAIV (H5N1) outbreaks in 3 poultry holdings in Germany were temporally, spatially, and phylogenetically linked to outbreaks in wild aquatic birds. Detection of HPAIV (H5N1) in frozen duck carcass samples of retained slaughter batches of 1 farm indicated that silent infection had occurred for some time before the incidental detection. Phylogenetic analysis established a direct epidemiologic link between HPAIV isolated from duck meat and strains isolated from 3 further outbreaks in December 2007 in backyard chickens that had access to uncooked offal from commercial deep-frozen duck carcasses. Measures that will prevent such undetected introduction of HPAIV (H5N1) into the food chain are urgently required. PMID:19193272

Discovery and small RNA profile of Pecan mosaic-associated virus, a novel potyvirus of pecan trees.

PubMed

Su, Xiu; Fu, Shuai; Qian, Yajuan; Zhang, Liqin; Xu, Yi; Zhou, Xueping

2016-05-26

A novel potyvirus was discovered in pecan (Carya illinoensis) showing leaf mosaic symptom through the use of deep sequencing of small RNAs. The complete genome of this virus was determined to comprise of 9,310 nucleotides (nt), and shared 24.0% to 58.9% nucleotide similarities with that of other Potyviridae viruses. The genome was deduced to encode a single open reading frame (polyprotein) on the plus strand. Phylogenetic analysis based on the whole genome sequence and coat protein amino acid sequence showed that this virus is most closely related to Lettuce mosaic virus. Using electron microscopy, the typical Potyvirus filamentous particles were identified in infected pecan leaves with mosaic symptoms. Our results clearly show that this virus is a new member of the genus Potyvirus in the family Potyviridae. The virus is tentatively named Pecan mosaic-associated virus (PMaV). Additionally, profiling of the PMaV-derived small RNA (PMaV-sRNA) showed that the most abundant PMaV-sRNAs were 21-nt in length. There are several hotspots for small RNA production along the PMaV genome; two 21-nt PMaV-sRNAs starting at 811 nt and 610 nt of the minus-strand genome were highly repeated.

Discovery and small RNA profile of Pecan mosaic-associated virus, a novel potyvirus of pecan trees

PubMed Central

Su, Xiu; Fu, Shuai; Qian, Yajuan; Zhang, Liqin; Xu, Yi; Zhou, Xueping

2016-01-01

A novel potyvirus was discovered in pecan (Carya illinoensis) showing leaf mosaic symptom through the use of deep sequencing of small RNAs. The complete genome of this virus was determined to comprise of 9,310 nucleotides (nt), and shared 24.0% to 58.9% nucleotide similarities with that of other Potyviridae viruses. The genome was deduced to encode a single open reading frame (polyprotein) on the plus strand. Phylogenetic analysis based on the whole genome sequence and coat protein amino acid sequence showed that this virus is most closely related to Lettuce mosaic virus. Using electron microscopy, the typical Potyvirus filamentous particles were identified in infected pecan leaves with mosaic symptoms. Our results clearly show that this virus is a new member of the genus Potyvirus in the family Potyviridae. The virus is tentatively named Pecan mosaic-associated virus (PMaV). Additionally, profiling of the PMaV-derived small RNA (PMaV-sRNA) showed that the most abundant PMaV-sRNAs were 21-nt in length. There are several hotspots for small RNA production along the PMaV genome; two 21-nt PMaV-sRNAs starting at 811 nt and 610 nt of the minus-strand genome were highly repeated. PMID:27226228

Potential impact of global climate change on benthic deep-sea microbes.

PubMed

Danovaro, Roberto; Corinaldesi, Cinzia; Dell'Anno, Antonio; Rastelli, Eugenio

2017-12-15

Benthic deep-sea environments are the largest ecosystem on Earth, covering ∼65% of the Earth surface. Microbes inhabiting this huge biome at all water depths represent the most abundant biological components and a relevant portion of the biomass of the biosphere, and play a crucial role in global biogeochemical cycles. Increasing evidence suggests that global climate changes are affecting also deep-sea ecosystems, both directly (causing shifts in bottom-water temperature, oxygen concentration and pH) and indirectly (through changes in surface oceans' productivity and in the consequent export of organic matter to the seafloor). However, the responses of the benthic deep-sea biota to such shifts remain largely unknown. This applies particularly to deep-sea microbes, which include bacteria, archaea, microeukaryotes and their viruses. Understanding the potential impacts of global change on the benthic deep-sea microbial assemblages and the consequences on the functioning of the ocean interior is a priority to better forecast the potential consequences at global scale. Here we explore the potential changes in the benthic deep-sea microbiology expected in the coming decades using case studies on specific systems used as test models. © FEMS 2017. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.

Comparative phyloinformatics of virus genes at micro and macro levels in a distributed computing environment.

PubMed

Singh, Dadabhai T; Trehan, Rahul; Schmidt, Bertil; Bretschneider, Timo

2008-01-01

Preparedness for a possible global pandemic caused by viruses such as the highly pathogenic influenza A subtype H5N1 has become a global priority. In particular, it is critical to monitor the appearance of any new emerging subtypes. Comparative phyloinformatics can be used to monitor, analyze, and possibly predict the evolution of viruses. However, in order to utilize the full functionality of available analysis packages for large-scale phyloinformatics studies, a team of computer scientists, biostatisticians and virologists is needed--a requirement which cannot be fulfilled in many cases. Furthermore, the time complexities of many algorithms involved leads to prohibitive runtimes on sequential computer platforms. This has so far hindered the use of comparative phyloinformatics as a commonly applied tool in this area. In this paper the graphical-oriented workflow design system called Quascade and its efficient usage for comparative phyloinformatics are presented. In particular, we focus on how this task can be effectively performed in a distributed computing environment. As a proof of concept, the designed workflows are used for the phylogenetic analysis of neuraminidase of H5N1 isolates (micro level) and influenza viruses (macro level). The results of this paper are hence twofold. Firstly, this paper demonstrates the usefulness of a graphical user interface system to design and execute complex distributed workflows for large-scale phyloinformatics studies of virus genes. Secondly, the analysis of neuraminidase on different levels of complexity provides valuable insights of this virus's tendency for geographical based clustering in the phylogenetic tree and also shows the importance of glycan sites in its molecular evolution. The current study demonstrates the efficiency and utility of workflow systems providing a biologist friendly approach to complex biological dataset analysis using high performance computing. In particular, the utility of the platform Quascade for deploying distributed and parallelized versions of a variety of computationally intensive phylogenetic algorithms has been shown. Secondly, the analysis of the utilized H5N1 neuraminidase datasets at macro and micro levels has clearly indicated a pattern of spatial clustering of the H5N1 viral isolates based on geographical distribution rather than temporal or host range based clustering.

Discovery of Culex pipiens associated tunisia virus: a new ssRNA(+) virus representing a new insect associated virus family

PubMed Central

Bigot, Diane; Atyame, Célestine M; Weill, Mylène; Justy, Fabienne

2018-01-01

Abstract In the global context of arboviral emergence, deep sequencing unlocks the discovery of new mosquito-borne viruses. Mosquitoes of the species Culex pipiens, C. torrentium, and C. hortensis were sampled from 22 locations worldwide for transcriptomic analyses. A virus discovery pipeline was used to analyze the dataset of 0.7 billion reads comprising 22 individual transcriptomes. Two closely related 6.8 kb viral genomes were identified in C. pipiens and named as Culex pipiens associated tunisia virus (CpATV) strains Ayed and Jedaida. The CpATV genome contained four ORFs. ORF1 possessed helicase and RNA-dependent RNA polymerase (RdRp) domains related to new viral sequences recently found mainly in dipterans. ORF2 and 4 contained a capsid protein domain showing strong homology with Virgaviridae plant viruses. ORF3 displayed similarities with eukaryotic Rhoptry domain and a merozoite surface protein (MSP7) domain only found in mosquito-transmitted Plasmodium, suggesting possible interactions between CpATV and vertebrate cells. Estimation of a strong purifying selection exerted on each ORFs and the presence of a polymorphism maintained in the coding region of ORF3 suggested that both CpATV sequences are genuine functional viruses. CpATV is part of an entirely new and highly diversified group of viruses recently found in insects, and that bears the genomic hallmarks of a new viral family. PMID:29340209

Provirophages and transpovirons as the diverse mobilome of giant viruses.

PubMed

Desnues, Christelle; La Scola, Bernard; Yutin, Natalya; Fournous, Ghislain; Robert, Catherine; Azza, Saïd; Jardot, Priscilla; Monteil, Sonia; Campocasso, Angélique; Koonin, Eugene V; Raoult, Didier

2012-10-30

A distinct class of infectious agents, the virophages that infect giant viruses of the Mimiviridae family, has been recently described. Here we report the simultaneous discovery of a giant virus of Acanthamoeba polyphaga (Lentille virus) that contains an integrated genome of a virophage (Sputnik 2), and a member of a previously unknown class of mobile genetic elements, the transpovirons. The transpovirons are linear DNA elements of ~7 kb that encompass six to eight protein-coding genes, two of which are homologous to virophage genes. Fluorescence in situ hybridization showed that the free form of the transpoviron replicates within the giant virus factory and accumulates in high copy numbers inside giant virus particles, Sputnik 2 particles, and amoeba cytoplasm. Analysis of deep-sequencing data showed that the virophage and the transpoviron can integrate in nearly any place in the chromosome of the giant virus host and that, although less frequently, the transpoviron can also be linked to the virophage chromosome. In addition, integrated fragments of transpoviron DNA were detected in several giant virus and Sputnik genomes. Analysis of 19 Mimivirus strains revealed three distinct transpovirons associated with three subgroups of Mimiviruses. The virophage, the transpoviron, and the previously identified self-splicing introns and inteins constitute the complex, interconnected mobilome of the giant viruses and are likely to substantially contribute to interviral gene transfer.

Provirophages and transpovirons as the diverse mobilome of giant viruses

PubMed Central

Desnues, Christelle; La Scola, Bernard; Yutin, Natalya; Fournous, Ghislain; Robert, Catherine; Azza, Saïd; Jardot, Priscilla; Monteil, Sonia; Campocasso, Angélique; Koonin, Eugene V.; Raoult, Didier

2012-01-01

A distinct class of infectious agents, the virophages that infect giant viruses of the Mimiviridae family, has been recently described. Here we report the simultaneous discovery of a giant virus of Acanthamoeba polyphaga (Lentille virus) that contains an integrated genome of a virophage (Sputnik 2), and a member of a previously unknown class of mobile genetic elements, the transpovirons. The transpovirons are linear DNA elements of ∼7 kb that encompass six to eight protein-coding genes, two of which are homologous to virophage genes. Fluorescence in situ hybridization showed that the free form of the transpoviron replicates within the giant virus factory and accumulates in high copy numbers inside giant virus particles, Sputnik 2 particles, and amoeba cytoplasm. Analysis of deep-sequencing data showed that the virophage and the transpoviron can integrate in nearly any place in the chromosome of the giant virus host and that, although less frequently, the transpoviron can also be linked to the virophage chromosome. In addition, integrated fragments of transpoviron DNA were detected in several giant virus and Sputnik genomes. Analysis of 19 Mimivirus strains revealed three distinct transpovirons associated with three subgroups of Mimiviruses. The virophage, the transpoviron, and the previously identified self-splicing introns and inteins constitute the complex, interconnected mobilome of the giant viruses and are likely to substantially contribute to interviral gene transfer. PMID:23071316

A robust and cost-effective approach to sequence and analyze complete genomes of small RNA viruses

USDA-ARS?s Scientific Manuscript database

Background: Next-generation sequencing (NGS) allows ultra-deep sequencing of nucleic acids. The use of sequence-independent amplification of viral nucleic acids without utilization of target-specific primers provides advantages over traditional sequencing methods and allows detection of unsuspected ...

High-Throughput Identification of Loss-of-Function Mutations for Anti-Interferon Activity in the Influenza A Virus NS Segment

PubMed Central

Wu, Nicholas C.; Young, Arthur P.; Al-Mawsawi, Laith Q.; Olson, C. Anders; Feng, Jun; Qi, Hangfei; Luan, Harding H.; Li, Xinmin; Wu, Ting-Ting

2014-01-01

ABSTRACT Viral proteins often display several functions which require multiple assays to dissect their genetic basis. Here, we describe a systematic approach to screen for loss-of-function mutations that confer a fitness disadvantage under a specified growth condition. Our methodology was achieved by genetically monitoring a mutant library under two growth conditions, with and without interferon, by deep sequencing. We employed a molecular tagging technique to distinguish true mutations from sequencing error. This approach enabled us to identify mutations that were negatively selected against, in addition to those that were positively selected for. Using this technique, we identified loss-of-function mutations in the influenza A virus NS segment that were sensitive to type I interferon in a high-throughput fashion. Mechanistic characterization further showed that a single substitution, D92Y, resulted in the inability of NS to inhibit RIG-I ubiquitination. The approach described in this study can be applied under any specified condition for any virus that can be genetically manipulated. IMPORTANCE Traditional genetics focuses on a single genotype-phenotype relationship, whereas high-throughput genetics permits phenotypic characterization of numerous mutants in parallel. High-throughput genetics often involves monitoring of a mutant library with deep sequencing. However, deep sequencing suffers from a high error rate (∼0.1 to 1%), which is usually higher than the occurrence frequency for individual point mutations within a mutant library. Therefore, only mutations that confer a fitness advantage can be identified with confidence due to an enrichment in the occurrence frequency. In contrast, it is impossible to identify deleterious mutations using most next-generation sequencing techniques. In this study, we have applied a molecular tagging technique to distinguish true mutations from sequencing errors. It enabled us to identify mutations that underwent negative selection, in addition to mutations that experienced positive selection. This study provides a proof of concept by screening for loss-of-function mutations on the influenza A virus NS segment that are involved in its anti-interferon activity. PMID:24965464

Deep Learning in Medical Image Analysis.

PubMed

Shen, Dinggang; Wu, Guorong; Suk, Heung-Il

2017-06-21

This review covers computer-assisted analysis of images in the field of medical imaging. Recent advances in machine learning, especially with regard to deep learning, are helping to identify, classify, and quantify patterns in medical images. At the core of these advances is the ability to exploit hierarchical feature representations learned solely from data, instead of features designed by hand according to domain-specific knowledge. Deep learning is rapidly becoming the state of the art, leading to enhanced performance in various medical applications. We introduce the fundamentals of deep learning methods and review their successes in image registration, detection of anatomical and cellular structures, tissue segmentation, computer-aided disease diagnosis and prognosis, and so on. We conclude by discussing research issues and suggesting future directions for further improvement.

Radiomic analysis in prediction of Human Papilloma Virus status.

PubMed

Yu, Kaixian; Zhang, Youyi; Yu, Yang; Huang, Chao; Liu, Rongjie; Li, Tengfei; Yang, Liuqing; Morris, Jeffrey S; Baladandayuthapani, Veerabhadran; Zhu, Hongtu

2017-12-01

Human Papilloma Virus (HPV) has been associated with oropharyngeal cancer prognosis. Traditionally the HPV status is tested through invasive lab test. Recently, the rapid development of statistical image analysis techniques has enabled precise quantitative analysis of medical images. The quantitative analysis of Computed Tomography (CT) provides a non-invasive way to assess HPV status for oropharynx cancer patients. We designed a statistical radiomics approach analyzing CT images to predict HPV status. Various radiomics features were extracted from CT scans, and analyzed using statistical feature selection and prediction methods. Our approach ranked the highest in the 2016 Medical Image Computing and Computer Assisted Intervention (MICCAI) grand challenge: Oropharynx Cancer (OPC) Radiomics Challenge, Human Papilloma Virus (HPV) Status Prediction. Further analysis on the most relevant radiomic features distinguishing HPV positive and negative subjects suggested that HPV positive patients usually have smaller and simpler tumors.

An intelligent and secure system for predicting and preventing Zika virus outbreak using Fog computing

NASA Astrophysics Data System (ADS)

Sareen, Sanjay; Gupta, Sunil Kumar; Sood, Sandeep K.

2017-10-01

Zika virus is a mosquito-borne disease that spreads very quickly in different parts of the world. In this article, we proposed a system to prevent and control the spread of Zika virus disease using integration of Fog computing, cloud computing, mobile phones and the Internet of things (IoT)-based sensor devices. Fog computing is used as an intermediary layer between the cloud and end users to reduce the latency time and extra communication cost that is usually found high in cloud-based systems. A fuzzy k-nearest neighbour is used to diagnose the possibly infected users, and Google map web service is used to provide the geographic positioning system (GPS)-based risk assessment to prevent the outbreak. It is used to represent each Zika virus (ZikaV)-infected user, mosquito-dense sites and breeding sites on the Google map that help the government healthcare authorities to control such risk-prone areas effectively and efficiently. The proposed system is deployed on Amazon EC2 cloud to evaluate its performance and accuracy using data set for 2 million users. Our system provides high accuracy of 94.5% for initial diagnosis of different users according to their symptoms and appropriate GPS-based risk assessment.

Novel insights into host responses and the reproductive pathophysiology of type 2 porcine reproductive and respiratory syndrome (PRRS)

USDA-ARS?s Scientific Manuscript database

A large-scale challenge experiment using type 2 porcine reproductive and respiratory virus (PRRSV) provided new insights into the pathophysiology of reproductive PRRS in third-trimester pregnant gilts. Deep phenotyping enabled identification of maternal and fetal factors predictive of PRRS severity ...

Contaminant transport pathways between urban sewer networks and water supply wells

USDA-ARS?s Scientific Manuscript database

Water supply wells and sanitary sewers are critical components of urban infrastructure, but sewer leakage threatens the quality of groundwater in sewered areas. Previous work by our group has documented the presence of human enteric viruses in deep public supply wells. Our current research uses such...

75 FR 25185 - Broadband Initiatives Program

Federal Register 2010, 2011, 2012, 2013, 2014

2010-05-07

..., excluding desktop or laptop computers, computer hardware and software (including anti-virus, anti-spyware, and other security software), audio or video equipment, computer network components... 10 desktop or laptop computers and individual workstations to be located within the rural library...

Accelerating Spaceborne SAR Imaging Using Multiple CPU/GPU Deep Collaborative Computing

PubMed Central

Zhang, Fan; Li, Guojun; Li, Wei; Hu, Wei; Hu, Yuxin

2016-01-01

With the development of synthetic aperture radar (SAR) technologies in recent years, the huge amount of remote sensing data brings challenges for real-time imaging processing. Therefore, high performance computing (HPC) methods have been presented to accelerate SAR imaging, especially the GPU based methods. In the classical GPU based imaging algorithm, GPU is employed to accelerate image processing by massive parallel computing, and CPU is only used to perform the auxiliary work such as data input/output (IO). However, the computing capability of CPU is ignored and underestimated. In this work, a new deep collaborative SAR imaging method based on multiple CPU/GPU is proposed to achieve real-time SAR imaging. Through the proposed tasks partitioning and scheduling strategy, the whole image can be generated with deep collaborative multiple CPU/GPU computing. In the part of CPU parallel imaging, the advanced vector extension (AVX) method is firstly introduced into the multi-core CPU parallel method for higher efficiency. As for the GPU parallel imaging, not only the bottlenecks of memory limitation and frequent data transferring are broken, but also kinds of optimized strategies are applied, such as streaming, parallel pipeline and so on. Experimental results demonstrate that the deep CPU/GPU collaborative imaging method enhances the efficiency of SAR imaging on single-core CPU by 270 times and realizes the real-time imaging in that the imaging rate outperforms the raw data generation rate. PMID:27070606

Accelerating Spaceborne SAR Imaging Using Multiple CPU/GPU Deep Collaborative Computing.

PubMed

Zhang, Fan; Li, Guojun; Li, Wei; Hu, Wei; Hu, Yuxin

2016-04-07

With the development of synthetic aperture radar (SAR) technologies in recent years, the huge amount of remote sensing data brings challenges for real-time imaging processing. Therefore, high performance computing (HPC) methods have been presented to accelerate SAR imaging, especially the GPU based methods. In the classical GPU based imaging algorithm, GPU is employed to accelerate image processing by massive parallel computing, and CPU is only used to perform the auxiliary work such as data input/output (IO). However, the computing capability of CPU is ignored and underestimated. In this work, a new deep collaborative SAR imaging method based on multiple CPU/GPU is proposed to achieve real-time SAR imaging. Through the proposed tasks partitioning and scheduling strategy, the whole image can be generated with deep collaborative multiple CPU/GPU computing. In the part of CPU parallel imaging, the advanced vector extension (AVX) method is firstly introduced into the multi-core CPU parallel method for higher efficiency. As for the GPU parallel imaging, not only the bottlenecks of memory limitation and frequent data transferring are broken, but also kinds of optimized strategies are applied, such as streaming, parallel pipeline and so on. Experimental results demonstrate that the deep CPU/GPU collaborative imaging method enhances the efficiency of SAR imaging on single-core CPU by 270 times and realizes the real-time imaging in that the imaging rate outperforms the raw data generation rate.

Environmental bacteriophages: viruses of microbes in aquatic ecosystems

PubMed Central

Sime-Ngando, Télesphore

2014-01-01

Since the discovery 2–3 decades ago that viruses of microbes are abundant in marine ecosystems, viral ecology has grown increasingly to reach the status of a full scientific discipline in environmental sciences. A dedicated ISVM society, the International Society for Viruses of Microorganisms, (http://www.isvm.org/) was recently launched. Increasing studies in viral ecology are sources of novel knowledge related to the biodiversity of living things, the functioning of ecosystems, and the evolution of the cellular world. This is because viruses are perhaps the most diverse, abundant, and ubiquitous biological entities in the biosphere, although local environmental conditions enrich for certain viral types through selective pressure. They exhibit various lifestyles that intimately depend on the deep-cellular mechanisms, and are ultimately replicated by members of all three domains of cellular life (Bacteria, Eukarya, Archaea), as well as by giant viruses of some eukaryotic cells. This establishes viral parasites as microbial killers but also as cell partners or metabolic manipulators in microbial ecology. The present chapter sought to review the literature on the diversity and functional roles of viruses of microbes in environmental microbiology, focusing primarily on prokaryotic viruses (i.e., phages) in aquatic ecosystems, which form the bulk of our knowledge in modern environmental viral ecology. PMID:25104950

Unusual RNA plant virus integration in the soybean genome leads to the production of small RNAs.

PubMed

da Fonseca, Guilherme Cordenonsi; de Oliveira, Luiz Felipe Valter; de Morais, Guilherme Loss; Abdelnor, Ricardo Vilela; Nepomuceno, Alexandre Lima; Waterhouse, Peter M; Farinelli, Laurent; Margis, Rogerio

2016-05-01

Horizontal gene transfer (HGT) is known to be a major force in genome evolution. The acquisition of genes from viruses by eukaryotic genomes is a well-studied example of HGT, including rare cases of non-retroviral RNA virus integration. The present study describes the integration of cucumber mosaic virus RNA-1 into soybean genome. After an initial metatranscriptomic analysis of small RNAs derived from soybean, the de novo assembly resulted a 3029-nt contig homologous to RNA-1. The integration of this sequence in the soybean genome was confirmed by DNA deep sequencing. The locus where the integration occurred harbors the full RNA-1 sequence followed by the partial sequence of an endogenous mRNA and another sequence of RNA-1 as an inverted repeat and allowing the formation of a hairpin structure. This region recombined into a retrotransposon located inside an exon of a soybean gene. The nucleotide similarity of the integrated sequence compared to other Cucumber mosaic virus sequences indicates that the integration event occurred recently. We described a rare event of non-retroviral RNA virus integration in soybean that leads to the production of a double-stranded RNA in a similar fashion to virus resistance RNAi plants. Copyright © 2016 Elsevier Ireland Ltd. All rights reserved.

A metagenomic survey of viral abundance and diversity in mosquitoes from Hubei province.

PubMed

Shi, Chenyan; Liu, Yi; Hu, Xiaomin; Xiong, Jinfeng; Zhang, Bo; Yuan, Zhiming

2015-01-01

Mosquitoes as one of the most common but important vectors have the potential to transmit or acquire a lot of viruses through biting, however viral flora in mosquitoes and its impact on mosquito-borne disease transmission has not been well investigated and evaluated. In this study, the metagenomic techniquehas been successfully employed in analyzing the abundance and diversity of viral community in three mosquito samples from Hubei, China. Among 92,304 reads produced through a run with 454 GS FLX system, 39% have high similarities with viral sequences belonging to identified bacterial, fungal, animal, plant and insect viruses, and 0.02% were classed into unidentified viral sequences, demonstrating high abundance and diversity of viruses in mosquitoes. Furthermore, two novel viruses in subfamily Densovirinae and family Dicistroviridae were identified, and six torque tenosus virus1 in family Anelloviridae, three porcine parvoviruses in subfamily Parvovirinae and a Culex tritaeniorhynchus rhabdovirus in Family Rhabdoviridae were preliminarily characterized. The viral metagenomic analysis offered us a deep insight into the viral population of mosquito which played an important role in viral initiative or passive transmission and evolution during the process.

Characterization of H9N2 avian influenza viruses from the Middle East demonstrates heterogeneity at amino acid position 226 in the hemagglutinin and potential for transmission to mammals.

PubMed

Chrzastek, Klaudia; Lee, Dong-Hun; Gharaibeh, Saad; Zsak, Aniko; Kapczynski, Darrell R

2018-05-01

Next-generation sequencing (NGS) technologies are a valuable tool to monitor changes in viral genomes and determine the genetic heterogeneity of viruses. In this study, NGS was applied to clinical poultry samples from Jordan to detect eleven H9N2 low pathogenic avian influenza viruses (LPAIV). All of the viruses tested belonged to Middle East A genetic group of G1 lineage. Deep sequencing demonstrated a high degree of heterogeneity of glutamine and leucine residues at position 226 in the hemagglutinin (HA) gene, which increases specificity to either avian or mammalian-type receptors. Moreover, additional amino acid changes in PB1, PA, M1, M2, and NS1 were identified among the viruses tested. Compared to single gene amplification, application of NGS for surveillance and characterization of H9N2 LPAIV provides a complete genetic profile of emerging isolates and better understanding of the potential of zoonotic transmissions to mammals. Published by Elsevier Inc.

Predicting RNA-protein binding sites and motifs through combining local and global deep convolutional neural networks.

PubMed

Pan, Xiaoyong; Shen, Hong-Bin

2018-05-02

RNA-binding proteins (RBPs) take over 5∼10% of the eukaryotic proteome and play key roles in many biological processes, e.g. gene regulation. Experimental detection of RBP binding sites is still time-intensive and high-costly. Instead, computational prediction of the RBP binding sites using pattern learned from existing annotation knowledge is a fast approach. From the biological point of view, the local structure context derived from local sequences will be recognized by specific RBPs. However, in computational modeling using deep learning, to our best knowledge, only global representations of entire RNA sequences are employed. So far, the local sequence information is ignored in the deep model construction process. In this study, we present a computational method iDeepE to predict RNA-protein binding sites from RNA sequences by combining global and local convolutional neural networks (CNNs). For the global CNN, we pad the RNA sequences into the same length. For the local CNN, we split a RNA sequence into multiple overlapping fixed-length subsequences, where each subsequence is a signal channel of the whole sequence. Next, we train deep CNNs for multiple subsequences and the padded sequences to learn high-level features, respectively. Finally, the outputs from local and global CNNs are combined to improve the prediction. iDeepE demonstrates a better performance over state-of-the-art methods on two large-scale datasets derived from CLIP-seq. We also find that the local CNN run 1.8 times faster than the global CNN with comparable performance when using GPUs. Our results show that iDeepE has captured experimentally verified binding motifs. https://github.com/xypan1232/iDeepE. xypan172436@gmail.com or hbshen@sjtu.edu.cn. Supplementary data are available at Bioinformatics online.

Virus World as an Evolutionary Network of Viruses and Capsidless Selfish Elements

PubMed Central

Dolja, Valerian V.

2014-01-01

SUMMARY Viruses were defined as one of the two principal types of organisms in the biosphere, namely, as capsid-encoding organisms in contrast to ribosome-encoding organisms, i.e., all cellular life forms. Structurally similar, apparently homologous capsids are present in a huge variety of icosahedral viruses that infect bacteria, archaea, and eukaryotes. These findings prompted the concept of the capsid as the virus “self” that defines the identity of deep, ancient viral lineages. However, several other widespread viral “hallmark genes” encode key components of the viral replication apparatus (such as polymerases and helicases) and combine with different capsid proteins, given the inherently modular character of viral evolution. Furthermore, diverse, widespread, capsidless selfish genetic elements, such as plasmids and various types of transposons, share hallmark genes with viruses. Viruses appear to have evolved from capsidless selfish elements, and vice versa, on multiple occasions during evolution. At the earliest, precellular stage of life's evolution, capsidless genetic parasites most likely emerged first and subsequently gave rise to different classes of viruses. In this review, we develop the concept of a greater virus world which forms an evolutionary network that is held together by shared conserved genes and includes both bona fide capsid-encoding viruses and different classes of capsidless replicons. Theoretical studies indicate that selfish replicons (genetic parasites) inevitably emerge in any sufficiently complex evolving ensemble of replicators. Therefore, the key signature of the greater virus world is not the presence of a capsid but rather genetic, informational parasitism itself, i.e., various degrees of reliance on the information processing systems of the host. PMID:24847023

DeepInfer: open-source deep learning deployment toolkit for image-guided therapy

NASA Astrophysics Data System (ADS)

Mehrtash, Alireza; Pesteie, Mehran; Hetherington, Jorden; Behringer, Peter A.; Kapur, Tina; Wells, William M.; Rohling, Robert; Fedorov, Andriy; Abolmaesumi, Purang

2017-03-01

Deep learning models have outperformed some of the previous state-of-the-art approaches in medical image analysis. Instead of using hand-engineered features, deep models attempt to automatically extract hierarchical representations at multiple levels of abstraction from the data. Therefore, deep models are usually considered to be more flexible and robust solutions for image analysis problems compared to conventional computer vision models. They have demonstrated significant improvements in computer-aided diagnosis and automatic medical image analysis applied to such tasks as image segmentation, classification and registration. However, deploying deep learning models often has a steep learning curve and requires detailed knowledge of various software packages. Thus, many deep models have not been integrated into the clinical research work ows causing a gap between the state-of-the-art machine learning in medical applications and evaluation in clinical research procedures. In this paper, we propose "DeepInfer" - an open-source toolkit for developing and deploying deep learning models within the 3D Slicer medical image analysis platform. Utilizing a repository of task-specific models, DeepInfer allows clinical researchers and biomedical engineers to deploy a trained model selected from the public registry, and apply it to new data without the need for software development or configuration. As two practical use cases, we demonstrate the application of DeepInfer in prostate segmentation for targeted MRI-guided biopsy and identification of the target plane in 3D ultrasound for spinal injections.

DeepInfer: Open-Source Deep Learning Deployment Toolkit for Image-Guided Therapy.

PubMed

Mehrtash, Alireza; Pesteie, Mehran; Hetherington, Jorden; Behringer, Peter A; Kapur, Tina; Wells, William M; Rohling, Robert; Fedorov, Andriy; Abolmaesumi, Purang

2017-02-11

Deep learning models have outperformed some of the previous state-of-the-art approaches in medical image analysis. Instead of using hand-engineered features, deep models attempt to automatically extract hierarchical representations at multiple levels of abstraction from the data. Therefore, deep models are usually considered to be more flexible and robust solutions for image analysis problems compared to conventional computer vision models. They have demonstrated significant improvements in computer-aided diagnosis and automatic medical image analysis applied to such tasks as image segmentation, classification and registration. However, deploying deep learning models often has a steep learning curve and requires detailed knowledge of various software packages. Thus, many deep models have not been integrated into the clinical research workflows causing a gap between the state-of-the-art machine learning in medical applications and evaluation in clinical research procedures. In this paper, we propose "DeepInfer" - an open-source toolkit for developing and deploying deep learning models within the 3D Slicer medical image analysis platform. Utilizing a repository of task-specific models, DeepInfer allows clinical researchers and biomedical engineers to deploy a trained model selected from the public registry, and apply it to new data without the need for software development or configuration. As two practical use cases, we demonstrate the application of DeepInfer in prostate segmentation for targeted MRI-guided biopsy and identification of the target plane in 3D ultrasound for spinal injections.

DeepInfer: Open-Source Deep Learning Deployment Toolkit for Image-Guided Therapy

PubMed Central

Mehrtash, Alireza; Pesteie, Mehran; Hetherington, Jorden; Behringer, Peter A.; Kapur, Tina; Wells, William M.; Rohling, Robert; Fedorov, Andriy; Abolmaesumi, Purang

2017-01-01

Deep learning models have outperformed some of the previous state-of-the-art approaches in medical image analysis. Instead of using hand-engineered features, deep models attempt to automatically extract hierarchical representations at multiple levels of abstraction from the data. Therefore, deep models are usually considered to be more flexible and robust solutions for image analysis problems compared to conventional computer vision models. They have demonstrated significant improvements in computer-aided diagnosis and automatic medical image analysis applied to such tasks as image segmentation, classification and registration. However, deploying deep learning models often has a steep learning curve and requires detailed knowledge of various software packages. Thus, many deep models have not been integrated into the clinical research workflows causing a gap between the state-of-the-art machine learning in medical applications and evaluation in clinical research procedures. In this paper, we propose “DeepInfer” – an open-source toolkit for developing and deploying deep learning models within the 3D Slicer medical image analysis platform. Utilizing a repository of task-specific models, DeepInfer allows clinical researchers and biomedical engineers to deploy a trained model selected from the public registry, and apply it to new data without the need for software development or configuration. As two practical use cases, we demonstrate the application of DeepInfer in prostate segmentation for targeted MRI-guided biopsy and identification of the target plane in 3D ultrasound for spinal injections. PMID:28615794

Why Do Computer Viruses Survive In The Internet?

NASA Astrophysics Data System (ADS)

Ifti, Margarita; Neumann, Paul

2010-01-01

We use the three-species cyclic competition model (Rock-Paper-Scissors), described by reactions A+B→2B, B+C→2C, C+A→2A, for emulating a computer network with e-mail viruses. Different topologies of the network bring about different dynamics of the epidemics. When the parameters of the network are varied, it is observed that very high clustering coefficients are necessary for a pandemics to happen. The differences between the networks of computer users, e-mail networks, and social networks, as well as their role in determining the nature of epidemics are also discussed.

A new method for enhancer prediction based on deep belief network.

PubMed

Bu, Hongda; Gan, Yanglan; Wang, Yang; Zhou, Shuigeng; Guan, Jihong

2017-10-16

Studies have shown that enhancers are significant regulatory elements to play crucial roles in gene expression regulation. Since enhancers are unrelated to the orientation and distance to their target genes, it is a challenging mission for scholars and researchers to accurately predicting distal enhancers. In the past years, with the high-throughout ChiP-seq technologies development, several computational techniques emerge to predict enhancers using epigenetic or genomic features. Nevertheless, the inconsistency of computational models across different cell-lines and the unsatisfactory prediction performance call for further research in this area. Here, we propose a new Deep Belief Network (DBN) based computational method for enhancer prediction, which is called EnhancerDBN. This method combines diverse features, composed of DNA sequence compositional features, DNA methylation and histone modifications. Our computational results indicate that 1) EnhancerDBN outperforms 13 existing methods in prediction, and 2) GC content and DNA methylation can serve as relevant features for enhancer prediction. Deep learning is effective in boosting the performance of enhancer prediction.

RNase L targets distinct sites in influenza A virus RNAs.

PubMed

Cooper, Daphne A; Banerjee, Shuvojit; Chakrabarti, Arindam; García-Sastre, Adolfo; Hesselberth, Jay R; Silverman, Robert H; Barton, David J

2015-03-01

Influenza A virus (IAV) infections are influenced by type 1 interferon-mediated antiviral defenses and by viral countermeasures to these defenses. When IAV NS1 protein is disabled, RNase L restricts virus replication; however, the RNAs targeted for cleavage by RNase L under these conditions have not been defined. In this study, we used deep-sequencing methods to identify RNase L cleavage sites within host and viral RNAs from IAV PR8ΔNS1-infected A549 cells. Short hairpin RNA knockdown of RNase L allowed us to distinguish between RNase L-dependent and RNase L-independent cleavage sites. RNase L-dependent cleavage sites were evident at discrete locations in IAV RNA segments (both positive and negative strands). Cleavage in PB2, PB1, and PA genomic RNAs suggests that viral RNPs are susceptible to cleavage by RNase L. Prominent amounts of cleavage mapped to specific regions within IAV RNAs, including some areas of increased synonymous-site conservation. Among cellular RNAs, RNase L-dependent cleavage was most frequent at precise locations in rRNAs. Our data show that RNase L targets specific sites in both host and viral RNAs to restrict influenza virus replication when NS1 protein is disabled. RNase L is a critical component of interferon-regulated and double-stranded-RNA-activated antiviral host responses. We sought to determine how RNase L exerts its antiviral activity during influenza virus infection. We enhanced the antiviral activity of RNase L by disabling a viral protein, NS1, that inhibits the activation of RNase L. Then, using deep-sequencing methods, we identified the host and viral RNAs targeted by RNase L. We found that RNase L cleaved viral RNAs and rRNAs at very precise locations. The direct cleavage of IAV RNAs by RNase L highlights an intimate battle between viral RNAs and an antiviral endonuclease. Copyright © 2015, American Society for Microbiology. All Rights Reserved.

