Sample records for computational approach identifies

  1. Protein interaction network topology uncovers melanogenesis regulatory network components within functional genomics datasets.

    PubMed

    Ho, Hsiang; Milenković, Tijana; Memisević, Vesna; Aruri, Jayavani; Przulj, Natasa; Ganesan, Anand K

    2010-06-15

    RNA-mediated interference (RNAi)-based functional genomics is a systems-level approach to identify novel genes that control biological phenotypes. Existing computational approaches can identify individual genes from RNAi datasets that regulate a given biological process. However, currently available methods cannot identify which RNAi screen "hits" are novel components of well-characterized biological pathways known to regulate the interrogated phenotype. In this study, we describe a method to identify genes from RNAi datasets that are novel components of known biological pathways. We experimentally validate our approach in the context of a recently completed RNAi screen to identify novel regulators of melanogenesis. In this study, we utilize a PPI network topology-based approach to identify targets within our RNAi dataset that may be components of known melanogenesis regulatory pathways. Our computational approach identifies a set of screen targets that cluster topologically in a human PPI network with the known pigment regulator Endothelin receptor type B (EDNRB). Validation studies reveal that these genes impact pigment production and EDNRB signaling in pigmented melanoma cells (MNT-1) and normal melanocytes. We present an approach that identifies novel components of well-characterized biological pathways from functional genomics datasets that could not have been identified by existing statistical and computational approaches.

  2. Protein interaction network topology uncovers melanogenesis regulatory network components within functional genomics datasets

    PubMed Central

    2010-01-01

    Background RNA-mediated interference (RNAi)-based functional genomics is a systems-level approach to identify novel genes that control biological phenotypes. Existing computational approaches can identify individual genes from RNAi datasets that regulate a given biological process. However, currently available methods cannot identify which RNAi screen "hits" are novel components of well-characterized biological pathways known to regulate the interrogated phenotype. In this study, we describe a method to identify genes from RNAi datasets that are novel components of known biological pathways. We experimentally validate our approach in the context of a recently completed RNAi screen to identify novel regulators of melanogenesis. Results In this study, we utilize a PPI network topology-based approach to identify targets within our RNAi dataset that may be components of known melanogenesis regulatory pathways. Our computational approach identifies a set of screen targets that cluster topologically in a human PPI network with the known pigment regulator Endothelin receptor type B (EDNRB). Validation studies reveal that these genes impact pigment production and EDNRB signaling in pigmented melanoma cells (MNT-1) and normal melanocytes. Conclusions We present an approach that identifies novel components of well-characterized biological pathways from functional genomics datasets that could not have been identified by existing statistical and computational approaches. PMID:20550706

  3. A comparison of approaches for finding minimum identifying codes on graphs

    NASA Astrophysics Data System (ADS)

    Horan, Victoria; Adachi, Steve; Bak, Stanley

    2016-05-01

    In order to formulate mathematical conjectures likely to be true, a number of base cases must be determined. However, many combinatorial problems are NP-hard and the computational complexity makes this research approach difficult using a standard brute force approach on a typical computer. One sample problem explored is that of finding a minimum identifying code. To work around the computational issues, a variety of methods are explored and consist of a parallel computing approach using MATLAB, an adiabatic quantum optimization approach using a D-Wave quantum annealing processor, and lastly using satisfiability modulo theory (SMT) and corresponding SMT solvers. Each of these methods requires the problem to be formulated in a unique manner. In this paper, we address the challenges of computing solutions to this NP-hard problem with respect to each of these methods.

  4. Role of Soft Computing Approaches in HealthCare Domain: A Mini Review.

    PubMed

    Gambhir, Shalini; Malik, Sanjay Kumar; Kumar, Yugal

    2016-12-01

    In the present era, soft computing approaches play a vital role in solving the different kinds of problems and provide promising solutions. Due to popularity of soft computing approaches, these approaches have also been applied in healthcare data for effectively diagnosing the diseases and obtaining better results in comparison to traditional approaches. Soft computing approaches have the ability to adapt itself according to problem domain. Another aspect is a good balance between exploration and exploitation processes. These aspects make soft computing approaches more powerful, reliable and efficient. The above mentioned characteristics make the soft computing approaches more suitable and competent for health care data. The first objective of this review paper is to identify the various soft computing approaches which are used for diagnosing and predicting the diseases. Second objective is to identify various diseases for which these approaches are applied. Third objective is to categories the soft computing approaches for clinical support system. In literature, it is found that large number of soft computing approaches have been applied for effectively diagnosing and predicting the diseases from healthcare data. Some of these are particle swarm optimization, genetic algorithm, artificial neural network, support vector machine etc. A detailed discussion on these approaches are presented in literature section. This work summarizes various soft computing approaches used in healthcare domain in last one decade. These approaches are categorized in five different categories based on the methodology, these are classification model based system, expert system, fuzzy and neuro fuzzy system, rule based system and case based system. Lot of techniques are discussed in above mentioned categories and all discussed techniques are summarized in the form of tables also. This work also focuses on accuracy rate of soft computing technique and tabular information is provided for each category including author details, technique, disease and utility/accuracy.

  5. Bioinformatics approaches to predict target genes from transcription factor binding data.

    PubMed

    Essebier, Alexandra; Lamprecht, Marnie; Piper, Michael; Bodén, Mikael

    2017-12-01

    Transcription factors regulate gene expression and play an essential role in development by maintaining proliferative states, driving cellular differentiation and determining cell fate. Transcription factors are capable of regulating multiple genes over potentially long distances making target gene identification challenging. Currently available experimental approaches to detect distal interactions have multiple weaknesses that have motivated the development of computational approaches. Although an improvement over experimental approaches, existing computational approaches are still limited in their application, with different weaknesses depending on the approach. Here, we review computational approaches with a focus on data dependency, cell type specificity and usability. With the aim of identifying transcription factor target genes, we apply available approaches to typical transcription factor experimental datasets. We show that approaches are not always capable of annotating all transcription factor binding sites; binding sites should be treated disparately; and a combination of approaches can increase the biological relevance of the set of genes identified as targets. Copyright © 2017 Elsevier Inc. All rights reserved.

  6. Introductory Computer Programming Course Teaching Improvement Using Immersion Language, Extreme Programming, and Education Theories

    ERIC Educational Resources Information Center

    Velez-Rubio, Miguel

    2013-01-01

    Teaching computer programming to freshmen students in Computer Sciences and other Information Technology areas has been identified as a complex activity. Different approaches have been studied looking for the best one that could help to improve this teaching process. A proposed approach was implemented which is based in the language immersion…

  7. Inhibitors of HIV-protease from computational design. A history of theory and synthesis still to be fully appreciated.

    PubMed

    Berti, Federico; Frecer, Vladimir; Miertus, Stanislav

    2014-01-01

    Despite the fact that HIV-Protease is an over 20 years old target, computational approaches to rational design of its inhibitors still have a great potential to stimulate the synthesis of new compounds and the discovery of new, potent derivatives, ever capable to overcome the problem of drug resistance. This review deals with successful examples of inhibitors identified by computational approaches, rather than by knowledge-based design. Such methodologies include the development of energy and scoring functions, docking protocols, statistical models, virtual combinatorial chemistry. Computations addressing drug resistance, and the development of related models as the substrate envelope hypothesis are also reviewed. In some cases, the identified structures required the development of synthetic approaches in order to obtain the desired target molecules; several examples are reported.

  8. Cyber Security: Big Data Think II Working Group Meeting

    NASA Technical Reports Server (NTRS)

    Hinke, Thomas; Shaw, Derek

    2015-01-01

    This presentation focuses on approaches that could be used by a data computation center to identify attacks and ensure malicious code and backdoors are identified if planted in system. The goal is to identify actionable security information from the mountain of data that flows into and out of an organization. The approaches are applicable to big data computational center and some must also use big data techniques to extract the actionable security information from the mountain of data that flows into and out of a data computational center. The briefing covers the detection of malicious delivery sites and techniques for reducing the mountain of data so that intrusion detection information can be useful, and not hidden in a plethora of false alerts. It also looks at the identification of possible unauthorized data exfiltration.

  9. VIOLENT FRAMES IN ACTION

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

    Sanfilippo, Antonio P.; McGrath, Liam R.; Whitney, Paul D.

    2011-11-17

    We present a computational approach to radical rhetoric that leverages the co-expression of rhetoric and action features in discourse to identify violent intent. The approach combines text mining and machine learning techniques with insights from Frame Analysis and theories that explain the emergence of violence in terms of moral disengagement, the violation of sacred values and social isolation in order to build computational models that identify messages from terrorist sources and estimate their proximity to an attack. We discuss a specific application of this approach to a body of documents from and about radical and terrorist groups in the Middlemore » East and present the results achieved.« less

  10. Discover binding pathways using the sliding binding-box docking approach: application to binding pathways of oseltamivir to avian influenza H5N1 neuraminidase

    NASA Astrophysics Data System (ADS)

    Tran, Diem-Trang T.; Le, Ly T.; Truong, Thanh N.

    2013-08-01

    Drug binding and unbinding are transient processes which are hardly observed by experiment and difficult to analyze by computational techniques. In this paper, we employed a cost-effective method called "pathway docking" in which molecular docking was used to screen ligand-receptor binding free energy surface to reveal possible paths of ligand approaching protein binding pocket. A case study was applied on oseltamivir, the key drug against influenza a virus. The equilibrium pathways identified by this method are found to be similar to those identified in prior studies using highly expensive computational approaches.

  11. Systems Biology and Birth Defects Prevention: Blockade of the Glucocorticoid Receptor Prevents Arsenic-Induced Birth Defects

    PubMed Central

    Ahir, Bhavesh K.; Sanders, Alison P.; Rager, Julia E.

    2013-01-01

    Background: The biological mechanisms by which environmental metals are associated with birth defects are largely unknown. Systems biology–based approaches may help to identify key pathways that mediate metal-induced birth defects as well as potential targets for prevention. Objectives: First, we applied a novel computational approach to identify a prioritized biological pathway that associates metals with birth defects. Second, in a laboratory setting, we sought to determine whether inhibition of the identified pathway prevents developmental defects. Methods: Seven environmental metals were selected for inclusion in the computational analysis: arsenic, cadmium, chromium, lead, mercury, nickel, and selenium. We used an in silico strategy to predict genes and pathways associated with both metal exposure and developmental defects. The most significant pathway was identified and tested using an in ovo whole chick embryo culture assay. We further evaluated the role of the pathway as a mediator of metal-induced toxicity using the in vitro midbrain micromass culture assay. Results: The glucocorticoid receptor pathway was computationally predicted to be a key mediator of multiple metal-induced birth defects. In the chick embryo model, structural malformations induced by inorganic arsenic (iAs) were prevented when signaling of the glucocorticoid receptor pathway was inhibited. Further, glucocorticoid receptor inhibition demonstrated partial to complete protection from both iAs- and cadmium-induced neurodevelopmental toxicity in vitro. Conclusions: Our findings highlight a novel approach to computationally identify a targeted biological pathway for examining birth defects prevention. PMID:23458687

  12. Combining fragment homology modeling with molecular dynamics aims at prediction of Ca2+ binding sites in CaBPs

    NASA Astrophysics Data System (ADS)

    Pang, ChunLi; Cao, TianGuang; Li, JunWei; Jia, MengWen; Zhang, SuHua; Ren, ShuXi; An, HaiLong; Zhan, Yong

    2013-08-01

    The family of calcium-binding proteins (CaBPs) consists of dozens of members and contributes to all aspects of the cell's function, from homeostasis to learning and memory. However, the Ca2+-binding mechanism is still unclear for most of CaBPs. To identify the Ca2+-binding sites of CaBPs, this study presented a computational approach which combined the fragment homology modeling with molecular dynamics simulation. For validation, we performed a two-step strategy as follows: first, the approach is used to identify the Ca2+-binding sites of CaBPs, which have the EF-hand Ca2+-binding site and the detailed binding mechanism. To accomplish this, eighteen crystal structures of CaBPs with 49 Ca2+-binding sites are selected to be analyzed including calmodulin. The computational method identified 43 from 49 Ca2+-binding sites. Second, we performed the approach to large-conductance Ca2+-activated K+ (BK) channels which don't have clear Ca2+-binding mechanism. The simulated results are consistent with the experimental data. The computational approach may shed some light on the identification of Ca2+-binding sites in CaBPs.

  13. Efficiently Identifying Significant Associations in Genome-wide Association Studies

    PubMed Central

    Eskin, Eleazar

    2013-01-01

    Abstract Over the past several years, genome-wide association studies (GWAS) have implicated hundreds of genes in common disease. More recently, the GWAS approach has been utilized to identify regions of the genome that harbor variation affecting gene expression or expression quantitative trait loci (eQTLs). Unlike GWAS applied to clinical traits, where only a handful of phenotypes are analyzed per study, in eQTL studies, tens of thousands of gene expression levels are measured, and the GWAS approach is applied to each gene expression level. This leads to computing billions of statistical tests and requires substantial computational resources, particularly when applying novel statistical methods such as mixed models. We introduce a novel two-stage testing procedure that identifies all of the significant associations more efficiently than testing all the single nucleotide polymorphisms (SNPs). In the first stage, a small number of informative SNPs, or proxies, across the genome are tested. Based on their observed associations, our approach locates the regions that may contain significant SNPs and only tests additional SNPs from those regions. We show through simulations and analysis of real GWAS datasets that the proposed two-stage procedure increases the computational speed by a factor of 10. Additionally, efficient implementation of our software increases the computational speed relative to the state-of-the-art testing approaches by a factor of 75. PMID:24033261

  14. A Comparative Analysis of Computational Approaches to Relative Protein Quantification Using Peptide Peak Intensities in Label-free LC-MS Proteomics Experiments

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

    Matzke, Melissa M.; Brown, Joseph N.; Gritsenko, Marina A.

    2013-02-01

    Liquid chromatography coupled with mass spectrometry (LC-MS) is widely used to identify and quantify peptides in complex biological samples. In particular, label-free shotgun proteomics is highly effective for the identification of peptides and subsequently obtaining a global protein profile of a sample. As a result, this approach is widely used for discovery studies. Typically, the objective of these discovery studies is to identify proteins that are affected by some condition of interest (e.g. disease, exposure). However, for complex biological samples, label-free LC-MS proteomics experiments measure peptides and do not directly yield protein quantities. Thus, protein quantification must be inferred frommore » one or more measured peptides. In recent years, many computational approaches to relative protein quantification of label-free LC-MS data have been published. In this review, we examine the most commonly employed quantification approaches to relative protein abundance from peak intensity values, evaluate their individual merits, and discuss challenges in the use of the various computational approaches.« less

  15. Taking the Mystery Out of Research in Computing Information Systems: A New Approach to Teaching Research Paradigm Architecture.

    ERIC Educational Resources Information Center

    Heslin, J. Alexander, Jr.

    In senior-level undergraduate research courses in Computer Information Systems (CIS), students are required to read and assimilate a large volume of current research literature. One course objective is to demonstrate to the student that there are patterns or models or paradigms of research. A new approach in identifying research paradigms is…

  16. Examining the Roles of Blended Learning Approaches in Computer-Supported Collaborative Learning (CSCL) Environments: A Delphi Study

    ERIC Educational Resources Information Center

    So, Hyo-Jeong; Bonk, Curtis J.

    2010-01-01

    In this study, a Delphi method was used to identify and predict the roles of blended learning approaches in computer-supported collaborative learning (CSCL) environments. The Delphi panel consisted of experts in online learning from different geographic regions of the world. This study discusses findings related to (a) pros and cons of blended…

  17. Computer Series, 75: Bits and Pieces, 30.

    ERIC Educational Resources Information Center

    Moore, John W., Ed.

    1986-01-01

    Identifies six computer-oriented approaches to teaching concepts in chemistry. Describes courseware and equipment involved in various experiments dealing with such topics as polymer configurations, stepper motors, conductometric titration, kinetic spectrophotometry, and overlap integrals. (TW)

  18. Identifying local structural states in atomic imaging by computer vision

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

    Laanait, Nouamane; Ziatdinov, Maxim; He, Qian

    The availability of atomically resolved imaging modalities enables an unprecedented view into the local structural states of materials, which manifest themselves by deviations from the fundamental assumptions of periodicity and symmetry. Consequently, approaches that aim to extract these local structural states from atomic imaging data with minimal assumptions regarding the average crystallographic configuration of a material are indispensable to advances in structural and chemical investigations of materials. Here, we present an approach to identify and classify local structural states that is rooted in computer vision. This approach introduces a definition of a structural state that is composed of both localmore » and non-local information extracted from atomically resolved images, and is wholly untethered from the familiar concepts of symmetry and periodicity. Instead, this approach relies on computer vision techniques such as feature detection, and concepts such as scale-invariance. We present the fundamental aspects of local structural state extraction and classification by application to simulated scanning transmission electron microscopy images, and analyze the robustness of this approach in the presence of common instrumental factors such as noise, limited spatial resolution, and weak contrast. Finally, we apply this computer vision-based approach for the unsupervised detection and classification of local structural states in an experimental electron micrograph of a complex oxides interface, and a scanning tunneling micrograph of a defect engineered multilayer graphene surface.« less

  19. Identifying local structural states in atomic imaging by computer vision

    DOE PAGES

    Laanait, Nouamane; Ziatdinov, Maxim; He, Qian; ...

    2016-11-02

    The availability of atomically resolved imaging modalities enables an unprecedented view into the local structural states of materials, which manifest themselves by deviations from the fundamental assumptions of periodicity and symmetry. Consequently, approaches that aim to extract these local structural states from atomic imaging data with minimal assumptions regarding the average crystallographic configuration of a material are indispensable to advances in structural and chemical investigations of materials. Here, we present an approach to identify and classify local structural states that is rooted in computer vision. This approach introduces a definition of a structural state that is composed of both localmore » and non-local information extracted from atomically resolved images, and is wholly untethered from the familiar concepts of symmetry and periodicity. Instead, this approach relies on computer vision techniques such as feature detection, and concepts such as scale-invariance. We present the fundamental aspects of local structural state extraction and classification by application to simulated scanning transmission electron microscopy images, and analyze the robustness of this approach in the presence of common instrumental factors such as noise, limited spatial resolution, and weak contrast. Finally, we apply this computer vision-based approach for the unsupervised detection and classification of local structural states in an experimental electron micrograph of a complex oxides interface, and a scanning tunneling micrograph of a defect engineered multilayer graphene surface.« less

  20. Computers in Science: Thinking Outside the Discipline.

    ERIC Educational Resources Information Center

    Hamilton, Todd M.

    2003-01-01

    Describes the Computers in Science course which integrates computer-related techniques into the science disciplines of chemistry, physics, biology, and Earth science. Uses a team teaching approach and teaches students how to solve chemistry problems with spreadsheets, identify minerals with X-rays, and chemical and force analysis. (Contains 14…

  1. A Computer Vision Approach to Identify Einstein Rings and Arcs

    NASA Astrophysics Data System (ADS)

    Lee, Chien-Hsiu

    2017-03-01

    Einstein rings are rare gems of strong lensing phenomena; the ring images can be used to probe the underlying lens gravitational potential at every position angles, tightly constraining the lens mass profile. In addition, the magnified images also enable us to probe high-z galaxies with enhanced resolution and signal-to-noise ratios. However, only a handful of Einstein rings have been reported, either from serendipitous discoveries or or visual inspections of hundred thousands of massive galaxies or galaxy clusters. In the era of large sky surveys, an automated approach to identify ring pattern in the big data to come is in high demand. Here, we present an Einstein ring recognition approach based on computer vision techniques. The workhorse is the circle Hough transform that recognise circular patterns or arcs in the images. We propose a two-tier approach by first pre-selecting massive galaxies associated with multiple blue objects as possible lens, than use Hough transform to identify circular pattern. As a proof-of-concept, we apply our approach to SDSS, with a high completeness, albeit with low purity. We also apply our approach to other lenses in DES, HSC-SSP, and UltraVISTA survey, illustrating the versatility of our approach.

  2. An Integrated Constraint Programming Approach to Scheduling Sports Leagues with Divisional and Round-robin Tournaments

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

    Carlsson, Mats; Johansson, Mikael; Larson, Jeffrey

    Previous approaches for scheduling a league with round-robin and divisional tournaments involved decomposing the problem into easier subproblems. This approach, used to schedule the top Swedish handball league Elitserien, reduces the problem complexity but can result in suboptimal schedules. This paper presents an integrated constraint programming model that allows to perform the scheduling in a single step. Particular attention is given to identifying implied and symmetry-breaking constraints that reduce the computational complexity significantly. The experimental evaluation of the integrated approach takes considerably less computational effort than the previous approach.

  3. Computer-aided drug discovery.

    PubMed

    Bajorath, Jürgen

    2015-01-01

    Computational approaches are an integral part of interdisciplinary drug discovery research. Understanding the science behind computational tools, their opportunities, and limitations is essential to make a true impact on drug discovery at different levels. If applied in a scientifically meaningful way, computational methods improve the ability to identify and evaluate potential drug molecules, but there remain weaknesses in the methods that preclude naïve applications. Herein, current trends in computer-aided drug discovery are reviewed, and selected computational areas are discussed. Approaches are highlighted that aid in the identification and optimization of new drug candidates. Emphasis is put on the presentation and discussion of computational concepts and methods, rather than case studies or application examples. As such, this contribution aims to provide an overview of the current methodological spectrum of computational drug discovery for a broad audience.

  4. A new computer approach to mixed feature classification for forestry application

    NASA Technical Reports Server (NTRS)

    Kan, E. P.

    1976-01-01

    A computer approach for mapping mixed forest features (i.e., types, classes) from computer classification maps is discussed. Mixed features such as mixed softwood/hardwood stands are treated as admixtures of softwood and hardwood areas. Large-area mixed features are identified and small-area features neglected when the nominal size of a mixed feature can be specified. The computer program merges small isolated areas into surrounding areas by the iterative manipulation of the postprocessing algorithm that eliminates small connected sets. For a forestry application, computer-classified LANDSAT multispectral scanner data of the Sam Houston National Forest were used to demonstrate the proposed approach. The technique was successful in cleaning the salt-and-pepper appearance of multiclass classification maps and in mapping admixtures of softwood areas and hardwood areas. However, the computer-mapped mixed areas matched very poorly with the ground truth because of inadequate resolution and inappropriate definition of mixed features.

  5. At the Crossroads of Learning and Culture: Identifying a Construct for Effective Computer-Assisted Language Learning for English Language Learners

    ERIC Educational Resources Information Center

    Shaw, Yun

    2010-01-01

    Many of the commercial Computer-Assisted Language Learning (CALL) programs available today typically take a generic approach. This approach standardizes the program so that it can be used to teach any language merely by translating the content from one language to another. These CALL programs rarely consider the cultural background or preferred…

  6. User interfaces for computational science: A domain specific language for OOMMF embedded in Python

    NASA Astrophysics Data System (ADS)

    Beg, Marijan; Pepper, Ryan A.; Fangohr, Hans

    2017-05-01

    Computer simulations are used widely across the engineering and science disciplines, including in the research and development of magnetic devices using computational micromagnetics. In this work, we identify and review different approaches to configuring simulation runs: (i) the re-compilation of source code, (ii) the use of configuration files, (iii) the graphical user interface, and (iv) embedding the simulation specification in an existing programming language to express the computational problem. We identify the advantages and disadvantages of different approaches and discuss their implications on effectiveness and reproducibility of computational studies and results. Following on from this, we design and describe a domain specific language for micromagnetics that is embedded in the Python language, and allows users to define the micromagnetic simulations they want to carry out in a flexible way. We have implemented this micromagnetic simulation description language together with a computational backend that executes the simulation task using the Object Oriented MicroMagnetic Framework (OOMMF). We illustrate the use of this Python interface for OOMMF by solving the micromagnetic standard problem 4. All the code is publicly available and is open source.