Online social networks—Paradise of computer viruses

NASA Astrophysics Data System (ADS)

Fan, W.; Yeung, K. H.

2011-01-01

Online social network services have attracted more and more users in recent years. So the security of social networks becomes a critical problem. In this paper, we propose a virus propagation model based on the application network of Facebook, which is the most popular among these social network service providers. We also study the virus propagation with an email virus model and compare the behaviors of a virus spreading on Facebook with the original email network. It is found that Facebook provides the same chance for a virus spreading while it gives a platform for application developers. And a virus will spread faster in the Facebook network if users of Facebook spend more time on it.

Global morphological analysis of marine viruses shows minimal regional variation and dominance of non-tailed viruses.

PubMed

Brum, Jennifer R; Schenck, Ryan O; Sullivan, Matthew B

2013-09-01

Viruses influence oceanic ecosystems by causing mortality of microorganisms, altering nutrient and organic matter flux via lysis and auxiliary metabolic gene expression and changing the trajectory of microbial evolution through horizontal gene transfer. Limited host range and differing genetic potential of individual virus types mean that investigations into the types of viruses that exist in the ocean and their spatial distribution throughout the world's oceans are critical to understanding the global impacts of marine viruses. Here we evaluate viral morphological characteristics (morphotype, capsid diameter and tail length) using a quantitative transmission electron microscopy (qTEM) method across six of the world's oceans and seas sampled through the Tara Oceans Expedition. Extensive experimental validation of the qTEM method shows that neither sample preservation nor preparation significantly alters natural viral morphological characteristics. The global sampling analysis demonstrated that morphological characteristics did not vary consistently with depth (surface versus deep chlorophyll maximum waters) or oceanic region. Instead, temperature, salinity and oxygen concentration, but not chlorophyll a concentration, were more explanatory in evaluating differences in viral assemblage morphological characteristics. Surprisingly, given that the majority of cultivated bacterial viruses are tailed, non-tailed viruses appear to numerically dominate the upper oceans as they comprised 51-92% of the viral particles observed. Together, these results document global marine viral morphological characteristics, show that their minimal variability is more explained by environmental conditions than geography and suggest that non-tailed viruses might represent the most ecologically important targets for future research.

Hunting in the Rainforest and Mayaro Virus Infection: An emerging Alphavirus in Ecuador.

PubMed

Izurieta, Ricardo O; Macaluso, Maurizio; Watts, Douglas M; Tesh, Robert B; Guerra, Bolivar; Cruz, Ligia M; Galwankar, Sagar; Vermund, Sten H

2011-10-01

The objectives of this report were to document the potential presence of Mayaro virus infection in Ecuador and to examine potential risk factors for Mayaro virus infection among the personnel of a military garrison in the Amazonian rainforest. The study population consisted of the personnel of a garrison located in the Ecuadorian Amazonian rainforest. The cross-sectional study employed interviews and seroepidemiological methods. Humoral immune response to Mayaro virus infection was assessed by evaluating IgM- and IgG-specific antibodies using ELISA. Of 338 subjects studied, 174 were from the Coastal zone of Ecuador, 73 from Andean zone, and 91 were native to the Amazonian rainforest. Seroprevalence of Mayaro virus infection was more than 20 times higher among Amazonian natives (46%) than among subjects born in other areas (2%). Age and hunting in the rainforest were significant predictors of Mayaro virus infection overall and among Amazonian natives. The results provide the first demonstration of the potential presence of Mayaro virus infection in Ecuador and a systematic evaluation of risk factors for the transmission of this alphavirus. The large difference in prevalence rates between Amazonian natives and other groups and between older and younger natives suggest that Mayaro virus is endemic and enzootic in the rainforest, with sporadic outbreaks that determine differences in risk between birth cohorts of natives. Deep forest hunting may selectively expose native men, descendants of the Shuar and Huaronai ethnic groups, to the arthropod vectors of Mayaro virus in areas close to primate reservoirs.

A new approach for detecting adventitious viruses shows Sf-rhabdovirus-negative Sf-RVN cells are suitable for safe biologicals production.

PubMed

Geisler, Christoph

2018-02-07

Adventitious viral contamination in cell substrates used for biologicals production is a major safety concern. A powerful new approach that can be used to identify adventitious viruses is a combination of bioinformatics tools with massively parallel sequencing technology. Typically, this involves mapping or BLASTN searching individual reads against viral nucleotide databases. Although extremely sensitive for known viruses, this approach can easily miss viruses that are too dissimilar to viruses in the database. Moreover, it is computationally intensive and requires reference cell genome databases. To avoid these drawbacks, we set out to develop an alternative approach. We reasoned that searching genome and transcriptome assemblies for adventitious viral contaminants using TBLASTN with a compact viral protein database covering extant viral diversity as the query could be fast and sensitive without a requirement for high performance computing hardware. We tested our approach on Spodoptera frugiperda Sf-RVN, a recently isolated insect cell line, to determine if it was contaminated with one or more adventitious viruses. We used Illumina reads to assemble the Sf-RVN genome and transcriptome and searched them for adventitious viral contaminants using TBLASTN with our viral protein database. We found no evidence of viral contamination, which was substantiated by the fact that our searches otherwise identified diverse sequences encoding virus-like proteins. These sequences included Maverick, R1 LINE, and errantivirus transposons, all of which are common in insect genomes. We also identified previously described as well as novel endogenous viral elements similar to ORFs encoded by diverse insect viruses. Our results demonstrate TBLASTN searching massively parallel sequencing (MPS) assemblies with a compact, manually curated viral protein database is more sensitive for adventitious virus detection than BLASTN, as we identified various sequences that encoded virus-like proteins, but had no similarity to viral sequences at the nucleotide level. Moreover, searches were fast without requiring high performance computing hardware. Our study also documents the enhanced biosafety profile of Sf-RVN as compared to other Sf cell lines, and supports the notion that Sf-RVN is highly suitable for the production of safe biologicals.

Crime, Abuse, and Hacker Ethics.

ERIC Educational Resources Information Center

Johnson, Deborah G.

1994-01-01

Discusses computer ethics and the use of computer networks. Topics addressed include computer hackers; software piracy; computer viruses and worms; intentional and unintentional abuse; intellectual property rights versus freedom of thought; the role of information in a democratic society; individual privacy; legislation; social attitudes; and the…

Antiviral resistance due to deletion in the neuraminidase gene and defective interfering-like viral polymerase basic 2 RNA of influenza A virus subtype H3N2.

PubMed

Trebbien, Ramona; Christiansen, Claus Bohn; Fischer, Thea Kølsen

2018-05-01

Antiviral treatment of influenza virus infections can lead to drug resistance of virus. This study investigates a selection of mutations in the full genome of H3N2 influenza A virus isolated from a patient in treatment with oseltamivir. Respiratory samples from a patient were collected before, during, and after antiviral treatment. Whole genome sequencing of the influenza virus by next generation sequencing, and low-frequency-variant analysis was performed. Neuraminidase-inhibition tests were performed with oseltamivir and zanamivir, and viruses were propagated in sial-transferase gene transfected Madin-Darby Canine Kidney cells. A deletion at amino acid position 245-248 in the neuraminidase gene occurred after initiation of treatment with oseltamivir. The deleted virus had highly reduced inhibition against oseltamivir but was sensitive to zanamivir. Nine days after discontinuation of oseltamivir treatment the deleted H3N2 virus was still present in the patient. After three passages of the deleted virus in cell culture, the deletion was retained. Several variant mutations appeared in the other genes of the H3N2 virus, where most striking were two major out-of-frame deletions in the polymerase basic 2 (PB2) gene, indicating defective interfering-like viral RNA. The viruses harboring the 245-248 deletion in the neuraminidase gene were still present after discontinuation of oseltamivir treatment and passages in cell cultures, indicating a potential risk for transmission of the deleted virus. Full genome deep sequencing was useful to reveal variant mutations that might be selected due to antiviral treatment, and defective interfering-like viral PB2 RNA in the respiratory samples was detected. Copyright © 2018 Elsevier B.V. All rights reserved.

Computational ghost imaging using deep learning

NASA Astrophysics Data System (ADS)

Shimobaba, Tomoyoshi; Endo, Yutaka; Nishitsuji, Takashi; Takahashi, Takayuki; Nagahama, Yuki; Hasegawa, Satoki; Sano, Marie; Hirayama, Ryuji; Kakue, Takashi; Shiraki, Atsushi; Ito, Tomoyoshi

2018-04-01

Computational ghost imaging (CGI) is a single-pixel imaging technique that exploits the correlation between known random patterns and the measured intensity of light transmitted (or reflected) by an object. Although CGI can obtain two- or three-dimensional images with a single or a few bucket detectors, the quality of the reconstructed images is reduced by noise due to the reconstruction of images from random patterns. In this study, we improve the quality of CGI images using deep learning. A deep neural network is used to automatically learn the features of noise-contaminated CGI images. After training, the network is able to predict low-noise images from new noise-contaminated CGI images.

Atypical manifestations of Epstein-Barr virus in children: a diagnostic challenge.

PubMed

Bolis, Vasileios; Karadedos, Christos; Chiotis, Ioannis; Chaliasos, Nikolaos; Tsabouri, Sophia

2016-01-01

Clarify the frequency and the pathophysiological mechanisms of the rare manifestations of Epstein-Barr virus infection. Original research studies published in English between 1985 and 2015 were selected through a computer-assisted literature search (PubMed and Scopus). Computer searches used combinations of key words relating to "EBV infections" and "atypical manifestation." Epstein-Barr virus is a herpes virus responsible for a lifelong latent infection in almost every adult. The primary infection concerns mostly children and presents with the clinical syndrome of infectious mononucleosis. However, Epstein-Barr virus infection may exhibit numerous rare, atypical and threatening manifestations. It may cause secondary infections and various complications of the respiratory, cardiovascular, genitourinary, gastrointestinal, and nervous systems. Epstein-Barr virus also plays a significant role in pathogenesis of autoimmune diseases, allergies, and neoplasms, with Burkitt lymphoma as the main representative of the latter. The mechanisms of these manifestations are still unresolved. Therefore, the main suggestions are direct viral invasion and chronic immune response due to the reactivation of the latent state of the virus, or even various DNA mutations. Physicians should be cautious about uncommon presentations of the viral infection and consider EBV as a causative agent when they encounter similar clinical pictures. Copyright © 2016 Sociedade Brasileira de Pediatria. Published by Elsevier Editora Ltda. All rights reserved.

Hybrid DNA virus in Chinese patients with seronegative hepatitis discovered by deep sequencing.

PubMed

Xu, Baoyan; Zhi, Ning; Hu, Gangqing; Wan, Zhihong; Zheng, Xiaobin; Liu, Xiaohong; Wong, Susan; Kajigaya, Sachiko; Zhao, Keji; Mao, Qing; Young, Neal S

2013-06-18

Seronegative hepatitis--non-A, non-B, non-C, non-D, non-E hepatitis--is poorly characterized but strongly associated with serious complications. We collected 92 sera specimens from patients with non-A-E hepatitis in Chongqing, China between 1999 and 2007. Ten sera pools were screened by Solexa deep sequencing. We discovered a 3,780-bp contig present in all 10 pools that yielded BLASTx E scores of 7e-05-0.008 against parvoviruses. The complete sequence of the in silico-assembled 3,780-bp contig was confirmed by gene amplification of overlapping regions over almost the entire genome, and the virus was provisionally designated NIH-CQV. Further analysis revealed that the contig was composed of two major ORFs. By protein BLAST, ORF1 and ORF2 were most homologous to the replication-associated protein of bat circovirus and the capsid protein of porcine parvovirus, respectively. Phylogenetic analysis indicated that NIH-CQV is located at the interface of Parvoviridae and Circoviridae. Prevalence of NIH-CQV in patients was determined by quantitative PCR. Sixty-three of 90 patient samples (70%) were positive, but all those from 45 healthy controls were negative. Average virus titer in the patient specimens was 1.05 e4 copies/µL. Specific antibodies against NIH-CQV were sought by immunoblotting. Eighty-four percent of patients were positive for IgG, and 31% were positive for IgM; in contrast, 78% of healthy controls were positive for IgG, but all were negative for IgM. Although more work is needed to determine the etiologic role of NIH-CQV in human disease, our data indicate that a parvovirus-like virus is highly prevalent in a cohort of patients with non-A-E hepatitis.

Simultaneous Quantification of Viral Antigen Expression Kinetics Using Data-Independent (DIA) Mass Spectrometry*

PubMed Central

Croft, Nathan P.; de Verteuil, Danielle A.; Smith, Stewart A.; Wong, Yik Chun; Schittenhelm, Ralf B.; Tscharke, David C.; Purcell, Anthony W.

2015-01-01

The generation of antigen-specific reagents is a significant bottleneck in the study of complex pathogens that express many hundreds to thousands of different proteins or to emerging or new strains of viruses that display potential pandemic qualities and therefore require rapid investigation. In these instances the development of antibodies for example can be prohibitively expensive to cover the full pathogen proteome, or the lead time may be unacceptably long in urgent cases where new highly pathogenic viral strains may emerge. Because genomic information on such pathogens can be rapidly acquired this opens up avenues using mass spectrometric approaches to study pathogen antigen expression, host responses and for screening the utility of therapeutics. In particular, data-independent acquisition (DIA) modalities on high-resolution mass spectrometers generate spectral information on all components of a complex sample providing depth of coverage hitherto only seen in genomic deep sequencing. The spectral information generated by DIA can be iteratively interrogated for potentially any protein of interest providing both evidence of protein expression and quantitation. Here we apply a solely DIA mass spectrometry based methodology to profile the viral antigen expression in cells infected with vaccinia virus up to 9 h post infection without the need for antigen specific antibodies or other reagents. We demonstrate deep coverage of the vaccinia virus proteome using a SWATH-MS acquisition approach, extracting quantitative kinetics of 100 virus proteins within a single experiment. The results highlight the complexity of vaccinia protein expression, complementing what is known at the transcriptomic level, and provide a valuable resource and technique for future studies of viral infection and replication kinetics. Furthermore, they highlight the utility of DIA and mass spectrometry in the dissection of host-pathogen interactions. PMID:25755296

Deep Unsupervised Learning on a Desktop PC: A Primer for Cognitive Scientists.

PubMed

Testolin, Alberto; Stoianov, Ivilin; De Filippo De Grazia, Michele; Zorzi, Marco

2013-01-01

Deep belief networks hold great promise for the simulation of human cognition because they show how structured and abstract representations may emerge from probabilistic unsupervised learning. These networks build a hierarchy of progressively more complex distributed representations of the sensory data by fitting a hierarchical generative model. However, learning in deep networks typically requires big datasets and it can involve millions of connection weights, which implies that simulations on standard computers are unfeasible. Developing realistic, medium-to-large-scale learning models of cognition would therefore seem to require expertise in programing parallel-computing hardware, and this might explain why the use of this promising approach is still largely confined to the machine learning community. Here we show how simulations of deep unsupervised learning can be easily performed on a desktop PC by exploiting the processors of low cost graphic cards (graphic processor units) without any specific programing effort, thanks to the use of high-level programming routines (available in MATLAB or Python). We also show that even an entry-level graphic card can outperform a small high-performance computing cluster in terms of learning time and with no loss of learning quality. We therefore conclude that graphic card implementations pave the way for a widespread use of deep learning among cognitive scientists for modeling cognition and behavior.

Deep Unsupervised Learning on a Desktop PC: A Primer for Cognitive Scientists

PubMed Central

Testolin, Alberto; Stoianov, Ivilin; De Filippo De Grazia, Michele; Zorzi, Marco

2013-01-01

Deep belief networks hold great promise for the simulation of human cognition because they show how structured and abstract representations may emerge from probabilistic unsupervised learning. These networks build a hierarchy of progressively more complex distributed representations of the sensory data by fitting a hierarchical generative model. However, learning in deep networks typically requires big datasets and it can involve millions of connection weights, which implies that simulations on standard computers are unfeasible. Developing realistic, medium-to-large-scale learning models of cognition would therefore seem to require expertise in programing parallel-computing hardware, and this might explain why the use of this promising approach is still largely confined to the machine learning community. Here we show how simulations of deep unsupervised learning can be easily performed on a desktop PC by exploiting the processors of low cost graphic cards (graphic processor units) without any specific programing effort, thanks to the use of high-level programming routines (available in MATLAB or Python). We also show that even an entry-level graphic card can outperform a small high-performance computing cluster in terms of learning time and with no loss of learning quality. We therefore conclude that graphic card implementations pave the way for a widespread use of deep learning among cognitive scientists for modeling cognition and behavior. PMID:23653617

Using small RNA (sRNA) deep sequencing to understand global virus distribution in plants

USDA-ARS?s Scientific Manuscript database

Small RNAs (sRNAs), a class of regulatory RNAs, have been used to serve as the specificity determinants of suppressing gene expression in plants and animals. Next generation sequencing (NGS) uncovered the sRNA landscape in most organisms including their associated microbes. In the current study, w...

Genome-resolved metagenomics reveals that sulfur metabolism dominates the microbial ecology of rising hydrothermal plumes

NASA Astrophysics Data System (ADS)

Anantharaman, K.; Breier, J. A., Jr.; Jain, S.; Reed, D. C.; Dick, G.

2015-12-01

Deep-sea hydrothermal plumes occur when hot fluids from hydrothermal vents replete with chemically reduced elements and compounds like sulfide, methane, hydrogen, ammonia, iron and manganese mix with cold, oxic seawater. Chemosynthetic microbes use these reduced chemicals to power primary production and are pervasive throughout the deep sea, even at sites far removed from hydrothermal vents. Although neutrally-buoyant hydrothermal plumes have been well-studied, rising hydrothermal plumes have received little attention even though they represent an important interface in the deep-sea where microbial metabolism and particle formation processes control the transformation of important elements and impact global biogeochemical cycles. In this study, we used genome-resolved metagenomic analyses and thermodynamic-bioenergetic modeling to study the microbial ecology of rising hydrothermal plumes at five different hydrothermal vents spanning a range of geochemical gradients at the Eastern Lau Spreading Center (ELSC) in the Western Pacific Ocean. Our analyses show that differences in the geochemistry of hydrothermal vents do not manifest in microbial diversity and community composition, both of which display only minor variance across ELSC hydrothermal plumes. Microbial metabolism is dominated by oxidation of reduced sulfur species and supports a diversity of bacteria, archaea and viruses that provide intriguing insights into metabolic plasticity and virus-mediated horizontal gene transfer in the microbial community. The manifestation of sulfur oxidation genes in hydrogen and methane oxidizing organisms hints at metabolic opportunism in deep-sea microbes that would enable them to respond to varying redox conditions in hydrothermal plumes. Finally, we infer that the abundance, diversity and metabolic versatility of microbes associated with sulfur oxidation impart functional redundancy that could allow it to persist in the dynamic settings of hydrothermal plumes.

Metaproteome of the viral concentrates from the deep chlorophyll maximum of the South China Sea

NASA Astrophysics Data System (ADS)

Xie, Zhang-Xian; Chen, Feng; Zhang, Shu-Feng; Wang, Ming-Hua; Zhang, Hao; Kong, Ling-Fen; Dai, Min-Han; Hong, Hua-Sheng; Lin, Lin; Wang, Da-Zhi

2016-04-01

Viral concentrates (VCs) have been commonly used for studying viral diversity, viral metagenomics and virus-host interactions in the natural ecosystem. However, the protein characteristics of VCs have not been explored. Here, we applied shotgun proteomics to characterize the proteins of VCs collected from the oligotrophic deep chlorophyll maximum of the South China Sea. We found that 34% of the identified proteins were assigned to the viruses, mainly being those of SAR11 related bacteria, cyanobacteria and picophytoeukaryotes. The remaining 66% were non-viral proteins mostly originating from diverse bacteria, such as SAR324, SAR11 and the Alteromonadales, and were functionally dominated by transport, translation, sulfur metabolism and one-carbon metabolism. Among the non-viral proteins, 28% were extracellular proteins and 10% were identified exclusively in the VCs, suggesting that non-viral entities might exist in the VCs. This study demonstrated that metaproteomics provides a valuable avenue to explore not only the diversity and structure of a viral community but also the novel ecological functions affiliated with microbes in the natural environment.

Deep Learning and Developmental Learning: Emergence of Fine-to-Coarse Conceptual Categories at Layers of Deep Belief Network.

PubMed

Sadeghi, Zahra

2016-09-01

In this paper, I investigate conceptual categories derived from developmental processing in a deep neural network. The similarity matrices of deep representation at each layer of neural network are computed and compared with their raw representation. While the clusters generated by raw representation stand at the basic level of abstraction, conceptual categories obtained from deep representation shows a bottom-up transition procedure. Results demonstrate a developmental course of learning from specific to general level of abstraction through learned layers of representations in a deep belief network. © The Author(s) 2016.

Computational Approaches to Viral Evolution and Rational Vaccine Design

NASA Astrophysics Data System (ADS)

Bhattacharya, Tanmoy

2006-10-01

Viral pandemics, including HIV, are a major health concern across the world. Experimental techniques available today have uncovered a great wealth of information about how these viruses infect, grow, and cause disease; as well as how our body attempts to defend itself against them. Nevertheless, due to the high variability and fast evolution of many of these viruses, the traditional method of developing vaccines by presenting a heuristically chosen strain to the body fails and an effective intervention strategy still eludes us. A large amount of carefully curated genomic data on a number of these viruses are now available, often annotated with disease and immunological context. The availability of parallel computers has now made it possible to carry out a systematic analysis of this data within an evolutionary framework. I will describe, as an example, how computations on such data has allowed us to understand the origins and diversification of HIV, the causative agent of AIDS. On the practical side, computations on the same data is now being used to inform choice or defign of optimal vaccine strains.

A Computer Security Course in the Undergraduate Computer Science Curriculum.

ERIC Educational Resources Information Center

Spillman, Richard

1992-01-01

Discusses the importance of computer security and considers criminal, national security, and personal privacy threats posed by security breakdown. Several examples are given, including incidents involving computer viruses. Objectives, content, instructional strategies, resources, and a sample examination for an experimental undergraduate computer…

Deep Convolutional Framelet Denosing for Low-Dose CT via Wavelet Residual Network.

PubMed

Kang, Eunhee; Chang, Won; Yoo, Jaejun; Ye, Jong Chul

2018-06-01

Model-based iterative reconstruction algorithms for low-dose X-ray computed tomography (CT) are computationally expensive. To address this problem, we recently proposed a deep convolutional neural network (CNN) for low-dose X-ray CT and won the second place in 2016 AAPM Low-Dose CT Grand Challenge. However, some of the textures were not fully recovered. To address this problem, here we propose a novel framelet-based denoising algorithm using wavelet residual network which synergistically combines the expressive power of deep learning and the performance guarantee from the framelet-based denoising algorithms. The new algorithms were inspired by the recent interpretation of the deep CNN as a cascaded convolution framelet signal representation. Extensive experimental results confirm that the proposed networks have significantly improved performance and preserve the detail texture of the original images.

Computed tomography of deep fat masses in multiple symmetrical lipomatosis

DOE Office of Scientific and Technical Information (OSTI.GOV)

Enzi, G.; Biondetti, P.R.; Fiore, D.

1982-07-01

Deep fat masses were evaluated by computed tomography (CT) in 15 patients with multiple symmetrical lipomatosis. In 4 patients, peritracheal accumulations of fat were observed. In 3 of them, tracheal compression by lipomatous tissue was demonstrated: 2 were asymptomatic and the third severe respiratory insufficiency secondary to blockage of the air was by the vocal cords as the result of recurrent nerve palsy. In 6 patients, lipomatous tissue occupied the potential space between the spina scapulae and the trapezius, supraspinatus, and infraspinatus muscles. In 2, calcification of lipomatous masses was observed. There was no relationship between extension of subcutaneous fatmore » and accumulation at deep sites. CT facilitates early detection of peritracheal lipomatous tissue and is helpful in follow-up when deep fat accumulation is responsible for space-occupying lesions requiring surgery.« less

DOE's Computer Incident Advisory Capability (CIAC)

DOE Office of Scientific and Technical Information (OSTI.GOV)

Schultz, E.

1990-09-01

Computer security is essential in maintaining quality in the computing environment. Computer security incidents, however, are becoming more sophisticated. The DOE Computer Incident Advisory Capability (CIAC) team was formed primarily to assist DOE sites in responding to computer security incidents. Among CIAC's other responsibilities are gathering and distributing information to DOE sites, providing training workshops, coordinating with other agencies, response teams, and vendors, creating guidelines for incident handling, and developing software tools. CIAC has already provided considerable assistance to DOE sites faced with virus infections and worm and hacker attacks, has issued over 40 information bulletins, and has developed andmore » presented a workshop on incident handling. CIAC's experience in helping sites has produced several lessons learned, including the need to follow effective procedures to avoid virus infections in small systems and the need for sound password management and system administration in networked systems. CIAC's activity and scope will expand in the future. 4 refs.« less

A Novel Benzoquinone Compound Isolated from Deep-Sea Hydrothermal Vent Triggers Apoptosis of Tumor Cells.

PubMed

Xu, Chenxi; Sun, Xumei; Jin, Min; Zhang, Xiaobo

2017-06-26

Microorganisms are important sources for screening bioactive natural products. However, natural products from deep-sea microbes have not been extensively explored. In this study, the metabolites of bacteriophage GVE2 -infected ( Geobacillus sp. E263 virus) thermophilic bacterium Geobacillus sp. E263, which was isolated from a deep-sea hydrothermal vent, were characterized. A novel quinoid compound, which had anti-tumor activity, was isolated from the phage-challenged thermophile. The chemical structure analysis showed that this novel quinoid compound was 2-amino-6-hydroxy-[1,4]-benzoquinone. The results indicated that 2-amino-6-hydroxy-[1,4]-benzoquinone and its two derivatives could trigger apoptosis of gastric cancer cells and breast cancer cells by inducing the accumulation of intracellular reactive oxygen species. Therefore, our study highlighted that the metabolites from the phage-challenged deep-sea microbes might be a kind of promising sources for anti-tumor drug discovery, because of the similarity of metabolic disorder between bacteriophage-infected microbes and tumor cells.

Microbial metabolisms in a 2.5-km-deep ecosystem created by hydraulic fracturing in shales.

PubMed

Daly, Rebecca A; Borton, Mikayla A; Wilkins, Michael J; Hoyt, David W; Kountz, Duncan J; Wolfe, Richard A; Welch, Susan A; Marcus, Daniel N; Trexler, Ryan V; MacRae, Jean D; Krzycki, Joseph A; Cole, David R; Mouser, Paula J; Wrighton, Kelly C

2016-09-05

Hydraulic fracturing is the industry standard for extracting hydrocarbons from shale formations. Attention has been paid to the economic benefits and environmental impacts of this process, yet the biogeochemical changes induced in the deep subsurface are poorly understood. Recent single-gene investigations revealed that halotolerant microbial communities were enriched after hydraulic fracturing. Here, the reconstruction of 31 unique genomes coupled to metabolite data from the Marcellus and Utica shales revealed that many of the persisting organisms play roles in methylamine cycling, ultimately supporting methanogenesis in the deep biosphere. Fermentation of injected chemical additives also sustains long-term microbial persistence, while thiosulfate reduction could produce sulfide, contributing to reservoir souring and infrastructure corrosion. Extensive links between viruses and microbial hosts demonstrate active viral predation, which may contribute to the release of labile cellular constituents into the extracellular environment. Our analyses show that hydraulic fracturing provides the organismal and chemical inputs for colonization and persistence in the deep terrestrial subsurface.

Microbial metabolisms in a 2.5-km-deep ecosystem created by hydraulic fracturing in shales

DOE Office of Scientific and Technical Information (OSTI.GOV)

Daly, Rebecca A.; Borton, Mikayla A.; Wilkins, Michael J.

Hydraulic fracturing is the industry standard for extracting hydrocarbons from shale formations. Attention has been paid to the economic benefits and environmental impacts of this process, yet the biogeochemical changes induced in the deep subsurface are poorly understood. Recent single-gene investigations revealed that halotolerant microbial communities were enriched after hydraulic fracturing. Here the reconstruction of 31 unique genomes coupled to metabolite data from the Marcellus and Utica shales revealed that methylamine cycling supports methanogenesis in the deep biosphere. Fermentation of injected chemical additives also sustains long-term microbial persistence, while sulfide generation from thiosulfate represents a poorly recognized corrosion mechanism inmore » shales. Extensive links between viruses and microbial hosts demonstrate active viral predation, which may contribute to the release of labile cellular constituents into the extracellular environment. Our analyses show that hydraulic fracturing provides the organismal and chemical inputs for colonization and persistence in the deep terrestrial subsurface.« less

Sensitive Deep-Sequencing-Based HIV-1 Genotyping Assay To Simultaneously Determine Susceptibility to Protease, Reverse Transcriptase, Integrase, and Maturation Inhibitors, as Well as HIV-1 Coreceptor Tropism

PubMed Central

Gibson, Richard M.; Meyer, Ashley M.; Winner, Dane; Archer, John; Feyertag, Felix; Ruiz-Mateos, Ezequiel; Leal, Manuel; Robertson, David L.; Schmotzer, Christine L.

2014-01-01

With 29 individual antiretroviral drugs available from six classes that are approved for the treatment of HIV-1 infection, a combination of different phenotypic and genotypic tests is currently needed to monitor HIV-infected individuals. In this study, we developed a novel HIV-1 genotypic assay based on deep sequencing (DeepGen HIV) to simultaneously assess HIV-1 susceptibilities to all drugs targeting the three viral enzymes and to predict HIV-1 coreceptor tropism. Patient-derived gag-p2/NCp7/p1/p6/pol-PR/RT/IN- and env-C2V3 PCR products were sequenced using the Ion Torrent Personal Genome Machine. Reads spanning the 3′ end of the Gag, protease (PR), reverse transcriptase (RT), integrase (IN), and V3 regions were extracted, truncated, translated, and assembled for genotype and HIV-1 coreceptor tropism determination. DeepGen HIV consistently detected both minority drug-resistant viruses and non-R5 HIV-1 variants from clinical specimens with viral loads of ≥1,000 copies/ml and from B and non-B subtypes. Additional mutations associated with resistance to PR, RT, and IN inhibitors, previously undetected by standard (Sanger) population sequencing, were reliably identified at frequencies as low as 1%. DeepGen HIV results correlated with phenotypic (original Trofile, 92%; enhanced-sensitivity Trofile assay [ESTA], 80%; TROCAI, 81%; and VeriTrop, 80%) and genotypic (population sequencing/Geno2Pheno with a 10% false-positive rate [FPR], 84%) HIV-1 tropism test results. DeepGen HIV (83%) and Trofile (85%) showed similar concordances with the clinical response following an 8-day course of maraviroc monotherapy (MCT). In summary, this novel all-inclusive HIV-1 genotypic and coreceptor tropism assay, based on deep sequencing of the PR, RT, IN, and V3 regions, permits simultaneous multiplex detection of low-level drug-resistant and/or non-R5 viruses in up to 96 clinical samples. This comprehensive test, the first of its class, will be instrumental in the development of new antiretroviral drugs and, more importantly, will aid in the treatment and management of HIV-infected individuals. PMID:24468782

Raw Sewage Harbors Diverse Viral Populations

PubMed Central

Cantalupo, Paul G.; Calgua, Byron; Zhao, Guoyan; Hundesa, Ayalkibet; Wier, Adam D.; Katz, Josh P.; Grabe, Michael; Hendrix, Roger W.; Girones, Rosina; Wang, David; Pipas, James M.

2011-01-01

ABSTRACT At this time, about 3,000 different viruses are recognized, but metagenomic studies suggest that these viruses are a small fraction of the viruses that exist in nature. We have explored viral diversity by deep sequencing nucleic acids obtained from virion populations enriched from raw sewage. We identified 234 known viruses, including 17 that infect humans. Plant, insect, and algal viruses as well as bacteriophages were also present. These viruses represented 26 taxonomic families and included viruses with single-stranded DNA (ssDNA), double-stranded DNA (dsDNA), positive-sense ssRNA [ssRNA(+)], and dsRNA genomes. Novel viruses that could be placed in specific taxa represented 51 different families, making untreated wastewater the most diverse viral metagenome (genetic material recovered directly from environmental samples) examined thus far. However, the vast majority of sequence reads bore little or no sequence relation to known viruses and thus could not be placed into specific taxa. These results show that the vast majority of the viruses on Earth have not yet been characterized. Untreated wastewater provides a rich matrix for identifying novel viruses and for studying virus diversity. Importance At this time, virology is focused on the study of a relatively small number of viral species. Specific viruses are studied either because they are easily propagated in the laboratory or because they are associated with disease. The lack of knowledge of the size and characteristics of the viral universe and the diversity of viral genomes is a roadblock to understanding important issues, such as the origin of emerging pathogens and the extent of gene exchange among viruses. Untreated wastewater is an ideal system for assessing viral diversity because virion populations from large numbers of individuals are deposited and because raw sewage itself provides a rich environment for the growth of diverse host species and thus their viruses. These studies suggest that the viral universe is far more vast and diverse than previously suspected. PMID:21972239

IMG/VR: a database of cultured and uncultured DNA Viruses and retroviruses

DOE PAGES

Paez-Espino, David; Chen, I. -Min A.; Palaniappan, Krishna; ...

2016-10-30

Viruses represent the most abundant life forms on the planet. Recent experimental and computational improvements have led to a dramatic increase in the number of viral genome sequences identified primarily from metagenomic samples. As a result of the expanding catalog of metagenomic viral sequences, there exists a need for a comprehensive computational platform integrating all these sequences with associated metadata and analytical tools. Here we present IMG/VR (https://img.jgi.doe.gov/vr/), the largest publicly available database of 3908 isolate reference DNA viruses with 264 413 computationally identified viral contigs from > 6000 ecologically diverse metagenomic samples. Approximately half of the viral contigs aremore » grouped into genetically distinct quasi-species clusters. Microbial hosts are predicted for 20 000 viral sequences, revealing nine microbial phyla previously unreported to be infected by viruses. Viral sequences can be queried using a variety of associated metadata, including habitat type and geographic location of the samples, or taxonomic classification according to hallmark viral genes. IMG/VR has a user-friendly interface that allows users to interrogate all integrated data and interact by comparingwith external sequences, thus serving as an essential resource in the viral genomics community.« less

IMG/VR: a database of cultured and uncultured DNA Viruses and retroviruses

DOE Office of Scientific and Technical Information (OSTI.GOV)

Paez-Espino, David; Chen, I. -Min A.; Palaniappan, Krishna

Viruses represent the most abundant life forms on the planet. Recent experimental and computational improvements have led to a dramatic increase in the number of viral genome sequences identified primarily from metagenomic samples. As a result of the expanding catalog of metagenomic viral sequences, there exists a need for a comprehensive computational platform integrating all these sequences with associated metadata and analytical tools. Here we present IMG/VR (https://img.jgi.doe.gov/vr/), the largest publicly available database of 3908 isolate reference DNA viruses with 264 413 computationally identified viral contigs from > 6000 ecologically diverse metagenomic samples. Approximately half of the viral contigs aremore » grouped into genetically distinct quasi-species clusters. Microbial hosts are predicted for 20 000 viral sequences, revealing nine microbial phyla previously unreported to be infected by viruses. Viral sequences can be queried using a variety of associated metadata, including habitat type and geographic location of the samples, or taxonomic classification according to hallmark viral genes. IMG/VR has a user-friendly interface that allows users to interrogate all integrated data and interact by comparingwith external sequences, thus serving as an essential resource in the viral genomics community.« less

Virus world as an evolutionary network of viruses and capsidless selfish elements.

PubMed

Koonin, Eugene V; Dolja, Valerian V

2014-06-01

Viruses were defined as one of the two principal types of organisms in the biosphere, namely, as capsid-encoding organisms in contrast to ribosome-encoding organisms, i.e., all cellular life forms. Structurally similar, apparently homologous capsids are present in a huge variety of icosahedral viruses that infect bacteria, archaea, and eukaryotes. These findings prompted the concept of the capsid as the virus "self" that defines the identity of deep, ancient viral lineages. However, several other widespread viral "hallmark genes" encode key components of the viral replication apparatus (such as polymerases and helicases) and combine with different capsid proteins, given the inherently modular character of viral evolution. Furthermore, diverse, widespread, capsidless selfish genetic elements, such as plasmids and various types of transposons, share hallmark genes with viruses. Viruses appear to have evolved from capsidless selfish elements, and vice versa, on multiple occasions during evolution. At the earliest, precellular stage of life's evolution, capsidless genetic parasites most likely emerged first and subsequently gave rise to different classes of viruses. In this review, we develop the concept of a greater virus world which forms an evolutionary network that is held together by shared conserved genes and includes both bona fide capsid-encoding viruses and different classes of capsidless replicons. Theoretical studies indicate that selfish replicons (genetic parasites) inevitably emerge in any sufficiently complex evolving ensemble of replicators. Therefore, the key signature of the greater virus world is not the presence of a capsid but rather genetic, informational parasitism itself, i.e., various degrees of reliance on the information processing systems of the host. Copyright © 2014, American Society for Microbiology. All Rights Reserved.