  7. BACLAB: A Computer Simulation of a Medical Bacteriology Laboratory--An Aid for Teaching Tertiary Level Microbiology.

    ERIC Educational Resources Information Center

    Lewington, J.; And Others

    1985-01-01

    Describes a computer simulation program which helps students learn the main biochemical tests and profiles for identifying medically important bacteria. Also discusses the advantages and applications of this type of approach. (ML)

  8. Integrating autonomous distributed control into a human-centric C4ISR environment

    NASA Astrophysics Data System (ADS)

    Straub, Jeremy

    2017-05-01

    This paper considers incorporating autonomy into human-centric Command, Control, Communications, Computers, Intelligence, Surveillance and Reconnaissance (C4ISR) environments. Specifically, it focuses on identifying ways that current autonomy technologies can augment human control and the challenges presented by additive autonomy. Three approaches to this challenge are considered, stemming from prior work in two converging areas. In the first, the problem is approached as augmenting what humans currently do with automation. In the alternate approach, the problem is approached as treating humans as actors within a cyber-physical system-of-systems (stemming from robotic distributed computing). A third approach, combines elements of both of the aforementioned.

  9. Prediction of Patient-Controlled Analgesic Consumption: A Multimodel Regression Tree Approach.

    PubMed

    Hu, Yuh-Jyh; Ku, Tien-Hsiung; Yang, Yu-Hung; Shen, Jia-Ying

    2018-01-01

    Several factors contribute to individual variability in postoperative pain, therefore, individuals consume postoperative analgesics at different rates. Although many statistical studies have analyzed postoperative pain and analgesic consumption, most have identified only the correlation and have not subjected the statistical model to further tests in order to evaluate its predictive accuracy. In this study involving 3052 patients, a multistrategy computational approach was developed for analgesic consumption prediction. This approach uses data on patient-controlled analgesia demand behavior over time and combines clustering, classification, and regression to mitigate the limitations of current statistical models. Cross-validation results indicated that the proposed approach significantly outperforms various existing regression methods. Moreover, a comparison between the predictions by anesthesiologists and medical specialists and those of the computational approach for an independent test data set of 60 patients further evidenced the superiority of the computational approach in predicting analgesic consumption because it produced markedly lower root mean squared errors.

  10. Hiding in Plain Sight: Identifying Computational Thinking in the Ontario Elementary School Curriculum

    ERIC Educational Resources Information Center

    Hennessey, Eden J. V.; Mueller, Julie; Beckett, Danielle; Fisher, Peter A.

    2017-01-01

    Given a growing digital economy with complex problems, demands are being made for education to address computational thinking (CT)--an approach to problem solving that draws on the tenets of computer science. We conducted a comprehensive content analysis of the Ontario elementary school curriculum documents for 44 CT-related terms to examine the…

  11. Examining Student Opinions on Computer Use Based on the Learning Styles in Mathematics Education

    ERIC Educational Resources Information Center

    Ozgen, Kemal; Bindak, Recep

    2012-01-01

    The purpose of this study is to identify the opinions of high school students, who have different learning styles, related to computer use in mathematics education. High school students' opinions on computer use in mathematics education were collected with both qualitative and quantitative approaches in the study conducted with a survey model. For…

  12. On Identifying the Sound Sources in a Turbulent Flow

    NASA Technical Reports Server (NTRS)

    Goldstein, M. E.

    2008-01-01

    A space-time filtering approach is used to divide an unbounded turbulent flow into its radiating and non-radiating components. The result is then used to clarify a number of issues including the possibility of identifying the sources of the sound in such flows. It is also used to investigate the efficacy of some of the more recent computational approaches.

  13. SELDI PROTEINCHIP-BASED LIVER BIOMARKERS IN FUNGICIDE EXPOSED ZEBRAFISH

    EPA Science Inventory

    The research presented here is part of a three-phased small fish computational toxicology project using a combination of 1) whole organism endpoints, 2) genomic, proteomic, and metabolomic approaches, and 3) computational modeling to (a) identify new molecular biomarkers of expos...

  14. Development and Validation of a Computational Model for Androgen Receptor Activity

    EPA Science Inventory

    Testing thousands of chemicals to identify potential androgen receptor (AR) agonists or antagonists would cost millions of dollars and take decades to complete using current validated methods. High-throughput in vitro screening (HTS) and computational toxicology approaches can mo...

  15. Exploring the role of computers in sex and relationship education within British families.

    PubMed

    Turnbull, Triece; van Schaik, Paul; van Wersch, Anna

    2013-04-01

    In this study, we aimed to identify the impact that computers can have in relation to sex and relationship education, as well as to provide a communication model that can be used within British families. We used a mixed-methods approach to explore the factors that influence communication of sexual matters within British families. Twenty families from the northeast of England were recruited through purposive sampling. First, semistructured interviews were conducted to identify how sexual matters were discussed within families. Interviews were recorded, transcribed verbatim, and then analyzed using the grounded theory approach. The second part of the research involved identifying the impact of using a computer program on knowledge and confidence within families to enhance communication about sexual matters. Although the majority of parents and their children were found to discuss sexual matters, the computer program was found to increase knowledge and confidence, which led to greater communication within families. The results highlighted the beneficial role that computer programs can have when educating and increasing communication within families. Future research needs to focus on improving access to information relating to sex and relationship education for parents so they can educate and talk openly about sexual matters with their children. A resource that does exactly this is www.safecoolsex.com.

  16. Insights into enzymatic halogenation from computational studies

    PubMed Central

    Senn, Hans M.

    2014-01-01

    The halogenases are a group of enzymes that have only come to the fore over the last 10 years thanks to the discovery and characterization of several novel representatives. They have revealed the fascinating variety of distinct chemical mechanisms that nature utilizes to activate halogens and introduce them into organic substrates. Computational studies using a range of approaches have already elucidated many details of the mechanisms of these enzymes, often in synergistic combination with experiment. This Review summarizes the main insights gained from these studies. It also seeks to identify open questions that are amenable to computational investigations. The studies discussed herein serve to illustrate some of the limitations of the current computational approaches and the challenges encountered in computational mechanistic enzymology. PMID:25426489

  17. Knowledge-driven lead discovery.

    PubMed

    Pirard, Bernard

    2005-11-01

    Virtual screening encompasses several computational approaches which have proven valuable for identifying novel leads. These approaches rely on available information. Herein, we review recent successful applications of virtual screening. The extension of virtual screening methodologies to target families is also briefly discussed.

  18. A Hybrid MPI/OpenMP Approach for Parallel Groundwater Model Calibration on Multicore Computers

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

    Tang, Guoping; D'Azevedo, Ed F; Zhang, Fan

    2010-01-01

    Groundwater model calibration is becoming increasingly computationally time intensive. We describe a hybrid MPI/OpenMP approach to exploit two levels of parallelism in software and hardware to reduce calibration time on multicore computers with minimal parallelization effort. At first, HydroGeoChem 5.0 (HGC5) is parallelized using OpenMP for a uranium transport model with over a hundred species involving nearly a hundred reactions, and a field scale coupled flow and transport model. In the first application, a single parallelizable loop is identified to consume over 97% of the total computational time. With a few lines of OpenMP compiler directives inserted into the code,more » the computational time reduces about ten times on a compute node with 16 cores. The performance is further improved by selectively parallelizing a few more loops. For the field scale application, parallelizable loops in 15 of the 174 subroutines in HGC5 are identified to take more than 99% of the execution time. By adding the preconditioned conjugate gradient solver and BICGSTAB, and using a coloring scheme to separate the elements, nodes, and boundary sides, the subroutines for finite element assembly, soil property update, and boundary condition application are parallelized, resulting in a speedup of about 10 on a 16-core compute node. The Levenberg-Marquardt (LM) algorithm is added into HGC5 with the Jacobian calculation and lambda search parallelized using MPI. With this hybrid approach, compute nodes at the number of adjustable parameters (when the forward difference is used for Jacobian approximation), or twice that number (if the center difference is used), are used to reduce the calibration time from days and weeks to a few hours for the two applications. This approach can be extended to global optimization scheme and Monte Carol analysis where thousands of compute nodes can be efficiently utilized.« less

  19. Identification of drug interactions in hospitals--computerized screening vs. bedside recording.

    PubMed

    Blix, H S; Viktil, K K; Moger, T A; Reikvam, A

    2008-04-01

    Managing drug interactions in hospitalized patients is important and challenging. The objective of the study was to compare two methods for identification of drug interactions (DDIs)--computerized screening and prospective bedside recording--with regard to capability of identifying DDIs. Patient characteristics were recorded for patients admitted to five hospitals. By bedside evaluation drug-related problems, including DDIs, were prospectively recorded by pharmacists and discussed in multidisciplinary teams. A computer screening programme was used to identify DDIs retrospectively--dividing DDIs into four classes: A, avoid; B, avoid/take precautions; C, take precautions; D, no action needed. Among 827 patients, computer screening identified DDIs in 544 patients (66%); 351 had DDIs introduced in hospital. The 1513 computer-identified DDIs had the following distribution: type A 78; type B 915; type C 38; type D 482. By bedside evaluation, 99 DDIs were identified in 73 patients (9%). The proportions of computer recorded DDIs which were also identified at the bedside were: 5%, 8%, 8%, 2% DDIs of types A, B, C and D respectively. In 10 patients, DDIs not registered by computer screening were identified by bedside evaluation. The drugs most frequently involved in DDIs, identified by computerized screening were acetylsalicylic acid, warfarin, furosemide and digitoxin compared with warfarin, simvastatin, theophylline and carbamazepine, by bedside evaluation. Despite an active prospective bedside search for DDIs, this approach identified less than one in 10 of the DDIs recorded by computer screening, including those regarded as hazardous. However, computer screening overestimates considerably when the objective is to identify clinically relevant DDIs.

  20. Categorisation of visualisation methods to support the design of Human-Computer Interaction Systems.

    PubMed

    Li, Katie; Tiwari, Ashutosh; Alcock, Jeffrey; Bermell-Garcia, Pablo

    2016-07-01

    During the design of Human-Computer Interaction (HCI) systems, the creation of visual artefacts forms an important part of design. On one hand producing a visual artefact has a number of advantages: it helps designers to externalise their thought and acts as a common language between different stakeholders. On the other hand, if an inappropriate visualisation method is employed it could hinder the design process. To support the design of HCI systems, this paper reviews the categorisation of visualisation methods used in HCI. A keyword search is conducted to identify a) current HCI design methods, b) approaches of selecting these methods. The resulting design methods are filtered to create a list of just visualisation methods. These are then categorised using the approaches identified in (b). As a result 23 HCI visualisation methods are identified and categorised in 5 selection approaches (The Recipient, Primary Purpose, Visual Archetype, Interaction Type, and The Design Process). Copyright © 2016 The Authors. Published by Elsevier Ltd.. All rights reserved.

  1. Towards Text Copyright Detection Using Metadata in Web Applications

    ERIC Educational Resources Information Center

    Poulos, Marios; Korfiatis, Nikolaos; Bokos, George

    2011-01-01

    Purpose: This paper aims to present the semantic content identifier (SCI), a permanent identifier, computed through a linear-time onion-peeling algorithm that enables the extraction of semantic features from a text, and the integration of this information within the permanent identifier. Design/methodology/approach: The authors employ SCI to…

  2. Applications of computer-aided approaches in the development of hepatitis C antiviral agents.

    PubMed

    Ganesan, Aravindhan; Barakat, Khaled

    2017-04-01

    Hepatitis C virus (HCV) is a global health problem that causes several chronic life-threatening liver diseases. The numbers of people affected by HCV are rising annually. Since 2011, the FDA has approved several anti-HCV drugs; while many other promising HCV drugs are currently in late clinical trials. Areas covered: This review discusses the applications of different computational approaches in HCV drug design. Expert opinion: Molecular docking and virtual screening approaches have emerged as a low-cost tool to screen large databases and identify potential small-molecule hits against HCV targets. Ligand-based approaches are useful for filtering-out compounds with rich physicochemical properties to inhibit HCV targets. Molecular dynamics (MD) remains a useful tool in optimizing the ligand-protein complexes and understand the ligand binding modes and drug resistance mechanisms in HCV. Despite their varied roles, the application of in-silico approaches in HCV drug design is still in its infancy. A more mature application should aim at modelling the whole HCV replicon in its active form and help to identify new effective druggable sites within the replicon system. With more technological advancements, the roles of computer-aided methods are only going to increase several folds in the development of next-generation HCV drugs.

  3. Characterization of potential driver mutations involved in human breast cancer by computational approaches

    PubMed Central

    Rajendran, Barani Kumar; Deng, Chu-Xia

    2017-01-01

    Breast cancer is the second most frequently occurring form of cancer and is also the second most lethal cancer in women worldwide. A genetic mutation is one of the key factors that alter multiple cellular regulatory pathways and drive breast cancer initiation and progression yet nature of these cancer drivers remains elusive. In this article, we have reviewed various computational perspectives and algorithms for exploring breast cancer driver mutation genes. Using both frequency based and mutational exclusivity based approaches, we identified 195 driver genes and shortlisted 63 of them as candidate drivers for breast cancer using various computational approaches. Finally, we conducted network and pathway analysis to explore their functions in breast tumorigenesis including tumor initiation, progression, and metastasis. PMID:28477017

  4. A computer-aided approach to detect the fetal behavioral states using multi-sensor Magnetocardiographic recordings.

    PubMed

    Vairavan, S; Ulusar, U D; Eswaran, H; Preissl, H; Wilson, J D; Mckelvey, S S; Lowery, C L; Govindan, R B

    2016-02-01

    We propose a novel computational approach to automatically identify the fetal heart rate patterns (fHRPs), which are reflective of sleep/awake states. By combining these patterns with presence or absence of movements, a fetal behavioral state (fBS) was determined. The expert scores were used as the gold standard and objective thresholds for the detection procedure were obtained using Receiver Operating Characteristics (ROC) analysis. To assess the performance, intraclass correlation was computed between the proposed approach and the mutually agreed expert scores. The detected fHRPs were then associated to their corresponding fBS based on the fetal movement obtained from fetal magnetocardiogaphic (fMCG) signals. This approach may aid clinicians in objectively assessing the fBS and monitoring fetal wellbeing. Copyright © 2015 Elsevier Ltd. All rights reserved.

  5. Analytical and computational approaches to define the Aspergillus niger secretome.

    PubMed

    Tsang, Adrian; Butler, Gregory; Powlowski, Justin; Panisko, Ellen A; Baker, Scott E

    2009-03-01

    We used computational and mass spectrometric approaches to characterize the Aspergillus niger secretome.The 11,200 gene models predicted in the genome of A. niger strain ATCC 1015 were the data source for the analysis. Depending on the computational methods used, 691 to 881 proteins were predicted to be secreted proteins. We cultured A. niger in six different media and analyzed the extracellular proteins produced using mass spectrometry. A total of 222 proteins were identified, with 39 proteins expressed under all six conditions and 74 proteins expressed under only one condition. The secreted proteins identified by mass spectrometry were used to guide the correction of about 20 gene models. Additional analysis focused on extracellular enzymes of interest for biomass processing. Of the 63 glycoside hydrolases predicted to be capable of hydrolyzing cellulose, hemicellulose or pectin, 94% of the exo-acting enzymes and only 18% of the endo-acting enzymes were experimentally detected.

  6. Petri-net-based 2D design of DNA walker circuits.

    PubMed

    Gilbert, David; Heiner, Monika; Rohr, Christian

    2018-01-01

    We consider localised DNA computation, where a DNA strand walks along a binary decision graph to compute a binary function. One of the challenges for the design of reliable walker circuits consists in leakage transitions, which occur when a walker jumps into another branch of the decision graph. We automatically identify leakage transitions, which allows for a detailed qualitative and quantitative assessment of circuit designs, design comparison, and design optimisation. The ability to identify leakage transitions is an important step in the process of optimising DNA circuit layouts where the aim is to minimise the computational error inherent in a circuit while minimising the area of the circuit. Our 2D modelling approach of DNA walker circuits relies on coloured stochastic Petri nets which enable functionality, topology and dimensionality all to be integrated in one two-dimensional model. Our modelling and analysis approach can be easily extended to 3-dimensional walker systems.

  7. Analytical and computational approaches to define the Aspergillus niger secretome

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

    Tsang, Adrian; Butler, Gregory D.; Powlowski, Justin

    2009-03-01

    We used computational and mass spectrometric approaches to characterize the Aspergillus niger secretome. The 11,200 gene models predicted in the genome of A. niger strain ATCC 1015 were the data source for the analysis. Depending on the computational methods used, 691 to 881 proteins were predicted to be secreted proteins. We cultured A. niger in six different media and analyzed the extracellular proteins produced using mass spectrometry. A total of 222 proteins were identified, with 39 proteins expressed under all six conditions and 74 proteins expressed under only one condition. The secreted proteins identified by mass spectrometry were used tomore » guide the correction of about 20 gene models. Additional analysis focused on extracellular enzymes of interest for biomass processing. Of the 63 glycoside hydrolases predicted to be capable of hydrolyzing cellulose, hemicellulose or pectin, 94% of the exo-acting enzymes and only 18% of the endo-acting enzymes were experimentally detected.« less

  8. Computing and Applying Atomic Regulons to Understand Gene Expression and Regulation

    DOE PAGES

    Faria, José P.; Davis, James J.; Edirisinghe, Janaka N.; ...

    2016-11-24

    Understanding gene function and regulation is essential for the interpretation, prediction, and ultimate design of cell responses to changes in the environment. A multitude of technologies, abstractions, and interpretive frameworks have emerged to answer the challenges presented by genome function and regulatory network inference. Here, we propose a new approach for producing biologically meaningful clusters of coexpressed genes, called Atomic Regulons (ARs), based on expression data, gene context, and functional relationships. We demonstrate this new approach by computing ARs for Escherichia coli, which we compare with the coexpressed gene clusters predicted by two prevalent existing methods: hierarchical clustering and k-meansmore » clustering. We test the consistency of ARs predicted by all methods against expected interactions predicted by the Context Likelihood of Relatedness (CLR) mutual information based method, finding that the ARs produced by our approach show better agreement with CLR interactions. We then apply our method to compute ARs for four other genomes: Shewanella oneidensis, Pseudomonas aeruginosa, Thermus thermophilus, and Staphylococcus aureus. We compare the AR clusters from all genomes to study the similarity of coexpression among a phylogenetically diverse set of species, identifying subsystems that show remarkable similarity over wide phylogenetic distances. We also study the sensitivity of our method for computing ARs to the expression data used in the computation, showing that our new approach requires less data than competing approaches to converge to a near final configuration of ARs. We go on to use our sensitivity analysis to identify the specific experiments that lead most rapidly to the final set of ARs for E. coli. As a result, this analysis produces insights into improving the design of gene expression experiments.« less

  9. Computing and Applying Atomic Regulons to Understand Gene Expression and Regulation

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

    Faria, José P.; Davis, James J.; Edirisinghe, Janaka N.

    Understanding gene function and regulation is essential for the interpretation, prediction, and ultimate design of cell responses to changes in the environment. A multitude of technologies, abstractions, and interpretive frameworks have emerged to answer the challenges presented by genome function and regulatory network inference. Here, we propose a new approach for producing biologically meaningful clusters of coexpressed genes, called Atomic Regulons (ARs), based on expression data, gene context, and functional relationships. We demonstrate this new approach by computing ARs for Escherichia coli, which we compare with the coexpressed gene clusters predicted by two prevalent existing methods: hierarchical clustering and k-meansmore » clustering. We test the consistency of ARs predicted by all methods against expected interactions predicted by the Context Likelihood of Relatedness (CLR) mutual information based method, finding that the ARs produced by our approach show better agreement with CLR interactions. We then apply our method to compute ARs for four other genomes: Shewanella oneidensis, Pseudomonas aeruginosa, Thermus thermophilus, and Staphylococcus aureus. We compare the AR clusters from all genomes to study the similarity of coexpression among a phylogenetically diverse set of species, identifying subsystems that show remarkable similarity over wide phylogenetic distances. We also study the sensitivity of our method for computing ARs to the expression data used in the computation, showing that our new approach requires less data than competing approaches to converge to a near final configuration of ARs. We go on to use our sensitivity analysis to identify the specific experiments that lead most rapidly to the final set of ARs for E. coli. As a result, this analysis produces insights into improving the design of gene expression experiments.« less

  10. Progress and challenges in bioinformatics approaches for enhancer identification

    PubMed Central

    Kleftogiannis, Dimitrios; Kalnis, Panos

    2016-01-01

    Enhancers are cis-acting DNA elements that play critical roles in distal regulation of gene expression. Identifying enhancers is an important step for understanding distinct gene expression programs that may reflect normal and pathogenic cellular conditions. Experimental identification of enhancers is constrained by the set of conditions used in the experiment. This requires multiple experiments to identify enhancers, as they can be active under specific cellular conditions but not in different cell types/tissues or cellular states. This has opened prospects for computational prediction methods that can be used for high-throughput identification of putative enhancers to complement experimental approaches. Potential functions and properties of predicted enhancers have been catalogued and summarized in several enhancer-oriented databases. Because the current methods for the computational prediction of enhancers produce significantly different enhancer predictions, it will be beneficial for the research community to have an overview of the strategies and solutions developed in this field. In this review, we focus on the identification and analysis of enhancers by bioinformatics approaches. First, we describe a general framework for computational identification of enhancers, present relevant data types and discuss possible computational solutions. Next, we cover over 30 existing computational enhancer identification methods that were developed since 2000. Our review highlights advantages, limitations and potentials, while suggesting pragmatic guidelines for development of more efficient computational enhancer prediction methods. Finally, we discuss challenges and open problems of this topic, which require further consideration. PMID:26634919

  11. Method of identifying clusters representing statistical dependencies in multivariate data

    NASA Technical Reports Server (NTRS)

    Borucki, W. J.; Card, D. H.; Lyle, G. C.

    1975-01-01

    Approach is first to cluster and then to compute spatial boundaries for resulting clusters. Next step is to compute, from set of Monte Carlo samples obtained from scrambled data, estimates of probabilities of obtaining at least as many points within boundaries as were actually observed in original data.

  12. Wikipedia Writing as Praxis: Computer-Mediated Socialization of Second-Language Writers

    ERIC Educational Resources Information Center

    King, Brian W.

    2015-01-01

    This study explores the writing of Wikipedia articles as a form of authentic writing for learners of English in Hong Kong. Adopting "Second Language Socialization and Language Learning & Identity" approaches to language learning inquiry, it responds to an identified shortage of research on computer-mediated language socialization.…

  13. An Multivariate Distance-Based Analytic Framework for Connectome-Wide Association Studies

    PubMed Central

    Shehzad, Zarrar; Kelly, Clare; Reiss, Philip T.; Craddock, R. Cameron; Emerson, John W.; McMahon, Katie; Copland, David A.; Castellanos, F. Xavier; Milham, Michael P.

    2014-01-01

    The identification of phenotypic associations in high-dimensional brain connectivity data represents the next frontier in the neuroimaging connectomics era. Exploration of brain-phenotype relationships remains limited by statistical approaches that are computationally intensive, depend on a priori hypotheses, or require stringent correction for multiple comparisons. Here, we propose a computationally efficient, data-driven technique for connectome-wide association studies (CWAS) that provides a comprehensive voxel-wise survey of brain-behavior relationships across the connectome; the approach identifies voxels whose whole-brain connectivity patterns vary significantly with a phenotypic variable. Using resting state fMRI data, we demonstrate the utility of our analytic framework by identifying significant connectivity-phenotype relationships for full-scale IQ and assessing their overlap with existent neuroimaging findings, as synthesized by openly available automated meta-analysis (www.neurosynth.org). The results appeared to be robust to the removal of nuisance covariates (i.e., mean connectivity, global signal, and motion) and varying brain resolution (i.e., voxelwise results are highly similar to results using 800 parcellations). We show that CWAS findings can be used to guide subsequent seed-based correlation analyses. Finally, we demonstrate the applicability of the approach by examining CWAS for three additional datasets, each encompassing a distinct phenotypic variable: neurotypical development, Attention-Deficit/Hyperactivity Disorder diagnostic status, and L-dopa pharmacological manipulation. For each phenotype, our approach to CWAS identified distinct connectome-wide association profiles, not previously attainable in a single study utilizing traditional univariate approaches. As a computationally efficient, extensible, and scalable method, our CWAS framework can accelerate the discovery of brain-behavior relationships in the connectome. PMID:24583255

  14. Parallel photonic information processing at gigabyte per second data rates using transient states

    NASA Astrophysics Data System (ADS)

    Brunner, Daniel; Soriano, Miguel C.; Mirasso, Claudio R.; Fischer, Ingo

    2013-01-01

    The increasing demands on information processing require novel computational concepts and true parallelism. Nevertheless, hardware realizations of unconventional computing approaches never exceeded a marginal existence. While the application of optics in super-computing receives reawakened interest, new concepts, partly neuro-inspired, are being considered and developed. Here we experimentally demonstrate the potential of a simple photonic architecture to process information at unprecedented data rates, implementing a learning-based approach. A semiconductor laser subject to delayed self-feedback and optical data injection is employed to solve computationally hard tasks. We demonstrate simultaneous spoken digit and speaker recognition and chaotic time-series prediction at data rates beyond 1Gbyte/s. We identify all digits with very low classification errors and perform chaotic time-series prediction with 10% error. Our approach bridges the areas of photonic information processing, cognitive and information science.