Novel microRNA-like viral small regulatory RNAs arising during human hepatitis A virus infection.

PubMed

Shi, Jiandong; Sun, Jing; Wang, Bin; Wu, Meini; Zhang, Jing; Duan, Zhiqing; Wang, Haixuan; Hu, Ningzhu; Hu, Yunzhang

2014-10-01

MicroRNAs (miRNAs), including host miRNAs and viral miRNAs, play vital roles in regulating host-virus interactions. DNA viruses encode miRNAs that regulate the viral life cycle. However, it is generally believed that cytoplasmic RNA viruses do not encode miRNAs, owing to inaccessible cellular miRNA processing machinery. Here, we provide a comprehensive genome-wide analysis and identification of miRNAs that were derived from hepatitis A virus (HAV; Hu/China/H2/1982), which is a typical cytoplasmic RNA virus. Using deep-sequencing and in silico approaches, we identified 2 novel virally encoded miRNAs, named hav-miR-1-5p and hav-miR-2-5p. Both of the novel virally encoded miRNAs were clearly detected in infected cells. Analysis of Dicer enzyme silencing demonstrated that HAV-derived miRNA biogenesis is Dicer dependent. Furthermore, we confirmed that HAV mature miRNAs were generated from viral miRNA precursors (pre-miRNAs) in host cells. Notably, naturally derived HAV miRNAs were biologically and functionally active and induced post-transcriptional gene silencing (PTGS). Genomic location analysis revealed novel miRNAs located in the coding region of the viral genome. Overall, our results show that HAV naturally generates functional miRNA-like small regulatory RNAs during infection. This is the first report of miRNAs derived from the coding region of genomic RNA of a cytoplasmic RNA virus. These observations demonstrate that a cytoplasmic RNA virus can naturally generate functional miRNAs, as DNA viruses do. These findings also contribute to improved understanding of host-RNA virus interactions mediated by RNA virus-derived miRNAs. © FASEB.

From Turing machines to computer viruses.

PubMed

Marion, Jean-Yves

2012-07-28

Self-replication is one of the fundamental aspects of computing where a program or a system may duplicate, evolve and mutate. Our point of view is that Kleene's (second) recursion theorem is essential to understand self-replication mechanisms. An interesting example of self-replication codes is given by computer viruses. This was initially explained in the seminal works of Cohen and of Adleman in the 1980s. In fact, the different variants of recursion theorems provide and explain constructions of self-replicating codes and, as a result, of various classes of malware. None of the results are new from the point of view of computability theory. We now propose a self-modifying register machine as a model of computation in which we can effectively deal with the self-reproduction and in which new offsprings can be activated as independent organisms.

Emergence of a new rhabdovirus associated with mass mortalities in eelpout (Zoarces viviparous) in the Baltic Sea.

PubMed

Axén, C; Hakhverdyan, M; Boutrup, T S; Blomkvist, E; Ljunghager, F; Alfjorden, A; Hagström, Å; Olesen, N J; Juremalm, M; Leijon, M; Valarcher, J-F

2017-02-01

We report the first description of a new Rhabdoviridae tentatively named eelpout rhabdovirus (EpRV genus Perhabdovirus). This virus was associated with mass mortalities in eelpout (Zoarces viviparous, Linnaeus) along the Swedish Baltic Sea coast line in 2014. Diseased fish showed signs of central nervous system infection, and brain lesions were confirmed by histology. A cytopathogenic effect was observed in cell culture, but ELISAs for the epizootic piscine viral haemorrhagic septicaemia virus (VHSV), infectious pancreas necrosis virus (IPNV), infectious haematopoietic necrosis virus (IHNV) and spring viraemia of carp virus (SVCV) were negative. Further investigations by chloroform inactivation, indirect fluorescence antibody test and electron microscopy indicated the presence of a rhabdovirus. By deep sequencing of original tissue suspension and infected cell culture supernatant, the full viral genome was assembled and we confirmed the presence of a rhabdovirus with 59.5% nucleotide similarity to the closest relative Siniperca chuatsi rhabdovirus. The full-genome sequence of this new virus, eelpout rhabdovirus (EpRV), has been deposited in GenBank under accession number KR612230. An RT-PCR based on the L-gene sequence confirmed the presence of EpRV in sick/dead eelpout, but the virus was not found in control fish. Additional investigations to characterize the pathogenicity of EpRV are planned. © 2016 John Wiley & Sons Ltd.

An influenza A virus (H7N9) anti-neuraminidase monoclonal antibody protects mice from morbidity without interfering with the development of protective immunity to subsequent homologous challenge.

PubMed

Wilson, Jason R; Belser, Jessica A; DaSilva, Juliana; Guo, Zhu; Sun, Xiangjie; Gansebom, Shane; Bai, Yaohui; Stark, Thomas J; Chang, Jessie; Carney, Paul; Levine, Min Z; Barnes, John; Stevens, James; Maines, Taronna R; Tumpey, Terrence M; York, Ian A

2017-11-01

The emergence of A(H7N9) virus strains with resistance to neuraminidase (NA) inhibitors highlights a critical need to discover new countermeasures for treatment of A(H7N9) virus-infected patients. We previously described an anti-NA mAb (3c10-3) that has prophylactic and therapeutic efficacy in mice lethally challenged with A(H7N9) virus when delivered intraperitoneally (i.p.). Here we show that intrananasal (i.n.) administration of 3c10-3 protects 100% of mice from mortality when treated 24h post-challenge and further characterize the protective efficacy of 3c10-3 using a nonlethal A(H7N9) challenge model. Administration of 3c10-3 i.p. 24h prior to challenge resulted in a significant decrease in viral lung titers and deep sequencing analysis indicated that treatment did not consistently select for viral variants in NA. Furthermore, prophylactic administration of 3c10-3 did not inhibit the development of protective immunity to subsequent homologous virus re-challenge. Taken together, 3c10-3 highlights the potential use of anti-NA mAb to mitigate influenza virus infection. Published by Elsevier Inc.

The Impact of the Network Topology on the Viral Prevalence: A Node-Based Approach

PubMed Central

Yang, Lu-Xing; Draief, Moez; Yang, Xiaofan

2015-01-01

This paper addresses the impact of the structure of the viral propagation network on the viral prevalence. For that purpose, a new epidemic model of computer virus, known as the node-based SLBS model, is proposed. Our analysis shows that the maximum eigenvalue of the underlying network is a key factor determining the viral prevalence. Specifically, the value range of the maximum eigenvalue is partitioned into three subintervals: viruses tend to extinction very quickly or approach extinction or persist depending on into which subinterval the maximum eigenvalue of the propagation network falls. Consequently, computer virus can be contained by adjusting the propagation network so that its maximum eigenvalue falls into the desired subinterval. PMID:26222539

Viruses, Trojan Horses, and Other Badware: Information and Implications for Online Searchers.

ERIC Educational Resources Information Center

Clancy, Steve

1988-01-01

Discusses the various forms of computer viruses and the threat they pose to online databases. Available protection programs are described, and a list of online sources of protection programs and news is provided. (14 references) (CLB)

Online Hand Holding in Fixing Computer Glitches

ERIC Educational Resources Information Center

Goldsborough, Reid

2005-01-01

According to most surveys, computer manufacturers such as HP puts out reliable products, and computers in general are less troublesome than in the past. But personal computers are still prone to bugs, conflicts, viruses, spyware infestations, hacker and phishing attacks, and--most of all--user error. Unfortunately, technical support from computer…

Novel Nipah virus immune-antagonism strategy revealed by experimental and computational study.

PubMed

Seto, Jeremy; Qiao, Liang; Guenzel, Carolin A; Xiao, Sa; Shaw, Megan L; Hayot, Fernand; Sealfon, Stuart C

2010-11-01

Nipah virus is an emerging pathogen that causes severe disease in humans. It expresses several antagonist proteins that subvert the immune response and that may contribute to its pathogenicity. Studies of its biology are difficult due to its high pathogenicity and requirement for biosafety level 4 containment. We integrated experimental and computational methods to elucidate the effects of Nipah virus immune antagonists. Individual Nipah virus immune antagonists (phosphoprotein and V and W proteins) were expressed from recombinant Newcastle disease viruses, and the responses of infected human monocyte-derived dendritic cells were determined. We developed an ordinary differential equation model of the infectious process that that produced results with a high degree of correlation with these experimental results. In order to simulate the effects of wild-type virus, the model was extended to incorporate published experimental data on the time trajectories of immune-antagonist production. These data showed that the RNA-editing mechanism utilized by the wild-type Nipah virus to produce immune antagonists leads to a delay in the production of the most effective immune antagonists, V and W. Model simulations indicated that this delay caused a disconnection between attenuation of the antiviral response and suppression of inflammation. While the antiviral cytokines were efficiently suppressed at early time points, some early inflammatory cytokine production occurred, which would be expected to increase vascular permeability and promote virus spread and pathogenesis. These results suggest that Nipah virus has evolved a unique immune-antagonist strategy that benefits from controlled expression of multiple antagonist proteins with various potencies.

Computational analysis of Ebolavirus data: prospects, promises and challenges.

PubMed

Michaelis, Martin; Rossman, Jeremy S; Wass, Mark N

2016-08-15

The ongoing Ebola virus (also known as Zaire ebolavirus, a member of the Ebolavirus family) outbreak in West Africa has so far resulted in >28000 confirmed cases compared with previous Ebolavirus outbreaks that affected a maximum of a few hundred individuals. Hence, Ebolaviruses impose a much greater threat than we may have expected (or hoped). An improved understanding of the virus biology is essential to develop therapeutic and preventive measures and to be better prepared for future outbreaks by members of the Ebolavirus family. Computational investigations can complement wet laboratory research for biosafety level 4 pathogens such as Ebolaviruses for which the wet experimental capacities are limited due to a small number of appropriate containment laboratories. During the current West Africa outbreak, sequence data from many Ebola virus genomes became available providing a rich resource for computational analysis. Here, we consider the studies that have already reported on the computational analysis of these data. A range of properties have been investigated including Ebolavirus evolution and pathogenicity, prediction of micro RNAs and identification of Ebolavirus specific signatures. However, the accuracy of the results remains to be confirmed by wet laboratory experiments. Therefore, communication and exchange between computational and wet laboratory researchers is necessary to make maximum use of computational analyses and to iteratively improve these approaches. © 2016 The Author(s). published by Portland Press Limited on behalf of the Biochemical Society.

Compensating for cross-reactions using avidity and computation in a suspension multiplex immunoassay for serotyping of Zika versus other flavivirus infections.

PubMed

Rönnberg, Bengt; Gustafsson, Åke; Vapalahti, Olli; Emmerich, Petra; Lundkvist, Åke; Schmidt-Chanasit, Jonas; Blomberg, Jonas

2017-10-01

The recent spread of Zika virus (ZIKV) in the Americas and Asia necessitates an increased preparedness for improved maternal and perinatal health and blood safety. However, serological cross-reactions, especially to Dengue virus (DENV), complicate ZIKV antibody serodiagnosis. A novel "pan-Flavi" suspension multiplex immunoassay (PFSMIA) using 25 antigens, whole virus (WV), non-structural protein 1 (NS1), and envelope (E) proteins, from 7 zoonotic flaviviruses for specific detection of ZIKV and DENV IgM and IgG was developed. Patterns of antibody cross-reactivity, avidity, and kinetics were established in 104 sera from returning travelers with known ZIKV and DENV infections. PFSMIA gave IgM- and IgG-sensitivities for both viruses of 96-100%, compared to an immunofluorescence assay. Main IgM cross-reactions were to NS1, for IgG to the E and WV antigens. Infecting virus yielded reactivity to several antigens of the homologous virus, while cross-reactions tended to occur only to a single antigen from heterologous virus(es). A specificity-enhancing computer procedure took into account antibody isotype, number of antibody-reactive antigens per virus, avidity, average degree of cross-reactivity to heterologous flavivirus antigens, and reactivity changes in serial sera. It classified all 50 cases correctly. Applied to sera from 200 pregnant women and 173 blood donors from Sweden, one blood donor was found ZIKV NS1 IgM positive, and another as ZIKV NS1 IgG positive. These samples did not react with other ZIKV antigens and were thereby judged as false-positives. PFSMIA provided sensitive and specific ZIKV and DENV serology, warranting high-throughput serological surveillance and a minimized need for laborious and expensive virus neutralization assays.

Global morphological analysis of marine viruses shows minimal regional variation and dominance of non-tailed viruses

PubMed Central

Brum, Jennifer R; Schenck, Ryan O; Sullivan, Matthew B

2013-01-01

Viruses influence oceanic ecosystems by causing mortality of microorganisms, altering nutrient and organic matter flux via lysis and auxiliary metabolic gene expression and changing the trajectory of microbial evolution through horizontal gene transfer. Limited host range and differing genetic potential of individual virus types mean that investigations into the types of viruses that exist in the ocean and their spatial distribution throughout the world's oceans are critical to understanding the global impacts of marine viruses. Here we evaluate viral morphological characteristics (morphotype, capsid diameter and tail length) using a quantitative transmission electron microscopy (qTEM) method across six of the world's oceans and seas sampled through the Tara Oceans Expedition. Extensive experimental validation of the qTEM method shows that neither sample preservation nor preparation significantly alters natural viral morphological characteristics. The global sampling analysis demonstrated that morphological characteristics did not vary consistently with depth (surface versus deep chlorophyll maximum waters) or oceanic region. Instead, temperature, salinity and oxygen concentration, but not chlorophyll a concentration, were more explanatory in evaluating differences in viral assemblage morphological characteristics. Surprisingly, given that the majority of cultivated bacterial viruses are tailed, non-tailed viruses appear to numerically dominate the upper oceans as they comprised 51–92% of the viral particles observed. Together, these results document global marine viral morphological characteristics, show that their minimal variability is more explained by environmental conditions than geography and suggest that non-tailed viruses might represent the most ecologically important targets for future research. PMID:23635867

Hunting in the Rainforest and Mayaro Virus Infection: An emerging Alphavirus in Ecuador

PubMed Central

Izurieta, Ricardo O; Macaluso, Maurizio; Watts, Douglas M; Tesh, Robert B; Guerra, Bolivar; Cruz, Ligia M; Galwankar, Sagar; Vermund, Sten H

2011-01-01

Objectives: The objectives of this report were to document the potential presence of Mayaro virus infection in Ecuador and to examine potential risk factors for Mayaro virus infection among the personnel of a military garrison in the Amazonian rainforest. Materials and Methods: The study population consisted of the personnel of a garrison located in the Ecuadorian Amazonian rainforest. The cross-sectional study employed interviews and seroepidemiological methods. Humoral immune response to Mayaro virus infection was assessed by evaluating IgM- and IgG-specific antibodies using ELISA. Results: Of 338 subjects studied, 174 were from the Coastal zone of Ecuador, 73 from Andean zone, and 91 were native to the Amazonian rainforest. Seroprevalence of Mayaro virus infection was more than 20 times higher among Amazonian natives (46%) than among subjects born in other areas (2%). Conclusions: Age and hunting in the rainforest were significant predictors of Mayaro virus infection overall and among Amazonian natives. The results provide the first demonstration of the potential presence of Mayaro virus infection in Ecuador and a systematic evaluation of risk factors for the transmission of this alphavirus. The large difference in prevalence rates between Amazonian natives and other groups and between older and younger natives suggest that Mayaro virus is endemic and enzootic in the rainforest, with sporadic outbreaks that determine differences in risk between birth cohorts of natives. Deep forest hunting may selectively expose native men, descendants of the Shuar and Huaronai ethnic groups, to the arthropod vectors of Mayaro virus in areas close to primate reservoirs. PMID:22223990

DeepX: Deep Learning Accelerator for Restricted Boltzmann Machine Artificial Neural Networks.

PubMed

Kim, Lok-Won

2018-05-01

Although there have been many decades of research and commercial presence on high performance general purpose processors, there are still many applications that require fully customized hardware architectures for further computational acceleration. Recently, deep learning has been successfully used to learn in a wide variety of applications, but their heavy computation demand has considerably limited their practical applications. This paper proposes a fully pipelined acceleration architecture to alleviate high computational demand of an artificial neural network (ANN) which is restricted Boltzmann machine (RBM) ANNs. The implemented RBM ANN accelerator (integrating network size, using 128 input cases per batch, and running at a 303-MHz clock frequency) integrated in a state-of-the art field-programmable gate array (FPGA) (Xilinx Virtex 7 XC7V-2000T) provides a computational performance of 301-billion connection-updates-per-second and about 193 times higher performance than a software solution running on general purpose processors. Most importantly, the architecture enables over 4 times (12 times in batch learning) higher performance compared with a previous work when both are implemented in an FPGA device (XC2VP70).

Host shifts result in parallel genetic changes when viruses evolve in closely related species

PubMed Central

Day, Jonathan P.; Smith, Sophia C. L.; Houslay, Thomas M.; Tagliaferri, Lucia

2018-01-01

Host shifts, where a pathogen invades and establishes in a new host species, are a major source of emerging infectious diseases. They frequently occur between related host species and often rely on the pathogen evolving adaptations that increase their fitness in the novel host species. To investigate genetic changes in novel hosts, we experimentally evolved replicate lineages of an RNA virus (Drosophila C Virus) in 19 different species of Drosophilidae and deep sequenced the viral genomes. We found a strong pattern of parallel evolution, where viral lineages from the same host were genetically more similar to each other than to lineages from other host species. When we compared viruses that had evolved in different host species, we found that parallel genetic changes were more likely to occur if the two host species were closely related. This suggests that when a virus adapts to one host it might also become better adapted to closely related host species. This may explain in part why host shifts tend to occur between related species, and may mean that when a new pathogen appears in a given species, closely related species may become vulnerable to the new disease. PMID:29649296

A Metagenomic Survey of Viral Abundance and Diversity in Mosquitoes from Hubei Province

PubMed Central

Shi, Chenyan; Liu, Yi; Hu, Xiaomin; Xiong, Jinfeng; Zhang, Bo; Yuan, Zhiming

2015-01-01

Mosquitoes as one of the most common but important vectors have the potential to transmit or acquire a lot of viruses through biting, however viral flora in mosquitoes and its impact on mosquito-borne disease transmission has not been well investigated and evaluated. In this study, the metagenomic techniquehas been successfully employed in analyzing the abundance and diversity of viral community in three mosquito samples from Hubei, China. Among 92,304 reads produced through a run with 454 GS FLX system, 39% have high similarities with viral sequences belonging to identified bacterial, fungal, animal, plant and insect viruses, and 0.02% were classed into unidentified viral sequences, demonstrating high abundance and diversity of viruses in mosquitoes. Furthermore, two novel viruses in subfamily Densovirinae and family Dicistroviridae were identified, and six torque tenosus virus1 in family Anelloviridae, three porcine parvoviruses in subfamily Parvovirinae and a Culex tritaeniorhynchus rhabdovirus in Family Rhabdoviridae were preliminarily characterized. The viral metagenomic analysis offered us a deep insight into the viral population of mosquito which played an important role in viral initiative or passive transmission and evolution during the process. PMID:26030271

Deep learning in mammography and breast histology, an overview and future trends.

PubMed

Hamidinekoo, Azam; Denton, Erika; Rampun, Andrik; Honnor, Kate; Zwiggelaar, Reyer

2018-07-01

Recent improvements in biomedical image analysis using deep learning based neural networks could be exploited to enhance the performance of Computer Aided Diagnosis (CAD) systems. Considering the importance of breast cancer worldwide and the promising results reported by deep learning based methods in breast imaging, an overview of the recent state-of-the-art deep learning based CAD systems developed for mammography and breast histopathology images is presented. In this study, the relationship between mammography and histopathology phenotypes is described, which takes biological aspects into account. We propose a computer based breast cancer modelling approach: the Mammography-Histology-Phenotype-Linking-Model, which develops a mapping of features/phenotypes between mammographic abnormalities and their histopathological representation. Challenges are discussed along with the potential contribution of such a system to clinical decision making and treatment management. Crown Copyright © 2018. Published by Elsevier B.V. All rights reserved.

Deep Learning in Medical Image Analysis

PubMed Central

Shen, Dinggang; Wu, Guorong; Suk, Heung-Il

2016-01-01

The computer-assisted analysis for better interpreting images have been longstanding issues in the medical imaging field. On the image-understanding front, recent advances in machine learning, especially, in the way of deep learning, have made a big leap to help identify, classify, and quantify patterns in medical images. Specifically, exploiting hierarchical feature representations learned solely from data, instead of handcrafted features mostly designed based on domain-specific knowledge, lies at the core of the advances. In that way, deep learning is rapidly proving to be the state-of-the-art foundation, achieving enhanced performances in various medical applications. In this article, we introduce the fundamentals of deep learning methods; review their successes to image registration, anatomical/cell structures detection, tissue segmentation, computer-aided disease diagnosis or prognosis, and so on. We conclude by raising research issues and suggesting future directions for further improvements. PMID:28301734

Physician use of updated anti-virus software in a tertiary Nigerian hospital.

PubMed

Laabes, E P; Nyango, D D; Ayedima, M M; Ladep, N G

2010-01-01

While physicians are becoming increasingly dependent on computers and the internet, highly lethal malware continue to be loaded into cyberspace. We sought to assess the proportion of physicians with updated anti-virus software in Jos University Teaching Hospital Nigeria and to determine perceived barriers to getting updates. We used a pre-tested semi-structured self-administered questionnaire to conduct a cross-sectional survey among 118 physicians. The mean age (+/- SD) of subjects was 34 (+/- 4) years, with 94 male and 24 female physicians. Forty-two (36.5%) of 115 physicians with anti-virus software used an updated program (95% Cl: 27, 45). The top-three antivirus software were: McAfee 40 (33.9%), AVG 37 (31.4%) and Norton 17 (14.4%). Common infections were: Trojan horse 22 (29.7%), Brontok worm 8 (10.8%), and Ravmonlog.exe 5 (6.8%). Internet browsing with a firewall was an independent determinant for use of updated anti-virus software [OR 4.3, 95% CI, 1.86, 10.02; P < 0.001]. Busy schedule, 40 (33.9%) and lack of credit card 39 (33.1%) were perceived barriers to updating antivirus software. The use of regularly updated anti-virus software is sub-optimal among physicians implying vulnerability to computer viruses. Physicians should be careful with flash drives and should avoid being victims of the raging arms race between malware producers and anti-virus software developers.

Unbiased whole-genome deep sequencing of human and porcine stool samples reveals circulation of multiple groups of rotaviruses and a putative zoonotic infection

PubMed Central

Phan, My V. T.; Anh, Pham Hong; Cuong, Nguyen Van; Munnink, Bas B. Oude; van der Hoek, Lia; My, Phuc Tran; Tri, Tue Ngo; Bryant, Juliet E.; Baker, Stephen; Thwaites, Guy; Woolhouse, Mark; Kellam, Paul; Rabaa, Maia A.

2016-01-01

Abstract Coordinated and synchronous surveillance for zoonotic viruses in both human clinical cases and animal reservoirs provides an opportunity to identify interspecies virus movement. Rotavirus (RV) is an important cause of viral gastroenteritis in humans and animals. In this study, we document the RV diversity within co-located humans and animals sampled from the Mekong delta region of Vietnam using a primer-independent, agnostic, deep sequencing approach. A total of 296 stool samples (146 from diarrhoeal human patients and 150 from pigs living in the same geographical region) were directly sequenced, generating the genomic sequences of sixty human rotaviruses (all group A) and thirty-one porcine rotaviruses (thirteen group A, seven group B, six group C, and five group H). Phylogenetic analyses showed the co-circulation of multiple distinct RV group A (RVA) genotypes/strains, many of which were divergent from the strain components of licensed RVA vaccines, as well as considerable virus diversity in pigs including full genomes of rotaviruses in groups B, C, and H, none of which have been previously reported in Vietnam. Furthermore, the detection of an atypical RVA genotype constellation (G4-P[6]-I1-R1-C1-M1-A8-N1-T7-E1-H1) in a human patient and a pig from the same region provides some evidence for a zoonotic event. PMID:28748110

Using deep learning in image hyper spectral segmentation, classification, and detection

NASA Astrophysics Data System (ADS)

Zhao, Xiuying; Su, Zhenyu

2018-02-01

Recent years have shown that deep learning neural networks are a valuable tool in the field of computer vision. Deep learning method can be used in applications like remote sensing such as Land cover Classification, Detection of Vehicle in Satellite Images, Hyper spectral Image classification. This paper addresses the use of the deep learning artificial neural network in Satellite image segmentation. Image segmentation plays an important role in image processing. The hue of the remote sensing image often has a large hue difference, which will result in the poor display of the images in the VR environment. Image segmentation is a pre processing technique applied to the original images and splits the image into many parts which have different hue to unify the color. Several computational models based on supervised, unsupervised, parametric, probabilistic region based image segmentation techniques have been proposed. Recently, one of the machine learning technique known as, deep learning with convolution neural network has been widely used for development of efficient and automatic image segmentation models. In this paper, we focus on study of deep neural convolution network and its variants for automatic image segmentation rather than traditional image segmentation strategies.

In Touch with Molecules: Improving Student Learning with Innovative Molecular Models

ERIC Educational Resources Information Center

Davenport, Jodi; Silberglitt, Matt; Olson, Arthur

2013-01-01

How do viruses self-assemble? Why do DNA bases pair the way they do? What factors determine whether strands of proteins fold into sheets or helices? Why does handedness matter? A deep understanding of core issues in biology requires students to understand both complex spatial structures of molecules and the interactions involved in dynamic…

Bacteriophage lytic to Desulfovibrio aespoeensis isolated from deep groundwater.

PubMed

Eydal, Hallgerd S C; Jägevall, Sara; Hermansson, Malte; Pedersen, Karsten

2009-10-01

Viruses were earlier found to be 10-fold more abundant than prokaryotes in deep granitic groundwater at the Aspö Hard Rock Laboratory (HRL). Using a most probable number (MPN) method, 8-30 000 cells of sulphate-reducing bacteria per ml were found in groundwater from seven boreholes at the Aspö HRL. The content of lytic phages infecting the indigenous bacterium Desulfovibrio aespoeensis in Aspö groundwater was analysed using the MPN technique for phages. In four of 10 boreholes, 0.2-80 phages per ml were found at depths of 342-450 m. Isolates of lytic phages were made from five cultures. Using transmission electron microscopy, these were characterized and found to be in the Podoviridae morphology group. The isolated phages were further analysed regarding host range and were found not to infect five other species of Desulfovibrio or 10 Desulfovibrio isolates with up to 99.9% 16S rRNA gene sequence identity to D. aespoeensis. To further analyse phage-host interactions, using a direct count method, growth of the phages and their host was followed in batch cultures, and the viral burst size was calculated to be approximately 170 phages per lytic event, after a latent period of approximately 70 h. When surviving cells from infected D. aespoeensis batch cultures were inoculated into new cultures and reinfected, immunity to the phages was found. The parasite-prey system found implies that viruses are important for microbial ecosystem diversity and activity, and for microbial numbers in deep subsurface groundwater.

Ultra-Deep Sequencing Analysis of the Hepatitis A Virus 5'-Untranslated Region among Cases of the Same Outbreak from a Single Source

PubMed Central

Wu, Shuang; Nakamoto, Shingo; Kanda, Tatsuo; Jiang, Xia; Nakamura, Masato; Miyamura, Tatsuo; Shirasawa, Hiroshi; Sugiura, Nobuyuki; Takahashi-Nakaguchi, Azusa; Gonoi, Tohru; Yokosuka, Osamu

2014-01-01

Hepatitis A virus (HAV) is a causative agent of acute viral hepatitis for which an effective vaccine has been developed. Here we describe ultra-deep pyrosequences (UDPSs) of HAV 5'-untranslated region (5'UTR) among cases of the same outbreak, which arose from a single source, associated with a revolving sushi bar. We determined the reference sequence from HAV-derived clone from an attendant by the Sanger method. Sixteen UDPSs from this outbreak and one from another sporadic case were compared with this reference. Nucleotide errors yielded a UDPS error rate of < 1%. This study confirmed that nucleotide substitutions of this region are transition mutations in outbreak cases, that insertion was observed only in non-severe cases, and that these nucleotide substitutions were different from those of the sporadic case. Analysis of UDPSs detected low-prevalence HAV variations in 5'UTR, but no specific mutations associated with severity in these outbreak cases. To our surprise, HAV strains in this outbreak conserved HAV IRES sequence even if we performed analysis of UDPSs. UDPS analysis of HAV 5'UTR gave us no association between the disease severity of hepatitis A and HAV 5'UTR substitutions. It might be more interesting to perform ultra-deep sequencing of full length HAV genome in order to reveal possible unknown genomic determinants associated with disease severity. Further studies will be needed. PMID:24396287

Histopathology of papilloma virus infection of the cervix uteri: the history, taxonomy, nomenclature and reporting of koilocytic dysplasias.

PubMed Central

Fletcher, S

1983-01-01

Papilloma virus infections of the squamous cervix are common and determine the incidence of koilocytes in cervical smears and biopsies. Typical exophytic condyloma acuminatum accounts for few of the infections. Most are "flat condylomata" (flat koilocytosis)--slightly raised plaques of flat profile. Superficial koilocytosis, dyskeratosis and often disturbed deep cells are their main histological features. The association between koilocytosis and dysplasia is considered and the different taxonomic approaches to the koilodysplastic states are compared. Equivalence is established between different nomenclatures by reference to the familiar dysplastic grades. Methods of reporting koilodysplasias are given and a prognostically difficult state of "full thickness" or "plenary" koilocytosis is described. In applying the "high technology" of nucleic acid hybridisation to confirm the lesion specificity of papilloma virus strains the lesions must be well defined and clearly identified by precise nomenclature: the nature and nomenclature of the lesions is critically discussed. Images PMID:6304149

The Expanding Family of Virophages.

PubMed

Bekliz, Meriem; Colson, Philippe; La Scola, Bernard

2016-11-23

Virophages replicate with giant viruses in the same eukaryotic cells. They are a major component of the specific mobilome of mimiviruses. Since their discovery in 2008, five other representatives have been isolated, 18 new genomes have been described, two of which being nearly completely sequenced, and they have been classified in a new viral family, Lavidaviridae . Virophages are small viruses with approximately 35-74 nm large icosahedral capsids and 17-29 kbp large double-stranded DNA genomes with 16-34 genes, among which a very small set is shared with giant viruses. Virophages have been isolated or detected in various locations and in a broad range of habitats worldwide, including the deep ocean and inland. Humans, therefore, could be commonly exposed to virophages, although currently limited evidence exists of their presence in humans based on serology and metagenomics. The distribution of virophages, the consequences of their infection and the interactions with their giant viral hosts within eukaryotic cells deserve further research.

Influenza A Virus Polymerase Is a Site for Adaptive Changes during Experimental Evolution in Bat Cells

PubMed Central

Poole, Daniel S.; Yú, Shuǐqìng; Caì, Yíngyún; Dinis, Jorge M.; Müller, Marcel A.; Jordan, Ingo; Friedrich, Thomas C.; Kuhn, Jens H.

2014-01-01

ABSTRACT The recent identification of highly divergent influenza A viruses in bats revealed a new, geographically dispersed viral reservoir. To investigate the molecular mechanisms of host-restricted viral tropism and the potential for transmission of viruses between humans and bats, we exposed a panel of cell lines from bats of diverse species to a prototypical human-origin influenza A virus. All of the tested bat cell lines were susceptible to influenza A virus infection. Experimental evolution of human and avian-like viruses in bat cells resulted in efficient replication and created highly cytopathic variants. Deep sequencing of adapted human influenza A virus revealed a mutation in the PA polymerase subunit not previously described, M285K. Recombinant virus with the PA M285K mutation completely phenocopied the adapted virus. Adaptation of an avian virus-like virus resulted in the canonical PB2 E627K mutation that is required for efficient replication in other mammals. None of the adaptive mutations occurred in the gene for viral hemagglutinin, a gene that frequently acquires changes to recognize host-specific variations in sialic acid receptors. We showed that human influenza A virus uses canonical sialic acid receptors to infect bat cells, even though bat influenza A viruses do not appear to use these receptors for virus entry. Our results demonstrate that bats are unique hosts that select for both a novel mutation and a well-known adaptive mutation in the viral polymerase to support replication. IMPORTANCE Bats constitute well-known reservoirs for viruses that may be transferred into human populations, sometimes with fatal consequences. Influenza A viruses have recently been identified in bats, dramatically expanding the known host range of this virus. Here we investigated the replication of human influenza A virus in bat cell lines and the barriers that the virus faces in this new host. Human influenza A and B viruses infected cells from geographically and evolutionarily diverse New and Old World bats. Viruses mutated during infections in bat cells, resulting in increased replication and cytopathic effects. These mutations were mapped to the viral polymerase and shown to be solely responsible for adaptation to bat cells. Our data suggest that replication of human influenza A viruses in a nonnative host drives the evolution of new variants and may be an important source of genetic diversity. PMID:25142579

The stimulus selectivity and connectivity of layer six principal cells reveals cortical microcircuits underlying visual processing.

PubMed

Vélez-Fort, Mateo; Rousseau, Charly V; Niedworok, Christian J; Wickersham, Ian R; Rancz, Ede A; Brown, Alexander P Y; Strom, Molly; Margrie, Troy W

2014-09-17

Sensory computations performed in the neocortex involve layer six (L6) cortico-cortical (CC) and cortico-thalamic (CT) signaling pathways. Developing an understanding of the physiological role of these circuits requires dissection of the functional specificity and connectivity of the underlying individual projection neurons. By combining whole-cell recording from identified L6 principal cells in the mouse primary visual cortex (V1) with modified rabies virus-based input mapping, we have determined the sensory response properties and upstream monosynaptic connectivity of cells mediating the CC or CT pathway. We show that CC-projecting cells encompass a broad spectrum of selectivity to stimulus orientation and are predominantly innervated by deep layer V1 neurons. In contrast, CT-projecting cells are ultrasparse firing, exquisitely tuned to orientation and direction information, and receive long-range input from higher cortical areas. This segregation in function and connectivity indicates that L6 microcircuits route specific contextual and stimulus-related information within and outside the cortical network. Copyright © 2014 The Authors. Published by Elsevier Inc. All rights reserved.

Risk factors for computed tomography angiography spot sign in deep and lobar intracerebral hemorrhage are shared.

PubMed

Radmanesh, Farid; Falcone, Guido J; Anderson, Christopher D; Battey, Thomas W K; Ayres, Alison M; Vashkevich, Anastasia; McNamara, Kristen A; Schwab, Kristin; Romero, Javier M; Viswanathan, Anand; Greenberg, Steven M; Goldstein, Joshua N; Rosand, Jonathan; Brouwers, H Bart

2014-06-01

Patients with intracerebral hemorrhage (ICH) who present with a spot sign on computed tomography angiography are at increased risk of hematoma expansion and poor outcome. Because primary ICH is the acute manifestation of chronic cerebral small vessel disease, we investigated whether different clinical or imaging characteristics predict spot sign presence, using ICH location as a surrogate for arteriolosclerosis- and cerebral amyloid angiopathy-related ICH. Patients with primary ICH and available computed tomography angiography at presentation were included. Predictors of spot sign were assessed using uni- and multivariable regression, stratified by ICH location. Seven hundred forty-one patients were eligible, 335 (45%) deep and 406 (55%) lobar ICH. At least one spot sign was present in 76 (23%) deep and 102 (25%) lobar ICH patients. In multivariable regression, warfarin (odds ratio [OR], 2.42; 95% confidence interval [CI], 1.01-5.71; P=0.04), baseline ICH volume (OR, 1.20; 95% CI, 1.09-1.33, per 10 mL increase; P<0.001), and time from symptom onset to computed tomography angiography (OR, 0.89; 95% CI, 0.80-0.96, per hour; P=0.009) were associated with the spot sign in deep ICH. Predictors of spot sign in lobar ICH were warfarin (OR, 3.95; 95% CI, 1.87-8.51; P<0.001) and baseline ICH volume (OR, 1.20; 95% CI, 1.10-1.31, per 10 mL increase; P<0.001). The most potent associations with spot sign are shared between deep and lobar ICH, suggesting that the acute bleeding process that arises in the setting of different chronic small vessel diseases shares commonalities. © 2014 American Heart Association, Inc.

vFitness: a web-based computing tool for improving estimation of in vitro HIV-1 fitness experiments

PubMed Central

2010-01-01

Background The replication rate (or fitness) between viral variants has been investigated in vivo and in vitro for human immunodeficiency virus (HIV). HIV fitness plays an important role in the development and persistence of drug resistance. The accurate estimation of viral fitness relies on complicated computations based on statistical methods. This calls for tools that are easy to access and intuitive to use for various experiments of viral fitness. Results Based on a mathematical model and several statistical methods (least-squares approach and measurement error models), a Web-based computing tool has been developed for improving estimation of virus fitness in growth competition assays of human immunodeficiency virus type 1 (HIV-1). Conclusions Unlike the two-point calculation used in previous studies, the estimation here uses linear regression methods with all observed data in the competition experiment to more accurately estimate relative viral fitness parameters. The dilution factor is introduced for making the computational tool more flexible to accommodate various experimental conditions. This Web-based tool is implemented in C# language with Microsoft ASP.NET, and is publicly available on the Web at http://bis.urmc.rochester.edu/vFitness/. PMID:20482791

vFitness: a web-based computing tool for improving estimation of in vitro HIV-1 fitness experiments.

PubMed

Ma, Jingming; Dykes, Carrie; Wu, Tao; Huang, Yangxin; Demeter, Lisa; Wu, Hulin

2010-05-18

The replication rate (or fitness) between viral variants has been investigated in vivo and in vitro for human immunodeficiency virus (HIV). HIV fitness plays an important role in the development and persistence of drug resistance. The accurate estimation of viral fitness relies on complicated computations based on statistical methods. This calls for tools that are easy to access and intuitive to use for various experiments of viral fitness. Based on a mathematical model and several statistical methods (least-squares approach and measurement error models), a Web-based computing tool has been developed for improving estimation of virus fitness in growth competition assays of human immunodeficiency virus type 1 (HIV-1). Unlike the two-point calculation used in previous studies, the estimation here uses linear regression methods with all observed data in the competition experiment to more accurately estimate relative viral fitness parameters. The dilution factor is introduced for making the computational tool more flexible to accommodate various experimental conditions. This Web-based tool is implemented in C# language with Microsoft ASP.NET, and is publicly available on the Web at http://bis.urmc.rochester.edu/vFitness/.

Deep convolutional neural networks for automatic classification of gastric carcinoma using whole slide images in digital histopathology.

PubMed

Sharma, Harshita; Zerbe, Norman; Klempert, Iris; Hellwich, Olaf; Hufnagl, Peter

2017-11-01

Deep learning using convolutional neural networks is an actively emerging field in histological image analysis. This study explores deep learning methods for computer-aided classification in H&E stained histopathological whole slide images of gastric carcinoma. An introductory convolutional neural network architecture is proposed for two computerized applications, namely, cancer classification based on immunohistochemical response and necrosis detection based on the existence of tumor necrosis in the tissue. Classification performance of the developed deep learning approach is quantitatively compared with traditional image analysis methods in digital histopathology requiring prior computation of handcrafted features, such as statistical measures using gray level co-occurrence matrix, Gabor filter-bank responses, LBP histograms, gray histograms, HSV histograms and RGB histograms, followed by random forest machine learning. Additionally, the widely known AlexNet deep convolutional framework is comparatively analyzed for the corresponding classification problems. The proposed convolutional neural network architecture reports favorable results, with an overall classification accuracy of 0.6990 for cancer classification and 0.8144 for necrosis detection. Copyright © 2017 Elsevier Ltd. All rights reserved.

Crispen's Five Antivirus Rules.

ERIC Educational Resources Information Center

Crispen, Patrick Douglas

2000-01-01

Explains five rules to protect computers from viruses. Highlights include commercial antivirus software programs and the need to upgrade them periodically (every year to 18 months); updating virus definitions at least weekly; scanning attached files from email with antivirus software before opening them; Microsoft Word macro protection; and the…

IMG/VR: a database of cultured and uncultured DNA Viruses and retroviruses.

PubMed

Paez-Espino, David; Chen, I-Min A; Palaniappan, Krishna; Ratner, Anna; Chu, Ken; Szeto, Ernest; Pillay, Manoj; Huang, Jinghua; Markowitz, Victor M; Nielsen, Torben; Huntemann, Marcel; K Reddy, T B; Pavlopoulos, Georgios A; Sullivan, Matthew B; Campbell, Barbara J; Chen, Feng; McMahon, Katherine; Hallam, Steve J; Denef, Vincent; Cavicchioli, Ricardo; Caffrey, Sean M; Streit, Wolfgang R; Webster, John; Handley, Kim M; Salekdeh, Ghasem H; Tsesmetzis, Nicolas; Setubal, Joao C; Pope, Phillip B; Liu, Wen-Tso; Rivers, Adam R; Ivanova, Natalia N; Kyrpides, Nikos C

2017-01-04

Viruses represent the most abundant life forms on the planet. Recent experimental and computational improvements have led to a dramatic increase in the number of viral genome sequences identified primarily from metagenomic samples. As a result of the expanding catalog of metagenomic viral sequences, there exists a need for a comprehensive computational platform integrating all these sequences with associated metadata and analytical tools. Here we present IMG/VR (https://img.jgi.doe.gov/vr/), the largest publicly available database of 3908 isolate reference DNA viruses with 264 413 computationally identified viral contigs from >6000 ecologically diverse metagenomic samples. Approximately half of the viral contigs are grouped into genetically distinct quasi-species clusters. Microbial hosts are predicted for 20 000 viral sequences, revealing nine microbial phyla previously unreported to be infected by viruses. Viral sequences can be queried using a variety of associated metadata, including habitat type and geographic location of the samples, or taxonomic classification according to hallmark viral genes. IMG/VR has a user-friendly interface that allows users to interrogate all integrated data and interact by comparing with external sequences, thus serving as an essential resource in the viral genomics community. © The Author(s) 2016. Published by Oxford University Press on behalf of Nucleic Acids Research.

Replication of Many Human Viruses Is Refractory to Inhibition by Endogenous Cellular MicroRNAs

PubMed Central

Bogerd, Hal P.; Skalsky, Rebecca L.; Kennedy, Edward M.; Furuse, Yuki; Whisnant, Adam W.; Flores, Omar; Schultz, Kimberly L. W.; Putnam, Nicole; Barrows, Nicholas J.; Sherry, Barbara; Scholle, Frank; Garcia-Blanco, Mariano A.; Griffin, Diane E.

2014-01-01

ABSTRACT The issue of whether viruses are subject to restriction by endogenous microRNAs (miRNAs) and/or by virus-induced small interfering RNAs (siRNAs) in infected human somatic cells has been controversial. Here, we address this question in two ways. First, using deep sequencing, we demonstrate that infection of human cells by the RNA virus dengue virus (DENV) or West Nile virus (WNV) does not result in the production of any virus-derived siRNAs or viral miRNAs. Second, to more globally assess the potential of small regulatory RNAs to inhibit virus replication, we used gene editing to derive human cell lines that lack a functional Dicer enzyme and that therefore are unable to produce miRNAs or siRNAs. Infection of these cells with a wide range of viruses, including DENV, WNV, yellow fever virus, Sindbis virus, Venezuelan equine encephalitis virus, measles virus, influenza A virus, reovirus, vesicular stomatitis virus, human immunodeficiency virus type 1, or herpes simplex virus 1 (HSV-1), failed to reveal any enhancement in the replication of any of these viruses, although HSV-1, which encodes at least eight Dicer-dependent viral miRNAs, did replicate somewhat more slowly in the absence of Dicer. We conclude that most, and perhaps all, human viruses have evolved to be resistant to inhibition by endogenous human miRNAs during productive replication and that dependence on a cellular miRNA, as seen with hepatitis C virus, is rare. How viruses have evolved to avoid inhibition by endogenous cellular miRNAs, which are generally highly conserved during metazoan evolution, remains to be determined. IMPORTANCE Eukaryotic cells express a wide range of small regulatory RNAs, including miRNAs, that have the potential to inhibit the expression of mRNAs that show sequence complementarity. Indeed, previous work has suggested that endogenous miRNAs have the potential to inhibit viral gene expression and replication. Here, we demonstrate that the replication of a wide range of pathogenic viruses is not enhanced in human cells engineered to be unable to produce miRNAs, indicating that viruses have evolved to be resistant to inhibition by miRNAs. This result is important, as it implies that manipulation of miRNA levels is not likely to prove useful in inhibiting virus replication. It also focuses attention on the question of how viruses have evolved to resist inhibition by miRNAs and whether virus mutants that have lost this resistance might prove useful, for example, in the development of attenuated virus vaccines. PMID:24807715

Computer Abuse: Vandalizing the Information Society.

ERIC Educational Resources Information Center

Furnell, Steven M.; Warren, Matthew J.

1997-01-01

Computing and telecommunications, key to an information-based society, are increasingly targets for criminals and mischief makers. This article examines the effects of malicious computer abuse: hacking and viruses, highlights the apparent increase in incidents, and examines their effect on public perceptions of technology. Presents broad…

Development of a TL-3 deep beam tubular backup bridge rail.

DOT National Transportation Integrated Search

2010-12-01

The objective of this study is to investigate the performance of the Ohio Department of Transportation (ODOT) Deep Beam bridge rail system per the National Cooperative Highway Research Program (NCHRP) Report 350 TL-3. Analytical study, computer simul...

Predictors of immune function in space flight

NASA Astrophysics Data System (ADS)

Shearer, William T.; Zhang, Shaojie; Reuben, James M.; Lee, Bang-Ning; Butel, Janet S.

2007-02-01

Of all of the environmental conditions of space flight that might have an adverse effect upon human immunity and the incidence of infection, space radiation stands out as the single-most important threat. As important as this would be on humans engaged in long and deep space flight, it obviously is not possible to plan Earth-bound radiation and infection studies in humans. Therefore, we propose to develop a murine model that could predict the adverse effects of space flight radiation and reactivation of latent virus infection for humans. Recent observations on the effects of gamma and latent virus infection demonstrate latent virus reactivation and loss of T cell mediated immune responses in a murine model. We conclude that using this small animal method of quantitating the amounts of radiation and latent virus infection and resulting alterations in immune responses, it may be possible to predict the degree of immunosuppression in interplanetary space travel for humans. Moreover, this model could be extended to include other space flight conditions, such as microgravity, sleep deprivation, and isolation, to obtain a more complete assessment of space flight risks for humans.

Effect of serial pig passages on the adaptation of an avian H9N2 influenza virus to swine.

PubMed

Mancera Gracia, Jose Carlos; Van den Hoecke, Silvie; Saelens, Xavier; Van Reeth, Kristien

2017-01-01

H9N2 avian influenza viruses are endemic in poultry in Asia and the Middle East. These viruses sporadically cause dead-end infections in pigs and humans raising concerns about their potential to adapt to mammals or reassort with human or swine influenza viruses. We performed ten serial passages with an avian H9N2 virus (A/quail/Hong Kong/G1/1997) in influenza naïve pigs to assess the potential of this virus to adapt to swine. Virus replication in the entire respiratory tract and nasal virus excretion were examined after each passage and we deep sequenced viral genomic RNA of the parental and passage four H9N2 virus isolated from the nasal mucosa and lung. The parental H9N2 virus caused a productive infection in pigs with a predominant tropism for the nasal mucosa, whereas only 50% lung samples were virus-positive. In contrast, inoculation of pigs with passage four virus resulted in viral replication in the entire respiratory tract. Subsequent passages were associated with reduced virus replication in the lungs and infectious virus was no longer detectable in the upper and lower respiratory tract of inoculated pigs at passage ten. The broader tissue tropism after four passages was associated with an amino acid residue substitution at position 225, within the receptor-binding site of the hemagglutinin. We also compared the parental H9N2, passage four H9N2 and the 2009 pandemic H1N1 (pH1N1) virus in a direct contact transmission experiment. Whereas only one out of six contact pigs showed nasal virus excretion of the wild-type H9N2 for more than four days, all six contact animals shed the passage four H9N2 virus. Nevertheless, the amount of excreted virus was significantly lower when compared to that of the pH1N1, which readily transmitted and replicated in all six contact animals. Our data demonstrate that serial passaging of H9N2 virus in pigs enhances its replication and transmissibility. However, full adaptation of an avian H9N2 virus to pigs likely requires an extensive set of mutations.

Effect of serial pig passages on the adaptation of an avian H9N2 influenza virus to swine

PubMed Central

Van den Hoecke, Silvie; Saelens, Xavier; Van Reeth, Kristien

2017-01-01

H9N2 avian influenza viruses are endemic in poultry in Asia and the Middle East. These viruses sporadically cause dead-end infections in pigs and humans raising concerns about their potential to adapt to mammals or reassort with human or swine influenza viruses. We performed ten serial passages with an avian H9N2 virus (A/quail/Hong Kong/G1/1997) in influenza naïve pigs to assess the potential of this virus to adapt to swine. Virus replication in the entire respiratory tract and nasal virus excretion were examined after each passage and we deep sequenced viral genomic RNA of the parental and passage four H9N2 virus isolated from the nasal mucosa and lung. The parental H9N2 virus caused a productive infection in pigs with a predominant tropism for the nasal mucosa, whereas only 50% lung samples were virus-positive. In contrast, inoculation of pigs with passage four virus resulted in viral replication in the entire respiratory tract. Subsequent passages were associated with reduced virus replication in the lungs and infectious virus was no longer detectable in the upper and lower respiratory tract of inoculated pigs at passage ten. The broader tissue tropism after four passages was associated with an amino acid residue substitution at position 225, within the receptor-binding site of the hemagglutinin. We also compared the parental H9N2, passage four H9N2 and the 2009 pandemic H1N1 (pH1N1) virus in a direct contact transmission experiment. Whereas only one out of six contact pigs showed nasal virus excretion of the wild-type H9N2 for more than four days, all six contact animals shed the passage four H9N2 virus. Nevertheless, the amount of excreted virus was significantly lower when compared to that of the pH1N1, which readily transmitted and replicated in all six contact animals. Our data demonstrate that serial passaging of H9N2 virus in pigs enhances its replication and transmissibility. However, full adaptation of an avian H9N2 virus to pigs likely requires an extensive set of mutations. PMID:28384328

Goldstone (GDSCC) administrative computing

NASA Technical Reports Server (NTRS)

Martin, H.

1981-01-01

The GDSCC Data Processing Unit provides various administrative computing services for Goldstone. Those activities, including finance, manpower and station utilization, deep-space station scheduling and engineering change order (ECO) control are discussed.

Discovery and full genome characterization of two highly divergent simian immunodeficiency viruses infecting black-and-white colobus monkeys (Colobus guereza) in Kibale National Park, Uganda.

PubMed

Lauck, Michael; Switzer, William M; Sibley, Samuel D; Hyeroba, David; Tumukunde, Alex; Weny, Geoffrey; Taylor, Bill; Shankar, Anupama; Ting, Nelson; Chapman, Colin A; Friedrich, Thomas C; Goldberg, Tony L; O'Connor, David H

2013-10-21

African non-human primates (NHPs) are natural hosts for simian immunodeficiency viruses (SIV), the zoonotic transmission of which led to the emergence of HIV-1 and HIV-2. However, our understanding of SIV diversity and evolution is limited by incomplete taxonomic and geographic sampling of NHPs, particularly in East Africa. In this study, we screened blood specimens from nine black-and-white colobus monkeys (Colobus guereza occidentalis) from Kibale National Park, Uganda, for novel SIVs using a combination of serology and "unbiased" deep-sequencing, a method that does not rely on genetic similarity to previously characterized viruses. We identified two novel and divergent SIVs, tentatively named SIVkcol-1 and SIVkcol-2, and assembled genomes covering the entire coding region for each virus. SIVkcol-1 and SIVkcol-2 were detected in three and four animals, respectively, but with no animals co-infected. Phylogenetic analyses showed that SIVkcol-1 and SIVkcol-2 form a lineage with SIVcol, previously discovered in black-and-white colobus from Cameroon. Although SIVkcol-1 and SIVkcol-2 were isolated from the same host population in Uganda, SIVkcol-1 is more closely related to SIVcol than to SIVkcol-2. Analysis of functional motifs in the extracellular envelope glycoprotein (gp120) revealed that SIVkcol-2 is unique among primate lentiviruses in containing only 16 conserved cysteine residues instead of the usual 18 or more. Our results demonstrate that the genetic diversity of SIVs infecting black-and-white colobus across equatorial Africa is greater than previously appreciated and that divergent SIVs can co-circulate in the same colobine population. We also show that the use of "unbiased" deep sequencing for the detection of SIV has great advantages over traditional serological approaches, especially for studies of unknown or poorly characterized viruses. Finally, the detection of the first SIV containing only 16 conserved cysteines in the extracellular envelope protein gp120 further expands the range of functional motifs observed among SIVs and highlights the complex evolutionary history of simian retroviruses.

Discovery and full genome characterization of two highly divergent simian immunodeficiency viruses infecting black-and-white colobus monkeys (Colobus guereza) in Kibale National Park, Uganda

PubMed Central

2013-01-01

Background African non-human primates (NHPs) are natural hosts for simian immunodeficiency viruses (SIV), the zoonotic transmission of which led to the emergence of HIV-1 and HIV-2. However, our understanding of SIV diversity and evolution is limited by incomplete taxonomic and geographic sampling of NHPs, particularly in East Africa. In this study, we screened blood specimens from nine black-and-white colobus monkeys (Colobus guereza occidentalis) from Kibale National Park, Uganda, for novel SIVs using a combination of serology and “unbiased” deep-sequencing, a method that does not rely on genetic similarity to previously characterized viruses. Results We identified two novel and divergent SIVs, tentatively named SIVkcol-1 and SIVkcol-2, and assembled genomes covering the entire coding region for each virus. SIVkcol-1 and SIVkcol-2 were detected in three and four animals, respectively, but with no animals co-infected. Phylogenetic analyses showed that SIVkcol-1 and SIVkcol-2 form a lineage with SIVcol, previously discovered in black-and-white colobus from Cameroon. Although SIVkcol-1 and SIVkcol-2 were isolated from the same host population in Uganda, SIVkcol-1 is more closely related to SIVcol than to SIVkcol-2. Analysis of functional motifs in the extracellular envelope glycoprotein (gp120) revealed that SIVkcol-2 is unique among primate lentiviruses in containing only 16 conserved cysteine residues instead of the usual 18 or more. Conclusions Our results demonstrate that the genetic diversity of SIVs infecting black-and-white colobus across equatorial Africa is greater than previously appreciated and that divergent SIVs can co-circulate in the same colobine population. We also show that the use of “unbiased” deep sequencing for the detection of SIV has great advantages over traditional serological approaches, especially for studies of unknown or poorly characterized viruses. Finally, the detection of the first SIV containing only 16 conserved cysteines in the extracellular envelope protein gp120 further expands the range of functional motifs observed among SIVs and highlights the complex evolutionary history of simian retroviruses. PMID:24139306

Among the New Worlds.

ERIC Educational Resources Information Center

Algeo, John; Algeo, Adele

1989-01-01

Presents definitions and examples of usage of late-twentieth century American words and terms, including: bite, dramedy, photo op, photo opportunist, safe computer practice, sound bite, teflon, trapdoor, vaccination program, computer virus, and wait state. (CB)

Do You Lock Your Network Doors? Some Network Management Precautions.

ERIC Educational Resources Information Center

Neray, Phil

1997-01-01

Discusses security problems and solutions for networked organizations with Internet connections. Topics include access to private networks from electronic mail information; computer viruses; computer software; corporate espionage; firewalls, that is computers that stand between a local network and the Internet; passwords; and physical security.…

Unveiling the role and life strategies of viruses from the surface to the dark ocean

PubMed Central

Lara, Elena; Vaqué, Dolors; Sà, Elisabet Laia; Boras, Julia A.; Gomes, Ana; Borrull, Encarna; Díez-Vives, Cristina; Teira, Eva; Pernice, Massimo C.; Garcia, Francisca C.; Forn, Irene; Castillo, Yaiza M.; Peiró, Aida; Salazar, Guillem; Morán, Xosé Anxelu G.; Massana, Ramon; Catalá, Teresa S.; Luna, Gian Marco; Agustí, Susana; Estrada, Marta; Gasol, Josep M.; Duarte, Carlos M.

2017-01-01

Viruses are a key component of marine ecosystems, but the assessment of their global role in regulating microbial communities and the flux of carbon is precluded by a paucity of data, particularly in the deep ocean. We assessed patterns in viral abundance and production and the role of viral lysis as a driver of prokaryote mortality, from surface to bathypelagic layers, across the tropical and subtropical oceans. Viral abundance showed significant differences between oceans in the epipelagic and mesopelagic, but not in the bathypelagic, and decreased with depth, with an average power-law scaling exponent of −1.03 km−1 from an average of 7.76 × 106 viruses ml−1 in the epipelagic to 0.62 × 106 viruses ml−1 in the bathypelagic layer with an average integrated (0 to 4000 m) viral stock of about 0.004 to 0.044 g C m−2, half of which is found below 775 m. Lysogenic viral production was higher than lytic viral production in surface waters, whereas the opposite was found in the bathypelagic, where prokaryotic mortality due to viruses was estimated to be 60 times higher than grazing. Free viruses had turnover times of 0.1 days in the bathypelagic, revealing that viruses in the bathypelagic are highly dynamic. On the basis of the rates of lysed prokaryotic cells, we estimated that viruses release 145 Gt C year−1 in the global tropical and subtropical oceans. The active viral processes reported here demonstrate the importance of viruses in the production of dissolved organic carbon in the dark ocean, a major pathway in carbon cycling. PMID:28913418

High performance flight computer developed for deep space applications

NASA Technical Reports Server (NTRS)

Bunker, Robert L.

1993-01-01

The development of an advanced space flight computer for real time embedded deep space applications which embodies the lessons learned on Galileo and modern computer technology is described. The requirements are listed and the design implementation that meets those requirements is described. The development of SPACE-16 (Spaceborne Advanced Computing Engine) (where 16 designates the databus width) was initiated to support the MM2 (Marine Mark 2) project. The computer is based on a radiation hardened emulation of a modern 32 bit microprocessor and its family of support devices including a high performance floating point accelerator. Additional custom devices which include a coprocessor to improve input/output capabilities, a memory interface chip, and an additional support chip that provide management of all fault tolerant features, are described. Detailed supporting analyses and rationale which justifies specific design and architectural decisions are provided. The six chip types were designed and fabricated. Testing and evaluation of a brass/board was initiated.

Deep, noninvasive imaging and surgical guidance of submillimeter tumors using targeted M13-stabilized single-walled carbon nanotubes

PubMed Central

Ghosh, Debadyuti; Bagley, Alexander F.; Na, Young Jeong; Birrer, Michael J.; Bhatia, Sangeeta N.; Belcher, Angela M.

2014-01-01

Highly sensitive detection of small, deep tumors for early diagnosis and surgical interventions remains a challenge for conventional imaging modalities. Second-window near-infrared light (NIR2, 950–1,400 nm) is promising for in vivo fluorescence imaging due to deep tissue penetration and low tissue autofluorescence. With their intrinsic fluorescence in the NIR2 regime and lack of photobleaching, single-walled carbon nanotubes (SWNTs) are potentially attractive contrast agents to detect tumors. Here, targeted M13 virus-stabilized SWNTs are used to visualize deep, disseminated tumors in vivo. This targeted nanoprobe, which uses M13 to stably display both tumor-targeting peptides and an SWNT imaging probe, demonstrates excellent tumor-to-background uptake and exhibits higher signal-to-noise performance compared with visible and near-infrared (NIR1) dyes for delineating tumor nodules. Detection and excision of tumors by a gynecological surgeon improved with SWNT image guidance and led to the identification of submillimeter tumors. Collectively, these findings demonstrate the promise of targeted SWNT nanoprobes for noninvasive disease monitoring and guided surgery. PMID:25214538

Deep, noninvasive imaging and surgical guidance of submillimeter tumors using targeted M13-stabilized single-walled carbon nanotubes.

PubMed

Ghosh, Debadyuti; Bagley, Alexander F; Na, Young Jeong; Birrer, Michael J; Bhatia, Sangeeta N; Belcher, Angela M

2014-09-23

Highly sensitive detection of small, deep tumors for early diagnosis and surgical interventions remains a challenge for conventional imaging modalities. Second-window near-infrared light (NIR2, 950-1,400 nm) is promising for in vivo fluorescence imaging due to deep tissue penetration and low tissue autofluorescence. With their intrinsic fluorescence in the NIR2 regime and lack of photobleaching, single-walled carbon nanotubes (SWNTs) are potentially attractive contrast agents to detect tumors. Here, targeted M13 virus-stabilized SWNTs are used to visualize deep, disseminated tumors in vivo. This targeted nanoprobe, which uses M13 to stably display both tumor-targeting peptides and an SWNT imaging probe, demonstrates excellent tumor-to-background uptake and exhibits higher signal-to-noise performance compared with visible and near-infrared (NIR1) dyes for delineating tumor nodules. Detection and excision of tumors by a gynecological surgeon improved with SWNT image guidance and led to the identification of submillimeter tumors. Collectively, these findings demonstrate the promise of targeted SWNT nanoprobes for noninvasive disease monitoring and guided surgery.

Deep learning for EEG-Based preference classification

NASA Astrophysics Data System (ADS)

Teo, Jason; Hou, Chew Lin; Mountstephens, James

2017-10-01

Electroencephalogram (EEG)-based emotion classification is rapidly becoming one of the most intensely studied areas of brain-computer interfacing (BCI). The ability to passively identify yet accurately correlate brainwaves with our immediate emotions opens up truly meaningful and previously unattainable human-computer interactions such as in forensic neuroscience, rehabilitative medicine, affective entertainment and neuro-marketing. One particularly useful yet rarely explored areas of EEG-based emotion classification is preference recognition [1], which is simply the detection of like versus dislike. Within the limited investigations into preference classification, all reported studies were based on musically-induced stimuli except for a single study which used 2D images. The main objective of this study is to apply deep learning, which has been shown to produce state-of-the-art results in diverse hard problems such as in computer vision, natural language processing and audio recognition, to 3D object preference classification over a larger group of test subjects. A cohort of 16 users was shown 60 bracelet-like objects as rotating visual stimuli on a computer display while their preferences and EEGs were recorded. After training a variety of machine learning approaches which included deep neural networks, we then attempted to classify the users' preferences for the 3D visual stimuli based on their EEGs. Here, we show that that deep learning outperforms a variety of other machine learning classifiers for this EEG-based preference classification task particularly in a highly challenging dataset with large inter- and intra-subject variability.

Big Data, Deep Learning and Tianhe-2 at Sun Yat-Sen University, Guangzhou

NASA Astrophysics Data System (ADS)

Yuen, D. A.; Dzwinel, W.; Liu, J.; Zhang, K.

2014-12-01

In this decade the big data revolution has permeated in many fields, ranging from financial transactions, medical surveys and scientific endeavors, because of the big opportunities people see ahead. What to do with all this data remains an intriguing question. This is where computer scientists together with applied mathematicians have made some significant inroads in developing deep learning techniques for unraveling new relationships among the different variables by means of correlation analysis and data-assimilation methods. Deep-learning and big data taken together is a grand challenge task in High-performance computing which demand both ultrafast speed and large memory. The Tianhe-2 recently installed at Sun Yat-Sen University in Guangzhou is well positioned to take up this challenge because it is currently the world's fastest computer at 34 Petaflops. Each compute node of Tianhe-2 has two CPUs of Intel Xeon E5-2600 and three Xeon Phi accelerators. The Tianhe-2 has a very large fast memory RAM of 88 Gigabytes on each node. The system has a total memory of 1,375 Terabytes. All of these technical features will allow very high dimensional (more than 10) problem in deep learning to be explored carefully on the Tianhe-2. Problems in seismology which can be solved include three-dimensional seismic wave simulations of the whole Earth with a few km resolution and the recognition of new phases in seismic wave form from assemblage of large data sets.

Microcephaly: computational and organotypic modeling of a ...

EPA Pesticide Factsheets

lecture discusses computational and organotypic models of microcephaly in an AOP Framework and ToxCast assays. Lecture slide presentation at UNC Chapel Hill for Advanced Toxicology course lecture on Computational Approaches to Developmental and Reproductive Toxicology with presentation on computational and organotypic modeling of a complex human birth defect microcephaly with is associated with the recent Zika virus outbreak.

Top 10 Threats to Computer Systems Include Professors and Students

ERIC Educational Resources Information Center

Young, Jeffrey R.

2008-01-01

User awareness is growing in importance when it comes to computer security. Not long ago, keeping college networks safe from cyberattackers mainly involved making sure computers around campus had the latest software patches. New computer worms or viruses would pop up, taking advantage of some digital hole in the Windows operating system or in…

Grapevine fleck virus-like viruses in Vitis.

PubMed

Sabanadzovic, S; Abou-Ghanem, N; Castellano, M A; Digiaro, M; Martelli, G P

2000-01-01

Two sets of degenerate primers for the specific amplification of 572-575 nt and 386 nt segments of the methyltransferase and RNA- dependent RNA polymerase cistrons of members of the genera Tymovirus and Marafivirus and of the unassigned virus Grapevine fleck virus (GFkV) were designed on the basis of available sequences. These primers were used for amplifying and subsequent cloning and sequencing part of the open reading frame 1 of the genome of GFkV, Grapevine asteroid mosaic-associated virus (GAMaV) and of another previously unreported virus, for which the name Grapevine red globe virus (GRGV) is proposed. Computer-assisted analysis of the amplified genome portions showed that the three grapevine viruses are phylogenetically related with one another and with sequenced tymoviruses and marafiviruses. The relationships with tymoviruses was confirmed by the type of ultrastructural modifications induced in the host cells. RdRp-specific degenerate primers were successfully used for the aspecific detection of the three viruses in crude grapevine sap extracts. Specific virus identification was obtained with RT-PCR using antisense virus-specific primers.

Event-Driven Random Back-Propagation: Enabling Neuromorphic Deep Learning Machines

PubMed Central

Neftci, Emre O.; Augustine, Charles; Paul, Somnath; Detorakis, Georgios

2017-01-01

An ongoing challenge in neuromorphic computing is to devise general and computationally efficient models of inference and learning which are compatible with the spatial and temporal constraints of the brain. One increasingly popular and successful approach is to take inspiration from inference and learning algorithms used in deep neural networks. However, the workhorse of deep learning, the gradient descent Gradient Back Propagation (BP) rule, often relies on the immediate availability of network-wide information stored with high-precision memory during learning, and precise operations that are difficult to realize in neuromorphic hardware. Remarkably, recent work showed that exact backpropagated gradients are not essential for learning deep representations. Building on these results, we demonstrate an event-driven random BP (eRBP) rule that uses an error-modulated synaptic plasticity for learning deep representations. Using a two-compartment Leaky Integrate & Fire (I&F) neuron, the rule requires only one addition and two comparisons for each synaptic weight, making it very suitable for implementation in digital or mixed-signal neuromorphic hardware. Our results show that using eRBP, deep representations are rapidly learned, achieving classification accuracies on permutation invariant datasets comparable to those obtained in artificial neural network simulations on GPUs, while being robust to neural and synaptic state quantizations during learning. PMID:28680387

Event-Driven Random Back-Propagation: Enabling Neuromorphic Deep Learning Machines.

PubMed

Neftci, Emre O; Augustine, Charles; Paul, Somnath; Detorakis, Georgios

2017-01-01

An ongoing challenge in neuromorphic computing is to devise general and computationally efficient models of inference and learning which are compatible with the spatial and temporal constraints of the brain. One increasingly popular and successful approach is to take inspiration from inference and learning algorithms used in deep neural networks. However, the workhorse of deep learning, the gradient descent Gradient Back Propagation (BP) rule, often relies on the immediate availability of network-wide information stored with high-precision memory during learning, and precise operations that are difficult to realize in neuromorphic hardware. Remarkably, recent work showed that exact backpropagated gradients are not essential for learning deep representations. Building on these results, we demonstrate an event-driven random BP (eRBP) rule that uses an error-modulated synaptic plasticity for learning deep representations. Using a two-compartment Leaky Integrate & Fire (I&F) neuron, the rule requires only one addition and two comparisons for each synaptic weight, making it very suitable for implementation in digital or mixed-signal neuromorphic hardware. Our results show that using eRBP, deep representations are rapidly learned, achieving classification accuracies on permutation invariant datasets comparable to those obtained in artificial neural network simulations on GPUs, while being robust to neural and synaptic state quantizations during learning.

Animal Viruses Probe dataset (AVPDS) for microarray-based diagnosis and identification of viruses.

PubMed

Yadav, Brijesh S; Pokhriyal, Mayank; Vasishtha, Dinesh P; Sharma, Bhaskar

2014-03-01

AVPDS (Animal Viruses Probe dataset) is a dataset of virus-specific and conserve oligonucleotides for identification and diagnosis of viruses infecting animals. The current dataset contain 20,619 virus specific probes for 833 viruses and their subtypes and 3,988 conserved probes for 146 viral genera. Dataset of virus specific probe has been divided into two fields namely virus name and probe sequence. Similarly conserved probes for virus genera table have genus, and subgroup within genus name and probe sequence. The subgroup within genus is artificially divided subgroups with no taxonomic significance and contains probes which identifies viruses in that specific subgroup of the genus. Using this dataset we have successfully diagnosed the first case of Newcastle disease virus in sheep and reported a mixed infection of Bovine viral diarrhea and Bovine herpesvirus in cattle. These dataset also contains probes which cross reacts across species experimentally though computationally they meet specifications. These probes have been marked. We hope that this dataset will be useful in microarray-based detection of viruses. The dataset can be accessed through the link https://dl.dropboxusercontent.com/u/94060831/avpds/HOME.html.

Comparison of the structures of three circoviruses: chicken anemia virus, porcine circovirus type 2, and beak and feather disease virus.

PubMed

Crowther, R A; Berriman, J A; Curran, W L; Allan, G M; Todd, D

2003-12-01

Circoviruses are small, nonenveloped icosahedral animal viruses characterized by circular single-stranded DNA genomes. Their genomes are the smallest possessed by animal viruses. Infections with circoviruses, which can lead to economically important diseases, frequently result in virus-induced damage to lymphoid tissue and immunosuppression. Within the family Circoviridae, different genera are distinguished by differences in genomic organization. Thus, Chicken anemia virus is in the genus Gyrovirus, while porcine circoviruses and Beak and feather disease virus belong to the genus CIRCOVIRUS: Little is known about the structures of circoviruses. Accordingly, we investigated the structures of these three viruses with a view to determining whether they are related. Three-dimensional maps computed from electron micrographs showed that all three viruses have a T=1 organization with capsids formed from 60 subunits. Porcine circovirus type 2 and beak and feather disease virus show similar capsid structures with flat pentameric morphological units, whereas chicken anemia virus has stikingly different protruding pentagonal trumpet-shaped units. It thus appears that the structures of viruses in the same genus are related but that those of viruses in different genera are unrelated.

Phylodynamic Inference with Kernel ABC and Its Application to HIV Epidemiology.

PubMed

Poon, Art F Y

2015-09-01

The shapes of phylogenetic trees relating virus populations are determined by the adaptation of viruses within each host, and by the transmission of viruses among hosts. Phylodynamic inference attempts to reverse this flow of information, estimating parameters of these processes from the shape of a virus phylogeny reconstructed from a sample of genetic sequences from the epidemic. A key challenge to phylodynamic inference is quantifying the similarity between two trees in an efficient and comprehensive way. In this study, I demonstrate that a new distance measure, based on a subset tree kernel function from computational linguistics, confers a significant improvement over previous measures of tree shape for classifying trees generated under different epidemiological scenarios. Next, I incorporate this kernel-based distance measure into an approximate Bayesian computation (ABC) framework for phylodynamic inference. ABC bypasses the need for an analytical solution of model likelihood, as it only requires the ability to simulate data from the model. I validate this "kernel-ABC" method for phylodynamic inference by estimating parameters from data simulated under a simple epidemiological model. Results indicate that kernel-ABC attained greater accuracy for parameters associated with virus transmission than leading software on the same data sets. Finally, I apply the kernel-ABC framework to study a recent outbreak of a recombinant HIV subtype in China. Kernel-ABC provides a versatile framework for phylodynamic inference because it can fit a broader range of models than methods that rely on the computation of exact likelihoods. © The Author 2015. Published by Oxford University Press on behalf of the Society for Molecular Biology and Evolution.

15 CFR 971.805 - Computation of time.

Code of Federal Regulations, 2010 CFR

2010-01-01

... 15 Commerce and Foreign Trade 3 2010-01-01 2010-01-01 false Computation of time. 971.805 Section... ENVIRONMENTAL DATA SERVICE DEEP SEABED MINING REGULATIONS FOR COMMERCIAL RECOVERY PERMITS Miscellaneous § 971.805 Computation of time. Except where otherwise specified, Saturdays, Sundays and Federal Government...

Performance evaluation of digital phase-locked loops for advanced deep space transponders

NASA Technical Reports Server (NTRS)

Nguyen, T. M.; Hinedi, S. M.; Yeh, H.-G.; Kyriacou, C.