  15. New Frontiers in Analyzing Dynamic Group Interactions: Bridging Social and Computer Science

    PubMed Central

    Lehmann-Willenbrock, Nale; Hung, Hayley; Keyton, Joann

    2017-01-01

    This special issue on advancing interdisciplinary collaboration between computer scientists and social scientists documents the joint results of the international Lorentz workshop, “Interdisciplinary Insights into Group and Team Dynamics,” which took place in Leiden, The Netherlands, July 2016. An equal number of scholars from social and computer science participated in the workshop and contributed to the papers included in this special issue. In this introduction, we first identify interaction dynamics as the core of group and team models and review how scholars in social and computer science have typically approached behavioral interactions in groups and teams. Next, we identify key challenges for interdisciplinary collaboration between social and computer scientists, and we provide an overview of the different articles in this special issue aimed at addressing these challenges. PMID:29249891

  16. New Frontiers in Analyzing Dynamic Group Interactions: Bridging Social and Computer Science.

    PubMed

    Lehmann-Willenbrock, Nale; Hung, Hayley; Keyton, Joann

    2017-10-01

    This special issue on advancing interdisciplinary collaboration between computer scientists and social scientists documents the joint results of the international Lorentz workshop, "Interdisciplinary Insights into Group and Team Dynamics," which took place in Leiden, The Netherlands, July 2016. An equal number of scholars from social and computer science participated in the workshop and contributed to the papers included in this special issue. In this introduction, we first identify interaction dynamics as the core of group and team models and review how scholars in social and computer science have typically approached behavioral interactions in groups and teams. Next, we identify key challenges for interdisciplinary collaboration between social and computer scientists, and we provide an overview of the different articles in this special issue aimed at addressing these challenges.

  17. Rate-equation modelling and ensemble approach to extraction of parameters for viral infection-induced cell apoptosis and necrosis

    NASA Astrophysics Data System (ADS)

    Domanskyi, Sergii; Schilling, Joshua E.; Gorshkov, Vyacheslav; Libert, Sergiy; Privman, Vladimir

    2016-09-01

    We develop a theoretical approach that uses physiochemical kinetics modelling to describe cell population dynamics upon progression of viral infection in cell culture, which results in cell apoptosis (programmed cell death) and necrosis (direct cell death). Several model parameters necessary for computer simulation were determined by reviewing and analyzing available published experimental data. By comparing experimental data to computer modelling results, we identify the parameters that are the most sensitive to the measured system properties and allow for the best data fitting. Our model allows extraction of parameters from experimental data and also has predictive power. Using the model we describe interesting time-dependent quantities that were not directly measured in the experiment and identify correlations among the fitted parameter values. Numerical simulation of viral infection progression is done by a rate-equation approach resulting in a system of "stiff" equations, which are solved by using a novel variant of the stochastic ensemble modelling approach. The latter was originally developed for coupled chemical reactions.

  18. Rate-equation modelling and ensemble approach to extraction of parameters for viral infection-induced cell apoptosis and necrosis

    NASA Astrophysics Data System (ADS)

    Domanskyi, Sergii; Schilling, Joshua; Gorshkov, Vyacheslav; Libert, Sergiy; Privman, Vladimir

    We develop a theoretical approach that uses physiochemical kinetics modelling to describe cell population dynamics upon progression of viral infection in cell culture, which results in cell apoptosis (programmed cell death) and necrosis (direct cell death). Several model parameters necessary for computer simulation were determined by reviewing and analyzing available published experimental data. By comparing experimental data to computer modelling results, we identify the parameters that are the most sensitive to the measured system properties and allow for the best data fitting. Our model allows extraction of parameters from experimental data and also has predictive power. Using the model we describe interesting time-dependent quantities that were not directly measured in the experiment and identify correlations among the fitted parameter values. Numerical simulation of viral infection progression is done by a rate-equation approach resulting in a system of ``stiff'' equations, which are solved by using a novel variant of the stochastic ensemble modelling approach. The latter was originally developed for coupled chemical reactions.

  19. Costs of fire suppression forces based on cost-aggregation approach

    Treesearch

    Gonz& aacute; lez-Cab& aacute; Armando n; Charles W. McKetta; Thomas J. Mills

    1984-01-01

    A cost-aggregation approach has been developed for determining the cost of Fire Management Inputs (FMls)-the direct fireline production units (personnel and equipment) used in initial attack and large-fire suppression activities. All components contributing to an FMI are identified, computed, and summed to estimate hourly costs. This approach can be applied to any FMI...

  20. Report: Unsupervised identification of malaria parasites using computer vision.

    PubMed

    Khan, Najeed Ahmed; Pervaz, Hassan; Latif, Arsalan; Musharaff, Ayesha

    2017-01-01

    Malaria in human is a serious and fatal tropical disease. This disease results from Anopheles mosquitoes that are infected by Plasmodium species. The clinical diagnosis of malaria based on the history, symptoms and clinical findings must always be confirmed by laboratory diagnosis. Laboratory diagnosis of malaria involves identification of malaria parasite or its antigen / products in the blood of the patient. Manual diagnosis of malaria parasite by the pathologists has proven to become cumbersome. Therefore, there is a need of automatic, efficient and accurate identification of malaria parasite. In this paper, we proposed a computer vision based approach to identify the malaria parasite from light microscopy images. This research deals with the challenges involved in the automatic detection of malaria parasite tissues. Our proposed method is based on the pixel-based approach. We used K-means clustering (unsupervised approach) for the segmentation to identify malaria parasite tissues.

  1. Frequency domain system identification methods - Matrix fraction description approach

    NASA Technical Reports Server (NTRS)

    Horta, Luca G.; Juang, Jer-Nan

    1993-01-01

    This paper presents the use of matrix fraction descriptions for least-squares curve fitting of the frequency spectra to compute two matrix polynomials. The matrix polynomials are intermediate step to obtain a linearized representation of the experimental transfer function. Two approaches are presented: first, the matrix polynomials are identified using an estimated transfer function; second, the matrix polynomials are identified directly from the cross/auto spectra of the input and output signals. A set of Markov parameters are computed from the polynomials and subsequently realization theory is used to recover a minimum order state space model. Unevenly spaced frequency response functions may be used. Results from a simple numerical example and an experiment are discussed to highlight some of the important aspect of the algorithm.

  2. Computational approaches to identify functional genetic variants in cancer genomes

    PubMed Central

    Gonzalez-Perez, Abel; Mustonen, Ville; Reva, Boris; Ritchie, Graham R.S.; Creixell, Pau; Karchin, Rachel; Vazquez, Miguel; Fink, J. Lynn; Kassahn, Karin S.; Pearson, John V.; Bader, Gary; Boutros, Paul C.; Muthuswamy, Lakshmi; Ouellette, B.F. Francis; Reimand, Jüri; Linding, Rune; Shibata, Tatsuhiro; Valencia, Alfonso; Butler, Adam; Dronov, Serge; Flicek, Paul; Shannon, Nick B.; Carter, Hannah; Ding, Li; Sander, Chris; Stuart, Josh M.; Stein, Lincoln D.; Lopez-Bigas, Nuria

    2014-01-01

    The International Cancer Genome Consortium (ICGC) aims to catalog genomic abnormalities in tumors from 50 different cancer types. Genome sequencing reveals hundreds to thousands of somatic mutations in each tumor, but only a minority drive tumor progression. We present the result of discussions within the ICGC on how to address the challenge of identifying mutations that contribute to oncogenesis, tumor maintenance or response to therapy, and recommend computational techniques to annotate somatic variants and predict their impact on cancer phenotype. PMID:23900255

  3. Systems Biology for Organotypic Cell Cultures

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

    Grego, Sonia; Dougherty, Edward R.; Alexander, Francis J.

    Translating in vitro biological data into actionable information related to human health holds the potential to improve disease treatment and risk assessment of chemical exposures. While genomics has identified regulatory pathways at the cellular level, translation to the organism level requires a multiscale approach accounting for intra-cellular regulation, inter-cellular interaction, and tissue/organ-level effects. Tissue-level effects can now be probed in vitro thanks to recently developed systems of three-dimensional (3D), multicellular, “organotypic” cell cultures, which mimic functional responses of living tissue. However, there remains a knowledge gap regarding interactions across different biological scales, complicating accurate prediction of health outcomes from molecular/genomicmore » data and tissue responses. Systems biology aims at mathematical modeling of complex, non-linear biological systems. We propose to apply a systems biology approach to achieve a computational representation of tissue-level physiological responses by integrating empirical data derived from organotypic culture systems with computational models of intracellular pathways to better predict human responses. Successful implementation of this integrated approach will provide a powerful tool for faster, more accurate and cost-effective screening of potential toxicants and therapeutics. On September 11, 2015, an interdisciplinary group of scientists, engineers, and clinicians gathered for a workshop in Research Triangle Park, North Carolina, to discuss this ambitious goal. Participants represented laboratory-based and computational modeling approaches to pharmacology and toxicology, as well as the pharmaceutical industry, government, non-profits, and academia. Discussions focused on identifying critical system perturbations to model, the computational tools required, and the experimental approaches best suited to generating key data. This consensus report summarizes the discussions held.« less

  4. Workshop Report: Systems Biology for Organotypic Cell Cultures

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

    Grego, Sonia; Dougherty, Edward R.; Alexander, Francis Joseph

    Translating in vitro biological data into actionable information related to human health holds the potential to improve disease treatment and risk assessment of chemical exposures. While genomics has identified regulatory pathways at the cellular level, translation to the organism level requires a multiscale approach accounting for intra-cellular regulation, inter-cellular interaction, and tissue/organ-level effects. Tissue-level effects can now be probed in vitro thanks to recently developed systems of three-dimensional (3D), multicellular, “organotypic” cell cultures, which mimic functional responses of living tissue. However, there remains a knowledge gap regarding interactions across different biological scales, complicating accurate prediction of health outcomes from molecular/genomicmore » data and tissue responses. Systems biology aims at mathematical modeling of complex, non-linear biological systems. We propose to apply a systems biology approach to achieve a computational representation of tissue-level physiological responses by integrating empirical data derived from organotypic culture systems with computational models of intracellular pathways to better predict human responses. Successful implementation of this integrated approach will provide a powerful tool for faster, more accurate and cost-effective screening of potential toxicants and therapeutics. On September 11, 2015, an interdisciplinary group of scientists, engineers, and clinicians gathered for a workshop in Research Triangle Park, North Carolina, to discuss this ambitious goal. Participants represented laboratory-based and computational modeling approaches to pharmacology and toxicology, as well as the pharmaceutical industry, government, non-profits, and academia. Discussions focused on identifying critical system perturbations to model, the computational tools required, and the experimental approaches best suited to generating key data.« less

  5. Workshop Report: Systems Biology for Organotypic Cell Cultures

    DOE PAGES

    Grego, Sonia; Dougherty, Edward R.; Alexander, Francis Joseph; ...

    2016-11-14

    Translating in vitro biological data into actionable information related to human health holds the potential to improve disease treatment and risk assessment of chemical exposures. While genomics has identified regulatory pathways at the cellular level, translation to the organism level requires a multiscale approach accounting for intra-cellular regulation, inter-cellular interaction, and tissue/organ-level effects. Tissue-level effects can now be probed in vitro thanks to recently developed systems of three-dimensional (3D), multicellular, “organotypic” cell cultures, which mimic functional responses of living tissue. However, there remains a knowledge gap regarding interactions across different biological scales, complicating accurate prediction of health outcomes from molecular/genomicmore » data and tissue responses. Systems biology aims at mathematical modeling of complex, non-linear biological systems. We propose to apply a systems biology approach to achieve a computational representation of tissue-level physiological responses by integrating empirical data derived from organotypic culture systems with computational models of intracellular pathways to better predict human responses. Successful implementation of this integrated approach will provide a powerful tool for faster, more accurate and cost-effective screening of potential toxicants and therapeutics. On September 11, 2015, an interdisciplinary group of scientists, engineers, and clinicians gathered for a workshop in Research Triangle Park, North Carolina, to discuss this ambitious goal. Participants represented laboratory-based and computational modeling approaches to pharmacology and toxicology, as well as the pharmaceutical industry, government, non-profits, and academia. Discussions focused on identifying critical system perturbations to model, the computational tools required, and the experimental approaches best suited to generating key data.« less

  6. Systems biology for organotypic cell cultures.

    PubMed

    Grego, Sonia; Dougherty, Edward R; Alexander, Francis J; Auerbach, Scott S; Berridge, Brian R; Bittner, Michael L; Casey, Warren; Cooley, Philip C; Dash, Ajit; Ferguson, Stephen S; Fennell, Timothy R; Hawkins, Brian T; Hickey, Anthony J; Kleensang, Andre; Liebman, Michael N J; Martin, Florian; Maull, Elizabeth A; Paragas, Jason; Qiao, Guilin Gary; Ramaiahgari, Sreenivasa; Sumner, Susan J; Yoon, Miyoung

    2017-01-01

    Translating in vitro biological data into actionable information related to human health holds the potential to improve disease treatment and risk assessment of chemical exposures. While genomics has identified regulatory pathways at the cellular level, translation to the organism level requires a multiscale approach accounting for intra-cellular regulation, inter-cellular interaction, and tissue/organ-level effects. Tissue-level effects can now be probed in vitro thanks to recently developed systems of three-dimensional (3D), multicellular, "organotypic" cell cultures, which mimic functional responses of living tissue. However, there remains a knowledge gap regarding interactions across different biological scales, complicating accurate prediction of health outcomes from molecular/genomic data and tissue responses. Systems biology aims at mathematical modeling of complex, non-linear biological systems. We propose to apply a systems biology approach to achieve a computational representation of tissue-level physiological responses by integrating empirical data derived from organotypic culture systems with computational models of intracellular pathways to better predict human responses. Successful implementation of this integrated approach will provide a powerful tool for faster, more accurate and cost-effective screening of potential toxicants and therapeutics. On September 11, 2015, an interdisciplinary group of scientists, engineers, and clinicians gathered for a workshop in Research Triangle Park, North Carolina, to discuss this ambitious goal. Participants represented laboratory-based and computational modeling approaches to pharmacology and toxicology, as well as the pharmaceutical industry, government, non-profits, and academia. Discussions focused on identifying critical system perturbations to model, the computational tools required, and the experimental approaches best suited to generating key data.

  7. Rapid computational identification of the targets of protein kinase inhibitors.

    PubMed

    Rockey, William M; Elcock, Adrian H

    2005-06-16

    We describe a method for rapidly computing the relative affinities of an inhibitor for all individual members of a family of homologous receptors. The approach, implemented in a new program, SCR, models inhibitor-receptor interactions in full atomic detail with an empirical energy function and includes an explicit account of flexibility in homology-modeled receptors through sampling of libraries of side chain rotamers. SCR's general utility was demonstrated by application to seven different protein kinase inhibitors: for each inhibitor, relative binding affinities with panels of approximately 20 protein kinases were computed and compared with experimental data. For five of the inhibitors (SB203580, purvalanol B, imatinib, H89, and hymenialdisine), SCR provided excellent reproduction of the experimental trends and, importantly, was capable of identifying the targets of inhibitors even when they belonged to different kinase families. The method's performance in a predictive setting was demonstrated by performing separate training and testing applications, and its key assumptions were tested by comparison with a number of alternative approaches employing the ligand-docking program AutoDock (Morris et al. J. Comput. Chem. 1998, 19, 1639-1662). These comparison tests included using AutoDock in nondocking and docking modes and performing energy minimizations of inhibitor-kinase complexes with the molecular mechanics code GROMACS (Berendsen et al. Comput. Phys. Commun. 1995, 91, 43-56). It was found that a surprisingly important aspect of SCR's approach is its assumption that the inhibitor be modeled in the same orientation for each kinase: although this assumption is in some respects unrealistic, calculations that used apparently more realistic approaches produced clearly inferior results. Finally, as a large-scale application of the method, SB203580, purvalanol B, and imatinib were screened against an almost full complement of 493 human protein kinases using SCR in order to identify potential new targets; the predicted targets of SB203580 were compared with those identified in recent proteomics-based experiments. These kinome-wide screens, performed within a day on a small cluster of PCs, indicate that explicit computation of inhibitor-receptor binding affinities has the potential to promote rapid discovery of new therapeutic targets for existing inhibitors.

  8. Linkage of exposure and effects using genomics, proteomics and metabolomics in small fish models (presentation)

    EPA Science Inventory

    This research project combines the use of whole organism endpoints, genomic, proteomic and metabolomic approaches, and computational modeling in a systems biology approach to 1) identify molecular indicators of exposure and biomarkers of effect to EDCs representing several modes/...

  9. Application of a hybrid MPI/OpenMP approach for parallel groundwater model calibration using multi-core computers

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

    Tang, Guoping; D'Azevedo, Ed F; Zhang, Fan

    2010-01-01

    Calibration of groundwater models involves hundreds to thousands of forward solutions, each of which may solve many transient coupled nonlinear partial differential equations, resulting in a computationally intensive problem. We describe a hybrid MPI/OpenMP approach to exploit two levels of parallelisms in software and hardware to reduce calibration time on multi-core computers. HydroGeoChem 5.0 (HGC5) is parallelized using OpenMP for direct solutions for a reactive transport model application, and a field-scale coupled flow and transport model application. In the reactive transport model, a single parallelizable loop is identified to account for over 97% of the total computational time using GPROF.more » Addition of a few lines of OpenMP compiler directives to the loop yields a speedup of about 10 on a 16-core compute node. For the field-scale model, parallelizable loops in 14 of 174 HGC5 subroutines that require 99% of the execution time are identified. As these loops are parallelized incrementally, the scalability is found to be limited by a loop where Cray PAT detects over 90% cache missing rates. With this loop rewritten, similar speedup as the first application is achieved. The OpenMP-parallelized code can be run efficiently on multiple workstations in a network or multiple compute nodes on a cluster as slaves using parallel PEST to speedup model calibration. To run calibration on clusters as a single task, the Levenberg Marquardt algorithm is added to HGC5 with the Jacobian calculation and lambda search parallelized using MPI. With this hybrid approach, 100 200 compute cores are used to reduce the calibration time from weeks to a few hours for these two applications. This approach is applicable to most of the existing groundwater model codes for many applications.« less

  10. A multidisciplinary approach to solving computer related vision problems.

    PubMed

    Long, Jennifer; Helland, Magne

    2012-09-01

    This paper proposes a multidisciplinary approach to solving computer related vision issues by including optometry as a part of the problem-solving team. Computer workstation design is increasing in complexity. There are at least ten different professions who contribute to workstation design or who provide advice to improve worker comfort, safety and efficiency. Optometrists have a role identifying and solving computer-related vision issues and in prescribing appropriate optical devices. However, it is possible that advice given by optometrists to improve visual comfort may conflict with other requirements and demands within the workplace. A multidisciplinary approach has been advocated for solving computer related vision issues. There are opportunities for optometrists to collaborate with ergonomists, who coordinate information from physical, cognitive and organisational disciplines to enact holistic solutions to problems. This paper proposes a model of collaboration and examples of successful partnerships at a number of professional levels including individual relationships between optometrists and ergonomists when they have mutual clients/patients, in undergraduate and postgraduate education and in research. There is also scope for dialogue between optometry and ergonomics professional associations. A multidisciplinary approach offers the opportunity to solve vision related computer issues in a cohesive, rather than fragmented way. Further exploration is required to understand the barriers to these professional relationships. © 2012 The College of Optometrists.

  11. Approaches to Privacy and Security in Computer Systems. Publication No. 404.

    ERIC Educational Resources Information Center

    Renninger, Clark R., Ed.

    A conference was held at the National Bureau of Standards on March 4-5, 1974 to continue the dialogue in search of ways to protect confidential information in computer systems. Proposals were presented for meeting governmental needs in safeguarding individual privacy and data confidentiality that were identified at a conference held in November…

  12. Accident Prevention through Driving Skills Assessment and Interventions for Older Drivers: A Programmatic Research Project.

    ERIC Educational Resources Information Center

    Yee, Darlene; Melichar, Joseph F.

    A study examined the effectiveness of a new computer-based multiphasic approach to identifying "at-risk" older drivers and remediating deficits in their driving and traffic safety attitudes, knowledge, and skills. Data collected from 250 older drivers in 3 states were used to develop specifications for computer-based driver improvement modules.…

  13. A Branch-and-Bound Algorithm for Fitting Anti-Robinson Structures to Symmetric Dissimilarity Matrices.

    ERIC Educational Resources Information Center

    Brusco, Michael J.

    2002-01-01

    Developed a branch-and-bound algorithm that can be used to seriate a symmetric dissimilarity matrix by identifying a reordering of rows and columns of the matrix optimizing an anti-Robinson criterion. Computational results suggest that with respect to computational efficiency, the approach is generally competitive with dynamic programming. (SLD)

  14. On the interconnection of stable protein complexes: inter-complex hubs and their conservation in Saccharomyces cerevisiae and Homo sapiens networks.

    PubMed

    Guerra, Concettina

    2015-01-01

    Protein complexes are key molecular entities that perform a variety of essential cellular functions. The connectivity of proteins within a complex has been widely investigated with both experimental and computational techniques. We developed a computational approach to identify and characterise proteins that play a role in interconnecting complexes. We computed a measure of inter-complex centrality, the crossroad index, based on disjoint paths connecting proteins in distinct complexes and identified inter-complex hubs as proteins with a high value of the crossroad index. We applied the approach to a set of stable complexes in Saccharomyces cerevisiae and in Homo sapiens. Just as done for hubs, we evaluated the topological and biological properties of inter-complex hubs addressing the following questions. Do inter-complex hubs tend to be evolutionary conserved? What is the relation between crossroad index and essentiality? We found a good correlation between inter-complex hubs and both evolutionary conservation and essentiality.

  15. Using a combined computational-experimental approach to predict antibody-specific B cell epitopes.

    PubMed

    Sela-Culang, Inbal; Benhnia, Mohammed Rafii-El-Idrissi; Matho, Michael H; Kaever, Thomas; Maybeno, Matt; Schlossman, Andrew; Nimrod, Guy; Li, Sheng; Xiang, Yan; Zajonc, Dirk; Crotty, Shane; Ofran, Yanay; Peters, Bjoern

    2014-04-08

    Antibody epitope mapping is crucial for understanding B cell-mediated immunity and required for characterizing therapeutic antibodies. In contrast to T cell epitope mapping, no computational tools are in widespread use for prediction of B cell epitopes. Here, we show that, utilizing the sequence of an antibody, it is possible to identify discontinuous epitopes on its cognate antigen. The predictions are based on residue-pairing preferences and other interface characteristics. We combined these antibody-specific predictions with results of cross-blocking experiments that identify groups of antibodies with overlapping epitopes to improve the predictions. We validate the high performance of this approach by mapping the epitopes of a set of antibodies against the previously uncharacterized D8 antigen, using complementary techniques to reduce method-specific biases (X-ray crystallography, peptide ELISA, deuterium exchange, and site-directed mutagenesis). These results suggest that antibody-specific computational predictions and simple cross-blocking experiments allow for accurate prediction of residues in conformational B cell epitopes. Copyright © 2014 Elsevier Ltd. All rights reserved.

  16. Prototyping manufacturing in the cloud

    NASA Astrophysics Data System (ADS)

    Ciortea, E. M.

    2017-08-01

    This paper attempts a theoretical approach to cloud systems with impacts on production systems. I call systems as cloud computing because form a relatively new concept in the field of informatics, representing an overall distributed computing services, applications, access to information and data storage without the user to know the physical location and configuration of systems. The advantages of this approach are especially computing speed and storage capacity without investment in additional configurations, synchronizing user data, data processing using web applications. The disadvantage is that it wants to identify a solution for data security, leading to mistrust users. The case study is applied to a module of the system of production, because the system is complex.

  17. Elucidation of Seventeen Human Peripheral Blood B cell Subsets and Quantification of the Tetanus Response Using a Density-Based Method for the Automated Identification of Cell Populations in Multidimensional Flow Cytometry Data

    PubMed Central

    Qian, Yu; Wei, Chungwen; Lee, F. Eun-Hyung; Campbell, John; Halliley, Jessica; Lee, Jamie A.; Cai, Jennifer; Kong, Megan; Sadat, Eva; Thomson, Elizabeth; Dunn, Patrick; Seegmiller, Adam C.; Karandikar, Nitin J.; Tipton, Chris; Mosmann, Tim; Sanz, Iñaki; Scheuermann, Richard H.

    2011-01-01

    Background Advances in multi-parameter flow cytometry (FCM) now allow for the independent detection of larger numbers of fluorochromes on individual cells, generating data with increasingly higher dimensionality. The increased complexity of these data has made it difficult to identify cell populations from high-dimensional FCM data using traditional manual gating strategies based on single-color or two-color displays. Methods To address this challenge, we developed a novel program, FLOCK (FLOw Clustering without K), that uses a density-based clustering approach to algorithmically identify biologically relevant cell populations from multiple samples in an unbiased fashion, thereby eliminating operator-dependent variability. Results FLOCK was used to objectively identify seventeen distinct B cell subsets in a human peripheral blood sample and to identify and quantify novel plasmablast subsets responding transiently to tetanus and other vaccinations in peripheral blood. FLOCK has been implemented in the publically available Immunology Database and Analysis Portal – ImmPort (http://www.immport.org) for open use by the immunology research community. Conclusions FLOCK is able to identify cell subsets in experiments that use multi-parameter flow cytometry through an objective, automated computational approach. The use of algorithms like FLOCK for FCM data analysis obviates the need for subjective and labor intensive manual gating to identify and quantify cell subsets. Novel populations identified by these computational approaches can serve as hypotheses for further experimental study. PMID:20839340

  18. An efficient graph theory based method to identify every minimal reaction set in a metabolic network

    PubMed Central

    2014-01-01

    Background Development of cells with minimal metabolic functionality is gaining importance due to their efficiency in producing chemicals and fuels. Existing computational methods to identify minimal reaction sets in metabolic networks are computationally expensive. Further, they identify only one of the several possible minimal reaction sets. Results In this paper, we propose an efficient graph theory based recursive optimization approach to identify all minimal reaction sets. Graph theoretical insights offer systematic methods to not only reduce the number of variables in math programming and increase its computational efficiency, but also provide efficient ways to find multiple optimal solutions. The efficacy of the proposed approach is demonstrated using case studies from Escherichia coli and Saccharomyces cerevisiae. In case study 1, the proposed method identified three minimal reaction sets each containing 38 reactions in Escherichia coli central metabolic network with 77 reactions. Analysis of these three minimal reaction sets revealed that one of them is more suitable for developing minimal metabolism cell compared to other two due to practically achievable internal flux distribution. In case study 2, the proposed method identified 256 minimal reaction sets from the Saccharomyces cerevisiae genome scale metabolic network with 620 reactions. The proposed method required only 4.5 hours to identify all the 256 minimal reaction sets and has shown a significant reduction (approximately 80%) in the solution time when compared to the existing methods for finding minimal reaction set. Conclusions Identification of all minimal reactions sets in metabolic networks is essential since different minimal reaction sets have different properties that effect the bioprocess development. The proposed method correctly identified all minimal reaction sets in a both the case studies. The proposed method is computationally efficient compared to other methods for finding minimal reaction sets and useful to employ with genome-scale metabolic networks. PMID:24594118

  19. Design requirements for ubiquitous computing environments for healthcare professionals.

    PubMed

    Bång, Magnus; Larsson, Anders; Eriksson, Henrik

    2004-01-01

    Ubiquitous computing environments can support clinical administrative routines in new ways. The aim of such computing approaches is to enhance routine physical work, thus it is important to identify specific design requirements. We studied healthcare professionals in an emergency room and developed the computer-augmented environment NOSTOS to support teamwork in that setting. NOSTOS uses digital pens and paper-based media as the primary input interface for data capture and as a means of controlling the system. NOSTOS also includes a digital desk, walk-up displays, and sensor technology that allow the system to track documents and activities in the workplace. We propose a set of requirements and discuss the value of tangible user interfaces for healthcare personnel. Our results suggest that the key requirements are flexibility in terms of system usage and seamless integration between digital and physical components. We also discuss how ubiquitous computing approaches like NOSTOS can be beneficial in the medical workplace.

  20. Computational analysis of conserved RNA secondary structure in transcriptomes and genomes.

    PubMed

    Eddy, Sean R

    2014-01-01

    Transcriptomics experiments and computational predictions both enable systematic discovery of new functional RNAs. However, many putative noncoding transcripts arise instead from artifacts and biological noise, and current computational prediction methods have high false positive rates. I discuss prospects for improving computational methods for analyzing and identifying functional RNAs, with a focus on detecting signatures of conserved RNA secondary structure. An interesting new front is the application of chemical and enzymatic experiments that probe RNA structure on a transcriptome-wide scale. I review several proposed approaches for incorporating structure probing data into the computational prediction of RNA secondary structure. Using probabilistic inference formalisms, I show how all these approaches can be unified in a well-principled framework, which in turn allows RNA probing data to be easily integrated into a wide range of analyses that depend on RNA secondary structure inference. Such analyses include homology search and genome-wide detection of new structural RNAs.

  1. Computational methods in metabolic engineering for strain design.

    PubMed

    Long, Matthew R; Ong, Wai Kit; Reed, Jennifer L

    2015-08-01

    Metabolic engineering uses genetic approaches to control microbial metabolism to produce desired compounds. Computational tools can identify new biological routes to chemicals and the changes needed in host metabolism to improve chemical production. Recent computational efforts have focused on exploring what compounds can be made biologically using native, heterologous, and/or enzymes with broad specificity. Additionally, computational methods have been developed to suggest different types of genetic modifications (e.g. gene deletion/addition or up/down regulation), as well as suggest strategies meeting different criteria (e.g. high yield, high productivity, or substrate co-utilization). Strategies to improve the runtime performances have also been developed, which allow for more complex metabolic engineering strategies to be identified. Future incorporation of kinetic considerations will further improve strain design algorithms. Copyright © 2015 Elsevier Ltd. All rights reserved.

  2. A Computational Approach to Qualitative Analysis in Large Textual Datasets

    PubMed Central

    Evans, Michael S.

    2014-01-01

    In this paper I introduce computational techniques to extend qualitative analysis into the study of large textual datasets. I demonstrate these techniques by using probabilistic topic modeling to analyze a broad sample of 14,952 documents published in major American newspapers from 1980 through 2012. I show how computational data mining techniques can identify and evaluate the significance of qualitatively distinct subjects of discussion across a wide range of public discourse. I also show how examining large textual datasets with computational methods can overcome methodological limitations of conventional qualitative methods, such as how to measure the impact of particular cases on broader discourse, how to validate substantive inferences from small samples of textual data, and how to determine if identified cases are part of a consistent temporal pattern. PMID:24498398

  3. Discovery of Boolean metabolic networks: integer linear programming based approach.

    PubMed

    Qiu, Yushan; Jiang, Hao; Ching, Wai-Ki; Cheng, Xiaoqing

    2018-04-11

    Traditional drug discovery methods focused on the efficacy of drugs rather than their toxicity. However, toxicity and/or lack of efficacy are produced when unintended targets are affected in metabolic networks. Thus, identification of biological targets which can be manipulated to produce the desired effect with minimum side-effects has become an important and challenging topic. Efficient computational methods are required to identify the drug targets while incurring minimal side-effects. In this paper, we propose a graph-based computational damage model that summarizes the impact of enzymes on compounds in metabolic networks. An efficient method based on Integer Linear Programming formalism is then developed to identify the optimal enzyme-combination so as to minimize the side-effects. The identified target enzymes for known successful drugs are then verified by comparing the results with those in the existing literature. Side-effects reduction plays a crucial role in the study of drug development. A graph-based computational damage model is proposed and the theoretical analysis states the captured problem is NP-completeness. The proposed approaches can therefore contribute to the discovery of drug targets. Our developed software is available at " http://hkumath.hku.hk/~wkc/APBC2018-metabolic-network.zip ".

  4. Estimation and Identifiability of Model Parameters in Human Nociceptive Processing Using Yes-No Detection Responses to Electrocutaneous Stimulation.

    PubMed

    Yang, Huan; Meijer, Hil G E; Buitenweg, Jan R; van Gils, Stephan A

    2016-01-01

    Healthy or pathological states of nociceptive subsystems determine different stimulus-response relations measured from quantitative sensory testing. In turn, stimulus-response measurements may be used to assess these states. In a recently developed computational model, six model parameters characterize activation of nerve endings and spinal neurons. However, both model nonlinearity and limited information in yes-no detection responses to electrocutaneous stimuli challenge to estimate model parameters. Here, we address the question whether and how one can overcome these difficulties for reliable parameter estimation. First, we fit the computational model to experimental stimulus-response pairs by maximizing the likelihood. To evaluate the balance between model fit and complexity, i.e., the number of model parameters, we evaluate the Bayesian Information Criterion. We find that the computational model is better than a conventional logistic model regarding the balance. Second, our theoretical analysis suggests to vary the pulse width among applied stimuli as a necessary condition to prevent structural non-identifiability. In addition, the numerically implemented profile likelihood approach reveals structural and practical non-identifiability. Our model-based approach with integration of psychophysical measurements can be useful for a reliable assessment of states of the nociceptive system.

  5. Identifying equivalent sound sources from aeroacoustic simulations using a numerical phased array

    NASA Astrophysics Data System (ADS)

    Pignier, Nicolas J.; O'Reilly, Ciarán J.; Boij, Susann

    2017-04-01

    An application of phased array methods to numerical data is presented, aimed at identifying equivalent flow sound sources from aeroacoustic simulations. Based on phased array data extracted from compressible flow simulations, sound source strengths are computed on a set of points in the source region using phased array techniques assuming monopole propagation. Two phased array techniques are used to compute the source strengths: an approach using a Moore-Penrose pseudo-inverse and a beamforming approach using dual linear programming (dual-LP) deconvolution. The first approach gives a model of correlated sources for the acoustic field generated from the flow expressed in a matrix of cross- and auto-power spectral values, whereas the second approach results in a model of uncorrelated sources expressed in a vector of auto-power spectral values. The accuracy of the equivalent source model is estimated by computing the acoustic spectrum at a far-field observer. The approach is tested first on an analytical case with known point sources. It is then applied to the example of the flow around a submerged air inlet. The far-field spectra obtained from the source models for two different flow conditions are in good agreement with the spectra obtained with a Ffowcs Williams-Hawkings integral, showing the accuracy of the source model from the observer's standpoint. Various configurations for the phased array and for the sources are used. The dual-LP beamforming approach shows better robustness to changes in the number of probes and sources than the pseudo-inverse approach. The good results obtained with this simulation case demonstrate the potential of the phased array approach as a modelling tool for aeroacoustic simulations.

  6. Scheduling language and algorithm development study. Appendix: Study approach and activity summary

    NASA Technical Reports Server (NTRS)

    1974-01-01

    The approach and organization of the study to develop a high level computer programming language and a program library are presented. The algorithm and problem modeling analyses are summarized. The approach used to identify and specify the capabilities required in the basic language is described. Results of the analyses used to define specifications for the scheduling module library are presented.

  7. A novel approach for discovering condition-specific correlations of gene expressions within biological pathways by using cloud computing technology.

    PubMed

    Chang, Tzu-Hao; Wu, Shih-Lin; Wang, Wei-Jen; Horng, Jorng-Tzong; Chang, Cheng-Wei

    2014-01-01

    Microarrays are widely used to assess gene expressions. Most microarray studies focus primarily on identifying differential gene expressions between conditions (e.g., cancer versus normal cells), for discovering the major factors that cause diseases. Because previous studies have not identified the correlations of differential gene expression between conditions, crucial but abnormal regulations that cause diseases might have been disregarded. This paper proposes an approach for discovering the condition-specific correlations of gene expressions within biological pathways. Because analyzing gene expression correlations is time consuming, an Apache Hadoop cloud computing platform was implemented. Three microarray data sets of breast cancer were collected from the Gene Expression Omnibus, and pathway information from the Kyoto Encyclopedia of Genes and Genomes was applied for discovering meaningful biological correlations. The results showed that adopting the Hadoop platform considerably decreased the computation time. Several correlations of differential gene expressions were discovered between the relapse and nonrelapse breast cancer samples, and most of them were involved in cancer regulation and cancer-related pathways. The results showed that breast cancer recurrence might be highly associated with the abnormal regulations of these gene pairs, rather than with their individual expression levels. The proposed method was computationally efficient and reliable, and stable results were obtained when different data sets were used. The proposed method is effective in identifying meaningful biological regulation patterns between conditions.

  8. A frequentist approach to computer model calibration

    DOE PAGES

    Wong, Raymond K. W.; Storlie, Curtis Byron; Lee, Thomas C. M.

    2016-05-05

    The paper considers the computer model calibration problem and provides a general frequentist solution. Under the framework proposed, the data model is semiparametric with a non-parametric discrepancy function which accounts for any discrepancy between physical reality and the computer model. In an attempt to solve a fundamentally important (but often ignored) identifiability issue between the computer model parameters and the discrepancy function, the paper proposes a new and identifiable parameterization of the calibration problem. It also develops a two-step procedure for estimating all the relevant quantities under the new parameterization. This estimation procedure is shown to enjoy excellent rates ofmore » convergence and can be straightforwardly implemented with existing software. For uncertainty quantification, bootstrapping is adopted to construct confidence regions for the quantities of interest. As a result, the practical performance of the methodology is illustrated through simulation examples and an application to a computational fluid dynamics model.« less

  9. Rate-equation modelling and ensemble approach to extraction of parameters for viral infection-induced cell apoptosis and necrosis

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

    Domanskyi, Sergii; Schilling, Joshua E.; Privman, Vladimir, E-mail: privman@clarkson.edu

    We develop a theoretical approach that uses physiochemical kinetics modelling to describe cell population dynamics upon progression of viral infection in cell culture, which results in cell apoptosis (programmed cell death) and necrosis (direct cell death). Several model parameters necessary for computer simulation were determined by reviewing and analyzing available published experimental data. By comparing experimental data to computer modelling results, we identify the parameters that are the most sensitive to the measured system properties and allow for the best data fitting. Our model allows extraction of parameters from experimental data and also has predictive power. Using the model wemore » describe interesting time-dependent quantities that were not directly measured in the experiment and identify correlations among the fitted parameter values. Numerical simulation of viral infection progression is done by a rate-equation approach resulting in a system of “stiff” equations, which are solved by using a novel variant of the stochastic ensemble modelling approach. The latter was originally developed for coupled chemical reactions.« less

  10. Identification of evolutionarily conserved Momordica charantia microRNAs using computational approach and its utility in phylogeny analysis.

    PubMed

    Thirugnanasambantham, Krishnaraj; Saravanan, Subramanian; Karikalan, Kulandaivelu; Bharanidharan, Rajaraman; Lalitha, Perumal; Ilango, S; HairulIslam, Villianur Ibrahim

    2015-10-01

    Momordica charantia (bitter gourd, bitter melon) is a monoecious Cucurbitaceae with anti-oxidant, anti-microbial, anti-viral and anti-diabetic potential. Molecular studies on this economically valuable plant are very essential to understand its phylogeny and evolution. MicroRNAs (miRNAs) are conserved, small, non-coding RNA with ability to regulate gene expression by bind the 3' UTR region of target mRNA and are evolved at different rates in different plant species. In this study we have utilized homology based computational approach and identified 27 mature miRNAs for the first time from this bio-medically important plant. The phylogenetic tree developed from binary data derived from the data on presence/absence of the identified miRNAs were noticed to be uncertain and biased. Most of the identified miRNAs were highly conserved among the plant species and sequence based phylogeny analysis of miRNAs resolved the above difficulties in phylogeny approach using miRNA. Predicted gene targets of the identified miRNAs revealed their importance in regulation of plant developmental process. Reported miRNAs held sequence conservation in mature miRNAs and the detailed phylogeny analysis of pre-miRNA sequences revealed genus specific segregation of clusters. Copyright © 2015 Elsevier Ltd. All rights reserved.

  11. Focal Cortical Dysplasia (FCD) lesion analysis with complex diffusion approach.

    PubMed

    Rajan, Jeny; Kannan, K; Kesavadas, C; Thomas, Bejoy

    2009-10-01

    Identification of Focal Cortical Dysplasia (FCD) can be difficult due to the subtle MRI changes. Though sequences like FLAIR (fluid attenuated inversion recovery) can detect a large majority of these lesions, there are smaller lesions without signal changes that can easily go unnoticed by the naked eye. The aim of this study is to improve the visibility of focal cortical dysplasia lesions in the T1 weighted brain MRI images. In the proposed method, we used a complex diffusion based approach for calculating the FCD affected areas. Based on the diffused image and thickness map, a complex map is created. From this complex map; FCD areas can be easily identified. MRI brains of 48 subjects selected by neuroradiologists were given to computer scientists who developed the complex map for identifying the cortical dysplasia. The scientists were blinded to the MRI interpretation result of the neuroradiologist. The FCD could be identified in all the patients in whom surgery was done, however three patients had false positive lesions. More lesions were identified in patients in whom surgery was not performed and lesions were seen in few of the controls. These were considered as false positive. This computer aided detection technique using complex diffusion approach can help detect focal cortical dysplasia in patients with epilepsy.

  12. GPU accelerated dynamic functional connectivity analysis for functional MRI data.

    PubMed

    Akgün, Devrim; Sakoğlu, Ünal; Esquivel, Johnny; Adinoff, Bryon; Mete, Mutlu

    2015-07-01

    Recent advances in multi-core processors and graphics card based computational technologies have paved the way for an improved and dynamic utilization of parallel computing techniques. Numerous applications have been implemented for the acceleration of computationally-intensive problems in various computational science fields including bioinformatics, in which big data problems are prevalent. In neuroimaging, dynamic functional connectivity (DFC) analysis is a computationally demanding method used to investigate dynamic functional interactions among different brain regions or networks identified with functional magnetic resonance imaging (fMRI) data. In this study, we implemented and analyzed a parallel DFC algorithm based on thread-based and block-based approaches. The thread-based approach was designed to parallelize DFC computations and was implemented in both Open Multi-Processing (OpenMP) and Compute Unified Device Architecture (CUDA) programming platforms. Another approach developed in this study to better utilize CUDA architecture is the block-based approach, where parallelization involves smaller parts of fMRI time-courses obtained by sliding-windows. Experimental results showed that the proposed parallel design solutions enabled by the GPUs significantly reduce the computation time for DFC analysis. Multicore implementation using OpenMP on 8-core processor provides up to 7.7× speed-up. GPU implementation using CUDA yielded substantial accelerations ranging from 18.5× to 157× speed-up once thread-based and block-based approaches were combined in the analysis. Proposed parallel programming solutions showed that multi-core processor and CUDA-supported GPU implementations accelerated the DFC analyses significantly. Developed algorithms make the DFC analyses more practical for multi-subject studies with more dynamic analyses. Copyright © 2015 Elsevier Ltd. All rights reserved.

  13. Protein displacements under external forces: An atomistic Langevin dynamics approach.

    PubMed

    Gnandt, David; Utz, Nadine; Blumen, Alexander; Koslowski, Thorsten

    2009-02-28

    We present a fully atomistic Langevin dynamics approach as a method to simulate biopolymers under external forces. In the harmonic regime, this approach permits the computation of the long-term dynamics using only the eigenvalues and eigenvectors of the Hessian matrix of second derivatives. We apply this scheme to identify polymorphs of model proteins by their mechanical response fingerprint, and we relate the averaged dynamics of proteins to their biological functionality, with the ion channel gramicidin A, a phosphorylase, and neuropeptide Y as examples. In an environment akin to dilute solutions, even small proteins show relaxation times up to 50 ns. Atomically resolved Langevin dynamics computations have been performed for the stretched gramicidin A ion channel.

  14. A hybrid computational approach for efficient Alzheimer's disease classification based on heterogeneous data.

    PubMed

    Ding, Xuemei; Bucholc, Magda; Wang, Haiying; Glass, David H; Wang, Hui; Clarke, Dave H; Bjourson, Anthony John; Dowey, Le Roy C; O'Kane, Maurice; Prasad, Girijesh; Maguire, Liam; Wong-Lin, KongFatt

    2018-06-27

    There is currently a lack of an efficient, objective and systemic approach towards the classification of Alzheimer's disease (AD), due to its complex etiology and pathogenesis. As AD is inherently dynamic, it is also not clear how the relationships among AD indicators vary over time. To address these issues, we propose a hybrid computational approach for AD classification and evaluate it on the heterogeneous longitudinal AIBL dataset. Specifically, using clinical dementia rating as an index of AD severity, the most important indicators (mini-mental state examination, logical memory recall, grey matter and cerebrospinal volumes from MRI and active voxels from PiB-PET brain scans, ApoE, and age) can be automatically identified from parallel data mining algorithms. In this work, Bayesian network modelling across different time points is used to identify and visualize time-varying relationships among the significant features, and importantly, in an efficient way using only coarse-grained data. Crucially, our approach suggests key data features and their appropriate combinations that are relevant for AD severity classification with high accuracy. Overall, our study provides insights into AD developments and demonstrates the potential of our approach in supporting efficient AD diagnosis.

  15. Core Binding Site of a Thioflavin-T-Derived Imaging Probe on Amyloid β Fibrils Predicted by Computational Methods.

    PubMed

    Kawai, Ryoko; Araki, Mitsugu; Yoshimura, Masashi; Kamiya, Narutoshi; Ono, Masahiro; Saji, Hideo; Okuno, Yasushi

    2018-05-16

    Development of new diagnostic imaging probes for Alzheimer's disease, such as positron emission tomography (PET) and single photon emission computed tomography (SPECT) probes, has been strongly desired. In this study, we investigated the most accessible amyloid β (Aβ) binding site of [ 123 I]IMPY, a Thioflavin-T-derived SPECT probe, using experimental and computational methods. First, we performed a competitive inhibition assay with Orange-G, which recognizes the KLVFFA region in Aβ fibrils, suggesting that IMPY and Orange-G bind to different sites in Aβ fibrils. Next, we precisely predicted the IMPY binding site on a multiple-protofilament Aβ fibril model using computational approaches, consisting of molecular dynamics and docking simulations. We generated possible IMPY-binding structures using docking simulations to identify candidates for probe-binding sites. The binding free energy of IMPY with the Aβ fibril was calculated by a free energy simulation method, MP-CAFEE. These computational results suggest that IMPY preferentially binds to an interfacial pocket located between two protofilaments and is stabilized mainly through hydrophobic interactions. Finally, our computational approach was validated by comparing it with the experimental results. The present study demonstrates the possibility of computational approaches to screen new PET/SPECT probes for Aβ imaging.