1994-01-01

The performances of the digital phase-locked loops (DPLL's) for the advanced deep-space transponders (ADT's) are investigated. DPLL's considered in this article are derived from the analog phase-locked loop, which is currently employed by the NASA standard deep space transponder, using S-domain to Z-domain mapping techniques. Three mappings are used to develop digital approximations of the standard deep space analog phase-locked loop, namely the bilinear transformation (BT), impulse invariant transformation (IIT), and step invariant transformation (SIT) techniques. The performance in terms of the closed loop phase and magnitude responses, carrier tracking jitter, and response of the loop to the phase offset (the difference between in incoming phase and reference phase) is evaluated for each digital approximation. Theoretical results of the carrier tracking jitter for command-on and command-off cases are then validated by computer simulation. Both theoretical and computer simulation results show that at high sampling frequency, the DPLL's approximated by all three transformations have the same tracking jitter. However, at low sampling frequency, the digital approximation using BT outperforms the others. The minimum sampling frequency for adequate tracking performance is determined for each digital approximation of the analog loop. In addition, computer simulation shows that the DPLL developed by BT provides faster response to the phase offset than IIT and SIT.

Efficiency of the neighbor-joining method in reconstructing deep and shallow evolutionary relationships in large phylogenies.

PubMed

Kumar, S; Gadagkar, S R

2000-12-01

The neighbor-joining (NJ) method is widely used in reconstructing large phylogenies because of its computational speed and the high accuracy in phylogenetic inference as revealed in computer simulation studies. However, most computer simulation studies have quantified the overall performance of the NJ method in terms of the percentage of branches inferred correctly or the percentage of replications in which the correct tree is recovered. We have examined other aspects of its performance, such as the relative efficiency in correctly reconstructing shallow (close to the external branches of the tree) and deep branches in large phylogenies; the contribution of zero-length branches to topological errors in the inferred trees; and the influence of increasing the tree size (number of sequences), evolutionary rate, and sequence length on the efficiency of the NJ method. Results show that the correct reconstruction of deep branches is no more difficult than that of shallower branches. The presence of zero-length branches in realized trees contributes significantly to the overall error observed in the NJ tree, especially in large phylogenies or slowly evolving genes. Furthermore, the tree size does not influence the efficiency of NJ in reconstructing shallow and deep branches in our simulation study, in which the evolutionary process is assumed to be homogeneous in all lineages.

A mixed SIR-SIS model to contain a virus spreading through networks with two degrees

NASA Astrophysics Data System (ADS)

Essouifi, Mohamed; Achahbar, Abdelfattah

Due to the fact that the “nodes” and “links” of real networks are heterogeneous, to model computer viruses prevalence throughout the Internet, we borrow the idea of the reduced scale free network which was introduced recently. The purpose of this paper is to extend the previous deterministic two subchains of Susceptible-Infected-Susceptible (SIS) model into a mixed Susceptible-Infected-Recovered and Susceptible-Infected-Susceptible (SIR-SIS) model to contain the computer virus spreading over networks with two degrees. Moreover, we develop its stochastic counterpart. Due to the high protection and security taken for hubs class, we suggest to treat it by using SIR epidemic model rather than the SIS one. The analytical study reveals that the proposed model admits a stable viral equilibrium. Thus, it is shown numerically that the mean dynamic behavior of the stochastic model is in agreement with the deterministic one. Unlike the infection densities i2 and i which both tend to a viral equilibrium for both approaches as in the previous study, i1 tends to the virus-free equilibrium. Furthermore, since a proportion of infectives are recovered, the global infection density i is minimized. Therefore, the permanent presence of viruses in the network due to the lower-degree nodes class. Many suggestions are put forward for containing viruses propagation and minimizing their damages.

Applications of Deep Learning and Reinforcement Learning to Biological Data.

PubMed

Mahmud, Mufti; Kaiser, Mohammed Shamim; Hussain, Amir; Vassanelli, Stefano

2018-06-01

Rapid advances in hardware-based technologies during the past decades have opened up new possibilities for life scientists to gather multimodal data in various application domains, such as omics, bioimaging, medical imaging, and (brain/body)-machine interfaces. These have generated novel opportunities for development of dedicated data-intensive machine learning techniques. In particular, recent research in deep learning (DL), reinforcement learning (RL), and their combination (deep RL) promise to revolutionize the future of artificial intelligence. The growth in computational power accompanied by faster and increased data storage, and declining computing costs have already allowed scientists in various fields to apply these techniques on data sets that were previously intractable owing to their size and complexity. This paper provides a comprehensive survey on the application of DL, RL, and deep RL techniques in mining biological data. In addition, we compare the performances of DL techniques when applied to different data sets across various application domains. Finally, we outline open issues in this challenging research area and discuss future development perspectives.

CGBVS-DNN: Prediction of Compound-protein Interactions Based on Deep Learning.

PubMed

Hamanaka, Masatoshi; Taneishi, Kei; Iwata, Hiroaki; Ye, Jun; Pei, Jianguo; Hou, Jinlong; Okuno, Yasushi

2017-01-01

Computational prediction of compound-protein interactions (CPIs) is of great importance for drug design as the first step in in-silico screening. We previously proposed chemical genomics-based virtual screening (CGBVS), which predicts CPIs by using a support vector machine (SVM). However, the CGBVS has problems when training using more than a million datasets of CPIs since SVMs require an exponential increase in the calculation time and computer memory. To solve this problem, we propose the CGBVS-DNN, in which we use deep neural networks, a kind of deep learning technique, instead of the SVM. Deep learning does not require learning all input data at once because the network can be trained with small mini-batches. Experimental results show that the CGBVS-DNN outperformed the original CGBVS with a quarter million CPIs. Results of cross-validation show that the accuracy of the CGBVS-DNN reaches up to 98.2 % (σ<0.01) with 4 million CPIs. © 2017 Wiley-VCH Verlag GmbH & Co. KGaA, Weinheim.

Comparative reduction of Norwalk virus, poliovirus type 1, F+ RNA coliphage MS2 and Escherichia coli in miniature soil columns.

PubMed

Meschke, J S; Sobsey, M D

2003-01-01

Norwalk-like viruses (NLVs) are important agents of waterborne illness and have been linked to several groundwater-related outbreaks. The presence of human enteric viruses, in particular the presence of NLVs, is difficult to detect in the environment. Consequently, surrogate organisms are typically used as indicators of viruses from faecal contamination. Whether traditional bacterial indicators are reliable indicators for viral pathogens remains uncertain. Few studies have directly compared mobility and reduction of bacterial indicators (e.g. coliforms, Escherichia coli) and other surrogate indicators (coliphages) with pathogenic human viruses in soil systems. In this study the mobility and comparative reduction of the prototype NLV, Norwalk Virus (NV), was compared to poliovirus 1 (PV1), a bacterial indicator (E coli, EC) and a viral indicator (coliphage MS2) through miniature soil columns. Replicate, 10 cm deep, miniature columns were prepared using three soils representing a range of soil textures (sand, organic muck, and clay). Columns were initially conditioned, then incubated at 10-14 degrees C, dosed twice weekly for 8 weeks with one column pore volume of virus-seeded groundwater per dose, followed by 8 weeks of dosing with one column pore volume per dose of unseeded, simulated rainwater. Columns were allowed to drain after each dosing until an effluent volume equivalent to an applied dose was collected. Column effluents and doses were assayed for all viruses and EC. Rapid mobility with minimal reduction was observed for all organisms in the sand. Similar reductions were observed in organic muck for most organisms but NV showed a greater reduction. No organisms were shown to pass through the clay columns. Elution of viruses, in particular PV1, from the columns was gradual. After cessation of microbe dosing, E. coli was less detectable than viruses in column effluents and, therefore, unreliable as a virus indicator.

High-Resolution Analysis of Coronavirus Gene Expression by RNA Sequencing and Ribosome Profiling

PubMed Central

Jones, Joshua D.; Chung, Betty Y.-W.; Siddell, Stuart G.; Brierley, Ian

2016-01-01

Members of the family Coronaviridae have the largest genomes of all RNA viruses, typically in the region of 30 kilobases. Several coronaviruses, such as Severe acute respiratory syndrome-related coronavirus (SARS-CoV) and Middle East respiratory syndrome-related coronavirus (MERS-CoV), are of medical importance, with high mortality rates and, in the case of SARS-CoV, significant pandemic potential. Other coronaviruses, such as Porcine epidemic diarrhea virus and Avian coronavirus, are important livestock pathogens. Ribosome profiling is a technique which exploits the capacity of the translating ribosome to protect around 30 nucleotides of mRNA from ribonuclease digestion. Ribosome-protected mRNA fragments are purified, subjected to deep sequencing and mapped back to the transcriptome to give a global “snap-shot” of translation. Parallel RNA sequencing allows normalization by transcript abundance. Here we apply ribosome profiling to cells infected with Murine coronavirus, mouse hepatitis virus, strain A59 (MHV-A59), a model coronavirus in the same genus as SARS-CoV and MERS-CoV. The data obtained allowed us to study the kinetics of virus transcription and translation with exquisite precision. We studied the timecourse of positive and negative-sense genomic and subgenomic viral RNA production and the relative translation efficiencies of the different virus ORFs. Virus mRNAs were not found to be translated more efficiently than host mRNAs; rather, virus translation dominates host translation at later time points due to high levels of virus transcripts. Triplet phasing of the profiling data allowed precise determination of translated reading frames and revealed several translated short open reading frames upstream of, or embedded within, known virus protein-coding regions. Ribosome pause sites were identified in the virus replicase polyprotein pp1a ORF and investigated experimentally. Contrary to expectations, ribosomes were not found to pause at the ribosomal frameshift site. To our knowledge this is the first application of ribosome profiling to an RNA virus. PMID:26919232

A GFP Expressing Influenza A Virus to Report In Vivo Tropism and Protection by a Matrix Protein 2 Ectodomain-Specific Monoclonal Antibody

PubMed Central

Van den Hoecke, Silvie; Smet, Anouk; Schotsaert, Michael; Job, Emma R.; Roose, Kenny; Schepens, Bert; Fiers, Walter; Saelens, Xavier

2015-01-01

The severity of influenza-related illness is mediated by many factors, including in vivo cell tropism, timing and magnitude of the immune response, and presence of pre-existing immunity. A direct way to study cell tropism and virus spread in vivo is with an influenza virus expressing a reporter gene. However, reporter gene-expressing influenza viruses are often attenuated in vivo and may be genetically unstable. Here, we describe the generation of an influenza A virus expressing GFP from a tri-cistronic NS segment. To reduce the size of this engineered gene segment, we used a truncated NS1 protein of 73 amino acids combined with a heterologous dimerization domain to increase protein stability. GFP and nuclear export protein coding information were fused in frame with the truncated NS1 open reading frame and separated from each other by 2A self-processing sites. The resulting PR8-NS1(1–73)GFP virus was successfully rescued and replicated as efficiently as the parental PR8 virus in vitro and was slightly attenuated in vivo. Flow cytometry-based monitoring of cells isolated from PR8-NS1(1–73)GFP virus infected BALB/c mice revealed that GFP expression peaked on day two in all cell types tested. In particular respiratory epithelial cells and myeloid cells known to be involved in antigen presentation, including dendritic cells (CD11c+) and inflammatory monocytes (CD11b+ GR1+), became GFP positive following infection. Prophylactic treatment with anti-M2e monoclonal antibody or oseltamivir reduced GFP expression in all cell types studied, demonstrating the usefulness of this reporter virus to analyze the efficacy of antiviral treatments in vivo. Finally, deep sequencing analysis, serial in vitro passages and ex vivo analysis of PR8-NS1(1–73)GFP virus, indicate that this virus is genetically and phenotypically stable. PMID:25816132

A GFP expressing influenza A virus to report in vivo tropism and protection by a matrix protein 2 ectodomain-specific monoclonal antibody.

PubMed

De Baets, Sarah; Verhelst, Judith; Van den Hoecke, Silvie; Smet, Anouk; Schotsaert, Michael; Job, Emma R; Roose, Kenny; Schepens, Bert; Fiers, Walter; Saelens, Xavier

2015-01-01

The severity of influenza-related illness is mediated by many factors, including in vivo cell tropism, timing and magnitude of the immune response, and presence of pre-existing immunity. A direct way to study cell tropism and virus spread in vivo is with an influenza virus expressing a reporter gene. However, reporter gene-expressing influenza viruses are often attenuated in vivo and may be genetically unstable. Here, we describe the generation of an influenza A virus expressing GFP from a tri-cistronic NS segment. To reduce the size of this engineered gene segment, we used a truncated NS1 protein of 73 amino acids combined with a heterologous dimerization domain to increase protein stability. GFP and nuclear export protein coding information were fused in frame with the truncated NS1 open reading frame and separated from each other by 2A self-processing sites. The resulting PR8-NS1(1-73)GFP virus was successfully rescued and replicated as efficiently as the parental PR8 virus in vitro and was slightly attenuated in vivo. Flow cytometry-based monitoring of cells isolated from PR8-NS1(1-73)GFP virus infected BALB/c mice revealed that GFP expression peaked on day two in all cell types tested. In particular respiratory epithelial cells and myeloid cells known to be involved in antigen presentation, including dendritic cells (CD11c+) and inflammatory monocytes (CD11b+ GR1+), became GFP positive following infection. Prophylactic treatment with anti-M2e monoclonal antibody or oseltamivir reduced GFP expression in all cell types studied, demonstrating the usefulness of this reporter virus to analyze the efficacy of antiviral treatments in vivo. Finally, deep sequencing analysis, serial in vitro passages and ex vivo analysis of PR8-NS1(1-73)GFP virus, indicate that this virus is genetically and phenotypically stable.

Molecular characterization of emaraviruses associated with Pigeonpea sterility mosaic disease.

PubMed

Kumar, Surender; Subbarao, B L; Hallan, Vipin

2017-09-19

Sterility Mosaic Disease (SMD) of pigeonpea (Cajanus cajan (L.) Millspaugh) is a complex disease due to various factors including the presence of a mixed infection. Comparison of dsRNA profile and small RNA (sRNA) deep sequencing analysis of samples from three locations revealed the presence of Pigeonpea sterility mosaic virus-I and II (PPSMV-I and II) from Chevella and only PPSMV-II from Bengaluru and Coimbatore. PPSMV-I genome consisted of four while PPSMV-II encompassed six RNAs. The two viruses have modest sequence homology between their corresponding RNA 1-4 encoding RdRp, glycoprotein precursor, nucleocapsid and movement proteins and the corresponding orthologs of other emaraviruses. However, PPSMV-II is more related to Fig mosaic virus (FMV) than to PPSMV-I. ELISA based detection methodology was standardized to identify these two viruses, uniquely. Mite inoculation of sub-isolate Chevella sometimes resulted in few- to- many pigeonpea plants containing PPSMV-I alone. The study shows that (i) the N-terminal region of RdRp (SRD-1) of both the viruses contain "cap-snatching" endonuclease domain and a 13 AA cap binding site at the C-terminal, essential for viral cap-dependent transcription similar to the members of Bunyaviridae family and (ii) P4 is the movement protein and may belong to '30 K superfamily' of MPs.

Evaluation of monkeypox virus infection of prairie dogs (Cynomys ludovicianus) using in vivo bioluminescent imaging

USGS Publications Warehouse

Falendysz, Elizabeth A.; Londoño-Navas, Angela M.; Meteyer, Carol U.; Pussini, Nicola; Lopera, Juan G.; Osorio, Jorge E.; Rocke, Tonie E.

2014-01-01

Monkeypox (MPX) is a re-emerging zoonotic disease that is endemic in Central and West Africa, where it can cause a smallpox-like disease in humans. Despite many epidemiologic and field investigations of MPX, no definitive reservoir species has been identified. Using recombinant viruses expressing the firefly luciferase (luc) gene, we previously demonstrated the suitability of in vivo bioluminescent imaging (BLI) to study the pathogenesis of MPX in animal models. Here, we evaluated BLI as a novel approach for tracking MPX virus infection in black-tailed prairie dogs (Cynomys ludovicianus). Prairie dogs were affected during a multistate outbreak of MPX in the US in 2003 and have since been used as an animal model of this disease. Our BLI results were compared with PCR and virus isolation from tissues collected postmortem. Virus was easily detected and quantified in skin and superficial tissues by BLI before and during clinical phases, as well as in subclinical secondary cases, but was not reliably detected in deep tissues such as the lung. Although there are limitations to viral detection in larger wild rodent species, BLI can enhance the use of prairie dogs as an animal model of MPX and can be used for the study of infection, disease progression, and transmission in potential wild rodent reservoirs.

Finding and identifying the viral needle in the metagenomic haystack: trends and challenges

PubMed Central

Soueidan, Hayssam; Schmitt, Louise-Amélie; Candresse, Thierry; Nikolski, Macha

2015-01-01

Collectively, viruses have the greatest genetic diversity on Earth, occupy extremely varied niches and are likely able to infect all living organisms. Viral infections are an important issue for human health and cause considerable economic losses when agriculturally important crops or husbandry animals are infected. The advent of metagenomics has provided a precious tool to study viruses by sampling them in natural environments and identifying the genomic composition of a sample. However, reaching a clear recognition and taxonomic assignment of the identified viruses has been hampered by the computational difficulty of these problems. In this perspective paper we examine the trends in current research for the identification of viral sequences in a metagenomic sample, pinpoint the intrinsic computational difficulties for the identification of novel viral sequences within metagenomic samples, and suggest possible avenues to overcome them. PMID:25610431

OpenZika: An IBM World Community Grid Project to Accelerate Zika Virus Drug Discovery

PubMed Central

Perryman, Alexander L.; Horta Andrade, Carolina

2016-01-01

The Zika virus outbreak in the Americas has caused global concern. To help accelerate this fight against Zika, we launched the OpenZika project. OpenZika is an IBM World Community Grid Project that uses distributed computing on millions of computers and Android devices to run docking experiments, in order to dock tens of millions of drug-like compounds against crystal structures and homology models of Zika proteins (and other related flavivirus targets). This will enable the identification of new candidates that can then be tested in vitro, to advance the discovery and development of new antiviral drugs against the Zika virus. The docking data is being made openly accessible so that all members of the global research community can use it to further advance drug discovery studies against Zika and other related flaviviruses. PMID:27764115

OpenZika: An IBM World Community Grid Project to Accelerate Zika Virus Drug Discovery.

PubMed

Ekins, Sean; Perryman, Alexander L; Horta Andrade, Carolina

2016-10-01

The Zika virus outbreak in the Americas has caused global concern. To help accelerate this fight against Zika, we launched the OpenZika project. OpenZika is an IBM World Community Grid Project that uses distributed computing on millions of computers and Android devices to run docking experiments, in order to dock tens of millions of drug-like compounds against crystal structures and homology models of Zika proteins (and other related flavivirus targets). This will enable the identification of new candidates that can then be tested in vitro, to advance the discovery and development of new antiviral drugs against the Zika virus. The docking data is being made openly accessible so that all members of the global research community can use it to further advance drug discovery studies against Zika and other related flaviviruses.

Early Mesozoic Coexistence of Amniotes and Hepadnaviridae

PubMed Central

Suh, Alexander; Weber, Claudia C.; Kehlmaier, Christian; Braun, Edward L.; Green, Richard E.; Fritz, Uwe; Ray, David A.; Ellegren, Hans

2014-01-01

Hepadnaviridae are double-stranded DNA viruses that infect some species of birds and mammals. This includes humans, where hepatitis B viruses (HBVs) are prevalent pathogens in considerable parts of the global population. Recently, endogenized sequences of HBVs (eHBVs) have been discovered in bird genomes where they constitute direct evidence for the coexistence of these viruses and their hosts from the late Mesozoic until present. Nevertheless, virtually nothing is known about the ancient host range of this virus family in other animals. Here we report the first eHBVs from crocodilian, snake, and turtle genomes, including a turtle eHBV that endogenized >207 million years ago. This genomic “fossil” is >125 million years older than the oldest avian eHBV and provides the first direct evidence that Hepadnaviridae already existed during the Early Mesozoic. This implies that the Mesozoic fossil record of HBV infection spans three of the five major groups of land vertebrates, namely birds, crocodilians, and turtles. We show that the deep phylogenetic relationships of HBVs are largely congruent with the deep phylogeny of their amniote hosts, which suggests an ancient amniote–HBV coexistence and codivergence, at least since the Early Mesozoic. Notably, the organization of overlapping genes as well as the structure of elements involved in viral replication has remained highly conserved among HBVs along that time span, except for the presence of the X gene. We provide multiple lines of evidence that the tumor-promoting X protein of mammalian HBVs lacks a homolog in all other hepadnaviruses and propose a novel scenario for the emergence of X via segmental duplication and overprinting of pre-existing reading frames in the ancestor of mammalian HBVs. Our study reveals an unforeseen host range of prehistoric HBVs and provides novel insights into the genome evolution of hepadnaviruses throughout their long-lasting association with amniote hosts. PMID:25501991

Simulation models examining the effect of Brugian filariasis on dengue epidemics.

PubMed

Vaughan, Jefferson A; Focks, Dana A; Turell, Michael J

2009-01-01

Concurrent ingestion of microfilariae (mf) and arboviruses by mosquitoes can enhance the transmission of virus compared with when virus is ingested alone. We studied the effect of mf enhancement on the extrinsic incubation period (EIP) of dengue 1 virus within Aedes aegypti mosquitoes by feeding mosquitoes on blood that either contained virus plus Brugia malayi mf or virus only. Mosquitoes were sampled over time to determine viral dissemination rates. Co-ingestion of mf and virus reduced viral EIP by over half. We used the computer simulation program, DENSiM, to compare the predicted patterns of dengue incidence that would result from such a shortened EIP versus the EIP derived from the control (i.e., virus only) group of mosquitoes. Results indicated that, over the 14-year simulation period, mf-induced acceleration of the EIP would generate more frequent (but not necessarily more severe) epidemics. Potential interactions between arboviruses and hematozoans deserve closer scrutiny.

Comparison of Hydrodynamic Load Predictions Between Engineering Models and Computational Fluid Dynamics for the OC4-DeepCwind Semi-Submersible: Preprint

DOE Office of Scientific and Technical Information (OSTI.GOV)

Benitz, M. A.; Schmidt, D. P.; Lackner, M. A.

Hydrodynamic loads on the platforms of floating offshore wind turbines are often predicted with computer-aided engineering tools that employ Morison's equation and/or potential-flow theory. This work compares results from one such tool, FAST, NREL's wind turbine computer-aided engineering tool, and the computational fluid dynamics package, OpenFOAM, for the OC4-DeepCwind semi-submersible analyzed in the International Energy Agency Wind Task 30 project. Load predictions from HydroDyn, the offshore hydrodynamics module of FAST, are compared with high-fidelity results from OpenFOAM. HydroDyn uses a combination of Morison's equations and potential flow to predict the hydrodynamic forces on the structure. The implications of the assumptionsmore » in HydroDyn are evaluated based on this code-to-code comparison.« less

Deep Water Ocean Acoustics (DWOA): The Philippine Sea, OBSANP, and THAAW Experiments

DTIC Science & Technology

2015-09-30

the travel times. 4 The ocean state estimates were then re-computed to fit the acoustic travel times as integrals of the sound speed, and...1 DISTRIBUTION STATEMENT A. Approved for public release; distribution is unlimited. Deep Water Ocean Acoustics (DWOA): The Philippine Sea...deep-water acoustic propagation and ambient noise has been collected in a wide variety of environments over the last few years with ONR support

Embellishing Problem-Solving Examples with Deep Structure Information Facilitates Transfer

ERIC Educational Resources Information Center

Lee, Hee Seung; Betts, Shawn; Anderson, John R.

2017-01-01

Appreciation of problem structure is critical to successful learning. Two experiments investigated effective ways of communicating problem structure in a computer-based learning environment and tested whether verbal instruction is necessary to specify solution steps, when deep structure is already embellished by instructional examples.…

a Discrete Mathematical Model to Simulate Malware Spreading

NASA Astrophysics Data System (ADS)

Del Rey, A. Martin; Sánchez, G. Rodriguez

2012-10-01

With the advent and worldwide development of Internet, the study and control of malware spreading has become very important. In this sense, some mathematical models to simulate malware propagation have been proposed in the scientific literature, and usually they are based on differential equations exploiting the similarities with mathematical epidemiology. The great majority of these models study the behavior of a particular type of malware called computer worms; indeed, to the best of our knowledge, no model has been proposed to simulate the spreading of a computer virus (the traditional type of malware which differs from computer worms in several aspects). In this sense, the purpose of this work is to introduce a new mathematical model not based on continuous mathematics tools but on discrete ones, to analyze and study the epidemic behavior of computer virus. Specifically, cellular automata are used in order to design such model.

Single-Molecule Sequencing Reveals Complex Genome Variation of Hepatitis B Virus during 15 Years of Chronic Infection following Liver Transplantation

PubMed Central

Betz-Stablein, B. D.; Töpfer, A.; Littlejohn, M.; Yuen, L.; Colledge, D.; Sozzi, V.; Angus, P.; Thompson, A.; Revill, P.; Beerenwinkel, N.; Warner, N.

2016-01-01

ABSTRACT Chronic hepatitis B (CHB) is prevalent worldwide. The infectious agent, hepatitis B virus (HBV), replicates via an RNA intermediate and is error prone, leading to the rapid generation of closely related but not identical viral variants, including those that can escape host immune responses and antiviral treatments. The complexity of CHB can be further enhanced by the presence of HBV variants with large deletions in the genome generated via splicing (spHBV variants). Although spHBV variants are incapable of autonomous replication, their replication is rescued by wild-type HBV. spHBV variants have been shown to enhance wild-type virus replication, and their prevalence increases with liver disease progression. Single-molecule deep sequencing was performed on whole HBV genomes extracted from samples, including the liver explant, longitudinally collected from a subject with CHB over a 15-year period after liver transplantation. By employing novel bioinformatics methods, this analysis showed that the dynamics of the viral population across a period of changing treatment regimens was complex. The spHBV variants detected in the liver explant remained present posttransplantation, and a highly diverse novel spHBV population as well as variants with multiple deletions in the pre-S genes emerged. The identification of novel mutations outside the HBV reverse transcriptase gene that co-occurred with known drug resistance-associated mutations highlights the relevance of using full-genome deep sequencing and supports the hypothesis that drug resistance involves interactions across the full length of the HBV genome. IMPORTANCE Single-molecule sequencing allowed the characterization, in unprecedented detail, of the evolution of HBV populations and offered unique insights into the dynamics of defective and spHBV variants following liver transplantation and complex treatment regimens. This analysis also showed the rapid adaptation of HBV populations to treatment regimens with evolving drug resistance phenotypes and evidence of purifying selection across the whole genome. Finally, the new open-source bioinformatics tools with the capacity to easily identify potential spliced variants from deep sequencing data are freely available. PMID:27252524

Exploring the Earth Using Deep Learning Techniques

NASA Astrophysics Data System (ADS)

Larraondo, P. R.; Evans, B. J. K.; Antony, J.

2016-12-01

Research using deep neural networks have significantly matured in recent times, and there is now a surge in interest to apply such methods to Earth systems science and the geosciences. When combined with Big Data, we believe there are opportunities for significantly transforming a number of areas relevant to researchers and policy makers. In particular, by using a combination of data from a range of satellite Earth observations as well as computer simulations from climate models and reanalysis, we can gain new insights into the information that is locked within the data. Global geospatial datasets describe a wide range of physical and chemical parameters, which are mostly available using regular grids covering large spatial and temporal extents. This makes them perfect candidates to apply deep learning methods. So far, these techniques have been successfully applied to image analysis through the use of convolutional neural networks. However, this is only one field of interest, and there is potential for many more use cases to be explored. The deep learning algorithms require fast access to large amounts of data in the form of tensors and make intensive use of CPU in order to train its models. The Australian National Computational Infrastructure (NCI) has recently augmented its Raijin 1.2 PFlop supercomputer with hardware accelerators. Together with NCI's 3000 core high performance OpenStack cloud, these computational systems have direct access to NCI's 10+ PBytes of datasets and associated Big Data software technologies (see http://geonetwork.nci.org.au/ and http://nci.org.au/systems-services/national-facility/nerdip/). To effectively use these computing infrastructures requires that both the data and software are organised in a way that readily supports the deep learning software ecosystem. Deep learning software, such as the open source TensorFlow library, has allowed us to demonstrate the possibility of generating geospatial models by combining information from our different data sources. This opens the door to an exciting new way of generating products and extracting features that have previously been labour intensive. In this paper, we will explore some of these geospatial use cases and share some of the lessons learned from this experience.

Genome-Wide Survey of Host Responses: Use of Computational Analysis to Classify Exposures and Extract Signatures of Unconventional Versus Common Viral Exposures

DTIC Science & Technology

2006-11-01

alpha viruses, among which was Venezuelan equine encephalitis (VEE). The “training sets” were constructed from in vitro exposures to purified...virus and 4 serotypes of Dengue), and the forth group include 2 alpha viruses, among which was Venezuelan equine encephalitis (VEE). We have used...We examined our hypothesis that in vitro exposures can predict exposures in vivo. We have used data from PBMC exposed to Venezuelan equine

Stochastic acidification, activation of hemagglutinin and escape of influenza viruses from an endosome

NASA Astrophysics Data System (ADS)

Lagache, Thibault; Sieben, Christian; Meyer, Tim; Herrmann, Andreas; Holcman, David

2017-06-01

Influenza viruses enter the cell inside an endosome. During the endosomal journey, acidification triggers a conformational change of the virus spike protein hemagglutinin (HA) that results in escape of the viral genome from the endosome into the cytoplasm. It is still unclear how the interplay between acidification and HA conformation changes affects the kinetics of the viral endosomal escape. We develop here a stochastic model to estimate the change of conformation of HAs inside the endosome nanodomain. Using a Markov process, we model the arrival of protons to HA binding sites and compute the kinetics of their accumulation. We compute the Mean First Passage Time (MFPT) of the number of HA bound sites to a threshold, which is used to estimate the HA activation rate for a given pH concentration. The present analysis reveals that HA proton binding sites possess a high chemical barrier, ensuring a stability of the spike protein at sub-acidic pH. We predict that activating more than 3 adjacent HAs is necessary to trigger endosomal fusion and this configuration prevents premature release of viruses from early endosomes

Evidence of pervasive biologically functional secondary structures within the genomes of eukaryotic single-stranded DNA viruses.

PubMed

Muhire, Brejnev Muhizi; Golden, Michael; Murrell, Ben; Lefeuvre, Pierre; Lett, Jean-Michel; Gray, Alistair; Poon, Art Y F; Ngandu, Nobubelo Kwanele; Semegni, Yves; Tanov, Emil Pavlov; Monjane, Adérito Luis; Harkins, Gordon William; Varsani, Arvind; Shepherd, Dionne Natalie; Martin, Darren Patrick

2014-02-01

Single-stranded DNA (ssDNA) viruses have genomes that are potentially capable of forming complex secondary structures through Watson-Crick base pairing between their constituent nucleotides. A few of the structural elements formed by such base pairings are, in fact, known to have important functions during the replication of many ssDNA viruses. Unknown, however, are (i) whether numerous additional ssDNA virus genomic structural elements predicted to exist by computational DNA folding methods actually exist and (ii) whether those structures that do exist have any biological relevance. We therefore computationally inferred lists of the most evolutionarily conserved structures within a diverse selection of animal- and plant-infecting ssDNA viruses drawn from the families Circoviridae, Anelloviridae, Parvoviridae, Nanoviridae, and Geminiviridae and analyzed these for evidence of natural selection favoring the maintenance of these structures. While we find evidence that is consistent with purifying selection being stronger at nucleotide sites that are predicted to be base paired than at sites predicted to be unpaired, we also find strong associations between sites that are predicted to pair with one another and site pairs that are apparently coevolving in a complementary fashion. Collectively, these results indicate that natural selection actively preserves much of the pervasive secondary structure that is evident within eukaryote-infecting ssDNA virus genomes and, therefore, that much of this structure is biologically functional. Lastly, we provide examples of various highly conserved but completely uncharacterized structural elements that likely have important functions within some of the ssDNA virus genomes analyzed here.

Evidence of Pervasive Biologically Functional Secondary Structures within the Genomes of Eukaryotic Single-Stranded DNA Viruses

PubMed Central

Muhire, Brejnev Muhizi; Golden, Michael; Murrell, Ben; Lefeuvre, Pierre; Lett, Jean-Michel; Gray, Alistair; Poon, Art Y. F.; Ngandu, Nobubelo Kwanele; Semegni, Yves; Tanov, Emil Pavlov; Monjane, Adérito Luis; Harkins, Gordon William; Varsani, Arvind; Shepherd, Dionne Natalie

2014-01-01

Single-stranded DNA (ssDNA) viruses have genomes that are potentially capable of forming complex secondary structures through Watson-Crick base pairing between their constituent nucleotides. A few of the structural elements formed by such base pairings are, in fact, known to have important functions during the replication of many ssDNA viruses. Unknown, however, are (i) whether numerous additional ssDNA virus genomic structural elements predicted to exist by computational DNA folding methods actually exist and (ii) whether those structures that do exist have any biological relevance. We therefore computationally inferred lists of the most evolutionarily conserved structures within a diverse selection of animal- and plant-infecting ssDNA viruses drawn from the families Circoviridae, Anelloviridae, Parvoviridae, Nanoviridae, and Geminiviridae and analyzed these for evidence of natural selection favoring the maintenance of these structures. While we find evidence that is consistent with purifying selection being stronger at nucleotide sites that are predicted to be base paired than at sites predicted to be unpaired, we also find strong associations between sites that are predicted to pair with one another and site pairs that are apparently coevolving in a complementary fashion. Collectively, these results indicate that natural selection actively preserves much of the pervasive secondary structure that is evident within eukaryote-infecting ssDNA virus genomes and, therefore, that much of this structure is biologically functional. Lastly, we provide examples of various highly conserved but completely uncharacterized structural elements that likely have important functions within some of the ssDNA virus genomes analyzed here. PMID:24284329

Evolutionary Scheduler for the Deep Space Network

NASA Technical Reports Server (NTRS)

Guillaume, Alexandre; Lee, Seungwon; Wang, Yeou-Fang; Zheng, Hua; Chau, Savio; Tung, Yu-Wen; Terrile, Richard J.; Hovden, Robert

2010-01-01

A computer program assists human schedulers in satisfying, to the maximum extent possible, competing demands from multiple spacecraft missions for utilization of the transmitting/receiving Earth stations of NASA s Deep Space Network. The program embodies a concept of optimal scheduling to attain multiple objectives in the presence of multiple constraints.

A resolution adaptive deep hierarchical (RADHicaL) learning scheme applied to nuclear segmentation of digital pathology images.

PubMed

Janowczyk, Andrew; Doyle, Scott; Gilmore, Hannah; Madabhushi, Anant

2018-01-01

Deep learning (DL) has recently been successfully applied to a number of image analysis problems. However, DL approaches tend to be inefficient for segmentation on large image data, such as high-resolution digital pathology slide images. For example, typical breast biopsy images scanned at 40× magnification contain billions of pixels, of which usually only a small percentage belong to the class of interest. For a typical naïve deep learning scheme, parsing through and interrogating all the image pixels would represent hundreds if not thousands of hours of compute time using high performance computing environments. In this paper, we present a resolution adaptive deep hierarchical (RADHicaL) learning scheme wherein DL networks at lower resolutions are leveraged to determine if higher levels of magnification, and thus computation, are necessary to provide precise results. We evaluate our approach on a nuclear segmentation task with a cohort of 141 ER+ breast cancer images and show we can reduce computation time on average by about 85%. Expert annotations of 12,000 nuclei across these 141 images were employed for quantitative evaluation of RADHicaL. A head-to-head comparison with a naïve DL approach, operating solely at the highest magnification, yielded the following performance metrics: .9407 vs .9854 Detection Rate, .8218 vs .8489 F -score, .8061 vs .8364 true positive rate and .8822 vs 0.8932 positive predictive value. Our performance indices compare favourably with state of the art nuclear segmentation approaches for digital pathology images.

Using Computer Technology to Foster Learning for Understanding

PubMed Central

VAN MELLE, ELAINE; TOMALTY, LEWIS

2000-01-01

The literature shows that students typically use either a surface approach to learning, in which the emphasis is on memorization of facts, or a deep approach to learning, in which learning for understanding is the primary focus. This paper describes how computer technology, specifically the use of a multimedia CD-ROM, was integrated into a microbiology curriculum as part of the transition from focusing on facts to fostering learning for understanding. Evaluation of the changes in approaches to learning over the course of the term showed a statistically significant shift in a deep approach to learning, as measured by the Study Process Questionnaire. Additional data collected showed that the use of computer technology supported this shift by providing students with the opportunity to apply what they had learned in class to order tests and interpret the test results in relation to specific patient-focused case studies. The extent of the impact, however, varied among different groups of students in the class. For example, students who were recent high school graduates did not show a statistically significant increase in deep learning scores over the course of the term and did not perform as well in the course. The results also showed that a surface approach to learning was an important aspect of learning for understanding, although only those students who were able to combine a surface with a deep approach to learning were successfully able to learn for understanding. Implications of this finding for the future use of computer technology and learning for understanding are considered. PMID:23653533

Differences in the Selection Bottleneck between Modes of Sexual Transmission Influence the Genetic Composition of the HIV-1 Founder Virus

PubMed Central

Tully, Damien C.; Ogilvie, Colin B.; Batorsky, Rebecca E.; Bean, David J.; Power, Karen A.; Ghebremichael, Musie; Bedard, Hunter E.; Gladden, Adrianne D.; Seese, Aaron M.; Amero, Molly A.; Lane, Kimberly; McGrath, Graham; Bazner, Suzane B.; Tinsley, Jake; Lennon, Niall J.; Henn, Matthew R.; Brumme, Zabrina L.; Norris, Philip J.; Rosenberg, Eric S.; Mayer, Kenneth H.; Jessen, Heiko; Kosakovsky Pond, Sergei L.; Walker, Bruce D.; Altfeld, Marcus; Carlson, Jonathan M.; Allen, Todd M.

2016-01-01

Due to the stringent population bottleneck that occurs during sexual HIV-1 transmission, systemic infection is typically established by a limited number of founder viruses. Elucidation of the precise forces influencing the selection of founder viruses may reveal key vulnerabilities that could aid in the development of a vaccine or other clinical interventions. Here, we utilize deep sequencing data and apply a genetic distance-based method to investigate whether the mode of sexual transmission shapes the nascent founder viral genome. Analysis of 74 acute and early HIV-1 infected subjects revealed that 83% of men who have sex with men (MSM) exhibit a single founder virus, levels similar to those previously observed in heterosexual (HSX) transmission. In a metadata analysis of a total of 354 subjects, including HSX, MSM and injecting drug users (IDU), we also observed no significant differences in the frequency of single founder virus infections between HSX and MSM transmissions. However, comparison of HIV-1 envelope sequences revealed that HSX founder viruses exhibited a greater number of codon sites under positive selection, as well as stronger transmission indices possibly reflective of higher fitness variants. Moreover, specific genetic “signatures” within MSM and HSX founder viruses were identified, with single polymorphisms within gp41 enriched among HSX viruses while more complex patterns, including clustered polymorphisms surrounding the CD4 binding site, were enriched in MSM viruses. While our findings do not support an influence of the mode of sexual transmission on the number of founder viruses, they do demonstrate that there are marked differences in the selection bottleneck that can significantly shape their genetic composition. This study illustrates the complex dynamics of the transmission bottleneck and reveals that distinct genetic bottleneck processes exist dependent upon the mode of HIV-1 transmission. PMID:27163788

Differences in the Selection Bottleneck between Modes of Sexual Transmission Influence the Genetic Composition of the HIV-1 Founder Virus.

PubMed

Tully, Damien C; Ogilvie, Colin B; Batorsky, Rebecca E; Bean, David J; Power, Karen A; Ghebremichael, Musie; Bedard, Hunter E; Gladden, Adrianne D; Seese, Aaron M; Amero, Molly A; Lane, Kimberly; McGrath, Graham; Bazner, Suzane B; Tinsley, Jake; Lennon, Niall J; Henn, Matthew R; Brumme, Zabrina L; Norris, Philip J; Rosenberg, Eric S; Mayer, Kenneth H; Jessen, Heiko; Kosakovsky Pond, Sergei L; Walker, Bruce D; Altfeld, Marcus; Carlson, Jonathan M; Allen, Todd M

2016-05-01

Due to the stringent population bottleneck that occurs during sexual HIV-1 transmission, systemic infection is typically established by a limited number of founder viruses. Elucidation of the precise forces influencing the selection of founder viruses may reveal key vulnerabilities that could aid in the development of a vaccine or other clinical interventions. Here, we utilize deep sequencing data and apply a genetic distance-based method to investigate whether the mode of sexual transmission shapes the nascent founder viral genome. Analysis of 74 acute and early HIV-1 infected subjects revealed that 83% of men who have sex with men (MSM) exhibit a single founder virus, levels similar to those previously observed in heterosexual (HSX) transmission. In a metadata analysis of a total of 354 subjects, including HSX, MSM and injecting drug users (IDU), we also observed no significant differences in the frequency of single founder virus infections between HSX and MSM transmissions. However, comparison of HIV-1 envelope sequences revealed that HSX founder viruses exhibited a greater number of codon sites under positive selection, as well as stronger transmission indices possibly reflective of higher fitness variants. Moreover, specific genetic "signatures" within MSM and HSX founder viruses were identified, with single polymorphisms within gp41 enriched among HSX viruses while more complex patterns, including clustered polymorphisms surrounding the CD4 binding site, were enriched in MSM viruses. While our findings do not support an influence of the mode of sexual transmission on the number of founder viruses, they do demonstrate that there are marked differences in the selection bottleneck that can significantly shape their genetic composition. This study illustrates the complex dynamics of the transmission bottleneck and reveals that distinct genetic bottleneck processes exist dependent upon the mode of HIV-1 transmission.

Is Multitask Deep Learning Practical for Pharma?

PubMed

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.

Middle East Respiratory Syndrome Coronavirus Intra-Host Populations Are Characterized by Numerous High Frequency Variants

DOE Office of Scientific and Technical Information (OSTI.GOV)

Borucki, Monica K.; Lao, Victoria; Hwang, Mona

Middle East respiratory syndrome coronavirus (MERS-CoV) is an emerging human pathogen related to SARS virus. In vitro studies indicate this virus may have a broad host range suggesting an increased pandemic potential. Genetic and epidemiological evidence indicate camels serve as a reservoir for MERS virus but the mechanism of cross species transmission is unclear and many questions remain regarding the susceptibility of humans to infection. Deep sequencing data was obtained from the nasal samples of three camels that had been experimentally infected with a human MERS-CoV isolate. A majority of the genome was covered and average coverage was greater thanmore » 12,000x depth. Although only 5 mutations were detected in the consensus sequences, 473 intrahost single nucleotide variants were identified. Lastly, many of these variants were present at high frequencies and could potentially influence viral phenotype and the sensitivity of detection assays that target these regions for primer or probe binding.« less

New hepatitis C virus genotype 1 subtype naturally harbouring resistance-associated mutations to NS5A inhibitors.

PubMed

Ordeig, Laura; Garcia-Cehic, Damir; Gregori, Josep; Soria, Maria Eugenia; Nieto-Aponte, Leonardo; Perales, Celia; Llorens, Meritxell; Chen, Qian; Riveiro-Barciela, Mar; Buti, Maria; Esteban, Rafael; Esteban, Juan Ignacio; Rodriguez-Frias, Francisco; Quer, Josep

2018-01-01

Hepatitis C virus (HCV) is a highly divergent virus currently classified into seven major genotypes and 86 subtypes (ICTV, June 2017), which can have differing responses to therapy. Accurate genotyping/subtyping using high-resolution HCV subtyping enables confident subtype identification, identifies mixed infections and allows detection of new subtypes. During routine genotyping/subtyping, one sample from an Equatorial Guinea patient could not be classified into any of the subtypes. The complete genomic sequence was compared to reference sequences by phylogenetic and sliding window analysis. Resistance-associated substitutions (RASs) were assessed by deep sequencing. The unclassified HCV genome did not belong to any of the existing genotype 1 (G1) subtypes. Sliding window analysis along the complete genome ruled out recombination phenomena suggesting that it belongs to a new HCV G1 subtype. Two NS5A RASs (L31V+Y93H) were found to be naturally combined in the genome which could limit treatment possibilities in patients infected with this subtype.

Endogenous Retroviruses in the Genomics Era.

PubMed

Johnson, Welkin E

2015-11-01

Endogenous retroviruses comprise millions of discrete genetic loci distributed within the genomes of extant vertebrates. These sequences, which are clearly related to exogenous retroviruses, represent retroviral infections of the deep past, and their abundance suggests that retroviruses were a near-constant presence throughout the evolutionary history of modern vertebrates. Endogenous retroviruses contribute in myriad ways to the evolution of host genomes, as mutagens and as sources of genetic novelty (both coding and regulatory) to be acted upon by the twin engines of random genetic drift and natural selection. Importantly, the richness and complexity of endogenous retrovirus data can be used to understand how viruses spread and adapt on evolutionary timescales by combining population genetics and evolutionary theory with a detailed understanding of retrovirus biology (gleaned from the study of extant retroviruses). In addition to revealing the impact of viruses on organismal evolution, such studies can help us better understand, by looking back in time, how life-history traits, as well as ecological and geological events, influence the movement of viruses within and between populations.

Parapoxviral vulvovaginitis in Holstein cows.

PubMed

Moeller, Robert B; Crossley, Beate; Adaska, John M; Hsia, Gary; Kahn, Richard; Blanchard, Patricia C

2018-05-01

A group of Holstein first-calved heifers developed small pustules and ulcers on the vulva and in the vagina during the first 1-4 wk postpartum. The lesions varied from small red pinpoint foci to pustules and ulcers, 3-5 mm diameter. Some ulcers coalesced to form large ulcerated areas up to 15 mm diameter. In some animals, these ulcers progressed to become deep ulceration of the vaginal and vulvar mucosa with >50% of the mucosa involved. Vaginal biopsies from 4 heifers and vaginal individual swabs from 8 heifers for a combined sampling of 9 heifers were taken for clinical assessment. Six of the 9 heifers had parapoxvirus based on histopathology and/or PCR. Histologic examination of the biopsies of the pustules identified ballooning degeneration of the epithelium with degenerate epithelium containing eosinophilic intracytoplasmic inclusions consistent with a parapoxvirus in 3 of 4 biopsies. Testing for bovine herpesvirus 1, 2, and 4, bovine viral diarrhea virus, bovine papular stomatitis virus, and orf virus remained negative.

Middle East Respiratory Syndrome Coronavirus Intra-Host Populations Are Characterized by Numerous High Frequency Variants

DOE PAGES

Borucki, Monica K.; Lao, Victoria; Hwang, Mona; ...

2016-01-20

Middle East respiratory syndrome coronavirus (MERS-CoV) is an emerging human pathogen related to SARS virus. In vitro studies indicate this virus may have a broad host range suggesting an increased pandemic potential. Genetic and epidemiological evidence indicate camels serve as a reservoir for MERS virus but the mechanism of cross species transmission is unclear and many questions remain regarding the susceptibility of humans to infection. Deep sequencing data was obtained from the nasal samples of three camels that had been experimentally infected with a human MERS-CoV isolate. A majority of the genome was covered and average coverage was greater thanmore » 12,000x depth. Although only 5 mutations were detected in the consensus sequences, 473 intrahost single nucleotide variants were identified. Lastly, many of these variants were present at high frequencies and could potentially influence viral phenotype and the sensitivity of detection assays that target these regions for primer or probe binding.« less

Parvovirus Family Conundrum: What Makes a Killer?

PubMed

Kailasan, Shweta; Agbandje-McKenna, Mavis; Parrish, Colin R

2015-11-01

Parvoviruses infect a wide variety of hosts, and their ancestors appear to have emerged tens to hundreds of millions of years ago and to have spread widely ever since. The diversity of parvoviruses is therefore extensive, and although they all appear to descend from a common ancestor and share common structures in their capsid and nonstructural proteins, there is often low homology at the DNA or protein level. The diversity of these viruses is also seen in the widely differing impacts they have on their hosts, which range from severe and even lethal disease to subclinical or nonpathogenic infections. In the past few years, deep sequencing of DNA samples from animals has shown just how widespread the parvoviruses are in nature, but most of the newly discovered viruses have not yet been associated with any disease. However, variants of some parvoviruses have altered their host ranges to create new epidemic or pandemic viruses. Here, we examine the properties of parvoviruses and their interactions with their hosts that are associated with these disparate pathogenic outcomes.

The Expanding Family of Virophages

PubMed Central

Bekliz, Meriem; Colson, Philippe; La Scola, Bernard

2016-01-01

Virophages replicate with giant viruses in the same eukaryotic cells. They are a major component of the specific mobilome of mimiviruses. Since their discovery in 2008, five other representatives have been isolated, 18 new genomes have been described, two of which being nearly completely sequenced, and they have been classified in a new viral family, Lavidaviridae. Virophages are small viruses with approximately 35–74 nm large icosahedral capsids and 17–29 kbp large double-stranded DNA genomes with 16–34 genes, among which a very small set is shared with giant viruses. Virophages have been isolated or detected in various locations and in a broad range of habitats worldwide, including the deep ocean and inland. Humans, therefore, could be commonly exposed to virophages, although currently limited evidence exists of their presence in humans based on serology and metagenomics. The distribution of virophages, the consequences of their infection and the interactions with their giant viral hosts within eukaryotic cells deserve further research. PMID:27886075

Predicted secondary structure similarity in the absence of primary amino acid sequence homology: hepatitis B virus open reading frames.

PubMed Central

Schaeffer, E; Sninsky, J J

1984-01-01

Proteins that are related evolutionarily may have diverged at the level of primary amino acid sequence while maintaining similar secondary structures. Computer analysis has been used to compare the open reading frames of the hepatitis B virus to those of the woodchuck hepatitis virus at the level of amino acid sequence, and to predict the relative hydrophilic character and the secondary structure of putative polypeptides. Similarity is seen at the levels of relative hydrophilicity and secondary structure, in the absence of sequence homology. These data reinforce the proposal that these open reading frames encode viral proteins. Computer analysis of this type can be more generally used to establish structural similarities between proteins that do not share obvious sequence homology as well as to assess whether an open reading frame is fortuitous or codes for a protein. PMID:6585835

HIV-1 Strategies of Immune Evasion

NASA Astrophysics Data System (ADS)

Castiglione, F.; Bernaschi, M.

We simulate the progression of the HIV-1 infection in untreated host organisms. The phenotype features of the virus are represented by the replication rate, the probability of activating the transcription, the mutation rate and the capacity to stimulate an immune response (the so-called immunogenicity). It is very difficult to study in-vivo or in-vitro how these characteristics of the virus influence the evolution of the disease. Therefore we resorted to simulations based on a computer model validated in previous studies. We observe, by means of computer experiments, that the virus continuously evolves under the selective pressure of an immune response whose effectiveness downgrades along with the disease progression. The results of the simulations show that immunogenicity is the most important factor in determining the rate of disease progression but, by itself, it is not sufficient to drive the disease to a conclusion in all cases.

Pulmonary Nodule Classification with Deep Convolutional Neural Networks on Computed Tomography Images.

PubMed

Li, Wei; Cao, Peng; Zhao, Dazhe; Wang, Junbo

2016-01-01

Computer aided detection (CAD) systems can assist radiologists by offering a second opinion on early diagnosis of lung cancer. Classification and feature representation play critical roles in false-positive reduction (FPR) in lung nodule CAD. We design a deep convolutional neural networks method for nodule classification, which has an advantage of autolearning representation and strong generalization ability. A specified network structure for nodule images is proposed to solve the recognition of three types of nodules, that is, solid, semisolid, and ground glass opacity (GGO). Deep convolutional neural networks are trained by 62,492 regions-of-interest (ROIs) samples including 40,772 nodules and 21,720 nonnodules from the Lung Image Database Consortium (LIDC) database. Experimental results demonstrate the effectiveness of the proposed method in terms of sensitivity and overall accuracy and that it consistently outperforms the competing methods.

A distributed data base management system. [for Deep Space Network

NASA Technical Reports Server (NTRS)

Bryan, A. I.

1975-01-01

Major system design features of a distributed data management system for the NASA Deep Space Network (DSN) designed for continuous two-way deep space communications are described. The reasons for which the distributed data base utilizing third-generation minicomputers is selected as the optimum approach for the DSN are threefold: (1) with a distributed master data base, valid data is available in real-time to support DSN management activities at each location; (2) data base integrity is the responsibility of local management; and (3) the data acquisition/distribution and processing power of a third-generation computer enables the computer to function successfully as a data handler or as an on-line process controller. The concept of the distributed data base is discussed along with the software, data base integrity, and hardware used. The data analysis/update constraint is examined.

Rewiring of cellular membrane homeostasis by picornaviruses.

PubMed

Belov, George A; Sztul, Elizabeth

2014-09-01

Viruses are obligatory intracellular parasites and utilize host elements to support key viral processes, including penetration of the plasma membrane, initiation of infection, replication, and suppression of the host's antiviral defenses. In this review, we focus on picornaviruses, a family of positive-strand RNA viruses, and discuss the mechanisms by which these viruses hijack the cellular machinery to form and operate membranous replication complexes. Studies aimed at revealing factors required for the establishment of viral replication structures identified several cellular-membrane-remodeling proteins and led to the development of models in which the virus used a preexisting cellular-membrane-shaping pathway "as is" for generating its replication organelles. However, as more data accumulate, this view is being increasingly questioned, and it is becoming clearer that viruses may utilize cellular factors in ways that are distinct from the normal functions of these proteins in uninfected cells. In addition, the proteincentric view is being supplemented by important new studies showing a previously unappreciated deep remodeling of lipid homeostasis, including extreme changes to phospholipid biosynthesis and cholesterol trafficking. The data on viral modifications of lipid biosynthetic pathways are still rudimentary, but it appears once again that the viruses may rewire existing pathways to generate novel functions. Despite remarkable progress, our understanding of how a handful of viral proteins can completely overrun the multilayered, complex mechanisms that control the membrane organization of a eukaryotic cell remains very limited. Copyright © 2014, American Society for Microbiology. All Rights Reserved.

De novo assembly of honey bee RNA viral genomes by tapping into the innate insect antiviral response pathway.

PubMed

Fung, Elisabeth; Hill, Kelly; Hogendoorn, Katja; Glatz, Richard V; Napier, Kathryn R; Bellgard, Matthew I; Barrero, Roberto A

2018-02-01

Bee pollination is critical for improving productivity of one third of all plants or plant products consumed by humans. The health of honey bees is in decline in many countries worldwide, and RNA viruses together with other biological, environmental and anthropogenic factors have been identified as the main causes. The rapid genetic variation of viruses represents a challenge for diagnosis. Thus, application of deep sequencing methods for detection and analysis of viruses has increased over the last years. In this study, we leverage from the innate Dicer-2 mediated antiviral response against viruses to reconstruct complete viral genomes using virus-derived small interfering RNAs (vsiRNAs). Symptomatic A. mellifera larvae collected from hives free of Colony Collapse Disorder (CCD) and the parasitic Varroa mite (Varroa destructor) were used to generate more than 107 million small RNA reads. We show that de novo assembly of insect viral sequences is less fragmented using only 22 nt long vsiRNAs rather than a combination of 21-22 nt small RNAs. Our results show that A. mellifera larvae activate the RNAi immune response in the presence of Sacbrood virus (SBV). We assembled three SBV genomes from three individual larvae from different hives in a single apiary, with 1-2% nucleotide sequence variability among them. We found 3-4% variability between SBV genomes generated in this study and earlier published Australian variants suggesting the presence of different SBV quasispecies within the country. Copyright © 2018. Published by Elsevier Inc.

Evolutionary Strategies of Viruses, Bacteria and Archaea in Hydrothermal Vent Ecosystems Revealed through Metagenomics

PubMed Central

Anderson, Rika E.; Sogin, Mitchell L.; Baross, John A.

2014-01-01

The deep-sea hydrothermal vent habitat hosts a diverse community of archaea and bacteria that withstand extreme fluctuations in environmental conditions. Abundant viruses in these systems, a high proportion of which are lysogenic, must also withstand these environmental extremes. Here, we explore the evolutionary strategies of both microorganisms and viruses in hydrothermal systems through comparative analysis of a cellular and viral metagenome, collected by size fractionation of high temperature fluids from a diffuse flow hydrothermal vent. We detected a high enrichment of mobile elements and proviruses in the cellular fraction relative to microorganisms in other environments. We observed a relatively high abundance of genes related to energy metabolism as well as cofactors and vitamins in the viral fraction compared to the cellular fraction, which suggest encoding of auxiliary metabolic genes on viral genomes. Moreover, the observation of stronger purifying selection in the viral versus cellular gene pool suggests viral strategies that promote prolonged host integration. Our results demonstrate that there is great potential for hydrothermal vent viruses to integrate into hosts, facilitate horizontal gene transfer, and express or transfer genes that manipulate the hosts’ functional capabilities. PMID:25279954

The use of conduction model in laser weld profile computation

NASA Astrophysics Data System (ADS)

Grabas, Bogusław

2007-02-01

Profiles of joints resulting from deep penetration laser beam welding of a flat workpiece of carbon steel were computed. A semi-analytical conduction model solved with Green's function method was used in computations. In the model, the moving heat source was attenuated exponentially in accordance with Beer-Lambert law. Computational results were compared with those in the experiment.

Divergent Viruses Discovered in Arthropods and Vertebrates Revise the Evolutionary History of the Flaviviridae and Related Viruses.

PubMed

Shi, Mang; Lin, Xian-Dan; Vasilakis, Nikos; Tian, Jun-Hua; Li, Ci-Xiu; Chen, Liang-Jun; Eastwood, Gillian; Diao, Xiu-Nian; Chen, Ming-Hui; Chen, Xiao; Qin, Xin-Cheng; Widen, Steven G; Wood, Thomas G; Tesh, Robert B; Xu, Jianguo; Holmes, Edward C; Zhang, Yong-Zhen

2016-01-15

Viruses of the family Flaviviridae are important pathogens of humans and other animals and are currently classified into four genera. To better understand their diversity, evolutionary history, and genomic flexibility, we used transcriptome sequencing (RNA-seq) to search for the viruses related to the Flaviviridae in a range of potential invertebrate and vertebrate hosts. Accordingly, we recovered the full genomes of five segmented jingmenviruses and 12 distant relatives of the known Flaviviridae ("flavi-like" viruses) from a range of arthropod species. Although these viruses are highly divergent, they share a similar genomic plan and common ancestry with the Flaviviridae in the NS3 and NS5 regions. Remarkably, although these viruses fill in major gaps in the phylogenetic diversity of the Flaviviridae, genomic comparisons reveal important changes in genome structure, genome size, and replication/gene regulation strategy during evolutionary history. In addition, the wide diversity of flavi-like viruses found in invertebrates, as well as their deep phylogenetic positions, suggests that they may represent the ancestral forms from which the vertebrate-infecting viruses evolved. For the vertebrate viruses, we expanded the previously mammal-only pegivirus-hepacivirus group to include a virus from the graceful catshark (Proscyllium habereri), which in turn implies that these viruses possess a larger host range than is currently known. In sum, our data show that the Flaviviridae infect a far wider range of hosts and exhibit greater diversity in genome structure than previously anticipated. The family Flaviviridae of RNA viruses contains several notorious human pathogens, including dengue virus, West Nile virus, and hepatitis C virus. To date, however, our understanding of the biodiversity and evolution of the Flaviviridae has largely been directed toward vertebrate hosts and their blood-feeding arthropod vectors. Therefore, we investigated an expanded group of potential arthropod and vertebrate host species that have generally been ignored by surveillance programs. Remarkably, these species contained diverse flaviviruses and related viruses that are characterized by major changes in genome size and genome structure, such that these traits are more flexible than previously thought. More generally, these data suggest that arthropods may be the ultimate reservoir of the Flaviviridae and related viruses, harboring considerable genetic and phenotypic diversity. In sum, this study revises the traditional view on the evolutionary history, host range, and genomic structures of a major group of RNA viruses. Copyright © 2015, American Society for Microbiology. All Rights Reserved.

A Constructive Induction Approach to Computer Immunology

DTIC Science & Technology

1999-03-01

LVM98] Lamont, Gary B., David A. Van Veldhuizen , and Robert E Marmelstein, A Distributed Architecture for a Self-Adaptive Computer Virus...Artificial Intelligence, Herndon, VA, 1995. [MVL98] Marmelstein, Robert E., David A. Van Veldhuizen , and Gary B. Lamont. Modeling & Analysis

Deep learning for hybrid EEG-fNIRS brain-computer interface: application to motor imagery classification.

PubMed

Chiarelli, Antonio Maria; Croce, Pierpaolo; Merla, Arcangelo; Zappasodi, Filippo

2018-06-01

Brain-computer interface (BCI) refers to procedures that link the central nervous system to a device. BCI was historically performed using electroencephalography (EEG). In the last years, encouraging results were obtained by combining EEG with other neuroimaging technologies, such as functional near infrared spectroscopy (fNIRS). A crucial step of BCI is brain state classification from recorded signal features. Deep artificial neural networks (DNNs) recently reached unprecedented complex classification outcomes. These performances were achieved through increased computational power, efficient learning algorithms, valuable activation functions, and restricted or back-fed neurons connections. By expecting significant overall BCI performances, we investigated the capabilities of combining EEG and fNIRS recordings with state-of-the-art deep learning procedures. We performed a guided left and right hand motor imagery task on 15 subjects with a fixed classification response time of 1 s and overall experiment length of 10 min. Left versus right classification accuracy of a DNN in the multi-modal recording modality was estimated and it was compared to standalone EEG and fNIRS and other classifiers. At a group level we obtained significant increase in performance when considering multi-modal recordings and DNN classifier with synergistic effect. BCI performances can be significantly improved by employing multi-modal recordings that provide electrical and hemodynamic brain activity information, in combination with advanced non-linear deep learning classification procedures.

Deep learning for hybrid EEG-fNIRS brain–computer interface: application to motor imagery classification

NASA Astrophysics Data System (ADS)

Chiarelli, Antonio Maria; Croce, Pierpaolo; Merla, Arcangelo; Zappasodi, Filippo

2018-06-01

Objective. Brain–computer interface (BCI) refers to procedures that link the central nervous system to a device. BCI was historically performed using electroencephalography (EEG). In the last years, encouraging results were obtained by combining EEG with other neuroimaging technologies, such as functional near infrared spectroscopy (fNIRS). A crucial step of BCI is brain state classification from recorded signal features. Deep artificial neural networks (DNNs) recently reached unprecedented complex classification outcomes. These performances were achieved through increased computational power, efficient learning algorithms, valuable activation functions, and restricted or back-fed neurons connections. By expecting significant overall BCI performances, we investigated the capabilities of combining EEG and fNIRS recordings with state-of-the-art deep learning procedures. Approach. We performed a guided left and right hand motor imagery task on 15 subjects with a fixed classification response time of 1 s and overall experiment length of 10 min. Left versus right classification accuracy of a DNN in the multi-modal recording modality was estimated and it was compared to standalone EEG and fNIRS and other classifiers. Main results. At a group level we obtained significant increase in performance when considering multi-modal recordings and DNN classifier with synergistic effect. Significance. BCI performances can be significantly improved by employing multi-modal recordings that provide electrical and hemodynamic brain activity information, in combination with advanced non-linear deep learning classification procedures.

Analytical and computational modeling of early penetration of non-enveloped icosahedral viruses into cells.

PubMed

Katzengold, Rona; Zaharov, Evgeniya; Gefen, Amit

2016-07-27

As obligate intracellular parasites, all viruses penetrate target cells to initiate replication and infection. This study introduces two approaches for evaluating the contact loads applied to a cell during early penetration of non-enveloped icosahedral viruses. The first approach is analytical modeling which is based on Hertz's theory for the contact of two elastic bodies; here we model the virus capsid as a triangle and the cell as an order-of-magnitude larger sphere. The second approach is finite element modeling, where we simulate three types of viruses: adeno-, papilloma- and polio- viruses, each interacting with a cell section. We find that the peak contact pressures and forces generated at the initial virus-cell contact depend on the virus geometry - that is both size and shape. With respect to shape, we show that the icosahedral virus shape induces greater peak pressures compared to a spherical virus shape. With respect to size, it is shown that the larger the virus is the greater are the contact loads in the attacked cell. Utilization of our modeling can be substantially useful not only for basic science studies, but also in other, more applied fields, such as in the field of gene therapy, or in `phage' virus studies.

Teaching neuroanatomy using computer-aided learning: What makes for successful outcomes?

PubMed

Svirko, Elena; Mellanby, Jane

2017-11-01

Computer-aided learning (CAL) is an integral part of many medical courses. The neuroscience course at Oxford University for medical students includes CAL course of neuroanatomy. CAL is particularly suited to this since neuroanatomy requires much detailed three-dimensional visualization, which can be presented on screen. The CAL course was evaluated using the concept of approach to learning. The aims of university teaching are congruent with the deep approach-seeking meaning and relating new information to previous knowledge-rather than to the surface approach of concentrating on rote learning of detail. Seven cohorts of medical students (N = 869) filled in approach to learning scale and a questionnaire investigating their engagement with the CAL course. The students' scores on CAL-course-based neuroanatomy assessment and later university examinations were obtained. Although the students reported less use of the deep approach for the neuroanatomy CAL course than for the rest of their neuroanatomy course (mean = 24.99 vs. 31.49, P < 0.001), deep approach for CAL was positively correlated with neuroanatomy assessment performance (r = 0.12, P < 0.001). Time spent on the CAL course, enjoyment of it, the amount of CAL videos watched and quizzes completed were each significantly positively related to deep approach. The relationship between deep approach and enjoyment was particularly notable (25.5% shared variance). Reported relationships between deep approach and academic performance support the desirability of deep approach in university students. It is proposed that enjoyment of the course and the deep approach could be increased by incorporation of more clinical material which is what the students liked most. Anat Sci Educ 10: 560-569. © 2017 American Association of Anatomists. © 2017 American Association of Anatomists.

Computer Aided Process Planning for Non-Axisymmetric Deep Drawing Products

NASA Astrophysics Data System (ADS)

Park, Dong Hwan; Yarlagadda, Prasad K. D. V.

2004-06-01

In general, deep drawing products have various cross-section shapes such as cylindrical, rectangular and non-axisymmetric shapes. The application of the surface area calculation to non-axisymmetric deep drawing process has not been published yet. In this research, a surface area calculation for non-axisymmetric deep drawing products with elliptical shape was constructed for a design of blank shape of deep drawing products by using an AutoLISP function of AutoCAD software. A computer-aided process planning (CAPP) system for rotationally symmetric deep drawing products has been developed. However, the application of the system to non-axisymmetric components has not been reported yet. Thus, the CAPP system for non-axisymmetric deep drawing products with elliptical shape was constructed by using process sequence design. The system developed in this work consists of four modules. The first is recognition of shape module to recognize non-axisymmetric products. The second is a three-dimensional (3-D) modeling module to calculate the surface area for non-axisymmetric products. The third is a blank design module to create an oval-shaped blank with the identical surface area. The forth is a process planning module based on the production rules that play the best important role in an expert system for manufacturing. The production rules are generated and upgraded by interviewing field engineers. Especially, the drawing coefficient, the punch and die radii for elliptical shape products are considered as main design parameters. The suitability of this system was verified by applying to a real deep drawing product. This CAPP system constructed would be very useful to reduce lead-time for manufacturing and improve an accuracy of products.

Proceedings of the Second Annual Deep Brain Stimulation Think Tank: What's in the Pipeline.

PubMed

Gunduz, Aysegul; Morita, Hokuto; Rossi, P Justin; Allen, William L; Alterman, Ron L; Bronte-Stewart, Helen; Butson, Christopher R; Charles, David; Deckers, Sjaak; de Hemptinne, Coralie; DeLong, Mahlon; Dougherty, Darin; Ellrich, Jens; Foote, Kelly D; Giordano, James; Goodman, Wayne; Greenberg, Benjamin D; Greene, David; Gross, Robert; Judy, Jack W; Karst, Edward; Kent, Alexander; Kopell, Brian; Lang, Anthony; Lozano, Andres; Lungu, Codrin; Lyons, Kelly E; Machado, Andre; Martens, Hubert; McIntyre, Cameron; Min, Hoon-Ki; Neimat, Joseph; Ostrem, Jill; Pannu, Sat; Ponce, Francisco; Pouratian, Nader; Reymers, Donnie; Schrock, Lauren; Sheth, Sameer; Shih, Ludy; Stanslaski, Scott; Steinke, G Karl; Stypulkowski, Paul; Tröster, Alexander I; Verhagen, Leo; Walker, Harrison; Okun, Michael S

2015-01-01

The proceedings of the 2nd Annual Deep Brain Stimulation Think Tank summarize the most contemporary clinical, electrophysiological, and computational work on DBS for the treatment of neurological and neuropsychiatric disease and represent the insights of a unique multidisciplinary ensemble of expert neurologists, neurosurgeons, neuropsychologists, psychiatrists, scientists, engineers and members of industry. Presentations and discussions covered a broad range of topics, including advocacy for DBS, improving clinical outcomes, innovations in computational models of DBS, understanding of the neurophysiology of Parkinson's disease (PD) and Tourette syndrome (TS) and evolving sensor and device technologies.

Proceedings of the Second Annual Deep Brain Stimulation Think Tank: What's in the Pipeline

PubMed Central

Gunduz, Aysegul; Morita, Hokuto; Rossi, P. Justin; Allen, William L.; Alterman, Ron L.; Bronte-Stewart, Helen; Butson, Christopher R.; Charles, David; Deckers, Sjaak; de Hemptinne, Coralie; DeLong, Mahlon; Dougherty, Darin; Ellrich, Jens; Foote, Kelly D.; Giordano, James; Goodman, Wayne; Greenberg, Benjamin D.; Greene, David; Gross, Robert; Judy, Jack W.; Karst, Edward; Kent, Alexander; Kopell, Brian; Lang, Anthony; Lozano, Andres; Lungu, Codrin; Lyons, Kelly E.; Machado, Andre; Martens, Hubert; McIntyre, Cameron; Min, Hoon-Ki; Neimat, Joseph; Ostrem, Jill; Pannu, Sat; Ponce, Francisco; Pouratian, Nader; Reymers, Donnie; Schrock, Lauren; Sheth, Sameer; Shih, Ludy; Stanslaski, Scott; Steinke, G. Karl; Stypulkowski, Paul; Tröster, Alexander I.; Verhagen, Leo; Walker, Harrison; Okun, Michael S.

2015-01-01

The proceedings of the 2nd Annual Deep Brain Stimulation Think Tank summarize the most contemporary clinical, electrophysiological, and computational work on DBS for the treatment of neurological and neuropsychiatric disease and represent the insights of a unique multidisciplinary ensemble of expert neurologists, neurosurgeons, neuropsychologists, psychiatrists, scientists, engineers and members of industry. Presentations and discussions covered a broad range of topics, including advocacy for DBS, improving clinical outcomes, innovations in computational models of DBS, understanding of the neurophysiology of Parkinson's disease (PD) and Tourette syndrome (TS) and evolving sensor and device technologies. PMID:25526555

Proceedings of the second annual deep brain stimulation think tank: What's in the pipeline

DOE Office of Scientific and Technical Information (OSTI.GOV)

Gunduz, Aysegul; Morita, Hokuto; Rossi, P. Justin

Here the proceedings of the 2nd Annual Deep Brain Stimulation Think Tank summarize the most contemporary clinical, electrophysiological, and computational work on DBS for the treatment of neurological and neuropsychiatric disease and represent the insights of a unique multidisciplinary ensemble of expert neurologists, neurosurgeons, neuropsychologists, psychiatrists, scientists, engineers and members of industry. Presentations and discussions covered a broad range of topics, including advocacy for DBS, improving clinical outcomes, innovations in computational models of DBS, understanding of the neurophysiology of Parkinson's disease (PD) and Tourette syndrome (TS) and evolving sensor and device technologies.

Proceedings of the second annual deep brain stimulation think tank: What's in the pipeline

DOE PAGES

Gunduz, Aysegul; Morita, Hokuto; Rossi, P. Justin; ...

2015-05-25

Here the proceedings of the 2nd Annual Deep Brain Stimulation Think Tank summarize the most contemporary clinical, electrophysiological, and computational work on DBS for the treatment of neurological and neuropsychiatric disease and represent the insights of a unique multidisciplinary ensemble of expert neurologists, neurosurgeons, neuropsychologists, psychiatrists, scientists, engineers and members of industry. Presentations and discussions covered a broad range of topics, including advocacy for DBS, improving clinical outcomes, innovations in computational models of DBS, understanding of the neurophysiology of Parkinson's disease (PD) and Tourette syndrome (TS) and evolving sensor and device technologies.

DCL System Using Deep Learning Approaches for Land-Based or Ship-Based Real Time Recognition and Localization of Marine Mammals

DTIC Science & Technology

2015-09-30

Clark (2014), "Using High Performance Computing to Explore Large Complex Bioacoustic Soundscapes : Case Study for Right Whale Acoustics," Procedia...34Using High Performance Computing to Explore Large Complex Bioacoustic Soundscapes : Case Study for Right Whale Acoustics," Procedia Computer Science 20

Detection of Emerging Vaccine-Related Polioviruses by Deep Sequencing.

PubMed

Sahoo, Malaya K; Holubar, Marisa; Huang, ChunHong; Mohamed-Hadley, Alisha; Liu, Yuanyuan; Waggoner, Jesse J; Troy, Stephanie B; Garcia-Garcia, Lourdes; Ferreyra-Reyes, Leticia; Maldonado, Yvonne; Pinsky, Benjamin A

2017-07-01

Oral poliovirus vaccine can mutate to regain neurovirulence. To date, evaluation of these mutations has been performed primarily on culture-enriched isolates by using conventional Sanger sequencing. We therefore developed a culture-independent, deep-sequencing method targeting the 5' untranslated region (UTR) and P1 genomic region to characterize vaccine-related poliovirus variants. Error analysis of the deep-sequencing method demonstrated reliable detection of poliovirus mutations at levels of <1%, depending on read depth. Sequencing of viral nucleic acids from the stool of vaccinated, asymptomatic children and their close contacts collected during a prospective cohort study in Veracruz, Mexico, revealed no vaccine-derived polioviruses. This was expected given that the longest duration between sequenced sample collection and the end of the most recent national immunization week was 66 days. However, we identified many low-level variants (<5%) distributed across the 5' UTR and P1 genomic region in all three Sabin serotypes, as well as vaccine-related viruses with multiple canonical mutations associated with phenotypic reversion present at high levels (>90%). These results suggest that monitoring emerging vaccine-related poliovirus variants by deep sequencing may aid in the poliovirus endgame and efforts to ensure global polio eradication. Copyright © 2017 Sahoo et al.