  16. A Critical Pedagogy Approach for Engaging Urban Youth in Mobile App Development in an After-School Program

    ERIC Educational Resources Information Center

    Vakil, Sepehr

    2014-01-01

    To understand the digital divide as a matter of social justice, I identify access to computational fluency as a civil rights issue. "Access" refers to material as well as social resources, including meaningful learning opportunities that create the conditions for urban youth to engage in computational thinking. In this article, I explore…

  17. Differentially Private Frequent Subgraph Mining

    PubMed Central

    Xu, Shengzhi; Xiong, Li; Cheng, Xiang; Xiao, Ke

    2016-01-01

    Mining frequent subgraphs from a collection of input graphs is an important topic in data mining research. However, if the input graphs contain sensitive information, releasing frequent subgraphs may pose considerable threats to individual's privacy. In this paper, we study the problem of frequent subgraph mining (FGM) under the rigorous differential privacy model. We introduce a novel differentially private FGM algorithm, which is referred to as DFG. In this algorithm, we first privately identify frequent subgraphs from input graphs, and then compute the noisy support of each identified frequent subgraph. In particular, to privately identify frequent subgraphs, we present a frequent subgraph identification approach which can improve the utility of frequent subgraph identifications through candidates pruning. Moreover, to compute the noisy support of each identified frequent subgraph, we devise a lattice-based noisy support derivation approach, where a series of methods has been proposed to improve the accuracy of the noisy supports. Through formal privacy analysis, we prove that our DFG algorithm satisfies ε-differential privacy. Extensive experimental results on real datasets show that the DFG algorithm can privately find frequent subgraphs with high data utility. PMID:27616876

  18. Computer screening for palliative care needs in primary care: a mixed-methods study.

    PubMed

    Mason, Bruce; Boyd, Kirsty; Steyn, John; Kendall, Marilyn; Macpherson, Stella; Murray, Scott A

    2018-05-01

    Though the majority of people could benefit from palliative care before they die, most do not receive this approach, especially those with multimorbidity and frailty. GPs find it difficult to identify such patients. To refine and evaluate the utility of a computer application (AnticiPal) to help primary care teams screen their registered patients for people who could benefit from palliative care. A mixed-methods study of eight GP practices in Scotland, conducted in 2016-2017. After a search development cycle the authors adopted a mixed-methods approach, combining analysis of the number of people identified by the search with qualitative observations of the computer search as used by primary care teams, and interviews with professionals and patients. The search identified 0.8% of 62 708 registered patients. A total of 27 multidisciplinary meetings were observed, and eight GPs and 10 patients were interviewed. GPs thought the search identified many unrecognised patients with advanced multimorbidity and frailty, but were concerned about workload implications of assessment and care planning. Patients and carers endorsed the value of proactive identification of people with advanced illness. GP practices can use computer searching to generate lists of patients for review and care planning. The challenges of starting a conversation about the future remain. However, most patients regard key components of palliative care (proactive planning, including sharing information with urgent care services) as important. Screening for people with deteriorating health at risk from unplanned care is a current focus for quality improvement and should not be limited by labelling it solely as 'palliative care'. © British Journal of General Practice 2018.

  19. Efficient marginalization to compute protein posterior probabilities from shotgun mass spectrometry data

    PubMed Central

    Serang, Oliver; MacCoss, Michael J.; Noble, William Stafford

    2010-01-01

    The problem of identifying proteins from a shotgun proteomics experiment has not been definitively solved. Identifying the proteins in a sample requires ranking them, ideally with interpretable scores. In particular, “degenerate” peptides, which map to multiple proteins, have made such a ranking difficult to compute. The problem of computing posterior probabilities for the proteins, which can be interpreted as confidence in a protein’s presence, has been especially daunting. Previous approaches have either ignored the peptide degeneracy problem completely, addressed it by computing a heuristic set of proteins or heuristic posterior probabilities, or by estimating the posterior probabilities with sampling methods. We present a probabilistic model for protein identification in tandem mass spectrometry that recognizes peptide degeneracy. We then introduce graph-transforming algorithms that facilitate efficient computation of protein probabilities, even for large data sets. We evaluate our identification procedure on five different well-characterized data sets and demonstrate our ability to efficiently compute high-quality protein posteriors. PMID:20712337

  20. A Computational Approach to Estimate Interorgan Metabolic Transport in a Mammal

    PubMed Central

    Cui, Xiao; Geffers, Lars; Eichele, Gregor; Yan, Jun

    2014-01-01

    In multicellular organisms metabolism is distributed across different organs, each of which has specific requirements to perform its own specialized task. But different organs also have to support the metabolic homeostasis of the organism as a whole by interorgan metabolite transport. Recent studies have successfully reconstructed global metabolic networks in tissues and cell types and attempts have been made to connect organs with interorgan metabolite transport. Instead of these complicated approaches to reconstruct global metabolic networks, we proposed in this study a novel approach to study interorgan metabolite transport focusing on transport processes mediated by solute carrier (Slc) transporters and their couplings to cognate enzymatic reactions. We developed a computational approach to identify and score potential interorgan metabolite transports based on the integration of metabolism and transports in different organs in the adult mouse from quantitative gene expression data. This allowed us to computationally estimate the connectivity between 17 mouse organs via metabolite transport. Finally, by applying our method to circadian metabolism, we showed that our approach can shed new light on the current understanding of interorgan metabolite transport at a whole-body level in mammals. PMID:24971892

  1. Bioisostere Identification by Determining the Amino Acid Binding Preferences of Common Chemical Fragments.

    PubMed

    Sato, Tomohiro; Hashimoto, Noriaki; Honma, Teruki

    2017-12-26

    To assist in the structural optimization of hit/lead compounds during drug discovery, various computational approaches to identify potentially useful bioisosteric conversions have been reported. Here, the preference of chemical fragments to hydrogen bonds with specific amino acid residues was used to identify potential bioisosteric conversions. We first compiled a data set of chemical fragments frequently occurring in complex structures contained in the Protein Data Bank. We then used a computational approach to determine the amino acids to which these chemical fragments most frequently hydrogen bonded. The results of the frequency analysis were used to hierarchically cluster chemical fragments according to their amino acid preferences. The Euclid distance between amino acid preferences of chemical fragments for hydrogen bonding was then compared to MMP information in the ChEMBL database. To demonstrate the applicability of the approach for compound optimization, the similarity of amino acid preferences was used to identify known bioisosteric conversions of the epidermal growth factor receptor inhibitor gefitinib. The amino acid preference distance successfully detected bioisosteric fragments corresponding to the morpholine ring in gefitinib with a higher ROC score compared to those based on topological similarity of substituents and frequency of MMP in the ChEMBL database.

  2. Energy Index For Aircraft Maneuvers

    NASA Technical Reports Server (NTRS)

    Chidester, Thomas R. (Inventor); Lynch, Robert E. (Inventor); Lawrence, Robert E. (Inventor); Amidan, Brett G. (Inventor); Ferryman, Thomas A. (Inventor); Drew, Douglas A. (Inventor); Ainsworth, Robert J. (Inventor); Prothero, Gary L. (Inventor); Romanowski, Tomothy P. (Inventor); Bloch, Laurent (Inventor)

    2006-01-01

    Method and system for analyzing, separately or in combination, kinetic energy and potential energy and/or their time derivatives, measured or estimated or computed, for an aircraft in approach phase or in takeoff phase, to determine if the aircraft is or will be put in an anomalous configuration in order to join a stable approach path or takeoff path. A 3 reference value of kinetic energy andor potential energy (or time derivatives thereof) is provided, and a comparison index .for the estimated energy and reference energy is computed and compared with a normal range of index values for a corresponding aircraft maneuver. If the computed energy index lies outside the normal index range, this phase of the aircraft is identified as anomalous, non-normal or potentially unstable.

  3. The discrete adjoint method for parameter identification in multibody system dynamics.

    PubMed

    Lauß, Thomas; Oberpeilsteiner, Stefan; Steiner, Wolfgang; Nachbagauer, Karin

    2018-01-01

    The adjoint method is an elegant approach for the computation of the gradient of a cost function to identify a set of parameters. An additional set of differential equations has to be solved to compute the adjoint variables, which are further used for the gradient computation. However, the accuracy of the numerical solution of the adjoint differential equation has a great impact on the gradient. Hence, an alternative approach is the discrete adjoint method , where the adjoint differential equations are replaced by algebraic equations. Therefore, a finite difference scheme is constructed for the adjoint system directly from the numerical time integration method. The method provides the exact gradient of the discretized cost function subjected to the discretized equations of motion.

  4. New Trends of Digital Data Storage in DNA

    PubMed Central

    2016-01-01

    With the exponential growth in the capacity of information generated and the emerging need for data to be stored for prolonged period of time, there emerges a need for a storage medium with high capacity, high storage density, and possibility to withstand extreme environmental conditions. DNA emerges as the prospective medium for data storage with its striking features. Diverse encoding models for reading and writing data onto DNA, codes for encrypting data which addresses issues of error generation, and approaches for developing codons and storage styles have been developed over the recent past. DNA has been identified as a potential medium for secret writing, which achieves the way towards DNA cryptography and stenography. DNA utilized as an organic memory device along with big data storage and analytics in DNA has paved the way towards DNA computing for solving computational problems. This paper critically analyzes the various methods used for encoding and encrypting data onto DNA while identifying the advantages and capability of every scheme to overcome the drawbacks identified priorly. Cryptography and stenography techniques have been analyzed in a critical approach while identifying the limitations of each method. This paper also identifies the advantages and limitations of DNA as a memory device and memory applications. PMID:27689089

  5. New Trends of Digital Data Storage in DNA.

    PubMed

    De Silva, Pavani Yashodha; Ganegoda, Gamage Upeksha

    With the exponential growth in the capacity of information generated and the emerging need for data to be stored for prolonged period of time, there emerges a need for a storage medium with high capacity, high storage density, and possibility to withstand extreme environmental conditions. DNA emerges as the prospective medium for data storage with its striking features. Diverse encoding models for reading and writing data onto DNA, codes for encrypting data which addresses issues of error generation, and approaches for developing codons and storage styles have been developed over the recent past. DNA has been identified as a potential medium for secret writing, which achieves the way towards DNA cryptography and stenography. DNA utilized as an organic memory device along with big data storage and analytics in DNA has paved the way towards DNA computing for solving computational problems. This paper critically analyzes the various methods used for encoding and encrypting data onto DNA while identifying the advantages and capability of every scheme to overcome the drawbacks identified priorly. Cryptography and stenography techniques have been analyzed in a critical approach while identifying the limitations of each method. This paper also identifies the advantages and limitations of DNA as a memory device and memory applications.

  6. Identifying Methods for Monitoring Foodborne Illness: Review of Existing Public Health Surveillance Techniques.

    PubMed

    Oldroyd, Rachel A; Morris, Michelle A; Birkin, Mark

    2018-06-06

    Traditional methods of monitoring foodborne illness are associated with problems of untimeliness and underreporting. In recent years, alternative data sources such as social media data have been used to monitor the incidence of disease in the population (infodemiology and infoveillance). These data sources prove timelier than traditional general practitioner data, they can help to fill the gaps in the reporting process, and they often include additional metadata that is useful for supplementary research. The aim of the study was to identify and formally analyze research papers using consumer-generated data, such as social media data or restaurant reviews, to quantify a disease or public health ailment. Studies of this nature are scarce within the food safety domain, therefore identification and understanding of transferrable methods in other health-related fields are of particular interest. Structured scoping methods were used to identify and analyze primary research papers using consumer-generated data for disease or public health surveillance. The title, abstract, and keyword fields of 5 databases were searched using predetermined search terms. A total of 5239 papers matched the search criteria, of which 145 were taken to full-text review-62 papers were deemed relevant and were subjected to data characterization and thematic analysis. The majority of studies (40/62, 65%) focused on the surveillance of influenza-like illness. Only 10 studies (16%) used consumer-generated data to monitor outbreaks of foodborne illness. Twitter data (58/62, 94%) and Yelp reviews (3/62, 5%) were the most commonly used data sources. Studies reporting high correlations against baseline statistics used advanced statistical and computational approaches to calculate the incidence of disease. These include classification and regression approaches, clustering approaches, and lexicon-based approaches. Although they are computationally intensive due to the requirement of training data, studies using classification approaches reported the best performance. By analyzing studies in digital epidemiology, computer science, and public health, this paper has identified and analyzed methods of disease monitoring that can be transferred to foodborne disease surveillance. These methods fall into 4 main categories: basic approach, classification and regression, clustering approaches, and lexicon-based approaches. Although studies using a basic approach to calculate disease incidence generally report good performance against baseline measures, they are sensitive to chatter generated by media reports. More computationally advanced approaches are required to filter spurious messages and protect predictive systems against false alarms. Research using consumer-generated data for monitoring influenza-like illness is expansive; however, research regarding the use of restaurant reviews and social media data in the context of food safety is limited. Considering the advantages reported in this review, methods using consumer-generated data for foodborne disease surveillance warrant further investment. ©Rachel A Oldroyd, Michelle A Morris, Mark Birkin. Originally published in JMIR Public Health and Surveillance (http://publichealth.jmir.org), 06.06.2018.

  7. CT venography: use in selecting a surgical approach for the treatment of petrous apex cholesterol granulomas.

    PubMed

    Isaacson, Brandon; Kutz, Joe Walter; Mendelsohn, Dianne; Roland, Peter S

    2009-04-01

    To demonstrate the use of computed tomographic (CT) venography in selecting a surgical approach for cholesterol granulomas. Retrospective case review. Tertiary referral center. Three patients presented with symptomatic petrous apex cholesterol granulomas with extensive bone erosion involving the jugular fossa. Computed tomographic venography was performed on each patient before selecting a surgical approach for drainage. Localization of the jugular bulb in relation to the petrous carotid artery and basal turn of the cochlea was ascertained in each subject. Three patients with large symptomatic cholesterol granulomas were identified. Conventional CT demonstrated extensive bone erosion involving the jugular fossa in each patient. The location of the jugular bulb and its proximity to the petrous carotid artery and basal turn of the cochlea could not be determined with conventional temporal bone CT and magnetic resonance imaging. Computed tomographic venography provided the exact location of the jugular bulb in all 3 patients. The favorable position of the jugular bulb in all 3 cases permitted drainage of these lesions using an infracochlear approach. Computed tomographic venography provided invaluable information in 3 patients with large symptomatic cholesterol granulomas. All 3 patients were previously thought to be unsuitable candidates for an infracochlear or infralabyrinthine approach because of the unknown location of the jugular bulb.

  8. Do Students' Approaches to Learning Affect Their Perceptions of Using Computing and Information Technology?

    ERIC Educational Resources Information Center

    Jelfs, Anne; Colbourn, Chris

    2002-01-01

    Discusses the use of communication and information technology (C&IT) in higher education in the United Kingdom and describes research that examined student perceptions of using C&IT for a virtual seminar series in psychology. Identified student learning approaches within the group and how it affected their adoption or rejection of the…

  9. A multifaceted approach to prioritize and design bank stabilization measures along the Big Sioux River, South Dakota, USA

    USDA-ARS?s Scientific Manuscript database

    A multifaceted approach was used to manage fine-grained sediment loadings from river bank erosion along the Big Sioux River between Dell Rapids and Sioux Falls, South Dakota, USA. Simulations with the RVR Meander and CONCEPTS river-morphodynamics computer models were conducted to identify stream-ban...

  10. A two-pronged approach to detecting ICB Stoneley modes

    NASA Astrophysics Data System (ADS)

    Jasperson, H. A.; Ye, J.; Shi, J.; De Hoop, M. V.

    2017-12-01

    Stoneley modes are special kinds of normal modes that are confined to the boundary between a fluid layer and a solid layer inside the Earth. Thus, these modes theoretically occur at the core-mantle boundary (CMB) and inner core boundary (ICB). CMB Stoneley modes were identified in observational data by Koelemeijer, et al. in 2013, but ICB Stoneley modes have remained relatively unexplored. Here we use a joint numerical and data-driven approach to identify ICB Stoneley modes from the deep 2013 Mw 8.3 Sea of Okhotsk earthquake. For the data-driven portion, we use 50 stacked traces from the USArray to identify potential occurrences of ICB Stoneley modes. Next, we verify each occurrence by comparing the spectrum to its equivalent from the shallow 2011 Mw 9.1 Tohoku earthquake. We also develop a novel computational approach to compute normal modes in a spherically symmetric non-rotating Earth building on the work of Wiggins (1976) and Buland and Gilbert (1984). We successfully resolve the clustering eigenvalue problem with non-orthogonal eigenfunctions from which Mineos suffers. By choosing the displacement/pressure formulation in the fluid outer core and handling boundary conditions properly, we remarkably project out the essential spectrum and provide the correct point spectrum. The utilization of weak variational form to perform the Rayleigh-Ritz procedure contributes to preserving the high accuracy across the solid-fluid boundary, which makes it possible to capture Stoneley modes' exponentially decaying behavior across the solid-fluid boundary, leading to more accurate and reliable eigenvalues and eigenfunctions. This allows us to compare the observation data and numerical computations. With this approach, we eliminate false signals from consideration, leaving only true ICB Stoneley mode peaks. In the future, information from these modes can be used to study the properties of the ICB and inner core.

  11. From genome to drug lead: identification of a small-molecule inhibitor of the SARS virus.

    PubMed

    Dooley, Andrea J; Shindo, Nice; Taggart, Barbara; Park, Jewn-Giew; Pang, Yuan-Ping

    2006-02-15

    Virtual screening, a fast, computational approach to identify drug leads [Perola, E.; Xu, K.; Kollmeyer, T. M.; Kaufmann, S. H.; Prendergast, F. G. J. Med. Chem.2000, 43, 401; Miller, M. A. Nat. Rev. Drug Disc.2002, 1 220], is limited by a known challenge in crystallographically determining flexible regions of proteins. This approach has not been able to identify active inhibitors of the severe acute respiratory syndrome-associated coronavirus (SARS-CoV) using solely the crystal structures of a SARS-CoV cysteine proteinase with a flexible loop in the active site [Yang, H. T.; Yang, M. J.; Ding, Y.; Liu, Y. W.; Lou, Z. Y. Proc. Natl. Acad. Sci. U.S.A.2003, 100, 13190; Jenwitheesuk, E.; Samudrala, R. Bioorg. Med. Chem. Lett.2003, 13, 3989; Rajnarayanan, R. V.; Dakshanamurthy, S.; Pattabiraman, N. Biochem. Biophys. Res. Commun.2004, 321, 370; Du, Q.; Wang, S.; Wei, D.; Sirois, S.; Chou, K. Anal. Biochem.2005, 337, 262; Du, Q.; Wang, S.; Zhu, Y.; Wei, D.; Guo, H. Peptides2004, 25, 1857; Lee, V.; Wittayanarakul, K.; Remsungenen, T.; Parasuk, V.; Sompornpisut, P. Science (Asia)2003, 29, 181; Toney, J.; Navas-Martin, S.; Weiss, S.; Koeller, A. J. Med. Chem.2004, 47, 1079; Zhang, X. W.; Yap, Y. L. Bioorg. Med. Chem.2004, 12, 2517]. This article demonstrates a genome-to-drug-lead approach that uses terascale computing to model flexible regions of proteins, thus permitting the utilization of genetic information to identify drug leads expeditiously. A small-molecule inhibitor of SARS-CoV, exhibiting an effective concentration (EC50) of 23 microM in cell-based assays, was identified through virtual screening against a computer-predicted model of the cysteine proteinase. Screening against two crystal structures of the same proteinase failed to identify the 23-microM inhibitor. This study suggests that terascale computing can complement crystallography, broaden the scope of virtual screening, and accelerate the development of therapeutics to treat emerging infectious diseases such as SARS and Bird Flu.

  12. Visualizing stressful aspects of repetitive motion tasks and opportunities for ergonomic improvements using computer vision.

    PubMed

    Greene, Runyu L; Azari, David P; Hu, Yu Hen; Radwin, Robert G

    2017-11-01

    Patterns of physical stress exposure are often difficult to measure, and the metrics of variation and techniques for identifying them is underdeveloped in the practice of occupational ergonomics. Computer vision has previously been used for evaluating repetitive motion tasks for hand activity level (HAL) utilizing conventional 2D videos. The approach was made practical by relaxing the need for high precision, and by adopting a semi-automatic approach for measuring spatiotemporal characteristics of the repetitive task. In this paper, a new method for visualizing task factors, using this computer vision approach, is demonstrated. After videos are made, the analyst selects a region of interest on the hand to track and the hand location and its associated kinematics are measured for every frame. The visualization method spatially deconstructs and displays the frequency, speed and duty cycle components of tasks that are part of the threshold limit value for hand activity for the purpose of identifying patterns of exposure associated with the specific job factors, as well as for suggesting task improvements. The localized variables are plotted as a heat map superimposed over the video, and displayed in the context of the task being performed. Based on the intensity of the specific variables used to calculate HAL, we can determine which task factors most contribute to HAL, and readily identify those work elements in the task that contribute more to increased risk for an injury. Work simulations and actual industrial examples are described. This method should help practitioners more readily measure and interpret temporal exposure patterns and identify potential task improvements. Copyright © 2017. Published by Elsevier Ltd.

  13. Computational Approaches for Designing Protein/Inhibitor Complexes and Membrane Protein Variants

    NASA Astrophysics Data System (ADS)

    Vijayendran, Krishna Gajan

    Drug discovery of small-molecule protein inhibitors is a vast enterprise that involves several scientific disciplines (i.e. genomics, cell biology, x-ray crystallography, chemistry, computer science, statistics), with each discipline focusing on a particular aspect of the process. In this thesis, I use computational and experimental approaches to explore the most fundamental aspect of drug discovery: the molecular interactions of small-molecules inhibitors with proteins. In Part I (Chapters I and II), I describe how computational docking approaches can be used to identify structurally diverse molecules that can inhibit multiple protein targets in the brain. I illustrate this approach using the examples of microtubule-stabilizing agents and inhibitors of cyclooxygenase(COX)-I and 5-lipoxygenase (5-LOX). In Part II (Chapters III and IV), I focus on membrane proteins, which are notoriously difficult to work with due to their low natural abundances, low yields for heterologous over expression, and propensities toward aggregation. I describe a general approach for designing water-soluble variants of membrane proteins, for the purpose of developing cell-free, label-free, detergent-free, solution-phase studies of protein structure and small-molecule binding. I illustrate this approach through the design of a water-soluble variant of the membrane protein Smoothened, wsSMO. This wsSMO stands to serve as a first-step towards developing membrane protein analogs of this important signaling protein and drug target.

  14. Identification of Program Signatures from Cloud Computing System Telemetry Data

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

    Nichols, Nicole M.; Greaves, Mark T.; Smith, William P.

    Malicious cloud computing activity can take many forms, including running unauthorized programs in a virtual environment. Detection of these malicious activities while preserving the privacy of the user is an important research challenge. Prior work has shown the potential viability of using cloud service billing metrics as a mechanism for proxy identification of malicious programs. Previously this novel detection method has been evaluated in a synthetic and isolated computational environment. In this paper we demonstrate the ability of billing metrics to identify programs, in an active cloud computing environment, including multiple virtual machines running on the same hypervisor. The openmore » source cloud computing platform OpenStack, is used for private cloud management at Pacific Northwest National Laboratory. OpenStack provides a billing tool (Ceilometer) to collect system telemetry measurements. We identify four different programs running on four virtual machines under the same cloud user account. Programs were identified with up to 95% accuracy. This accuracy is dependent on the distinctiveness of telemetry measurements for the specific programs we tested. Future work will examine the scalability of this approach for a larger selection of programs to better understand the uniqueness needed to identify a program. Additionally, future work should address the separation of signatures when multiple programs are running on the same virtual machine.« less

  15. Identifying Ghanaian Pre-Service Teachers' Readiness for Computer Use: A Technology Acceptance Model Approach

    ERIC Educational Resources Information Center

    Gyamfi, Stephen Adu

    2016-01-01

    This study extends the technology acceptance model to identify factors that influence technology acceptance among pre-service teachers in Ghana. Data from 380 usable questionnaires were tested against the research model. Utilising the extended technology acceptance model (TAM) as a research framework, the study found that: pre-service teachers'…

  16. The Health Care Costs of Violence against Women

    ERIC Educational Resources Information Center

    Kruse, Marie; Sorensen, Jan; Bronnum-Hansen, Henrik; Helweg-Larsen, Karin

    2011-01-01

    The aim of this study is to analyze the health care costs of violence against women. For the study, we used a register-based approach where we identified victims of violence and assessed their actual health care costs at individual level in a bottom-up analysis. Furthermore, we identified a reference population. We computed the attributable costs,…

  17. Becoming an Interdisciplinary Scientist: An Analysis of Students' Experiences in Three Computer Science Doctoral Programmes

    ERIC Educational Resources Information Center

    Calatrava Moreno, María del Carmen; Danowitz, Mary Ann

    2016-01-01

    The aim of this study was to identify how and why doctoral students do interdisciplinary research. A mixed-methods approach utilising bibliometric analysis of the publications of 195 students identified those who had published interdisciplinary research. This objective measurement of the interdisciplinarity, applying the Rao-Stirling index to Web…

  18. A computational visual saliency model based on statistics and machine learning.

    PubMed

    Lin, Ru-Je; Lin, Wei-Song

    2014-08-01

    Identifying the type of stimuli that attracts human visual attention has been an appealing topic for scientists for many years. In particular, marking the salient regions in images is useful for both psychologists and many computer vision applications. In this paper, we propose a computational approach for producing saliency maps using statistics and machine learning methods. Based on four assumptions, three properties (Feature-Prior, Position-Prior, and Feature-Distribution) can be derived and combined by a simple intersection operation to obtain a saliency map. These properties are implemented by a similarity computation, support vector regression (SVR) technique, statistical analysis of training samples, and information theory using low-level features. This technique is able to learn the preferences of human visual behavior while simultaneously considering feature uniqueness. Experimental results show that our approach performs better in predicting human visual attention regions than 12 other models in two test databases. © 2014 ARVO.