Architecture for removable media USB-ARM

DOEpatents

Shue, Craig A.; Lamb, Logan M.; Paul, Nathanael R.

2015-07-14

A storage device is coupled to a computing system comprising an operating system and application software. Access to the storage device is blocked by a kernel filter driver, except exclusive access is granted to a first anti-virus engine. The first anti-virus engine is directed to scan the storage device for malicious software and report results. Exclusive access may be granted to one or more other anti-virus engines and they may be directed to scan the storage device and report results. Approval of all or a portion of the information on the storage device is based on the results from the first anti-virus engine and the other anti-virus engines. The storage device is presented to the operating system and access is granted to the approved information. The operating system may be a Microsoft Windows operating system. The kernel filter driver and usage of anti-virus engines may be configurable by a user.

Comparison of the Live Attenuated Yellow Fever Vaccine 17D-204 Strain to Its Virulent Parental Strain Asibi by Deep Sequencing

PubMed Central

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

2014-01-01

Background. 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. Methods. 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. Results. 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. Conclusions. 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. PMID:24141982

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

PubMed

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.

RSTensorFlow: GPU Enabled TensorFlow for Deep Learning on Commodity Android Devices

PubMed Central

Alzantot, Moustafa; Wang, Yingnan; Ren, Zhengshuang; Srivastava, Mani B.

2018-01-01

Mobile devices have become an essential part of our daily lives. By virtue of both their increasing computing power and the recent progress made in AI, mobile devices evolved to act as intelligent assistants in many tasks rather than a mere way of making phone calls. However, popular and commonly used tools and frameworks for machine intelligence are still lacking the ability to make proper use of the available heterogeneous computing resources on mobile devices. In this paper, we study the benefits of utilizing the heterogeneous (CPU and GPU) computing resources available on commodity android devices while running deep learning models. We leveraged the heterogeneous computing framework RenderScript to accelerate the execution of deep learning models on commodity Android devices. Our system is implemented as an extension to the popular open-source framework TensorFlow. By integrating our acceleration framework tightly into TensorFlow, machine learning engineers can now easily make benefit of the heterogeneous computing resources on mobile devices without the need of any extra tools. We evaluate our system on different android phones models to study the trade-offs of running different neural network operations on the GPU. We also compare the performance of running different models architectures such as convolutional and recurrent neural networks on CPU only vs using heterogeneous computing resources. Our result shows that although GPUs on the phones are capable of offering substantial performance gain in matrix multiplication on mobile devices. Therefore, models that involve multiplication of large matrices can run much faster (approx. 3 times faster in our experiments) due to GPU support. PMID:29629431

RSTensorFlow: GPU Enabled TensorFlow for Deep Learning on Commodity Android Devices.

PubMed

Alzantot, Moustafa; Wang, Yingnan; Ren, Zhengshuang; Srivastava, Mani B

2017-06-01

Mobile devices have become an essential part of our daily lives. By virtue of both their increasing computing power and the recent progress made in AI, mobile devices evolved to act as intelligent assistants in many tasks rather than a mere way of making phone calls. However, popular and commonly used tools and frameworks for machine intelligence are still lacking the ability to make proper use of the available heterogeneous computing resources on mobile devices. In this paper, we study the benefits of utilizing the heterogeneous (CPU and GPU) computing resources available on commodity android devices while running deep learning models. We leveraged the heterogeneous computing framework RenderScript to accelerate the execution of deep learning models on commodity Android devices. Our system is implemented as an extension to the popular open-source framework TensorFlow. By integrating our acceleration framework tightly into TensorFlow, machine learning engineers can now easily make benefit of the heterogeneous computing resources on mobile devices without the need of any extra tools. We evaluate our system on different android phones models to study the trade-offs of running different neural network operations on the GPU. We also compare the performance of running different models architectures such as convolutional and recurrent neural networks on CPU only vs using heterogeneous computing resources. Our result shows that although GPUs on the phones are capable of offering substantial performance gain in matrix multiplication on mobile devices. Therefore, models that involve multiplication of large matrices can run much faster (approx. 3 times faster in our experiments) due to GPU support.

Who Benefits from a Low versus High Guidance CSCL Script and Why?

ERIC Educational Resources Information Center

Mende, Stephan; Proske, Antje; Körndle, Hermann; Narciss, Susanne

2017-01-01

Computer-supported collaborative learning (CSCL) scripts can foster learners' deep text comprehension. However, this depends on (a) the extent to which the learning activities targeted by a script promote deep text comprehension and (b) whether the guidance level provided by the script is adequate to induce the targeted learning activities…

Machine Learning in Ultrasound Computer-Aided Diagnostic Systems: A Survey

PubMed Central

Zhang, Fan; Li, Xuelong

2018-01-01

The ultrasound imaging is one of the most common schemes to detect diseases in the clinical practice. There are many advantages of ultrasound imaging such as safety, convenience, and low cost. However, reading ultrasound imaging is not easy. To support the diagnosis of clinicians and reduce the load of doctors, many ultrasound computer-aided diagnosis (CAD) systems are proposed. In recent years, the success of deep learning in the image classification and segmentation led to more and more scholars realizing the potential of performance improvement brought by utilizing the deep learning in the ultrasound CAD system. This paper summarized the research which focuses on the ultrasound CAD system utilizing machine learning technology in recent years. This study divided the ultrasound CAD system into two categories. One is the traditional ultrasound CAD system which employed the manmade feature and the other is the deep learning ultrasound CAD system. The major feature and the classifier employed by the traditional ultrasound CAD system are introduced. As for the deep learning ultrasound CAD, newest applications are summarized. This paper will be useful for researchers who focus on the ultrasound CAD system. PMID:29687000

Machine Learning in Ultrasound Computer-Aided Diagnostic Systems: A Survey.

PubMed

Huang, Qinghua; Zhang, Fan; Li, Xuelong

2018-01-01

The ultrasound imaging is one of the most common schemes to detect diseases in the clinical practice. There are many advantages of ultrasound imaging such as safety, convenience, and low cost. However, reading ultrasound imaging is not easy. To support the diagnosis of clinicians and reduce the load of doctors, many ultrasound computer-aided diagnosis (CAD) systems are proposed. In recent years, the success of deep learning in the image classification and segmentation led to more and more scholars realizing the potential of performance improvement brought by utilizing the deep learning in the ultrasound CAD system. This paper summarized the research which focuses on the ultrasound CAD system utilizing machine learning technology in recent years. This study divided the ultrasound CAD system into two categories. One is the traditional ultrasound CAD system which employed the manmade feature and the other is the deep learning ultrasound CAD system. The major feature and the classifier employed by the traditional ultrasound CAD system are introduced. As for the deep learning ultrasound CAD, newest applications are summarized. This paper will be useful for researchers who focus on the ultrasound CAD system.

Quantum-assisted Helmholtz machines: A quantum–classical deep learning framework for industrial datasets in near-term devices

NASA Astrophysics Data System (ADS)

Benedetti, Marcello; Realpe-Gómez, John; Perdomo-Ortiz, Alejandro

2018-07-01

Machine learning has been presented as one of the key applications for near-term quantum technologies, given its high commercial value and wide range of applicability. In this work, we introduce the quantum-assisted Helmholtz machine:a hybrid quantum–classical framework with the potential of tackling high-dimensional real-world machine learning datasets on continuous variables. Instead of using quantum computers only to assist deep learning, as previous approaches have suggested, we use deep learning to extract a low-dimensional binary representation of data, suitable for processing on relatively small quantum computers. Then, the quantum hardware and deep learning architecture work together to train an unsupervised generative model. We demonstrate this concept using 1644 quantum bits of a D-Wave 2000Q quantum device to model a sub-sampled version of the MNIST handwritten digit dataset with 16 × 16 continuous valued pixels. Although we illustrate this concept on a quantum annealer, adaptations to other quantum platforms, such as ion-trap technologies or superconducting gate-model architectures, could be explored within this flexible framework.

DIGGING DEEPER INTO DEEP DATA: MOLECULAR DOCKING AS A HYPOTHESIS-DRIVEN BIOPHYSICAL INTERROGATION SYSTEM IN COMPUTATIONAL TOXICOLOGY.

EPA Science Inventory

Developing and evaluating prediactive strategies to elucidate the mode of biological activity of environmental chemicals is a major objective of the concerted efforts of the US-EPA's computational toxicology program.

Characterisation of divergent flavivirus NS3 and NS5 protein sequences detected in Rhipicephalus microplus ticks from Brazil

PubMed Central

Maruyama, Sandra Regina; Castro-Jorge, Luiza Antunes; Ribeiro, José Marcos Chaves; Gardinassi, Luiz Gustavo; Garcia, Gustavo Rocha; Brandão, Lucinda Giampietro; Rodrigues, Aline Rezende; Okada, Marcos Ituo; Abrão, Emiliana Pereira; Ferreira, Beatriz Rossetti; da Fonseca, Benedito Antonio Lopes; de Miranda-Santos, Isabel Kinney Ferreira

2013-01-01

Transcripts similar to those that encode the nonstructural (NS) proteins NS3 and NS5 from flaviviruses were found in a salivary gland (SG) complementary DNA (cDNA) library from the cattle tick Rhipicephalus microplus. Tick extracts were cultured with cells to enable the isolation of viruses capable of replicating in cultured invertebrate and vertebrate cells. Deep sequencing of the viral RNA isolated from culture supernatants provided the complete coding sequences for the NS3 and NS5 proteins and their molecular characterisation confirmed similarity with the NS3 and NS5 sequences from other flaviviruses. Despite this similarity, phylogenetic analyses revealed that this potentially novel virus may be a highly divergent member of the genus Flavivirus. Interestingly, we detected the divergent NS3 and NS5 sequences in ticks collected from several dairy farms widely distributed throughout three regions of Brazil. This is the first report of flavivirus-like transcripts in R. microplus ticks. This novel virus is a potential arbovirus because it replicated in arthropod and mammalian cells; furthermore, it was detected in a cDNA library from tick SGs and therefore may be present in tick saliva. It is important to determine whether and by what means this potential virus is transmissible and to monitor the virus as a potential emerging tick-borne zoonotic pathogen. PMID:24626302

Cyber War: The Next Frontier for NATO

DTIC Science & Technology

2015-03-01

cyber-attacks as a way to advance their agenda. Common examples of cyber- attacks include computer viruses, worms , malware, and distributed denial of...take advantage of security holes and cause damage to computer systems, steal financial data, or acquire sensitive secrets. As technology becomes

Safeguarding Databases Basic Concepts Revisited.

ERIC Educational Resources Information Center

Cardinali, Richard

1995-01-01

Discusses issues of database security and integrity, including computer crime and vandalism, human error, computer viruses, employee and user access, and personnel policies. Suggests some precautions to minimize system vulnerability such as careful personnel screening, audit systems, passwords, and building and software security systems. (JKP)

Resistance-Associated NS5A Variants of Hepatitis C Virus Are Susceptible to Interferon-Based Therapy.

PubMed

Itakura, Jun; Kurosaki, Masayuki; Higuchi, Mayu; Takada, Hitomi; Nakakuki, Natsuko; Itakura, Yoshie; Tamaki, Nobuharu; Yasui, Yutaka; Suzuki, Shoko; Tsuchiya, Kaoru; Nakanishi, Hiroyuki; Takahashi, Yuka; Maekawa, Shinya; Enomoto, Nobuyuki; Izumi, Namiki

2015-01-01

The presence of resistance-associated variants (RAVs) of hepatitis C virus (HCV) attenuates the efficacy of direct acting antivirals (DAAs). The objective of this study was to characterize the susceptibility of RAVs to interferon-based therapy. Direct and deep sequencing were performed to detect Y93H RAV in the NS5A region. Twenty nine genotype 1b patients with detectable RAV at baseline were treated by a combination of simeprevir, pegylated interferon and ribavirin. The longitudinal changes in the proportion of Y93H RAV during therapy and at breakthrough or relapse were determined. By direct sequencing, Y93H RAV became undetectable or decreased in proportion at an early time point during therapy (within 7 days) in 57% of patients with both the Y93H variant and wild type virus at baseline when HCV RNA was still detectable. By deep sequencing, the proportion of Y93H RAV against Y93 wild type was 52.7% (5.8%- 97.4%) at baseline which significantly decreased to 29.7% (0.16%- 98.3%) within 7 days of initiation of treatment (p = 0.023). The proportion of Y93H RAV was reduced in 21 of 29 cases (72.4%) and a marked reduction of more than 10% was observed in 14 cases (48.7%). HCV RNA reduction was significantly greater for Y93H RAV (-3.65±1.3 logIU/mL/day) than the Y93 wild type (-3.35±1.0 logIU/mL/day) (p<0.001). Y93H RAV is more susceptible to interferon-based therapy than the Y93 wild type.

Evaluation of Deep Learning Based Stereo Matching Methods: from Ground to Aerial Images

NASA Astrophysics Data System (ADS)

Liu, J.; Ji, S.; Zhang, C.; Qin, Z.

2018-05-01

Dense stereo matching has been extensively studied in photogrammetry and computer vision. In this paper we evaluate the application of deep learning based stereo methods, which were raised from 2016 and rapidly spread, on aerial stereos other than ground images that are commonly used in computer vision community. Two popular methods are evaluated. One learns matching cost with a convolutional neural network (known as MC-CNN); the other produces a disparity map in an end-to-end manner by utilizing both geometry and context (known as GC-net). First, we evaluate the performance of the deep learning based methods for aerial stereo images by a direct model reuse. The models pre-trained on KITTI 2012, KITTI 2015 and Driving datasets separately, are directly applied to three aerial datasets. We also give the results of direct training on target aerial datasets. Second, the deep learning based methods are compared to the classic stereo matching method, Semi-Global Matching(SGM), and a photogrammetric software, SURE, on the same aerial datasets. Third, transfer learning strategy is introduced to aerial image matching based on the assumption of a few target samples available for model fine tuning. It experimentally proved that the conventional methods and the deep learning based methods performed similarly, and the latter had greater potential to be explored.

LiteNet: Lightweight Neural Network for Detecting Arrhythmias at Resource-Constrained Mobile Devices.

PubMed

He, Ziyang; Zhang, Xiaoqing; Cao, Yangjie; Liu, Zhi; Zhang, Bo; Wang, Xiaoyan

2018-04-17

By running applications and services closer to the user, edge processing provides many advantages, such as short response time and reduced network traffic. Deep-learning based algorithms provide significantly better performances than traditional algorithms in many fields but demand more resources, such as higher computational power and more memory. Hence, designing deep learning algorithms that are more suitable for resource-constrained mobile devices is vital. In this paper, we build a lightweight neural network, termed LiteNet which uses a deep learning algorithm design to diagnose arrhythmias, as an example to show how we design deep learning schemes for resource-constrained mobile devices. Compare to other deep learning models with an equivalent accuracy, LiteNet has several advantages. It requires less memory, incurs lower computational cost, and is more feasible for deployment on resource-constrained mobile devices. It can be trained faster than other neural network algorithms and requires less communication across different processing units during distributed training. It uses filters of heterogeneous size in a convolutional layer, which contributes to the generation of various feature maps. The algorithm was tested using the MIT-BIH electrocardiogram (ECG) arrhythmia database; the results showed that LiteNet outperforms comparable schemes in diagnosing arrhythmias, and in its feasibility for use at the mobile devices.

LiteNet: Lightweight Neural Network for Detecting Arrhythmias at Resource-Constrained Mobile Devices

PubMed Central

Zhang, Xiaoqing; Cao, Yangjie; Liu, Zhi; Zhang, Bo; Wang, Xiaoyan

2018-01-01

By running applications and services closer to the user, edge processing provides many advantages, such as short response time and reduced network traffic. Deep-learning based algorithms provide significantly better performances than traditional algorithms in many fields but demand more resources, such as higher computational power and more memory. Hence, designing deep learning algorithms that are more suitable for resource-constrained mobile devices is vital. In this paper, we build a lightweight neural network, termed LiteNet which uses a deep learning algorithm design to diagnose arrhythmias, as an example to show how we design deep learning schemes for resource-constrained mobile devices. Compare to other deep learning models with an equivalent accuracy, LiteNet has several advantages. It requires less memory, incurs lower computational cost, and is more feasible for deployment on resource-constrained mobile devices. It can be trained faster than other neural network algorithms and requires less communication across different processing units during distributed training. It uses filters of heterogeneous size in a convolutional layer, which contributes to the generation of various feature maps. The algorithm was tested using the MIT-BIH electrocardiogram (ECG) arrhythmia database; the results showed that LiteNet outperforms comparable schemes in diagnosing arrhythmias, and in its feasibility for use at the mobile devices. PMID:29673171

Computational foundations of the visual number sense.

PubMed

Stoianov, Ivilin Peev; Zorzi, Marco

2017-01-01

We provide an emergentist perspective on the computational mechanism underlying numerosity perception, its development, and the role of inhibition, based on our deep neural network model. We argue that the influence of continuous visual properties does not challenge the notion of number sense, but reveals limit conditions for the computation that yields invariance in numerosity perception. Alternative accounts should be formalized in a computational model.

A fast technique for computing syndromes of BCH and RS codes. [deep space network

NASA Technical Reports Server (NTRS)

Reed, I. S.; Truong, T. K.; Miller, R. L.

1979-01-01

A combination of the Chinese Remainder Theorem and Winograd's algorithm is used to compute transforms of odd length over GF(2 to the m power). Such transforms are used to compute the syndromes needed for decoding CBH and RS codes. The present scheme requires substantially fewer multiplications and additions than the conventional method of computing the syndromes directly.

Slime mould biotechnology

NASA Astrophysics Data System (ADS)

Mayne, Richard

2015-03-01

Slime mould computing is an inherently multi-disciplinary subfield of unconventional computing that draws upon aspects of not only theoretical computer science and electronics, but also the natural sciences. This chapter focuses on the biology of slime moulds and expounds the viewpoint that a deep, intuitive understanding of slime mould life processes is a fundamental requirement for understanding -- and, hence, harnessing -- the incredible behaviour patterns we may characterise as "computation"...

Computer simulated building energy consumption for verification of energy conservation measures in network facilities

NASA Technical Reports Server (NTRS)

Plankey, B.

1981-01-01

A computer program called ECPVER (Energy Consumption Program - Verification) was developed to simulate all energy loads for any number of buildings. The program computes simulated daily, monthly, and yearly energy consumption which can be compared with actual meter readings for the same time period. Such comparison can lead to validation of the model under a variety of conditions, which allows it to be used to predict future energy saving due to energy conservation measures. Predicted energy saving can then be compared with actual saving to verify the effectiveness of those energy conservation changes. This verification procedure is planned to be an important advancement in the Deep Space Network Energy Project, which seeks to reduce energy cost and consumption at all DSN Deep Space Stations.

Pathogenic seedborne viruses are rare but Phaseolus vulgaris endornaviruses are common in bean varieties grown in Nicaragua and Tanzania.

PubMed

Nordenstedt, Noora; Marcenaro, Delfia; Chilagane, Daudi; Mwaipopo, Beatrice; Rajamäki, Minna-Liisa; Nchimbi-Msolla, Susan; Njau, Paul J R; Mbanzibwa, Deusdedith R; Valkonen, Jari P T

2017-01-01

Common bean (Phaseolus vulgaris) is an annual grain legume that was domesticated in Mesoamerica (Central America) and the Andes. It is currently grown widely also on other continents including Africa. We surveyed seedborne viruses in new common bean varieties introduced to Nicaragua (Central America) and in landraces and improved varieties grown in Tanzania (eastern Africa). Bean seeds, harvested from Nicaragua and Tanzania, were grown in insect-controlled greenhouse or screenhouse, respectively, to obtain leaf material for virus testing. Equal amounts of total RNA from different samples were pooled (30-36 samples per pool), and small RNAs were deep-sequenced (Illumina). Assembly of the reads (21-24 nt) to contiguous sequences and searches for homologous viral sequences in databases revealed Phaseolus vulgaris endornavirus 1 (PvEV-1) and PvEV-2 in the bean varieties in Nicaragua and Tanzania. These viruses are not known to cause symptoms in common bean and are considered non-pathogenic. The small-RNA reads from each pool of samples were mapped to the previously characterized complete PvEV-1 and PvEV-2 sequences (genome lengths ca. 14 kb and 15 kb, respectively). Coverage of the viral genomes was 87.9-99.9%, depending on the pool. Coverage per nucleotide ranged from 5 to 471, confirming virus identification. PvEV-1 and PvEV-2 are known to occur in Phaseolus spp. in Central America, but there is little previous information about their occurrence in Nicaragua, and no information about occurrence in Africa. Aside from Cowpea mild mosaic virus detected in bean plants grown from been seeds harvested from one region in Tanzania, no other pathogenic seedborne viruses were detected. The low incidence of infections caused by pathogenic viruses transmitted via bean seeds may be attributable to new, virus-resistant CB varieties released by breeding programs in Nicaragua and Tanzania.

Impact of the HIV-1 genetic background and HIV-1 population size on the evolution of raltegravir resistance.

PubMed

Fun, Axel; Leitner, Thomas; Vandekerckhove, Linos; Däumer, Martin; Thielen, Alexander; Buchholz, Bernd; Hoepelman, Andy I M; Gisolf, Elizabeth H; Schipper, Pauline J; Wensing, Annemarie M J; Nijhuis, Monique

2018-01-05

Emergence of resistance against integrase inhibitor raltegravir in human immunodeficiency virus type 1 (HIV-1) patients is generally associated with selection of one of three signature mutations: Y143C/R, Q148K/H/R or N155H, representing three distinct resistance pathways. The mechanisms that drive selection of a specific pathway are still poorly understood. We investigated the impact of the HIV-1 genetic background and population dynamics on the emergence of raltegravir resistance. Using deep sequencing we analyzed the integrase coding sequence (CDS) in longitudinal samples from five patients who initiated raltegravir plus optimized background therapy at viral loads > 5000 copies/ml. To investigate the role of the HIV-1 genetic background we created recombinant viruses containing the viral integrase coding region from pre-raltegravir samples from two patients in whom raltegravir resistance developed through different pathways. The in vitro selections performed with these recombinant viruses were designed to mimic natural population bottlenecks. Deep sequencing analysis of the viral integrase CDS revealed that the virological response to raltegravir containing therapy inversely correlated with the relative amount of unique sequence variants that emerged suggesting diversifying selection during drug pressure. In 4/5 patients multiple signature mutations representing different resistance pathways were observed. Interestingly, the resistant population can consist of a single resistant variant that completely dominates the population but also of multiple variants from different resistance pathways that coexist in the viral population. We also found evidence for increased diversification after stronger bottlenecks. In vitro selections with low viral titers, mimicking population bottlenecks, revealed that both recombinant viruses and HXB2 reference virus were able to select mutations from different resistance pathways, although typically only one resistance pathway emerged in each individual culture. The generation of a specific raltegravir resistant variant is not predisposed in the genetic background of the viral integrase CDS. Typically, in the early phases of therapy failure the sequence space is explored and multiple resistance pathways emerge and then compete for dominance which frequently results in a switch of the dominant population over time towards the fittest variant or even multiple variants of similar fitness that can coexist in the viral population.

Unsteady viscous calculations of supersonic flows past deep and shallow three-dimensional cavities

NASA Technical Reports Server (NTRS)

Baysal, O.; Srinivasan, S.; Stallings, R. L.

1988-01-01

Computational simulations were performed for supersonic, turbulent flows over deep and shallow three-dimensional cavities. The width and the depth of these cavities were fixed at 2.5 in. and 0.5 in., respectively. Length-to-depth ratio of the deep cavity was 6 and that of the shallow cavity was 16. Freestream values of Mach number and Reynolds number were 1.50 and 2.0 x 10 to the 6th/ft., respectively, at a total temperature of 585 R. The thickness of the turbulent boundary layer at the front lip of the cavity was 0.2 in. Simulations of these oscillatory flows were generated through time-accurate solutions of Reynolds-averaged full Navier-Stokes equations using the explicit MacCormack scheme. The solutions are validated through comparisons with experimental data. The features of open and closed cavity flows and effects of the third dimension are illustrated through computational graphics.

Deep learning for cardiac computer-aided diagnosis: benefits, issues & solutions.

PubMed

Loh, Brian C S; Then, Patrick H H

2017-01-01

Cardiovascular diseases are one of the top causes of deaths worldwide. In developing nations and rural areas, difficulties with diagnosis and treatment are made worse due to the deficiency of healthcare facilities. A viable solution to this issue is telemedicine, which involves delivering health care and sharing medical knowledge at a distance. Additionally, mHealth, the utilization of mobile devices for medical care, has also proven to be a feasible choice. The integration of telemedicine, mHealth and computer-aided diagnosis systems with the fields of machine and deep learning has enabled the creation of effective services that are adaptable to a multitude of scenarios. The objective of this review is to provide an overview of heart disease diagnosis and management, especially within the context of rural healthcare, as well as discuss the benefits, issues and solutions of implementing deep learning algorithms to improve the efficacy of relevant medical applications.

Computer analysis of the negative differential resistance switching phenomenon of double-injection devices

NASA Technical Reports Server (NTRS)

Shieh, Tsay-Jiu

1989-01-01

By directly solving the semiconductor differential equations for the double-injection (DI) devices involving two interacting deep levels, the authors studied the negative differential resistance switching characteristic and its relationship with the device dimension, doping level, and dependence on the deep impurity profile. Computer simulation showed that although one can increase the threshold voltage by increasing the device length, the excessive holding voltage that would follow would put this device in a very limited application such as pulse power source. The excessive leakage current in the low conductance state also jeopardizes the attempt to use the device for any practical purpose. Unless there are new materials and deep impurities found that have a great differential hole and electron capture cross sections and a reasonable energy bandgap for low intrinsic carrier concentration, no big improvement in the fate of DI devices is expected in the near future.

Quicklook Constituent Abundance and Stretch Parameter Retrieval for the Juno Microwave Radiometer using Neural Networks

NASA Astrophysics Data System (ADS)

Bellotti, A.; Steffes, P. G.

2016-12-01

The Juno Microwave Radiometer (MWR) has six channels ranging from 1.36-50 cm and the ability to peer deep into the Jovian atmosphere. An Artifical Neural Network algorithm has been developed to rapidly perform inversion for the deep abundance of ammonia, the deep abundance of water vapor, and atmospheric "stretch" (a parameter that reflects the deviation from a wet adiabate in the higher atmosphere). This algorithm is "trained" by using simulated emissions at the six wavelengths computed using the Juno atmospheric microwave radiative transfer (JAMRT) model presented by Oyafuso et al. (This meeting). By exploiting the emission measurements conducted at six wavelengths and at various incident angles, the neural network can provide preliminary results to a useful precison in a computational method hundreds of times faster than conventional methods. This can quickly provide important insights into the variability and structure of the Jovian atmosphere.

Deep learning for cardiac computer-aided diagnosis: benefits, issues & solutions

PubMed Central

Then, Patrick H. H.

2017-01-01

Cardiovascular diseases are one of the top causes of deaths worldwide. In developing nations and rural areas, difficulties with diagnosis and treatment are made worse due to the deficiency of healthcare facilities. A viable solution to this issue is telemedicine, which involves delivering health care and sharing medical knowledge at a distance. Additionally, mHealth, the utilization of mobile devices for medical care, has also proven to be a feasible choice. The integration of telemedicine, mHealth and computer-aided diagnosis systems with the fields of machine and deep learning has enabled the creation of effective services that are adaptable to a multitude of scenarios. The objective of this review is to provide an overview of heart disease diagnosis and management, especially within the context of rural healthcare, as well as discuss the benefits, issues and solutions of implementing deep learning algorithms to improve the efficacy of relevant medical applications. PMID:29184897

Deep learning for computational biology.

PubMed

Angermueller, Christof; Pärnamaa, Tanel; Parts, Leopold; Stegle, Oliver

2016-07-29

Technological advances in genomics and imaging have led to an explosion of molecular and cellular profiling data from large numbers of samples. This rapid increase in biological data dimension and acquisition rate is challenging conventional analysis strategies. Modern machine learning methods, such as deep learning, promise to leverage very large data sets for finding hidden structure within them, and for making accurate predictions. In this review, we discuss applications of this new breed of analysis approaches in regulatory genomics and cellular imaging. We provide background of what deep learning is, and the settings in which it can be successfully applied to derive biological insights. In addition to presenting specific applications and providing tips for practical use, we also highlight possible pitfalls and limitations to guide computational biologists when and how to make the most use of this new technology. © 2016 The Authors. Published under the terms of the CC BY 4.0 license.

Computed Tomography-Assisted Thoracoscopic Surgery: A Novel, Innovative Approach in Patients With Deep Intrapulmonary Lesions of Unknown Malignant Status.

PubMed

Kostrzewa, Michael; Kara, Kerim; Rathmann, Nils; Tsagogiorgas, Charalambos; Henzler, Thomas; Schoenberg, Stefan O; Hohenberger, Peter; Diehl, Steffen J; Roessner, Eric D

2017-06-01

Minimally invasive resection of small, deep intrapulmonary lesions can be challenging due to the difficulty of localizing them during video-assisted thoracoscopic surgery (VATS). We report our preliminary results evaluating the feasibility of an image-guided, minimally invasive, 1-stop-shop approach for the resection of small, deep intrapulmonary lesions in a hybrid operating room (OR). Fifteen patients (5 men, 10 women; mean age, 63 years) with a total of 16 solitary, deep intrapulmonary nodules of unknown malignant status were identified for intraoperative wire marking. Patients were placed on the operating table for resection by VATS. A marking wire was placed within the lesion under 3D laser and fluoroscopic guidance using a cone beam computed tomography system. Then, wedge resection by VATS was performed in the same setting without repositioning the patient. Complete resection with adequate safety margins was confirmed for all lesions. Marking wire placement facilitated resection in 15 of 16 lesions. Eleven lesions proved to be malignant, either primary or secondary; 5 were benign. Mean lesion size was 7.7 mm; mean distance to the pleural surface was 15.1 mm (mean lesion depth-diameter ratio, 2.2). Mean procedural time for marking wire placement was 35 minutes; mean VATS duration was 36 minutes. Computed tomography-assisted thoracoscopic surgery is a new, safe, and effective procedure for minimally invasive resection of small, deeply localized intrapulmonary lesions. The benefits of computed tomography-assisted thoracoscopic surgery are 1. One-stop-shop procedure, 2. Lower risk for the patient (no patient relocation, no marking wire loss), and 3. No need to coordinate scheduling between the CT room and OR.

A New Approach to Develop Computer-aided Diagnosis Scheme of Breast Mass Classification Using Deep Learning Technology

PubMed Central

Qiu, Yuchen; Yan, Shiju; Gundreddy, Rohith Reddy; Wang, Yunzhi; Cheng, Samuel; Liu, Hong; Zheng, Bin

2017-01-01

PURPOSE To develop and test a deep learning based computer-aided diagnosis (CAD) scheme of mammograms for classifying between malignant and benign masses. METHODS An image dataset involving 560 regions of interest (ROIs) extracted from digital mammograms was used. After down-sampling each ROI from 512×512 to 64×64 pixel size, we applied an 8 layer deep learning network that involves 3 pairs of convolution-max-pooling layers for automatic feature extraction and a multiple layer perceptron (MLP) classifier for feature categorization to process ROIs. The 3 pairs of convolution layers contain 20, 10, and 5 feature maps, respectively. Each convolution layer is connected with a max-pooling layer to improve the feature robustness. The output of the sixth layer is fully connected with a MLP classifier, which is composed of one hidden layer and one logistic regression layer. The network then generates a classification score to predict the likelihood of ROI depicting a malignant mass. A four-fold cross validation method was applied to train and test this deep learning network. RESULTS The results revealed that this CAD scheme yields an area under the receiver operation characteristic curve (AUC) of 0.696±0.044, 0.802±0.037, 0.836±0.036, and 0.822±0.035 for fold 1 to 4 testing datasets, respectively. The overall AUC of the entire dataset is 0.790±0.019. CONCLUSIONS This study demonstrates the feasibility of applying a deep learning based CAD scheme to classify between malignant and benign breast masses without a lesion segmentation, image feature computation and selection process. PMID:28436410

A new approach to develop computer-aided diagnosis scheme of breast mass classification using deep learning technology.

PubMed

Qiu, Yuchen; Yan, Shiju; Gundreddy, Rohith Reddy; Wang, Yunzhi; Cheng, Samuel; Liu, Hong; Zheng, Bin

2017-01-01

To develop and test a deep learning based computer-aided diagnosis (CAD) scheme of mammograms for classifying between malignant and benign masses. An image dataset involving 560 regions of interest (ROIs) extracted from digital mammograms was used. After down-sampling each ROI from 512×512 to 64×64 pixel size, we applied an 8 layer deep learning network that involves 3 pairs of convolution-max-pooling layers for automatic feature extraction and a multiple layer perceptron (MLP) classifier for feature categorization to process ROIs. The 3 pairs of convolution layers contain 20, 10, and 5 feature maps, respectively. Each convolution layer is connected with a max-pooling layer to improve the feature robustness. The output of the sixth layer is fully connected with a MLP classifier, which is composed of one hidden layer and one logistic regression layer. The network then generates a classification score to predict the likelihood of ROI depicting a malignant mass. A four-fold cross validation method was applied to train and test this deep learning network. The results revealed that this CAD scheme yields an area under the receiver operation characteristic curve (AUC) of 0.696±0.044, 0.802±0.037, 0.836±0.036, and 0.822±0.035 for fold 1 to 4 testing datasets, respectively. The overall AUC of the entire dataset is 0.790±0.019. This study demonstrates the feasibility of applying a deep learning based CAD scheme to classify between malignant and benign breast masses without a lesion segmentation, image feature computation and selection process.

Prediction of pharmacologically induced baroreflex sensitivity from local time and frequency domain indices of R-R interval and systolic blood pressure signals obtained during deep breathing.

PubMed

Arica, Sami; Firat Ince, N; Bozkurt, Abdi; Tewfik, Ahmed H; Birand, Ahmet

2011-07-01

Pharmacological measurement of baroreflex sensitivity (BRS) is widely accepted and used in clinical practice. Following the introduction of pharmacologically induced BRS (p-BRS), alternative assessment methods eliminating the use of drugs were in the center of interest of the cardiovascular research community. In this study we investigated whether p-BRS using phenylephrine injection can be predicted from non-pharmacological time and frequency domain indices computed from electrocardiogram (ECG) and blood pressure (BP) data acquired during deep breathing. In this scheme, ECG and BP data were recorded from 16 subjects in a two-phase experiment. In the first phase the subjects performed irregular deep breaths and in the second phase the subjects received phenylephrine injection. From the first phase of the experiment, a large pool of predictors describing the local characteristic of beat-to-beat interval tachogram (RR) and systolic blood pressure (SBP) were extracted in time and frequency domains. A subset of these indices was selected using twelve subjects with an exhaustive search fused with a leave one subject out cross validation procedure. The selected indices were used to predict the p-BRS on the remaining four test subjects. A multivariate regression was used in all prediction steps. The algorithm achieved best prediction accuracy with only two features extracted from the deep breathing data, one from the frequency and the other from the time domain. The normalized L2-norm error was computed as 22.9% and the correlation coefficient was 0.97 (p=0.03). These results suggest that the p-BRS can be estimated from non-pharmacological indices computed from ECG and invasive BP data related to deep breathing. Copyright © 2011 Elsevier Ltd. All rights reserved.

Do deep convolutional neural networks really need to be deep when applied for remote scene classification?

NASA Astrophysics Data System (ADS)

Luo, Chang; Wang, Jie; Feng, Gang; Xu, Suhui; Wang, Shiqiang

2017-10-01

Deep convolutional neural networks (CNNs) have been widely used to obtain high-level representation in various computer vision tasks. However, for remote scene classification, there are not sufficient images to train a very deep CNN from scratch. From two viewpoints of generalization power, we propose two promising kinds of deep CNNs for remote scenes and try to find whether deep CNNs need to be deep for remote scene classification. First, we transfer successful pretrained deep CNNs to remote scenes based on the theory that depth of CNNs brings the generalization power by learning available hypothesis for finite data samples. Second, according to the opposite viewpoint that generalization power of deep CNNs comes from massive memorization and shallow CNNs with enough neural nodes have perfect finite sample expressivity, we design a lightweight deep CNN (LDCNN) for remote scene classification. With five well-known pretrained deep CNNs, experimental results on two independent remote-sensing datasets demonstrate that transferred deep CNNs can achieve state-of-the-art results in an unsupervised setting. However, because of its shallow architecture, LDCNN cannot obtain satisfactory performance, regardless of whether in an unsupervised, semisupervised, or supervised setting. CNNs really need depth to obtain general features for remote scenes. This paper also provides baseline for applying deep CNNs to other remote sensing tasks.