  19. Fingerprint-Based Structure Retrieval Using Electron Density

    PubMed Central

    Yin, Shuangye; Dokholyan, Nikolay V.

    2010-01-01

    We present a computational approach that can quickly search a large protein structural database to identify structures that fit a given electron density, such as determined by cryo-electron microscopy. We use geometric invariants (fingerprints) constructed using 3D Zernike moments to describe the electron density, and reduce the problem of fitting of the structure to the electron density to simple fingerprint comparison. Using this approach, we are able to screen the entire Protein Data Bank and identify structures that fit two experimental electron densities determined by cryo-electron microscopy. PMID:21287628

  20. Fingerprint-based structure retrieval using electron density.

    PubMed

    Yin, Shuangye; Dokholyan, Nikolay V

    2011-03-01

    We present a computational approach that can quickly search a large protein structural database to identify structures that fit a given electron density, such as determined by cryo-electron microscopy. We use geometric invariants (fingerprints) constructed using 3D Zernike moments to describe the electron density, and reduce the problem of fitting of the structure to the electron density to simple fingerprint comparison. Using this approach, we are able to screen the entire Protein Data Bank and identify structures that fit two experimental electron densities determined by cryo-electron microscopy. Copyright © 2010 Wiley-Liss, Inc.

  1. Petascale supercomputing to accelerate the design of high-temperature alloys

    DOE PAGES

    Shin, Dongwon; Lee, Sangkeun; Shyam, Amit; ...

    2017-10-25

    Recent progress in high-performance computing and data informatics has opened up numerous opportunities to aid the design of advanced materials. Herein, we demonstrate a computational workflow that includes rapid population of high-fidelity materials datasets via petascale computing and subsequent analyses with modern data science techniques. We use a first-principles approach based on density functional theory to derive the segregation energies of 34 microalloying elements at the coherent and semi-coherent interfaces between the aluminium matrix and the θ'-Al 2Cu precipitate, which requires several hundred supercell calculations. We also perform extensive correlation analyses to identify materials descriptors that affect the segregation behaviourmore » of solutes at the interfaces. Finally, we show an example of leveraging machine learning techniques to predict segregation energies without performing computationally expensive physics-based simulations. As a result, the approach demonstrated in the present work can be applied to any high-temperature alloy system for which key materials data can be obtained using high-performance computing.« less

  2. Petascale supercomputing to accelerate the design of high-temperature alloys

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

    Shin, Dongwon; Lee, Sangkeun; Shyam, Amit

    Recent progress in high-performance computing and data informatics has opened up numerous opportunities to aid the design of advanced materials. Herein, we demonstrate a computational workflow that includes rapid population of high-fidelity materials datasets via petascale computing and subsequent analyses with modern data science techniques. We use a first-principles approach based on density functional theory to derive the segregation energies of 34 microalloying elements at the coherent and semi-coherent interfaces between the aluminium matrix and the θ'-Al 2Cu precipitate, which requires several hundred supercell calculations. We also perform extensive correlation analyses to identify materials descriptors that affect the segregation behaviourmore » of solutes at the interfaces. Finally, we show an example of leveraging machine learning techniques to predict segregation energies without performing computationally expensive physics-based simulations. As a result, the approach demonstrated in the present work can be applied to any high-temperature alloy system for which key materials data can be obtained using high-performance computing.« less

  3. The year 2000 threat: preparing radiology for nine realms of risk.

    PubMed

    Berland, L L

    1999-01-01

    The year 2000 computer problem arises from a long-standing and often-duplicated computer programming error. Affected programs use only two digits to represent years, which may lead to a variety of computer malfunctions and data errors related to crossing from 1999 (99) to 2000 (00), at which point computers may interpret 00 as 1900 or other incorrect dates. Radiology and medicine may be seriously affected by this problem as it relates to the function of its equipment; business functions such as scheduling, billing and purchasing; the reliability of infrastructure such as power and telecommunications; the availability of supplies; and many other issues. It is crucial that radiologists, as practitioners of one of the most computer-oriented medical specialties, help lead the effort to ensure continuity of operations as the year 2000 boundary approaches and passes. This article provides suggestions for a structured approach, as well as tools and checklists, to guide project leaders attempting to identify and remediate year 2000-associated problems within radiology facilities.

  4. Petascale supercomputing to accelerate the design of high-temperature alloys

    NASA Astrophysics Data System (ADS)

    Shin, Dongwon; Lee, Sangkeun; Shyam, Amit; Haynes, J. Allen

    2017-12-01

    Recent progress in high-performance computing and data informatics has opened up numerous opportunities to aid the design of advanced materials. Herein, we demonstrate a computational workflow that includes rapid population of high-fidelity materials datasets via petascale computing and subsequent analyses with modern data science techniques. We use a first-principles approach based on density functional theory to derive the segregation energies of 34 microalloying elements at the coherent and semi-coherent interfaces between the aluminium matrix and the θ‧-Al2Cu precipitate, which requires several hundred supercell calculations. We also perform extensive correlation analyses to identify materials descriptors that affect the segregation behaviour of solutes at the interfaces. Finally, we show an example of leveraging machine learning techniques to predict segregation energies without performing computationally expensive physics-based simulations. The approach demonstrated in the present work can be applied to any high-temperature alloy system for which key materials data can be obtained using high-performance computing.

  5. 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.

  6. Identifying Broadband Rotational Spectra with Neural Networks

    NASA Astrophysics Data System (ADS)

    Zaleski, Daniel P.; Prozument, Kirill

    2017-06-01

    A typical broadband rotational spectrum may contain several thousand observable transitions, spanning many species. Identifying the individual spectra, particularly when the dynamic range reaches 1,000:1 or even 10,000:1, can be challenging. One approach is to apply automated fitting routines. In this approach, combinations of 3 transitions can be created to form a "triple", which allows fitting of the A, B, and C rotational constants in a Watson-type Hamiltonian. On a standard desktop computer, with a target molecule of interest, a typical AUTOFIT routine takes 2-12 hours depending on the spectral density. A new approach is to utilize machine learning to train a computer to recognize the patterns (frequency spacing and relative intensities) inherit in rotational spectra and to identify the individual spectra in a raw broadband rotational spectrum. Here, recurrent neural networks have been trained to identify different types of rotational spectra and classify them accordingly. Furthermore, early results in applying convolutional neural networks for spectral object recognition in broadband rotational spectra appear promising. Perez et al. "Broadband Fourier transform rotational spectroscopy for structure determination: The water heptamer." Chem. Phys. Lett., 2013, 571, 1-15. Seifert et al. "AUTOFIT, an Automated Fitting Tool for Broadband Rotational Spectra, and Applications to 1-Hexanal." J. Mol. Spectrosc., 2015, 312, 13-21. Bishop. "Neural networks for pattern recognition." Oxford university press, 1995.

  7. Determining coding CpG islands by identifying regions significant for pattern statistics on Markov chains.

    PubMed

    Singer, Meromit; Engström, Alexander; Schönhuth, Alexander; Pachter, Lior

    2011-09-23

    Recent experimental and computational work confirms that CpGs can be unmethylated inside coding exons, thereby showing that codons may be subjected to both genomic and epigenomic constraint. It is therefore of interest to identify coding CpG islands (CCGIs) that are regions inside exons enriched for CpGs. The difficulty in identifying such islands is that coding exons exhibit sequence biases determined by codon usage and constraints that must be taken into account. We present a method for finding CCGIs that showcases a novel approach we have developed for identifying regions of interest that are significant (with respect to a Markov chain) for the counts of any pattern. Our method begins with the exact computation of tail probabilities for the number of CpGs in all regions contained in coding exons, and then applies a greedy algorithm for selecting islands from among the regions. We show that the greedy algorithm provably optimizes a biologically motivated criterion for selecting islands while controlling the false discovery rate. We applied this approach to the human genome (hg18) and annotated CpG islands in coding exons. The statistical criterion we apply to evaluating islands reduces the number of false positives in existing annotations, while our approach to defining islands reveals significant numbers of undiscovered CCGIs in coding exons. Many of these appear to be examples of functional epigenetic specialization in coding exons.

  8. An allotment planning concept and related computer software for planning the fixed satellite service at the 1988 space WARC

    NASA Technical Reports Server (NTRS)

    Miller, Edward F.; Heyward, Ann O.; Ponchak, Denise S.; Spence, Rodney L.; Whyte, Wayne A., Jr.

    1987-01-01

    The authors describe a two-phase approach to allotment planning suitable for use in planning the fixed satellite service at the 1988 Space World Administrative radio Conference (ORB-88). The two phases are (1) the identification of predetermined geostationary arc segments common to groups of administrations and (2) the use of a synthesis program to identify example scenarios of space station placements. The planning approach is described in detail and is related to the objectives of the conference. Computer software has been developed to implement the concepts, and the logic and rationale for identifying predetermined arc segments is discussed. Example scenarios are evaluated to give guidance in the selection of the technical characteristics of space communications systems to be planned. The allotment planning concept described guarantees equitable access to the geostationary orbit, provides flexibility in implementation, and reduces the need for coordination among administrations.

  9. An allotment planning concept and related computer software for planning the fixed satellite service at the 1988 space WARC

    NASA Technical Reports Server (NTRS)

    Miller, Edward F.; Heyward, Ann O.; Ponchak, Denise S.; Spence, Rodney L.; Whyte, Wayne A., Jr.; Zuzek, John E.

    1987-01-01

    Described is a two-phase approach to allotment planning suitable for use in establishing the fixed satellite service at the 1988 Space World Administrative Radio Conference (ORB-88). The two phases are (1) the identification of predetermined geostationary arc segments common togroups of administrations, and (2) the use of a synthesis program to identify example scenarios of space station placements. The planning approach is described in detail and is related to the objectives of the confernece. Computer software has been developed to implement the concepts, and a complete discussion on the logic and rationale for identifying predetermined arc segments is given. Example scenarios are evaluated to give guidance in the selection of the technical characteristics of space communications systems to be planned. The allotment planning concept described guarantees in practice equitable access to the geostationary orbit, provides flexibility in implementation, and reduces the need for coordination among administrations.

  10. Computational analysis of cell-to-cell heterogeneity in single-cell RNA-sequencing data reveals hidden subpopulations of cells.

    PubMed

    Buettner, Florian; Natarajan, Kedar N; Casale, F Paolo; Proserpio, Valentina; Scialdone, Antonio; Theis, Fabian J; Teichmann, Sarah A; Marioni, John C; Stegle, Oliver

    2015-02-01

    Recent technical developments have enabled the transcriptomes of hundreds of cells to be assayed in an unbiased manner, opening up the possibility that new subpopulations of cells can be found. However, the effects of potential confounding factors, such as the cell cycle, on the heterogeneity of gene expression and therefore on the ability to robustly identify subpopulations remain unclear. We present and validate a computational approach that uses latent variable models to account for such hidden factors. We show that our single-cell latent variable model (scLVM) allows the identification of otherwise undetectable subpopulations of cells that correspond to different stages during the differentiation of naive T cells into T helper 2 cells. Our approach can be used not only to identify cellular subpopulations but also to tease apart different sources of gene expression heterogeneity in single-cell transcriptomes.

  11. Human-based approaches to pharmacology and cardiology: an interdisciplinary and intersectorial workshop.

    PubMed

    Rodriguez, Blanca; Carusi, Annamaria; Abi-Gerges, Najah; Ariga, Rina; Britton, Oliver; Bub, Gil; Bueno-Orovio, Alfonso; Burton, Rebecca A B; Carapella, Valentina; Cardone-Noott, Louie; Daniels, Matthew J; Davies, Mark R; Dutta, Sara; Ghetti, Andre; Grau, Vicente; Harmer, Stephen; Kopljar, Ivan; Lambiase, Pier; Lu, Hua Rong; Lyon, Aurore; Minchole, Ana; Muszkiewicz, Anna; Oster, Julien; Paci, Michelangelo; Passini, Elisa; Severi, Stefano; Taggart, Peter; Tinker, Andy; Valentin, Jean-Pierre; Varro, Andras; Wallman, Mikael; Zhou, Xin

    2016-09-01

    Both biomedical research and clinical practice rely on complex datasets for the physiological and genetic characterization of human hearts in health and disease. Given the complexity and variety of approaches and recordings, there is now growing recognition of the need to embed computational methods in cardiovascular medicine and science for analysis, integration and prediction. This paper describes a Workshop on Computational Cardiovascular Science that created an international, interdisciplinary and inter-sectorial forum to define the next steps for a human-based approach to disease supported by computational methodologies. The main ideas highlighted were (i) a shift towards human-based methodologies, spurred by advances in new in silico, in vivo, in vitro, and ex vivo techniques and the increasing acknowledgement of the limitations of animal models. (ii) Computational approaches complement, expand, bridge, and integrate in vitro, in vivo, and ex vivo experimental and clinical data and methods, and as such they are an integral part of human-based methodologies in pharmacology and medicine. (iii) The effective implementation of multi- and interdisciplinary approaches, teams, and training combining and integrating computational methods with experimental and clinical approaches across academia, industry, and healthcare settings is a priority. (iv) The human-based cross-disciplinary approach requires experts in specific methodologies and domains, who also have the capacity to communicate and collaborate across disciplines and cross-sector environments. (v) This new translational domain for human-based cardiology and pharmacology requires new partnerships supported financially and institutionally across sectors. Institutional, organizational, and social barriers must be identified, understood and overcome in each specific setting. © The Author 2015. Published by Oxford University Press on behalf of the European Society of Cardiology.

  12. Numerical Analysis of Crack Tip Plasticity and History Effects under Mixed Mode Conditions

    NASA Astrophysics Data System (ADS)

    Lopez-Crespo, Pablo; Pommier, Sylvie

    The plastic behaviour in the crack tip region has a strong influence on the fatigue life of engineering components. In general, residual stresses developed as a consequence of the plasticity being constrained around the crack tip have a significant role on both the direction of crack propagation and the propagation rate. Finite element methods (FEM) are commonly employed in order to model plasticity. However, if millions of cycles need to be modelled to predict the fatigue behaviour of a component, the method becomes computationally too expensive. By employing a multiscale approach, very precise analyses computed by FEM can be brought to a global scale. The data generated using the FEM enables us to identify a global cyclic elastic-plastic model for the crack tip region. Once this model is identified, it can be employed directly, with no need of additional FEM computations, resulting in fast computations. This is done by partitioning local displacement fields computed by FEM into intensity factors (global data) and spatial fields. A Karhunen-Loeve algorithm developed for image processing was employed for this purpose. In addition, the partitioning is done such as to distinguish into elastic and plastic components. Each of them is further divided into opening mode and shear mode parts. The plastic flow direction was determined with the above approach on a centre cracked panel subjected to a wide range of mixed-mode loading conditions. It was found to agree well with the maximum tangential stress criterion developed by Erdogan and Sih, provided that the loading direction is corrected for residual stresses. In this approach, residual stresses are measured at the global scale through internal intensity factors.

  13. Identifying the relationship between feedback provided in computer-assisted instructional modules, science self-efficacy, and academic achievement

    NASA Astrophysics Data System (ADS)

    Mazingo, Diann Etsuko

    Feedback has been identified as a key variable in developing academic self-efficacy. The types of feedback can vary from a traditional, objectivist approach that focuses on minimizing learner errors to a more constructivist approach, focusing on facilitating understanding. The influx of computer-based courses, whether online or through a series of computer-assisted instruction (CAI) modules require that the current research of effective feedback techniques in the classroom be extended to computer environments in order to impact their instructional design. In this study, exposure to different types of feedback during a chemistry CAI module was studied in relation to science self-efficacy (SSE) and performance on an objective-driven assessment (ODA) of the chemistry concepts covered in the unit. The quantitative analysis consisted of two separate ANCOVAs on the dependent variables, using pretest as the covariate and group as the fixed factor. No significant differences were found for either variable between the three groups on adjusted posttest means for the ODA and SSE measures (.95F(2, 106) = 1.311, p = 0.274 and .95F(2, 106) = 1.080, p = 0.344, respectively). However, a mixed methods approach yielded valuable qualitative insights into why only one overall quantitative effect was observed. These findings are discussed in relation to the need to further refine the instruments and methods used in order to more fully explore the possibility that type of feedback might play a role in developing SSE, and consequently, improve academic performance in science. Future research building on this study may reveal significance that could impact instructional design practices for developing online and computer-based instruction.

  14. Computational and empirical studies predict Mycobacterium tuberculosis-specific T cells as a biomarker for infection outcome

    DOE PAGES

    Marino, Simeone; Gideon, Hannah P.; Gong, Chang; ...

    2016-04-11

    Identifying biomarkers for tuberculosis (TB) is an ongoing challenge in developing immunological correlates of infection outcome and protection. Biomarker discovery is also necessary for aiding design and testing of new treatments and vaccines. To effectively predict biomarkers for infection progression in any disease, including TB, large amounts of experimental data are required to reach statistical power and make accurate predictions. We took a two-pronged approach using both experimental and computational modeling to address this problem. We first collected 200 blood samples over a 2-year period from 28 non-human primates (NHP) infected with a low dose of Mycobacterium tuberculosis. We identifiedmore » T cells and the cytokines that they were producing (single and multiple) from each sample along with monkey status and infection progression data. Machine learning techniques were used to interrogate the experimental NHP datasets without identifying any potential TB biomarker. In parallel, we used our extensive novel NHP datasets to build and calibrate a multi-organ computational model that combines what is occurring at the site of infection (e.g., lung) at a single granuloma scale with blood level readouts that can be tracked in monkeys and humans. We then generated a large in silico repository of in silico granulomas coupled to lymph node and blood dynamics and developed an in silico tool to scale granuloma level results to a full host scale to identify what best predicts Mycobacterium tuberculosis (Mtb) infection outcomes. The analysis of in silico blood measures identifies Mtb-specific frequencies of effector T cell phenotypes at various time points post infection as promising indicators of infection outcome. As a result, we emphasize that pairing wetlab and computational approaches holds great promise to accelerate TB biomarker discovery.« less

  15. Computational and empirical studies predict Mycobacterium tuberculosis-specific T cells as a biomarker for infection outcome

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

    Marino, Simeone; Gideon, Hannah P.; Gong, Chang

    Identifying biomarkers for tuberculosis (TB) is an ongoing challenge in developing immunological correlates of infection outcome and protection. Biomarker discovery is also necessary for aiding design and testing of new treatments and vaccines. To effectively predict biomarkers for infection progression in any disease, including TB, large amounts of experimental data are required to reach statistical power and make accurate predictions. We took a two-pronged approach using both experimental and computational modeling to address this problem. We first collected 200 blood samples over a 2-year period from 28 non-human primates (NHP) infected with a low dose of Mycobacterium tuberculosis. We identifiedmore » T cells and the cytokines that they were producing (single and multiple) from each sample along with monkey status and infection progression data. Machine learning techniques were used to interrogate the experimental NHP datasets without identifying any potential TB biomarker. In parallel, we used our extensive novel NHP datasets to build and calibrate a multi-organ computational model that combines what is occurring at the site of infection (e.g., lung) at a single granuloma scale with blood level readouts that can be tracked in monkeys and humans. We then generated a large in silico repository of in silico granulomas coupled to lymph node and blood dynamics and developed an in silico tool to scale granuloma level results to a full host scale to identify what best predicts Mycobacterium tuberculosis (Mtb) infection outcomes. The analysis of in silico blood measures identifies Mtb-specific frequencies of effector T cell phenotypes at various time points post infection as promising indicators of infection outcome. As a result, we emphasize that pairing wetlab and computational approaches holds great promise to accelerate TB biomarker discovery.« less

  16. Computational exploration of microRNAs from expressed sequence tags of Humulus lupulus, target predictions and expression analysis.

    PubMed

    Mishra, Ajay Kumar; Duraisamy, Ganesh Selvaraj; Týcová, Anna; Matoušek, Jaroslav

    2015-12-01

    Among computationally predicted and experimentally validated plant miRNAs, several are conserved across species boundaries in the plant kingdom. In this study, a combined experimental-in silico computational based approach was adopted for the identification and characterization of miRNAs in Humulus lupulus (hop), which is widely cultivated for use by the brewing industry and apart from, used as a medicinal herb. A total of 22 miRNAs belonging to 17 miRNA families were identified in hop following comparative computational approach and EST-based homology search according to a series of filtering criteria. Selected miRNAs were validated by end-point PCR and quantitative reverse transcription-polymerase chain reaction (qRT-PCR), confirmed the existence of conserved miRNAs in hop. Based on the characteristic that miRNAs exhibit perfect or nearly perfect complementarity with their targeted mRNA sequences, a total of 47 potential miRNA targets were identified in hop. Strikingly, the majority of predicted targets were belong to transcriptional factors which could regulate hop growth and development, including leaf, root and even cone development. Moreover, the identified miRNAs may also be involved in other cellular and metabolic processes, such as stress response, signal transduction, and other physiological processes. The cis-regulatory elements relevant to biotic and abiotic stress, plant hormone response, flavonoid biosynthesis were identified in the promoter regions of those miRNA genes. Overall, findings from this study will accelerate the way for further researches of miRNAs, their functions in hop and shows a path for the prediction and analysis of miRNAs to those species whose genomes are not available. Copyright © 2015 Elsevier Ltd. All rights reserved.

  17. Cogeneration computer model assessment: Advanced cogeneration research study

    NASA Technical Reports Server (NTRS)

    Rosenberg, L.

    1983-01-01

    Cogeneration computer simulation models to recommend the most desirable models or their components for use by the Southern California Edison Company (SCE) in evaluating potential cogeneration projects was assessed. Existing cogeneration modeling capabilities are described, preferred models are identified, and an approach to the development of a code which will best satisfy SCE requirements is recommended. Five models (CELCAP, COGEN 2, CPA, DEUS, and OASIS) are recommended for further consideration.

  18. Computational pan-genomics: status, promises and challenges.

    PubMed

    2018-01-01

    Many disciplines, from human genetics and oncology to plant breeding, microbiology and virology, commonly face the challenge of analyzing rapidly increasing numbers of genomes. In case of Homo sapiens, the number of sequenced genomes will approach hundreds of thousands in the next few years. Simply scaling up established bioinformatics pipelines will not be sufficient for leveraging the full potential of such rich genomic data sets. Instead, novel, qualitatively different computational methods and paradigms are needed. We will witness the rapid extension of computational pan-genomics, a new sub-area of research in computational biology. In this article, we generalize existing definitions and understand a pan-genome as any collection of genomic sequences to be analyzed jointly or to be used as a reference. We examine already available approaches to construct and use pan-genomes, discuss the potential benefits of future technologies and methodologies and review open challenges from the vantage point of the above-mentioned biological disciplines. As a prominent example for a computational paradigm shift, we particularly highlight the transition from the representation of reference genomes as strings to representations as graphs. We outline how this and other challenges from different application domains translate into common computational problems, point out relevant bioinformatics techniques and identify open problems in computer science. With this review, we aim to increase awareness that a joint approach to computational pan-genomics can help address many of the problems currently faced in various domains. © The Author 2016. Published by Oxford University Press.

  19. Genome wide approaches to identify protein-DNA interactions.

    PubMed

    Ma, Tao; Ye, Zhenqing; Wang, Liguo

    2018-05-29

    Transcription factors are DNA-binding proteins that play key roles in many fundamental biological processes. Unraveling their interactions with DNA is essential to identify their target genes and understand the regulatory network. Genome-wide identification of their binding sites became feasible thanks to recent progress in experimental and computational approaches. ChIP-chip, ChIP-seq, and ChIP-exo are three widely used techniques to demarcate genome-wide transcription factor binding sites. This review aims to provide an overview of these three techniques including their experiment procedures, computational approaches, and popular analytic tools. ChIP-chip, ChIP-seq, and ChIP-exo have been the major techniques to study genome-wide in vivo protein-DNA interaction. Due to the rapid development of next-generation sequencing technology, array-based ChIP-chip is deprecated and ChIP-seq has become the most widely used technique to identify transcription factor binding sites in genome-wide. The newly developed ChIP-exo further improves the spatial resolution to single nucleotide. Numerous tools have been developed to analyze ChIP-chip, ChIP-seq and ChIP-exo data. However, different programs may employ different mechanisms or underlying algorithms thus each will inherently include its own set of statistical assumption and bias. So choosing the most appropriate analytic program for a given experiment needs careful considerations. Moreover, most programs only have command line interface so their installation and usage will require basic computation expertise in Unix/Linux. Copyright© Bentham Science Publishers; For any queries, please email at epub@benthamscience.org.

  20. Hotspot-Centric De Novo Design of Protein Binders

    PubMed Central

    Fleishman, Sarel J.; Corn, Jacob E.; Strauch, Eva-Maria; Whitehead, Timothy A.; Karanicolas, John; Baker, David

    2014-01-01

    Protein–protein interactions play critical roles in biology, and computational design of interactions could be useful in a range of applications. We describe in detail a general approach to de novo design of protein interactions based on computed, energetically optimized interaction hotspots, which was recently used to produce high-affinity binders of influenza hemagglutinin. We present several alternative approaches to identify and build the key hotspot interactions within both core secondary structural elements and variable loop regions and evaluate the method's performance in natural-interface recapitulation. We show that the method generates binding surfaces that are more conformationally restricted than previous design methods, reducing opportunities for off-target interactions. PMID:21945116

  1. Video image processing

    NASA Technical Reports Server (NTRS)

    Murray, N. D.

    1985-01-01

    Current technology projections indicate a lack of availability of special purpose computing for Space Station applications. Potential functions for video image special purpose processing are being investigated, such as smoothing, enhancement, restoration and filtering, data compression, feature extraction, object detection and identification, pixel interpolation/extrapolation, spectral estimation and factorization, and vision synthesis. Also, architectural approaches are being identified and a conceptual design generated. Computationally simple algorithms will be research and their image/vision effectiveness determined. Suitable algorithms will be implimented into an overall architectural approach that will provide image/vision processing at video rates that are flexible, selectable, and programmable. Information is given in the form of charts, diagrams and outlines.

  2. Convolutional neural networks and face recognition task

    NASA Astrophysics Data System (ADS)

    Sochenkova, A.; Sochenkov, I.; Makovetskii, A.; Vokhmintsev, A.; Melnikov, A.

    2017-09-01

    Computer vision tasks are remaining very important for the last couple of years. One of the most complicated problems in computer vision is face recognition that could be used in security systems to provide safety and to identify person among the others. There is a variety of different approaches to solve this task, but there is still no universal solution that would give adequate results in some cases. Current paper presents following approach. Firstly, we extract an area containing face, then we use Canny edge detector. On the next stage we use convolutional neural networks (CNN) to finally solve face recognition and person identification task.

  3. High-performance parallel approaches for three-dimensional light detection and ranging point clouds gridding

    NASA Astrophysics Data System (ADS)

    Rizki, Permata Nur Miftahur; Lee, Heezin; Lee, Minsu; Oh, Sangyoon

    2017-01-01

    With the rapid advance of remote sensing technology, the amount of three-dimensional point-cloud data has increased extraordinarily, requiring faster processing in the construction of digital elevation models. There have been several attempts to accelerate the computation using parallel methods; however, little attention has been given to investigating different approaches for selecting the most suited parallel programming model for a given computing environment. We present our findings and insights identified by implementing three popular high-performance parallel approaches (message passing interface, MapReduce, and GPGPU) on time demanding but accurate kriging interpolation. The performances of the approaches are compared by varying the size of the grid and input data. In our empirical experiment, we demonstrate the significant acceleration by all three approaches compared to a C-implemented sequential-processing method. In addition, we also discuss the pros and cons of each method in terms of usability, complexity infrastructure, and platform limitation to give readers a better understanding of utilizing those parallel approaches for gridding purposes.

  4. Computational approaches to protein inference in shotgun proteomics

    PubMed Central

    2012-01-01

    Shotgun proteomics has recently emerged as a powerful approach to characterizing proteomes in biological samples. Its overall objective is to identify the form and quantity of each protein in a high-throughput manner by coupling liquid chromatography with tandem mass spectrometry. As a consequence of its high throughput nature, shotgun proteomics faces challenges with respect to the analysis and interpretation of experimental data. Among such challenges, the identification of proteins present in a sample has been recognized as an important computational task. This task generally consists of (1) assigning experimental tandem mass spectra to peptides derived from a protein database, and (2) mapping assigned peptides to proteins and quantifying the confidence of identified proteins. Protein identification is fundamentally a statistical inference problem with a number of methods proposed to address its challenges. In this review we categorize current approaches into rule-based, combinatorial optimization and probabilistic inference techniques, and present them using integer programing and Bayesian inference frameworks. We also discuss the main challenges of protein identification and propose potential solutions with the goal of spurring innovative research in this area. PMID:23176300

  5. Traveling waves and their tails in locally resonant granular systems

    DOE PAGES

    Xu, H.; Kevrekidis, P. G.; Stefanov, A.

    2015-04-22

    In the present study, we revisit the theme of wave propagation in locally resonant granular crystal systems, also referred to as mass-in-mass systems. We use three distinct approaches to identify relevant traveling waves. In addition, the first consists of a direct solution of the traveling wave problem. The second one consists of the solution of the Fourier tranformed variant of the problem, or, more precisely, of its convolution reformulation (upon an inverse Fourier transform) in real space. Finally, our third approach will restrict considerations to a finite domain, utilizing the notion of Fourier series for important technical reasons, namely themore » avoidance of resonances, which will be discussed in detail. All three approaches can be utilized in either the displacement or the strain formulation. Typical resulting computations in finite domains result in the solitary waves bearing symmetric non-vanishing tails at both ends of the computational domain. Importantly, however, a countably infinite set of anti-resonance conditions is identified for which solutions with genuinely rapidly decaying tails arise.« less

  6. Evaluation of designed ligands by a multiple screening method: Application to glycogen phosphorylase inhibitors constructed with a variety of approaches

    NASA Astrophysics Data System (ADS)

    So, Sung-Sau; Karplus, Martin

    2001-07-01

    Glycogen phosphorylase (GP) is an important enzyme that regulates blood glucose level and a key therapeutic target for the treatment of type II diabetes. In this study, a number of potential GP inhibitors are designed with a variety of computational approaches. They include the applications of MCSS, LUDI and CoMFA to identify additional fragments that can be attached to existing lead molecules; the use of 2D and 3D similarity-based QSAR models (HQSAR and SMGNN) and of the LUDI program to identify novel molecules that may bind to the glucose binding site. The designed ligands are evaluated by a multiple screening method, which is a combination of commercial and in-house ligand-receptor binding affinity prediction programs used in a previous study (So and Karplus, J. Comp.-Aid. Mol. Des., 13 (1999), 243-258). Each method is used at an appropriate point in the screening, as determined by both the accuracy of the calculations and the computational cost. A comparison of the strengths and weaknesses of the ligand design approaches is made.

  7. Traveling waves and their tails in locally resonant granular systems

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

    Xu, H.; Kevrekidis, P. G.; Stefanov, A.

    In the present study, we revisit the theme of wave propagation in locally resonant granular crystal systems, also referred to as mass-in-mass systems. We use three distinct approaches to identify relevant traveling waves. In addition, the first consists of a direct solution of the traveling wave problem. The second one consists of the solution of the Fourier tranformed variant of the problem, or, more precisely, of its convolution reformulation (upon an inverse Fourier transform) in real space. Finally, our third approach will restrict considerations to a finite domain, utilizing the notion of Fourier series for important technical reasons, namely themore » avoidance of resonances, which will be discussed in detail. All three approaches can be utilized in either the displacement or the strain formulation. Typical resulting computations in finite domains result in the solitary waves bearing symmetric non-vanishing tails at both ends of the computational domain. Importantly, however, a countably infinite set of anti-resonance conditions is identified for which solutions with genuinely rapidly decaying tails arise.« less

  8. A Computer Program which Uses an Expert Systems Approach to Identifying Minerals.

    ERIC Educational Resources Information Center

    Hart, Allan Bruce; And Others

    1988-01-01

    Described is a mineral identification program which uses a shell system for creating expert systems of a classification nature. Discusses identification of minerals in hand specimens, thin sections, and polished sections of rocks. (Author/CW)

  9. Music Information Retrieval.

    ERIC Educational Resources Information Center

    Downie, J. Stephen

    2003-01-01

    Identifies MIR (Music Information Retrieval) computer system problems, historic influences, current state-of-the-art, and future MIR solutions through an examination of the multidisciplinary approach to MIR. Highlights include pitch; temporal factors; harmonics; tone; editorial, textual, and bibliographic facets; multicultural factors; locating…

  10. Quantitative Predictive Models for Systemic Toxicity (SOT)

    EPA Science Inventory

    Models to identify systemic and specific target organ toxicity were developed to help transition the field of toxicology towards computational models. By leveraging multiple data sources to incorporate read-across and machine learning approaches, a quantitative model of systemic ...

  11. Computational Approaches for Identifying Adverse Outcome Pathways

    EPA Science Inventory

    Adverse Outcome Pathways (AOPs) provide a framework for organizing toxicity information to improve predictions of the potential adverse impact of environment stressors on humans or wildlife populations, but these benefits are currently limited by the small number of AOPs currentl...

  12. Integrative prescreening in analysis of multiple cancer genomic studies

    PubMed Central

    2012-01-01

    Background In high throughput cancer genomic studies, results from the analysis of single datasets often suffer from a lack of reproducibility because of small sample sizes. Integrative analysis can effectively pool and analyze multiple datasets and provides a cost effective way to improve reproducibility. In integrative analysis, simultaneously analyzing all genes profiled may incur high computational cost. A computationally affordable remedy is prescreening, which fits marginal models, can be conducted in a parallel manner, and has low computational cost. Results An integrative prescreening approach is developed for the analysis of multiple cancer genomic datasets. Simulation shows that the proposed integrative prescreening has better performance than alternatives, particularly including prescreening with individual datasets, an intensity approach and meta-analysis. We also analyze multiple microarray gene profiling studies on liver and pancreatic cancers using the proposed approach. Conclusions The proposed integrative prescreening provides an effective way to reduce the dimensionality in cancer genomic studies. It can be coupled with existing analysis methods to identify cancer markers. PMID:22799431

  13. A survey of computational methods and error rate estimation procedures for peptide and protein identification in shotgun proteomics

    PubMed Central

    Nesvizhskii, Alexey I.

    2010-01-01

    This manuscript provides a comprehensive review of the peptide and protein identification process using tandem mass spectrometry (MS/MS) data generated in shotgun proteomic experiments. The commonly used methods for assigning peptide sequences to MS/MS spectra are critically discussed and compared, from basic strategies to advanced multi-stage approaches. A particular attention is paid to the problem of false-positive identifications. Existing statistical approaches for assessing the significance of peptide to spectrum matches are surveyed, ranging from single-spectrum approaches such as expectation values to global error rate estimation procedures such as false discovery rates and posterior probabilities. The importance of using auxiliary discriminant information (mass accuracy, peptide separation coordinates, digestion properties, and etc.) is discussed, and advanced computational approaches for joint modeling of multiple sources of information are presented. This review also includes a detailed analysis of the issues affecting the interpretation of data at the protein level, including the amplification of error rates when going from peptide to protein level, and the ambiguities in inferring the identifies of sample proteins in the presence of shared peptides. Commonly used methods for computing protein-level confidence scores are discussed in detail. The review concludes with a discussion of several outstanding computational issues. PMID:20816881

  14. Discovery of Novel MDR-Mycobacterium tuberculosis Inhibitor by New FRIGATE Computational Screen

    PubMed Central

    Vértessy, Beáta; Pütter, Vera; Grolmusz, Vince; Schade, Markus

    2011-01-01

    With 1.6 million casualties annually and 2 billion people being infected, tuberculosis is still one of the most pressing healthcare challenges. Here we report on the new computational docking algorithm FRIGATE which unites continuous local optimization techniques (conjugate gradient method) with an inherently discrete computational approach in forcefield computation, resulting in equal or better scoring accuracies than several benchmark docking programs. By utilizing FRIGATE for a virtual screen of the ZINC library against the Mycobacterium tuberculosis (Mtb) enzyme antigen 85C, we identified novel small molecule inhibitors of multiple drug-resistant Mtb, which bind in vitro to the catalytic site of antigen 85C. PMID:22164290

  15. Efficient and accurate Greedy Search Methods for mining functional modules in protein interaction networks.

    PubMed

    He, Jieyue; Li, Chaojun; Ye, Baoliu; Zhong, Wei

    2012-06-25

    Most computational algorithms mainly focus on detecting highly connected subgraphs in PPI networks as protein complexes but ignore their inherent organization. Furthermore, many of these algorithms are computationally expensive. However, recent analysis indicates that experimentally detected protein complexes generally contain Core/attachment structures. In this paper, a Greedy Search Method based on Core-Attachment structure (GSM-CA) is proposed. The GSM-CA method detects densely connected regions in large protein-protein interaction networks based on the edge weight and two criteria for determining core nodes and attachment nodes. The GSM-CA method improves the prediction accuracy compared to other similar module detection approaches, however it is computationally expensive. Many module detection approaches are based on the traditional hierarchical methods, which is also computationally inefficient because the hierarchical tree structure produced by these approaches cannot provide adequate information to identify whether a network belongs to a module structure or not. In order to speed up the computational process, the Greedy Search Method based on Fast Clustering (GSM-FC) is proposed in this work. The edge weight based GSM-FC method uses a greedy procedure to traverse all edges just once to separate the network into the suitable set of modules. The proposed methods are applied to the protein interaction network of S. cerevisiae. Experimental results indicate that many significant functional modules are detected, most of which match the known complexes. Results also demonstrate that the GSM-FC algorithm is faster and more accurate as compared to other competing algorithms. Based on the new edge weight definition, the proposed algorithm takes advantages of the greedy search procedure to separate the network into the suitable set of modules. Experimental analysis shows that the identified modules are statistically significant. The algorithm can reduce the computational time significantly while keeping high prediction accuracy.

  16. Petri net modeling of high-order genetic systems using grammatical evolution.

    PubMed

    Moore, Jason H; Hahn, Lance W

    2003-11-01

    Understanding how DNA sequence variations impact human health through a hierarchy of biochemical and physiological systems is expected to improve the diagnosis, prevention, and treatment of common, complex human diseases. We have previously developed a hierarchical dynamic systems approach based on Petri nets for generating biochemical network models that are consistent with genetic models of disease susceptibility. This modeling approach uses an evolutionary computation approach called grammatical evolution as a search strategy for optimal Petri net models. We have previously demonstrated that this approach routinely identifies biochemical network models that are consistent with a variety of genetic models in which disease susceptibility is determined by nonlinear interactions between two DNA sequence variations. In the present study, we evaluate whether the Petri net approach is capable of identifying biochemical networks that are consistent with disease susceptibility due to higher order nonlinear interactions between three DNA sequence variations. The results indicate that our model-building approach is capable of routinely identifying good, but not perfect, Petri net models. Ideas for improving the algorithm for this high-dimensional problem are presented.

  17. Discovery of novel bacterial toxins by genomics and computational biology.

    PubMed

    Doxey, Andrew C; Mansfield, Michael J; Montecucco, Cesare

    2018-06-01

    Hundreds and hundreds of bacterial protein toxins are presently known. Traditionally, toxin identification begins with pathological studies of bacterial infectious disease. Following identification and cultivation of a bacterial pathogen, the protein toxin is purified from the culture medium and its pathogenic activity is studied using the methods of biochemistry and structural biology, cell biology, tissue and organ biology, and appropriate animal models, supplemented by bioimaging techniques. The ongoing and explosive development of high-throughput DNA sequencing and bioinformatic approaches have set in motion a revolution in many fields of biology, including microbiology. One consequence is that genes encoding novel bacterial toxins can be identified by bioinformatic and computational methods based on previous knowledge accumulated from studies of the biology and pathology of thousands of known bacterial protein toxins. Starting from the paradigmatic cases of diphtheria toxin, tetanus and botulinum neurotoxins, this review discusses traditional experimental approaches as well as bioinformatics and genomics-driven approaches that facilitate the discovery of novel bacterial toxins. We discuss recent work on the identification of novel botulinum-like toxins from genera such as Weissella, Chryseobacterium, and Enteroccocus, and the implications of these computationally identified toxins in the field. Finally, we discuss the promise of metagenomics in the discovery of novel toxins and their ecological niches, and present data suggesting the existence of uncharacterized, botulinum-like toxin genes in insect gut metagenomes. Copyright © 2018. Published by Elsevier Ltd.

  18. Uncovering stability mechanisms in microbial ecosystems - combining microcosm experiments, computational modelling and ecological theory in a multidisciplinary approach

    NASA Astrophysics Data System (ADS)

    Worrich, Anja; König, Sara; Banitz, Thomas; Centler, Florian; Frank, Karin; Kästner, Matthias; Miltner, Anja; Thullner, Martin; Wick, Lukas

    2015-04-01

    Although bacterial degraders in soil are commonly exposed to fluctuating environmental conditions, the functional performance of the biodegradation processes can often be maintained by resistance and resilience mechanisms. However, there is still a gap in the mechanistic understanding of key factors contributing to the stability of such an ecosystem service. Therefore we developed an integrated approach combining microcosm experiments, simulation models and ecological theory to directly make use of the strengths of these disciplines. In a continuous interplay process, data, hypotheses, and central questions are exchanged between disciplines to initiate new experiments and models to ultimately identify buffer mechanisms and factors providing functional stability. We focus on drying and rewetting-cycles in soil ecosystems, which are a major abiotic driver for bacterial activity. Functional recovery of the system was found to depend on different spatial processes in the computational model. In particular, bacterial motility is a prerequisite for biodegradation if either bacteria or substrate are heterogeneously distributed. Hence, laboratory experiments focussing on bacterial dispersal processes were conducted and confirmed this finding also for functional resistance. Obtained results will be incorporated into the model in the next step. Overall, the combination of computational modelling and laboratory experiments identified spatial processes as the main driving force for functional stability in the considered system, and has proved a powerful methodological approach.

  19. Algorithmic design of a noise-resistant and efficient closed-loop deep brain stimulation system: A computational approach.

    PubMed

    Karamintziou, Sofia D; Custódio, Ana Luísa; Piallat, Brigitte; Polosan, Mircea; Chabardès, Stéphan; Stathis, Pantelis G; Tagaris, George A; Sakas, Damianos E; Polychronaki, Georgia E; Tsirogiannis, George L; David, Olivier; Nikita, Konstantina S

    2017-01-01

    Advances in the field of closed-loop neuromodulation call for analysis and modeling approaches capable of confronting challenges related to the complex neuronal response to stimulation and the presence of strong internal and measurement noise in neural recordings. Here we elaborate on the algorithmic aspects of a noise-resistant closed-loop subthalamic nucleus deep brain stimulation system for advanced Parkinson's disease and treatment-refractory obsessive-compulsive disorder, ensuring remarkable performance in terms of both efficiency and selectivity of stimulation, as well as in terms of computational speed. First, we propose an efficient method drawn from dynamical systems theory, for the reliable assessment of significant nonlinear coupling between beta and high-frequency subthalamic neuronal activity, as a biomarker for feedback control. Further, we present a model-based strategy through which optimal parameters of stimulation for minimum energy desynchronizing control of neuronal activity are being identified. The strategy integrates stochastic modeling and derivative-free optimization of neural dynamics based on quadratic modeling. On the basis of numerical simulations, we demonstrate the potential of the presented modeling approach to identify, at a relatively low computational cost, stimulation settings potentially associated with a significantly higher degree of efficiency and selectivity compared with stimulation settings determined post-operatively. Our data reinforce the hypothesis that model-based control strategies are crucial for the design of novel stimulation protocols at the backstage of clinical applications.

  20. Morphology control in polymer blend fibers—a high throughput computing approach

    NASA Astrophysics Data System (ADS)

    Sesha Sarath Pokuri, Balaji; Ganapathysubramanian, Baskar

    2016-08-01

    Fibers made from polymer blends have conventionally enjoyed wide use, particularly in textiles. This wide applicability is primarily aided by the ease of manufacturing such fibers. More recently, the ability to tailor the internal morphology of polymer blend fibers by carefully designing processing conditions has enabled such fibers to be used in technologically relevant applications. Some examples include anisotropic insulating properties for heat and anisotropic wicking of moisture, coaxial morphologies for optical applications as well as fibers with high internal surface area for filtration and catalysis applications. However, identifying the appropriate processing conditions from the large space of possibilities using conventional trial-and-error approaches is a tedious and resource-intensive process. Here, we illustrate a high throughput computational approach to rapidly explore and characterize how processing conditions (specifically blend ratio and evaporation rates) affect the internal morphology of polymer blends during solvent based fabrication. We focus on a PS: PMMA system and identify two distinct classes of morphologies formed due to variations in the processing conditions. We subsequently map the processing conditions to the morphology class, thus constructing a ‘phase diagram’ that enables rapid identification of processing parameters for specific morphology class. We finally demonstrate the potential for time dependent processing conditions to get desired features of the morphology. This opens up the possibility of rational stage-wise design of processing pathways for tailored fiber morphology using high throughput computing.

  1. A Digital Computer Approach to the Unsymmetric Rigid Body Problem.

    DTIC Science & Technology

    1982-03-01

    FORCE SYSTEMS COMMAND WRIGHT-PATTERSON AIR FORCE BASE , OHIO 45433 A 82 04 19 027 NOTICE When Government drawings, specifications, or other data are...Laboratory (AFWAL/AAA) March 1982 Air Force Wright Aeronautical Laboratories (AFSC) 13. NUMBER OF PAGES Wright-Patterson Air Force Base , Ohio 45433 67 14...reverse aide it necessary and Identify by block number ) The use of a computer approximation technique based on trial functions is investigated for the

  2. Precise detection of de novo single nucleotide variants in human genomes.

    PubMed

    Gómez-Romero, Laura; Palacios-Flores, Kim; Reyes, José; García, Delfino; Boege, Margareta; Dávila, Guillermo; Flores, Margarita; Schatz, Michael C; Palacios, Rafael

    2018-05-22

    The precise determination of de novo genetic variants has enormous implications across different fields of biology and medicine, particularly personalized medicine. Currently, de novo variations are identified by mapping sample reads from a parent-offspring trio to a reference genome, allowing for a certain degree of differences. While widely used, this approach often introduces false-positive (FP) results due to misaligned reads and mischaracterized sequencing errors. In a previous study, we developed an alternative approach to accurately identify single nucleotide variants (SNVs) using only perfect matches. However, this approach could be applied only to haploid regions of the genome and was computationally intensive. In this study, we present a unique approach, coverage-based single nucleotide variant identification (COBASI), which allows the exploration of the entire genome using second-generation short sequence reads without extensive computing requirements. COBASI identifies SNVs using changes in coverage of exactly matching unique substrings, and is particularly suited for pinpointing de novo SNVs. Unlike other approaches that require population frequencies across hundreds of samples to filter out any methodological biases, COBASI can be applied to detect de novo SNVs within isolated families. We demonstrate this capability through extensive simulation studies and by studying a parent-offspring trio we sequenced using short reads. Experimental validation of all 58 candidate de novo SNVs and a selection of non-de novo SNVs found in the trio confirmed zero FP calls. COBASI is available as open source at https://github.com/Laura-Gomez/COBASI for any researcher to use. Copyright © 2018 the Author(s). Published by PNAS.