Characterization of New Isolates of Apricot vein clearing-associated virus and of a New Prunus-Infecting Virus: Evidence for Recombination as a Driving Force in Betaflexiviridae Evolution.

PubMed

Marais, Armelle; Faure, Chantal; Mustafayev, Eldar; Candresse, Thierry

2015-01-01

Double stranded RNAs from Prunus samples gathered from various surveys were analyzed by a deep-sequencing approach. Contig annotations revealed the presence of a potential new viral species in an Azerbaijani almond tree (Prunus amygdalus) and its genome sequence was completed. Its genomic organization is similar to that of the recently described Apricot vein clearing associated virus (AVCaV) for which two new isolates were also characterized, in a similar fashion, from two Japanese plums (Prunus salicina) from a French germplasm collection. The amino acid identity values between the four proteins encoded by the genome of the new virus have identity levels with those of AVCaV which fall clearly outside the species demarcation criteria. The new virus should therefore be considered as a new species for which the name of Caucasus prunus virus (CPrV) has been proposed. Phylogenetic relationships and nucleotide comparisons suggested that together with AVCaV, CPrV could define a new genus (proposed name: Prunevirus) in the family Betaflexiviridae. A molecular test targeting both members of the new genus was developed, allowing the detection of additional AVCaV isolates, and therefore extending the known geographical distribution and the host range of AVCaV. Moreover, the phylogenetic trees reconstructed with the amino acid sequences of replicase, movement and coat proteins of representative Betaflexiviridae members suggest that Citrus leaf blotch virus (CLBV, type member of the genus Citrivirus) may have evolved from a recombination event involving a Prunevirus, further highlighting the importance of recombination as a driving force in Betaflexiviridae evolution. The sequences reported in the present manuscript have been deposited in the GenBank database under accession numbers KM507061-KM504070.

Characterization of New Isolates of Apricot vein clearing-associated virus and of a New Prunus-Infecting Virus: Evidence for Recombination as a Driving Force in Betaflexiviridae Evolution

PubMed Central

Marais, Armelle; Faure, Chantal; Mustafayev, Eldar; Candresse, Thierry

2015-01-01

Double stranded RNAs from Prunus samples gathered from various surveys were analyzed by a deep-sequencing approach. Contig annotations revealed the presence of a potential new viral species in an Azerbaijani almond tree (Prunus amygdalus) and its genome sequence was completed. Its genomic organization is similar to that of the recently described Apricot vein clearing associated virus (AVCaV) for which two new isolates were also characterized, in a similar fashion, from two Japanese plums (Prunus salicina) from a French germplasm collection. The amino acid identity values between the four proteins encoded by the genome of the new virus have identity levels with those of AVCaV which fall clearly outside the species demarcation criteria. The new virus should therefore be considered as a new species for which the name of Caucasus prunus virus (CPrV) has been proposed. Phylogenetic relationships and nucleotide comparisons suggested that together with AVCaV, CPrV could define a new genus (proposed name: Prunevirus) in the family Betaflexiviridae. A molecular test targeting both members of the new genus was developed, allowing the detection of additional AVCaV isolates, and therefore extending the known geographical distribution and the host range of AVCaV. Moreover, the phylogenetic trees reconstructed with the amino acid sequences of replicase, movement and coat proteins of representative Betaflexiviridae members suggest that Citrus leaf blotch virus (CLBV, type member of the genus Citrivirus) may have evolved from a recombination event involving a Prunevirus, further highlighting the importance of recombination as a driving force in Betaflexiviridae evolution. The sequences reported in the present manuscript have been deposited in the GenBank database under accession numbers KM507061-KM504070. PMID:26086395

Profile of small interfering RNAs from cotton plants infected with the polerovirus Cotton leafroll dwarf virus.

PubMed

Silva, Tatiane F; Romanel, Elisson A C; Andrade, Roberto R S; Farinelli, Laurent; Østerås, Magne; Deluen, Cécile; Corrêa, Régis L; Schrago, Carlos E G; Vaslin, Maite F S

2011-08-24

In response to infection, viral genomes are processed by Dicer-like (DCL) ribonuclease proteins into viral small RNAs (vsRNAs) of discrete sizes. vsRNAs are then used as guides for silencing the viral genome. The profile of vsRNAs produced during the infection process has been extensively studied for some groups of viruses. However, nothing is known about the vsRNAs produced during infections of members of the economically important family Luteoviridae, a group of phloem-restricted viruses. Here, we report the characterization of a population of vsRNAs from cotton plants infected with Cotton leafroll dwarf virus (CLRDV), a member of the genus Polerovirus, family Luteoviridae. Deep sequencing of small RNAs (sRNAs) from leaves of CLRDV-infected cotton plants revealed that the vsRNAs were 21- to 24-nucleotides (nt) long and that their sequences matched the viral genome, with higher frequencies of matches in the 3- region. There were equivalent amounts of sense and antisense vsRNAs, and the 22-nt class of small RNAs was predominant. During infection, cotton Dcl transcripts appeared to be up-regulated, while Dcl2 appeared to be down-regulated. This is the first report on the profile of sRNAs in a plant infected with a virus from the family Luteoviridae. Our sequence data strongly suggest that virus-derived double-stranded RNA functions as one of the main precursors of vsRNAs. Judging by the profiled size classes, all cotton DCLs might be working to silence the virus. The possible causes for the unexpectedly high accumulation of 22-nt vsRNAs are discussed. CLRDV is the causal agent of Cotton blue disease, which occurs worldwide. Our results are an important contribution for understanding the molecular mechanisms involved in this and related diseases.

Comparison of Dengue Virus Type 2-Specific Small RNAs from RNA Interference-Competent and –Incompetent Mosquito Cells

PubMed Central

Scott, Jaclyn C.; Brackney, Doug E.; Campbell, Corey L.; Bondu-Hawkins, Virginie; Hjelle, Brian; Ebel, Greg D.; Olson, Ken E.; Blair, Carol D.

2010-01-01

The exogenous RNA interference (RNAi) pathway is an important antiviral defense against arboviruses in mosquitoes, and virus-specific small interfering (si)RNAs are key components of this pathway. Understanding the biogenesis of siRNAs in mosquitoes could have important ramifications in using RNAi to control arbovirus transmission. Using deep sequencing technology, we characterized dengue virus type 2 (DENV2)-specific small RNAs produced during infection of Aedes aegypti mosquitoes and A. aegypti Aag2 cell cultures and compared them to those produced in the C6/36 Aedes albopictus cell line. We show that the size and mixed polarity of virus-specific small RNAs from DENV-infected A. aegypti cells indicate that they are products of Dicer-2 (Dcr2) cleavage of long dsRNA, whereas C6/36 cells generate DENV2-specific small RNAs that are longer and predominantly positive polarity, suggesting that they originate from a different small RNA pathway. Examination of virus-specific small RNAs after infection of the two mosquito cell lines with the insect-only flavivirus cell fusing agent virus (CFAV) corroborated these findings. An in vitro assay also showed that Aag2 A. aegypti cells are capable of siRNA production, while C6/36 A. albopictus cells exhibit inefficient Dcr2 cleavage of long dsRNA. Defective expression or function of Dcr2, the key initiator of the RNAi pathway, might explain the comparatively robust growth of arthropod-borne viruses in the C6/36 cell line, which has been used frequently as a surrogate for studying molecular interactions between arboviruses and cells of their mosquito hosts. PMID:21049014

Virus detection by PCR following vaccination of naive calves with intranasal or injectable multivalent modified-live viral vaccines.

PubMed

Walz, Paul H; Newcomer, Benjamin W; Riddell, Kay P; Scruggs, Daniel W; Cortese, Victor S

2017-09-01

We evaluated duration of PCR-positive results following administration of modified-live viral (MLV) vaccines to beef calves. Twenty beef calves were randomly assigned to either group 1 and vaccinated intranasally with a MLV vaccine containing bovine alphaherpesvirus 1 (BoHV-1), bovine respiratory syncytial virus (BRSV), and bovine parainfluenza virus 3 (BPIV-3), or to group 2 and vaccinated subcutaneously with a MLV vaccine containing bovine viral diarrhea virus 1 and 2 (BVDV-1, -2), BoHV-1, BRSV, and BPIV-3. Deep nasopharyngeal swabs (NPS) and transtracheal washes (TTW) were collected from all calves, and whole blood was collected from group 2 calves and tested by PCR. In group 1, the proportions of calves that tested PCR-positive to BVDV, BoHV-1, BRSV, and BPIV-3 on any sample at any time were 0%, 100%, 100%, and 10%, respectively. In group 1 calves, 100% of calves became PCR-positive for BoHV-1 by day 3 post-vaccination and 100% of calves became PCR-positive for BRSV by day 7 post-vaccination. In group 2, the proportions of calves that tested positive to BVDV, BoHV-1, BRSV, and BPIV-3 on any sample at any time were 50%, 40%, 10%, and 0%, respectively. All threshold cycle (Ct) values were >30 in group 2 calves, irrespective of virus; however, Ct values <25 were observed in group 1 calves from PCR-positive results for BoHV-1 and BRSV. All calves were PCR-negative for all viruses after day 28. Following intranasal MLV viral vaccination, PCR results and Ct values for BRSV and BoHV-1 suggest that attempts to differentiate vaccine virus from natural infection is unreliable.

How to Keep Your Campus Safe from Infection

ERIC Educational Resources Information Center

Brown, Scott

2005-01-01

In this article, the author explains how antivirus programs work. He also explains how performances of various antivirus programs vary from one to another. He also takes a look at 13 antivirus programs and explains which of these will keep computers protected. These programs include: (1) Sophos Anti-Virus Version 3.86.2; (2) McAfee VirusScan 9.0;…

Computer Security-Risks, Threats, and Safeguards.

ERIC Educational Resources Information Center

Ekhaml, Leticia

2001-01-01

Describes a variety of Internet threats to computers and networks used in schools. Discusses electronic trashing; clearing hard drives; cyber spying on Web sites visited; protection against cyber spying, including disposable email accounts; password sniffers; privacy policies; email snooping; email attachments that carry viruses; and hoaxes. (LRW)

Deep Learning for Prediction of Obstructive Disease From Fast Myocardial Perfusion SPECT: A Multicenter Study.

PubMed

Betancur, Julian; Commandeur, Frederic; Motlagh, Mahsaw; Sharir, Tali; Einstein, Andrew J; Bokhari, Sabahat; Fish, Mathews B; Ruddy, Terrence D; Kaufmann, Philipp; Sinusas, Albert J; Miller, Edward J; Bateman, Timothy M; Dorbala, Sharmila; Di Carli, Marcelo; Germano, Guido; Otaki, Yuka; Tamarappoo, Balaji K; Dey, Damini; Berman, Daniel S; Slomka, Piotr J

2018-03-12

The study evaluated the automatic prediction of obstructive disease from myocardial perfusion imaging (MPI) by deep learning as compared with total perfusion deficit (TPD). Deep convolutional neural networks trained with a large multicenter population may provide improved prediction of per-patient and per-vessel coronary artery disease from single-photon emission computed tomography MPI. A total of 1,638 patients (67% men) without known coronary artery disease, undergoing stress 99m Tc-sestamibi or tetrofosmin MPI with new generation solid-state scanners in 9 different sites, with invasive coronary angiography performed within 6 months of MPI, were studied. Obstructive disease was defined as ≥70% narrowing of coronary arteries (≥50% for left main artery). Left ventricular myocardium was segmented using clinical nuclear cardiology software and verified by an expert reader. Stress TPD was computed using sex- and camera-specific normal limits. Deep learning was trained using raw and quantitative polar maps and evaluated for prediction of obstructive stenosis in a stratified 10-fold cross-validation procedure. A total of 1,018 (62%) patients and 1,797 of 4,914 (37%) arteries had obstructive disease. Area under the receiver-operating characteristic curve for disease prediction by deep learning was higher than for TPD (per patient: 0.80 vs. 0.78; per vessel: 0.76 vs. 0.73: p < 0.01). With deep learning threshold set to the same specificity as TPD, per-patient sensitivity improved from 79.8% (TPD) to 82.3% (deep learning) (p < 0.05), and per-vessel sensitivity improved from 64.4% (TPD) to 69.8% (deep learning) (p < 0.01). Deep learning has the potential to improve automatic interpretation of MPI as compared with current clinical methods. Copyright © 2018 American College of Cardiology Foundation. Published by Elsevier Inc. All rights reserved.

Diagnosis and prediction of periodontally compromised teeth using a deep learning-based convolutional neural network algorithm.

PubMed

Lee, Jae-Hong; Kim, Do-Hyung; Jeong, Seong-Nyum; Choi, Seong-Ho

2018-04-01

The aim of the current study was to develop a computer-assisted detection system based on a deep convolutional neural network (CNN) algorithm and to evaluate the potential usefulness and accuracy of this system for the diagnosis and prediction of periodontally compromised teeth (PCT). Combining pretrained deep CNN architecture and a self-trained network, periapical radiographic images were used to determine the optimal CNN algorithm and weights. The diagnostic and predictive accuracy, sensitivity, specificity, positive predictive value, negative predictive value, receiver operating characteristic (ROC) curve, area under the ROC curve, confusion matrix, and 95% confidence intervals (CIs) were calculated using our deep CNN algorithm, based on a Keras framework in Python. The periapical radiographic dataset was split into training (n=1,044), validation (n=348), and test (n=348) datasets. With the deep learning algorithm, the diagnostic accuracy for PCT was 81.0% for premolars and 76.7% for molars. Using 64 premolars and 64 molars that were clinically diagnosed as severe PCT, the accuracy of predicting extraction was 82.8% (95% CI, 70.1%-91.2%) for premolars and 73.4% (95% CI, 59.9%-84.0%) for molars. We demonstrated that the deep CNN algorithm was useful for assessing the diagnosis and predictability of PCT. Therefore, with further optimization of the PCT dataset and improvements in the algorithm, a computer-aided detection system can be expected to become an effective and efficient method of diagnosing and predicting PCT.

Training Deep Spiking Neural Networks Using Backpropagation.

PubMed

Lee, Jun Haeng; Delbruck, Tobi; Pfeiffer, Michael

2016-01-01

Deep spiking neural networks (SNNs) hold the potential for improving the latency and energy efficiency of deep neural networks through data-driven event-based computation. However, training such networks is difficult due to the non-differentiable nature of spike events. In this paper, we introduce a novel technique, which treats the membrane potentials of spiking neurons as differentiable signals, where discontinuities at spike times are considered as noise. This enables an error backpropagation mechanism for deep SNNs that follows the same principles as in conventional deep networks, but works directly on spike signals and membrane potentials. Compared with previous methods relying on indirect training and conversion, our technique has the potential to capture the statistics of spikes more precisely. We evaluate the proposed framework on artificially generated events from the original MNIST handwritten digit benchmark, and also on the N-MNIST benchmark recorded with an event-based dynamic vision sensor, in which the proposed method reduces the error rate by a factor of more than three compared to the best previous SNN, and also achieves a higher accuracy than a conventional convolutional neural network (CNN) trained and tested on the same data. We demonstrate in the context of the MNIST task that thanks to their event-driven operation, deep SNNs (both fully connected and convolutional) trained with our method achieve accuracy equivalent with conventional neural networks. In the N-MNIST example, equivalent accuracy is achieved with about five times fewer computational operations.

Designing and Testing Broadly-Protective Filoviral Vaccines Optimized for Cytotoxic T-Lymphocyte Epitope Coverage

PubMed Central

Fenimore, Paul W.; Foley, Brian T.; Bakken, Russell R.; Thurmond, James R.; Yusim, Karina; Yoon, Hyejin; Parker, Michael; Hart, Mary Kate; Dye, John M.; Korber, Bette; Kuiken, Carla

2012-01-01

We report the rational design and in vivo testing of mosaic proteins for a polyvalent pan-filoviral vaccine using a computational strategy designed for the Human Immunodeficiency Virus type 1 (HIV-1) but also appropriate for Hepatitis C virus (HCV) and potentially other diverse viruses. Mosaics are sets of artificial recombinant proteins that are based on natural proteins. The recombinants are computationally selected using a genetic algorithm to optimize the coverage of potential cytotoxic T lymphocyte (CTL) epitopes. Because evolutionary history differs markedly between HIV-1 and filoviruses, we devised an adapted computational technique that is effective for sparsely sampled taxa; our first significant result is that the mosaic technique is effective in creating high-quality mosaic filovirus proteins. The resulting coverage of potential epitopes across filovirus species is superior to coverage by any natural variants, including current vaccine strains with demonstrated cross-reactivity. The mosaic cocktails are also robust: mosaics substantially outperformed natural strains when computationally tested against poorly sampled species and more variable genes. Furthermore, in a computational comparison of cross-reactive potential a design constructed prior to the Bundibugyo outbreak performed nearly as well against all species as an updated design that included Bundibugyo. These points suggest that the mosaic designs would be more resilient than natural-variant vaccines against future Ebola outbreaks dominated by novel viral variants. We demonstrate in vivo immunogenicity and protection against a heterologous challenge in a mouse model. This design work delineates the likely requirements and limitations on broadly-protective filoviral CTL vaccines. PMID:23056184

Temperature control simulation for a microwave transmitter cooling system. [deep space network

NASA Technical Reports Server (NTRS)

Yung, C. S.

1980-01-01

The thermal performance of a temperature control system for the antenna microwave transmitter (klystron tube) of the Deep Space Network antenna tracking system is discussed. In particular the mathematical model is presented along with the details of a computer program which is written for the system simulation and the performance parameterization. Analytical expressions are presented.

Preliminary Classification of Novel Hemorrhagic Fever-Causing Viruses Using Sequence-Based PAirwise Sequence Comparison (PASC) Analysis.

PubMed

Bào, Yīmíng; Kuhn, Jens H

2018-01-01

During the last decade, genome sequence-based classification of viruses has become increasingly prominent. Viruses can be even classified based on coding-complete genome sequence data alone. Nevertheless, classification remains arduous as experts are required to establish phylogenetic trees to depict the evolutionary relationships of such sequences for preliminary taxonomic placement. Pairwise sequence comparison (PASC) of genomes is one of several novel methods for establishing relationships among viruses. This method, provided by the US National Center for Biotechnology Information as an open-access tool, circumvents phylogenetics, and yet PASC results are often in agreement with those of phylogenetic analyses. Computationally inexpensive, PASC can be easily performed by non-taxonomists. Here we describe how to use the PASC tool for the preliminary classification of novel viral hemorrhagic fever-causing viruses.

Deep Direct Reinforcement Learning for Financial Signal Representation and Trading.

PubMed

Deng, Yue; Bao, Feng; Kong, Youyong; Ren, Zhiquan; Dai, Qionghai

2017-03-01

Can we train the computer to beat experienced traders for financial assert trading? In this paper, we try to address this challenge by introducing a recurrent deep neural network (NN) for real-time financial signal representation and trading. Our model is inspired by two biological-related learning concepts of deep learning (DL) and reinforcement learning (RL). In the framework, the DL part automatically senses the dynamic market condition for informative feature learning. Then, the RL module interacts with deep representations and makes trading decisions to accumulate the ultimate rewards in an unknown environment. The learning system is implemented in a complex NN that exhibits both the deep and recurrent structures. Hence, we propose a task-aware backpropagation through time method to cope with the gradient vanishing issue in deep training. The robustness of the neural system is verified on both the stock and the commodity future markets under broad testing conditions.

Manifold learning of brain MRIs by deep learning.

PubMed

Brosch, Tom; Tam, Roger

2013-01-01

Manifold learning of medical images plays a potentially important role for modeling anatomical variability within a population with pplications that include segmentation, registration, and prediction of clinical parameters. This paper describes a novel method for learning the manifold of 3D brain images that, unlike most existing manifold learning methods, does not require the manifold space to be locally linear, and does not require a predefined similarity measure or a prebuilt proximity graph. Our manifold learning method is based on deep learning, a machine learning approach that uses layered networks (called deep belief networks, or DBNs) and has received much attention recently in the computer vision field due to their success in object recognition tasks. DBNs have traditionally been too computationally expensive for application to 3D images due to the large number of trainable parameters. Our primary contributions are (1) a much more computationally efficient training method for DBNs that makes training on 3D medical images with a resolution of up to 128 x 128 x 128 practical, and (2) the demonstration that DBNs can learn a low-dimensional manifold of brain volumes that detects modes of variations that correlate to demographic and disease parameters.

Automated analysis of high-content microscopy data with deep learning.

PubMed

Kraus, Oren Z; Grys, Ben T; Ba, Jimmy; Chong, Yolanda; Frey, Brendan J; Boone, Charles; Andrews, Brenda J

2017-04-18

Existing computational pipelines for quantitative analysis of high-content microscopy data rely on traditional machine learning approaches that fail to accurately classify more than a single dataset without substantial tuning and training, requiring extensive analysis. Here, we demonstrate that the application of deep learning to biological image data can overcome the pitfalls associated with conventional machine learning classifiers. Using a deep convolutional neural network (DeepLoc) to analyze yeast cell images, we show improved performance over traditional approaches in the automated classification of protein subcellular localization. We also demonstrate the ability of DeepLoc to classify highly divergent image sets, including images of pheromone-arrested cells with abnormal cellular morphology, as well as images generated in different genetic backgrounds and in different laboratories. We offer an open-source implementation that enables updating DeepLoc on new microscopy datasets. This study highlights deep learning as an important tool for the expedited analysis of high-content microscopy data. © 2017 The Authors. Published under the terms of the CC BY 4.0 license.

DEEP: Database of Energy Efficiency Performance

DOE Office of Scientific and Technical Information (OSTI.GOV)

Hong, Tianzhen; Piette, Mary; Lee, Sang Hoon

A database of energy efficiency performance (DEEP) is a presimulated database to enable quick and accurate assessment of energy retrofit of commercial buildings. DEEP was compiled from results of about 10 million EnergyPlus simulations. DEEP provides energy savings for screening and evaluation of retrofit measures targeting the small and medium-sized office and retail buildings in California. The prototype building models are developed for a comprehensive assessment of building energy performance based on DOE commercial reference buildings and the California DEER [sic] prototype buildings. The prototype buildings represent seven building types across six vintages of constructions and 16 California climate zones.more » DEEP uses these prototypes to evaluate energy performance of about 100 energy conservation measures covering envelope, lighting, heating, ventilation, air conditioning, plug loads, and domestic hot war. DEEP consists the energy simulation results for individual retrofit measures as well as packages of measures to consider interactive effects between multiple measures. The large scale EnergyPlus simulations are being conducted on the super computers at the National Energy Research Scientific Computing Center (NERSC) of Lawrence Berkeley National Laboratory. The pre-simulation database is a part of the CEC PIER project to develop a web-based retrofit toolkit for small and medium-sized commercial buildings in California, which provides real-time energy retrofit feedback by querying DEEP with recommended measures, estimated energy savings and financial payback period based on users' decision criteria of maximizing energy savings, energy cost savings, carbon reduction, or payback of investment. The pre-simulated database and associated comprehensive measure analysis enhances the ability to performance assessments of retrofits to reduce energy use for small and medium buildings and business owners who typically do not have resources to conduct costly building energy audit.« less

A computationally designed inhibitor of an Epstein-Barr viral Bcl-2 protein induces apoptosis in infected cells

PubMed Central

Shen, Betty W.; Song, Yifan; Frayo, Shani; Convertine, Anthony J.; Margineantu, Daciana; Booth, Garrett; Correia, Bruno E.; Cheng, Yuanhua; Schief, William R.; Hockenbery, David M.; Press, Oliver W.; Stoddard, Barry L.; Stayton, Patrick S.; Baker, David

2014-01-01

SUMMARY Since apoptosis of infected cells can limit virus production and spread, some viruses have co-opted prosurvival genes from the host. This includes the Epstein-Barr virus (EBV) gene BHRF1, a homologue of human Bcl-2 proteins that block apoptosis and are associated with cancer. Computational design and experimental optimization were used to generate a novel protein called BINDI that binds BHRF1 with picomolar affinity. BINDI recognizes the hydrophobic cleft of BHRF1 in a manner similar to other Bcl-2 protein interactions, but makes many additional contacts to achieve exceptional affinity and specificity. BINDI induces apoptosis in EBV-infected cancer lines, and when delivered with an antibody-targeted intracellular delivery carrier, BINDI suppressed tumor growth and extended survival in a xenograft disease model of EBV-positive human lymphoma. High specificity designed proteins that selectively kill target cells may provide an advantage over the toxic compounds used in current generation antibody-drug conjugates. PMID:24949974

Quantum neuromorphic hardware for quantum artificial intelligence

NASA Astrophysics Data System (ADS)

Prati, Enrico

2017-08-01

The development of machine learning methods based on deep learning boosted the field of artificial intelligence towards unprecedented achievements and application in several fields. Such prominent results were made in parallel with the first successful demonstrations of fault tolerant hardware for quantum information processing. To which extent deep learning can take advantage of the existence of a hardware based on qubits behaving as a universal quantum computer is an open question under investigation. Here I review the convergence between the two fields towards implementation of advanced quantum algorithms, including quantum deep learning.

A Parameter-Free Dynamic Alternative to Hyper-Viscosity for Coupled Transport Equations: Application to the Simulation of 3D Squall Lines Using Spectral Elements

DTIC Science & Technology

2015-06-04

that involve physics coupling with phase change in the simulation of 3D deep convection. We show that the VMS+DC approach is a robust technique that can...of 3D deep convection. We show that the VMS+DC approach is a robust technique that can damp the high order modes characterizing the spectral element...of Spectral Elements, Deep Convection, Kessler Microphysics Preprint J. Comput. Phys. 283 (2015) 360-373 June 4, 2015 1. Introduction In the field of

A computationally optimized broadly reactive H5 hemagglutinin vaccine provides protection against homologous and heterologous H5N1 highly pathogenic avian influenza virus infection in chickens

USDA-ARS?s Scientific Manuscript database

Since its emergence in 1996 in China, H5N1 highly pathogenic avian influenza (HPAI) virus has continuously evolved into different genetic clades that have created challenges to maintaining antigenically relevant H5N1 vaccine seeds. Therefore, a universal (multi-hemagglutinin [HA] subtype) or more c...

Computer Security: Virus Highlights Need for Improved Internet Management

DTIC Science & Technology

1989-06-01

and Finance Committee on Energy and Commerce House of Representatives Dear Mr. Chairman: This report responds to your October 14,1988, request and...virus incident, the Chairman, Subcommittee on Telecommunications and Finance , House Committee on Energy and Commerce, asked GAO to provide an overview...Chairman, Subcom- mittee on Telecommunications and Finance , House Committee on Energy and Commerce, and subsequent agreements with his office, the

Epstein-Barr virus: a paradigm for persistent infection - for real and in virtual reality.

PubMed

Thorley-Lawson, David A; Duca, Karen A; Shapiro, Michael

2008-04-01

The really interesting thing about herpesviruses is that they can establish lifelong persistant infections in immunocompetent hosts. At first glance, they would seem to have very different ways of doing this. Here we will use as a model our current understanding of how the human herpesvirus Epstein-Barr virus establishes and maintains such an infection. We apply information from a wide range of sources including laboratory experimentation, clinical observation, animal models and a new computer simulation. We propose that the detailed mechanisms for establishing infection are dependent on the virus and tissues involved, but the strategy is the same - to persist in a long-lived cell type where the virus is invisible to the immune system and nonpathogenic.

Epizootic emergence of Usutu virus in wild and captive birds in Germany.

PubMed

Becker, Norbert; Jöst, Hanna; Ziegler, Ute; Eiden, Martin; Höper, Dirk; Emmerich, Petra; Fichet-Calvet, Elisabeth; Ehichioya, Deborah U; Czajka, Christina; Gabriel, Martin; Hoffmann, Bernd; Beer, Martin; Tenner-Racz, Klara; Racz, Paul; Günther, Stephan; Wink, Michael; Bosch, Stefan; Konrad, Armin; Pfeffer, Martin; Groschup, Martin H; Schmidt-Chanasit, Jonas

2012-01-01

This study aimed to identify the causative agent of mass mortality in wild and captive birds in southwest Germany and to gather insights into the phylogenetic relationship and spatial distribution of the pathogen. Since June 2011, 223 dead birds were collected and tested for the presence of viral pathogens. Usutu virus (USUV) RNA was detected by real-time RT-PCR in 86 birds representing 6 species. The virus was isolated in cell culture from the heart of 18 Blackbirds (Turdus merula). USUV-specific antigen was demonstrated by immunohistochemistry in brain, heart, liver, and lung of infected Blackbirds. The complete polyprotein coding sequence was obtained by deep sequencing of liver and spleen samples of a dead Blackbird from Mannheim (BH65/11-02-03). Phylogenetic analysis of the German USUV strain BH65/11-02-03 revealed a close relationship with strain Vienna that caused mass mortality among birds in Austria in 2001. Wild birds from lowland river valleys in southwest Germany were mainly affected by USUV, but also birds kept in aviaries. Our data suggest that after the initial detection of USUV in German mosquitoes in 2010, the virus spread in 2011 and caused epizootics among wild and captive birds in southwest Germany. The data also indicate an increased risk of USUV infections in humans in Germany.

Integrated Sensing and Information Processing Theme-Based Redesign of the Undergraduate Electrical and Computer Engineering Curriculum at Duke University

ERIC Educational Resources Information Center

Ybarra, Gary A.; Collins, Leslie M.; Huettel, Lisa G.; Brown, April S.; Coonley, Kip D.; Massoud, Hisham Z.; Board, John A.; Cummer, Steven A.; Choudhury, Romit Roy; Gustafson, Michael R.; Jokerst, Nan M.; Brooke, Martin A.; Willett, Rebecca M.; Kim, Jungsang; Absher, Martha S.

2011-01-01

The field of electrical and computer engineering has evolved significantly in the past two decades. This evolution has broadened the field of ECE, and subfields have seen deep penetration into very specialized areas. Remarkable devices and systems arising from innovative processes, exotic materials, high speed computer simulations, and complex…

Comparative analysis among the small RNA populations of source, sink and conductive tissues in two different plant-virus pathosystems.

PubMed

Herranz, Mari Carmen; Navarro, Jose Antonio; Sommen, Evelien; Pallas, Vicente

2015-02-22

In plants, RNA silencing plays a fundamental role as defence mechanism against viruses. During last years deep-sequencing technology has allowed to analyze the sRNA profile of a large variety of virus-infected tissues. Nevertheless, the majority of these studies have been restricted to a unique tissue and no comparative analysis between phloem and source/sink tissues has been conducted. In the present work, we compared the sRNA populations of source, sink and conductive (phloem) tissues in two different plant virus pathosystems. We chose two cucurbit species infected with two viruses very different in genome organization and replication strategy; Melon necrotic spot virus (MNSV) and Prunus necrotic ringspot virus (PNRSV). Our findings showed, in both systems, an increase of the 21-nt total sRNAs together with a decrease of those with a size of 24-nt in all the infected tissues, except for the phloem where the ratio of 21/24-nt sRNA species remained constant. Comparing the vsRNAs, both PNRSV- and MNSV-infected plants share the same vsRNA size distribution in all the analyzed tissues. Similar accumulation levels of sense and antisense vsRNAs were observed in both systems except for roots that showed a prevalence of (+) vsRNAs in both pathosystems. Additionally, the presence of overrepresented discrete sites along the viral genome, hot spots, were identified and validated by stem-loop RT-PCR. Despite that in PNRSV-infected plants the presence of vsRNAs was scarce both viruses modulated the host sRNA profile. We compare for the first time the sRNA profile of four different tissues, including source, sink and conductive (phloem) tissues, in two plant-virus pathosystems. Our results indicate that antiviral silencing machinery in melon and cucumber acts mainly through DCL4. Upon infection, the total sRNA pattern in phloem remains unchanged in contrast to the rest of the analyzed tissues indicating a certain tissue-tropism to this polulation. Independently of the accumulation level of the vsRNAs both viruses were able to modulate the host sRNA pattern.

The Classroom, Board Room, Chat Room, and Court Room: School Computers at the Crossroads.

ERIC Educational Resources Information Center

Stewart, Michael

2000-01-01

In schools' efforts to maximize technology's benefits, ethical considerations have often taken a back seat. Computer misuse is growing exponentially and assuming many forms: unauthorized data access, hacking, piracy, information theft, fraud, virus creation, harassment, defamation, and discrimination. Integrated-learning activities will help…

Space lab system analysis: Advanced Solid Rocket Motor (ASRM) communications networks analysis

NASA Technical Reports Server (NTRS)

Ingels, Frank M.; Moorhead, Robert J., II; Moorhead, Jane N.; Shearin, C. Mark; Thompson, Dale R.

1990-01-01

A synopsis of research on computer viruses and computer security is presented. A review of seven technical meetings attended is compiled. A technical discussion on the communication plans for the ASRM facility is presented, with a brief tutorial on the potential local area network media and protocols.

The Hidden Costs of Wireless Computer Labs

ERIC Educational Resources Information Center

Daly, Una

2005-01-01

Various elementary schools and middle schools across the U.S. have purchased one or more mobile laboratories. Although the wireless labs have provided more classroom computing, teachers and technology aides still have mixed views about their cost-benefit ratio. This is because the proliferation of viruses and spyware has dramatically increased…

Modeling and Analyzing Intrusion Attempts to a Computer Network Operating in a Defense in Depth Posture

DTIC Science & Technology

2004-09-01

protection. Firewalls, Intrusion Detection Systems (IDS’s), Anti-Virus (AV) software , and routers are such tools used. In recent years, computer security...associated with operating systems, application software , and computing hardware. When IDS’s are utilized on a host computer or network, there are two...primary approaches to detecting and / or preventing attacks. Traditional IDS’s, like most AV software , rely on known “signatures” to detect attacks

All-atom molecular dynamics of virus capsids as drug targets

DOE PAGES

Perilla, Juan R.; Hadden, Jodi A.; Goh, Boon Chong; ...

2016-04-29

Virus capsids are protein shells that package the viral genome. Although their morphology and biological functions can vary markedly, capsids often play critical roles in regulating viral infection pathways. A detailed knowledge of virus capsids, including their dynamic structure, interactions with cellular factors, and the specific roles that they play in the replication cycle, is imperative for the development of antiviral therapeutics. The following Perspective introduces an emerging area of computational biology that focuses on the dynamics of virus capsids and capsid–protein assemblies, with particular emphasis on the effects of small-molecule drug binding on capsid structure, stability, and allosteric pathways.more » When performed at chemical detail, molecular dynamics simulations can reveal subtle changes in virus capsids induced by drug molecules a fraction of their size. Finally, the current challenges of performing all-atom capsid–drug simulations are discussed, along with an outlook on the applicability of virus capsid simulations to reveal novel drug targets.« less

All-atom molecular dynamics of virus capsids as drug targets

DOE Office of Scientific and Technical Information (OSTI.GOV)

Perilla, Juan R.; Hadden, Jodi A.; Goh, Boon Chong

Virus capsids are protein shells that package the viral genome. Although their morphology and biological functions can vary markedly, capsids often play critical roles in regulating viral infection pathways. A detailed knowledge of virus capsids, including their dynamic structure, interactions with cellular factors, and the specific roles that they play in the replication cycle, is imperative for the development of antiviral therapeutics. The following Perspective introduces an emerging area of computational biology that focuses on the dynamics of virus capsids and capsid–protein assemblies, with particular emphasis on the effects of small-molecule drug binding on capsid structure, stability, and allosteric pathways.more » When performed at chemical detail, molecular dynamics simulations can reveal subtle changes in virus capsids induced by drug molecules a fraction of their size. Finally, the current challenges of performing all-atom capsid–drug simulations are discussed, along with an outlook on the applicability of virus capsid simulations to reveal novel drug targets.« less

Virus Particle Detection by Convolutional Neural Network in Transmission Electron Microscopy Images.

PubMed

Ito, Eisuke; Sato, Takaaki; Sano, Daisuke; Utagawa, Etsuko; Kato, Tsuyoshi

2018-06-01

A new computational method for the detection of virus particles in transmission electron microscopy (TEM) images is presented. Our approach is to use a convolutional neural network that transforms a TEM image to a probabilistic map that indicates where virus particles exist in the image. Our proposed approach automatically and simultaneously learns both discriminative features and classifier for virus particle detection by machine learning, in contrast to existing methods that are based on handcrafted features that yield many false positives and require several postprocessing steps. The detection performance of the proposed method was assessed against a dataset of TEM images containing feline calicivirus particles and compared with several existing detection methods, and the state-of-the-art performance of the developed method for detecting virus was demonstrated. Since our method is based on supervised learning that requires both the input images and their corresponding annotations, it is basically used for detection of already-known viruses. However, the method is highly flexible, and the convolutional networks can adapt themselves to any virus particles by learning automatically from an annotated dataset.