  3. Soft Computing Methods for Disulfide Connectivity Prediction.

    PubMed

    Márquez-Chamorro, Alfonso E; Aguilar-Ruiz, Jesús S

    2015-01-01

    The problem of protein structure prediction (PSP) is one of the main challenges in structural bioinformatics. To tackle this problem, PSP can be divided into several subproblems. One of these subproblems is the prediction of disulfide bonds. The disulfide connectivity prediction problem consists in identifying which nonadjacent cysteines would be cross-linked from all possible candidates. Determining the disulfide bond connectivity between the cysteines of a protein is desirable as a previous step of the 3D PSP, as the protein conformational search space is highly reduced. The most representative soft computing approaches for the disulfide bonds connectivity prediction problem of the last decade are summarized in this paper. Certain aspects, such as the different methodologies based on soft computing approaches (artificial neural network or support vector machine) or features of the algorithms, are used for the classification of these methods.

  4. Automatic discovery of the communication network topology for building a supercomputer model

    NASA Astrophysics Data System (ADS)

    Sobolev, Sergey; Stefanov, Konstantin; Voevodin, Vadim

    2016-10-01

    The Research Computing Center of Lomonosov Moscow State University is developing the Octotron software suite for automatic monitoring and mitigation of emergency situations in supercomputers so as to maximize hardware reliability. The suite is based on a software model of the supercomputer. The model uses a graph to describe the computing system components and their interconnections. One of the most complex components of a supercomputer that needs to be included in the model is its communication network. This work describes the proposed approach for automatically discovering the Ethernet communication network topology in a supercomputer and its description in terms of the Octotron model. This suite automatically detects computing nodes and switches, collects information about them and identifies their interconnections. The application of this approach is demonstrated on the "Lomonosov" and "Lomonosov-2" supercomputers.

  5. A cross-species bi-clustering approach to identifying conserved co-regulated genes.

    PubMed

    Sun, Jiangwen; Jiang, Zongliang; Tian, Xiuchun; Bi, Jinbo

    2016-06-15

    A growing number of studies have explored the process of pre-implantation embryonic development of multiple mammalian species. However, the conservation and variation among different species in their developmental programming are poorly defined due to the lack of effective computational methods for detecting co-regularized genes that are conserved across species. The most sophisticated method to date for identifying conserved co-regulated genes is a two-step approach. This approach first identifies gene clusters for each species by a cluster analysis of gene expression data, and subsequently computes the overlaps of clusters identified from different species to reveal common subgroups. This approach is ineffective to deal with the noise in the expression data introduced by the complicated procedures in quantifying gene expression. Furthermore, due to the sequential nature of the approach, the gene clusters identified in the first step may have little overlap among different species in the second step, thus difficult to detect conserved co-regulated genes. We propose a cross-species bi-clustering approach which first denoises the gene expression data of each species into a data matrix. The rows of the data matrices of different species represent the same set of genes that are characterized by their expression patterns over the developmental stages of each species as columns. A novel bi-clustering method is then developed to cluster genes into subgroups by a joint sparse rank-one factorization of all the data matrices. This method decomposes a data matrix into a product of a column vector and a row vector where the column vector is a consistent indicator across the matrices (species) to identify the same gene cluster and the row vector specifies for each species the developmental stages that the clustered genes co-regulate. Efficient optimization algorithm has been developed with convergence analysis. This approach was first validated on synthetic data and compared to the two-step method and several recent joint clustering methods. We then applied this approach to two real world datasets of gene expression during the pre-implantation embryonic development of the human and mouse. Co-regulated genes consistent between the human and mouse were identified, offering insights into conserved functions, as well as similarities and differences in genome activation timing between the human and mouse embryos. The R package containing the implementation of the proposed method in C ++ is available at: https://github.com/JavonSun/mvbc.git and also at the R platform https://www.r-project.org/ jinbo@engr.uconn.edu. © The Author 2016. Published by Oxford University Press.

  6. Computational evolution: taking liberties.

    PubMed

    Correia, Luís

    2010-09-01

    Evolution has, for a long time, inspired computer scientists to produce computer models mimicking its behavior. Evolutionary algorithm (EA) is one of the areas where this approach has flourished. EAs have been used to model and study evolution, but they have been especially developed for their aptitude as optimization tools for engineering. Developed models are quite simple in comparison with their natural sources of inspiration. However, since EAs run on computers, we have the freedom, especially in optimization models, to test approaches both realistic and outright speculative, from the biological point of view. In this article, we discuss different common evolutionary algorithm models, and then present some alternatives of interest. These include biologically inspired models, such as co-evolution and, in particular, symbiogenetics and outright artificial operators and representations. In each case, the advantages of the modifications to the standard model are identified. The other area of computational evolution, which has allowed us to study basic principles of evolution and ecology dynamics, is the development of artificial life platforms for open-ended evolution of artificial organisms. With these platforms, biologists can test theories by directly manipulating individuals and operators, observing the resulting effects in a realistic way. An overview of the most prominent of such environments is also presented. If instead of artificial platforms we use the real world for evolving artificial life, then we are dealing with evolutionary robotics (ERs). A brief description of this area is presented, analyzing its relations to biology. Finally, we present the conclusions and identify future research avenues in the frontier of computation and biology. Hopefully, this will help to draw the attention of more biologists and computer scientists to the benefits of such interdisciplinary research.

  7. Whole earth modeling: developing and disseminating scientific software for computational geophysics.

    NASA Astrophysics Data System (ADS)

    Kellogg, L. H.

    2016-12-01

    Historically, a great deal of specialized scientific software for modeling and data analysis has been developed by individual researchers or small groups of scientists working on their own specific research problems. As the magnitude of available data and computer power has increased, so has the complexity of scientific problems addressed by computational methods, creating both a need to sustain existing scientific software, and expand its development to take advantage of new algorithms, new software approaches, and new computational hardware. To that end, communities like the Computational Infrastructure for Geodynamics (CIG) have been established to support the use of best practices in scientific computing for solid earth geophysics research and teaching. Working as a scientific community enables computational geophysicists to take advantage of technological developments, improve the accuracy and performance of software, build on prior software development, and collaborate more readily. The CIG community, and others, have adopted an open-source development model, in which code is developed and disseminated by the community in an open fashion, using version control and software repositories like Git. One emerging issue is how to adequately identify and credit the intellectual contributions involved in creating open source scientific software. The traditional method of disseminating scientific ideas, peer reviewed publication, was not designed for review or crediting scientific software, although emerging publication strategies such software journals are attempting to address the need. We are piloting an integrated approach in which authors are identified and credited as scientific software is developed and run. Successful software citation requires integration with the scholarly publication and indexing mechanisms as well, to assign credit, ensure discoverability, and provide provenance for software.

  8. Cloud computing approaches to accelerate drug discovery value chain.

    PubMed

    Garg, Vibhav; Arora, Suchir; Gupta, Chitra

    2011-12-01

    Continued advancements in the area of technology have helped high throughput screening (HTS) evolve from a linear to parallel approach by performing system level screening. Advanced experimental methods used for HTS at various steps of drug discovery (i.e. target identification, target validation, lead identification and lead validation) can generate data of the order of terabytes. As a consequence, there is pressing need to store, manage, mine and analyze this data to identify informational tags. This need is again posing challenges to computer scientists to offer the matching hardware and software infrastructure, while managing the varying degree of desired computational power. Therefore, the potential of "On-Demand Hardware" and "Software as a Service (SAAS)" delivery mechanisms cannot be denied. This on-demand computing, largely referred to as Cloud Computing, is now transforming the drug discovery research. Also, integration of Cloud computing with parallel computing is certainly expanding its footprint in the life sciences community. The speed, efficiency and cost effectiveness have made cloud computing a 'good to have tool' for researchers, providing them significant flexibility, allowing them to focus on the 'what' of science and not the 'how'. Once reached to its maturity, Discovery-Cloud would fit best to manage drug discovery and clinical development data, generated using advanced HTS techniques, hence supporting the vision of personalized medicine.

  9. HIV-TRACE (Transmission Cluster Engine): a tool for large scale molecular epidemiology of HIV-1 and other rapidly evolving pathogens.

    PubMed

    Kosakovsky Pond, Sergei L; Weaver, Steven; Leigh Brown, Andrew J; Wertheim, Joel O

    2018-01-31

    In modern applications of molecular epidemiology, genetic sequence data are routinely used to identify clusters of transmission in rapidly evolving pathogens, most notably HIV-1. Traditional 'shoeleather' epidemiology infers transmission clusters by tracing chains of partners sharing epidemiological connections (e.g., sexual contact). Here, we present a computational tool for identifying a molecular transmission analog of such clusters: HIV-TRACE (TRAnsmission Cluster Engine). HIV-TRACE implements an approach inspired by traditional epidemiology, by identifying chains of partners whose viral genetic relatedness imply direct or indirect epidemiological connections. Molecular transmission clusters are constructed using codon-aware pairwise alignment to a reference sequence followed by pairwise genetic distance estimation among all sequences. This approach is computationally tractable and is capable of identifying HIV-1 transmission clusters in large surveillance databases comprising tens or hundreds of thousands of sequences in near real time, i.e., on the order of minutes to hours. HIV-TRACE is available at www.hivtrace.org and from github.com/veg/hivtrace, along with the accompanying result visualization module from github.com/veg/hivtrace-viz. Importantly, the approach underlying HIV-TRACE is not limited to the study of HIV-1 and can be applied to study outbreaks and epidemics of other rapidly evolving pathogens. © The Author 2018. Published by Oxford University Press on behalf of the Society for Molecular Biology and Evolution. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.

  10. Automated Measurement of Patient-Specific Tibial Slopes from MRI

    PubMed Central

    Amerinatanzi, Amirhesam; Summers, Rodney K.; Ahmadi, Kaveh; Goel, Vijay K.; Hewett, Timothy E.; Nyman, Edward

    2017-01-01

    Background: Multi-planar proximal tibial slopes may be associated with increased likelihood of osteoarthritis and anterior cruciate ligament injury, due in part to their role in checking the anterior-posterior stability of the knee. Established methods suffer repeatability limitations and lack computational efficiency for intuitive clinical adoption. The aims of this study were to develop a novel automated approach and to compare the repeatability and computational efficiency of the approach against previously established methods. Methods: Tibial slope geometries were obtained via MRI and measured using an automated Matlab-based approach. Data were compared for repeatability and evaluated for computational efficiency. Results: Mean lateral tibial slope (LTS) for females (7.2°) was greater than for males (1.66°). Mean LTS in the lateral concavity zone was greater for females (7.8° for females, 4.2° for males). Mean medial tibial slope (MTS) for females was greater (9.3° vs. 4.6°). Along the medial concavity zone, female subjects demonstrated greater MTS. Conclusion: The automated method was more repeatable and computationally efficient than previously identified methods and may aid in the clinical assessment of knee injury risk, inform surgical planning, and implant design efforts. PMID:28952547

  11. The paradigm compiler: Mapping a functional language for the connection machine

    NASA Technical Reports Server (NTRS)

    Dennis, Jack B.

    1989-01-01

    The Paradigm Compiler implements a new approach to compiling programs written in high level languages for execution on highly parallel computers. The general approach is to identify the principal data structures constructed by the program and to map these structures onto the processing elements of the target machine. The mapping is chosen to maximize performance as determined through compile time global analysis of the source program. The source language is Sisal, a functional language designed for scientific computations, and the target language is Paris, the published low level interface to the Connection Machine. The data structures considered are multidimensional arrays whose dimensions are known at compile time. Computations that build such arrays usually offer opportunities for highly parallel execution; they are data parallel. The Connection Machine is an attractive target for these computations, and the parallel for construct of the Sisal language is a convenient high level notation for data parallel algorithms. The principles and organization of the Paradigm Compiler are discussed.

  12. Deciphering Epithelial–Mesenchymal Transition Regulatory Networks in Cancer through Computational Approaches

    PubMed Central

    Burger, Gerhard A.; Danen, Erik H. J.; Beltman, Joost B.

    2017-01-01

    Epithelial–mesenchymal transition (EMT), the process by which epithelial cells can convert into motile mesenchymal cells, plays an important role in development and wound healing but is also involved in cancer progression. It is increasingly recognized that EMT is a dynamic process involving multiple intermediate or “hybrid” phenotypes rather than an “all-or-none” process. However, the role of EMT in various cancer hallmarks, including metastasis, is debated. Given the complexity of EMT regulation, computational modeling has proven to be an invaluable tool for cancer research, i.e., to resolve apparent conflicts in experimental data and to guide experiments by generating testable hypotheses. In this review, we provide an overview of computational modeling efforts that have been applied to regulation of EMT in the context of cancer progression and its associated tumor characteristics. Moreover, we identify possibilities to bridge different modeling approaches and point out outstanding questions in which computational modeling can contribute to advance our understanding of pathological EMT. PMID:28824874

  13. Privacy-Preserving Integration of Medical Data : A Practical Multiparty Private Set Intersection.

    PubMed

    Miyaji, Atsuko; Nakasho, Kazuhisa; Nishida, Shohei

    2017-03-01

    Medical data are often maintained by different organizations. However, detailed analyses sometimes require these datasets to be integrated without violating patient or commercial privacy. Multiparty Private Set Intersection (MPSI), which is an important privacy-preserving protocol, computes an intersection of multiple private datasets. This approach ensures that only designated parties can identify the intersection. In this paper, we propose a practical MPSI that satisfies the following requirements: The size of the datasets maintained by the different parties is independent of the others, and the computational complexity of the dataset held by each party is independent of the number of parties. Our MPSI is based on the use of an outsourcing provider, who has no knowledge of the data inputs or outputs. This reduces the computational complexity. The performance of the proposed MPSI is evaluated by implementing a prototype on a virtual private network to enable parallel computation in multiple threads. Our protocol is confirmed to be more efficient than comparable existing approaches.

  14. Cheminformatic models based on machine learning for pyruvate kinase inhibitors of Leishmania mexicana.

    PubMed

    Jamal, Salma; Scaria, Vinod

    2013-11-19

    Leishmaniasis is a neglected tropical disease which affects approx. 12 million individuals worldwide and caused by parasite Leishmania. The current drugs used in the treatment of Leishmaniasis are highly toxic and has seen widespread emergence of drug resistant strains which necessitates the need for the development of new therapeutic options. The high throughput screen data available has made it possible to generate computational predictive models which have the ability to assess the active scaffolds in a chemical library followed by its ADME/toxicity properties in the biological trials. In the present study, we have used publicly available, high-throughput screen datasets of chemical moieties which have been adjudged to target the pyruvate kinase enzyme of L. mexicana (LmPK). The machine learning approach was used to create computational models capable of predicting the biological activity of novel antileishmanial compounds. Further, we evaluated the molecules using the substructure based approach to identify the common substructures contributing to their activity. We generated computational models based on machine learning methods and evaluated the performance of these models based on various statistical figures of merit. Random forest based approach was determined to be the most sensitive, better accuracy as well as ROC. We further added a substructure based approach to analyze the molecules to identify potentially enriched substructures in the active dataset. We believe that the models developed in the present study would lead to reduction in cost and length of clinical studies and hence newer drugs would appear faster in the market providing better healthcare options to the patients.

  15. Computational Approaches and Tools for Exposure Prioritization and Biomonitoring Data Interpretation

    EPA Science Inventory

    The ability to describe the source-environment-exposure-dose-response continuum is essential for identifying exposures of greater concern to prioritize chemicals for toxicity testing or risk assessment, as well as for interpreting biomarker data for better assessment of exposure ...

  16. COMPETITIVE METAGENOMIC DNA HYBRIDIZATION IDENTIFIES HOST-SPECIFIC GENETIC MARKERS IN HUMAN FECAL MICROBIAL COMMUNITIES

    EPA Science Inventory

    Although recent technological advances in DNA sequencing and computational biology now allow scientists to compare entire microbial genomes, the use of these approaches to discern key genomic differences between natural microbial communities remains prohibitively expensive for mo...

  17. Protecting clinical data on Web client computers: the PCASSO approach.

    PubMed Central

    Masys, D. R.; Baker, D. B.

    1998-01-01

    The ubiquity and ease of use of the Web have made it an increasingly popular medium for communication of health-related information. Web interfaces to commercially available clinical information systems are now available or under development by most major vendors. To the extent that such interfaces involve the use of unprotected operating systems, they are vulnerable to security limitations of Web client software environments. The Patient Centered Access to Secure Systems Online (PCASSO) project extends the protections for person-identifiable health data on Web client computers. PCASSO uses several approaches, including physical protection of authentication information, execution containment, graphical displays, and monitoring the client system for intrusions and co-existing programs that may compromise security. PMID:9929243

  18. dCLIP: a computational approach for comparative CLIP-seq analyses

    PubMed Central

    2014-01-01

    Although comparison of RNA-protein interaction profiles across different conditions has become increasingly important to understanding the function of RNA-binding proteins (RBPs), few computational approaches have been developed for quantitative comparison of CLIP-seq datasets. Here, we present an easy-to-use command line tool, dCLIP, for quantitative CLIP-seq comparative analysis. The two-stage method implemented in dCLIP, including a modified MA normalization method and a hidden Markov model, is shown to be able to effectively identify differential binding regions of RBPs in four CLIP-seq datasets, generated by HITS-CLIP, iCLIP and PAR-CLIP protocols. dCLIP is freely available at http://qbrc.swmed.edu/software/. PMID:24398258

  19. Space Network Control Conference on Resource Allocation Concepts and Approaches

    NASA Technical Reports Server (NTRS)

    Moe, Karen L. (Editor)

    1991-01-01

    The results are presented of the Space Network Control (SNC) Conference. In the late 1990s, when the Advanced Tracking and Data Relay Satellite System is operational, Space Network communication services will be supported and controlled by the SNC. The goals of the conference were to survey existing resource allocation concepts and approaches, to identify solutions applicable to the Space Network, and to identify avenues of study in support of the SNC development. The conference was divided into three sessions: (1) Concepts for Space Network Allocation; (2) SNC and User Payload Operations Control Center (POCC) Human-Computer Interface Concepts; and (3) Resource Allocation Tools, Technology, and Algorithms. Key recommendations addressed approaches to achieving higher levels of automation in the scheduling process.

  20. Unique migration of a dental needle into the parapharyngeal space: successful removal by an intraoral approach and simulation for tracking visibility in X-ray fluoroscopy.

    PubMed

    Okumura, Yuri; Hidaka, Hiroshi; Seiji, Kazumasa; Nomura, Kazuhiro; Takata, Yusuke; Suzuki, Takahiro; Katori, Yukio

    2015-02-01

    The first objective was to describe a novel case of migration of a broken dental needle into the parapharyngeal space. The second was to address the importance of simulation elucidating visualization of such a thin needle under X-ray fluoroscopy. Clinical case records (including computed tomography [CT] and surgical approaches) were reviewed, and a simulation experiment using a head phantom was conducted using the same settings applied intraoperatively. A 36-year-old man was referred after failure to locate a broken 31-G dental needle. Computed tomography revealed migration of the needle into the parapharyngeal space. Intraoperative X-ray fluoroscopy failed to identify the needle, so a steel wire was applied as a reference during X-ray to locate the foreign body. The needle was successfully removed using an intraoral approach with tonsillectomy under surgical microscopy. The simulation showed that the dental needle was able to be identified only after applying an appropriate compensating filter, contrasting with the steel wire. Meticulous preoperative simulation regarding visual identification of dental needle foreign bodies is mandatory. Intraoperative radiography and an intraoral approach with tonsillectomy under surgical microscopy offer benefits for accessing the parapharyngeal space, specifically for cases medial to the great vessels. © The Author(s) 2014.

  1. Xtalk: a path-based approach for identifying crosstalk between signaling pathways

    PubMed Central

    Tegge, Allison N.; Sharp, Nicholas; Murali, T. M.

    2016-01-01

    Motivation: Cells communicate with their environment via signal transduction pathways. On occasion, the activation of one pathway can produce an effect downstream of another pathway, a phenomenon known as crosstalk. Existing computational methods to discover such pathway pairs rely on simple overlap statistics. Results: We present Xtalk, a path-based approach for identifying pairs of pathways that may crosstalk. Xtalk computes the statistical significance of the average length of multiple short paths that connect receptors in one pathway to the transcription factors in another. By design, Xtalk reports the precise interactions and mechanisms that support the identified crosstalk. We applied Xtalk to signaling pathways in the KEGG and NCI-PID databases. We manually curated a gold standard set of 132 crosstalking pathway pairs and a set of 140 pairs that did not crosstalk, for which Xtalk achieved an area under the receiver operator characteristic curve of 0.65, a 12% improvement over the closest competing approach. The area under the receiver operator characteristic curve varied with the pathway, suggesting that crosstalk should be evaluated on a pathway-by-pathway level. We also analyzed an extended set of 658 pathway pairs in KEGG and to a set of more than 7000 pathway pairs in NCI-PID. For the top-ranking pairs, we found substantial support in the literature (81% for KEGG and 78% for NCI-PID). We provide examples of networks computed by Xtalk that accurately recovered known mechanisms of crosstalk. Availability and implementation: The XTALK software is available at http://bioinformatics.cs.vt.edu/~murali/software. Crosstalk networks are available at http://graphspace.org/graphs?tags=2015-bioinformatics-xtalk. Contact: ategge@vt.edu, murali@cs.vt.edu Supplementary information: Supplementary data are available at Bioinformatics online. PMID:26400040

  2. Spotting and designing promiscuous ligands for drug discovery.

    PubMed

    Schneider, P; Röthlisberger, M; Reker, D; Schneider, G

    2016-01-21

    The promiscuous binding behavior of bioactive compounds forms a mechanistic basis for understanding polypharmacological drug action. We present the development and prospective application of a computational tool for identifying potential promiscuous drug-like ligands. In combination with computational target prediction methods, the approach provides a working concept for rationally designing such molecular structures. We could confirm the multi-target binding of a de novo generated compound in a proof-of-concept study relying on the new method.

  3. Computational approach to compact Riemann surfaces

    NASA Astrophysics Data System (ADS)

    Frauendiener, Jörg; Klein, Christian

    2017-01-01

    A purely numerical approach to compact Riemann surfaces starting from plane algebraic curves is presented. The critical points of the algebraic curve are computed via a two-dimensional Newton iteration. The starting values for this iteration are obtained from the resultants with respect to both coordinates of the algebraic curve and a suitable pairing of their zeros. A set of generators of the fundamental group for the complement of these critical points in the complex plane is constructed from circles around these points and connecting lines obtained from a minimal spanning tree. The monodromies are computed by solving the defining equation of the algebraic curve on collocation points along these contours and by analytically continuing the roots. The collocation points are chosen to correspond to Chebychev collocation points for an ensuing Clenshaw-Curtis integration of the holomorphic differentials which gives the periods of the Riemann surface with spectral accuracy. At the singularities of the algebraic curve, Puiseux expansions computed by contour integration on the circles around the singularities are used to identify the holomorphic differentials. The Abel map is also computed with the Clenshaw-Curtis algorithm and contour integrals. As an application of the code, solutions to the Kadomtsev-Petviashvili equation are computed on non-hyperelliptic Riemann surfaces.

  4. Real-world clinical applicability of pathogenicity predictors assessed on SERPINA1 mutations in alpha-1-antitrypsin deficiency.

    PubMed

    Giacopuzzi, Edoardo; Laffranchi, Mattia; Berardelli, Romina; Ravasio, Viola; Ferrarotti, Ilaria; Gooptu, Bibek; Borsani, Giuseppe; Fra, Annamaria

    2018-06-07

    The growth of publicly available data informing upon genetic variations, mechanisms of disease and disease sub-phenotypes offers great potential for personalised medicine. Computational approaches are likely required to assess large numbers of novel genetic variants. However, the integration of genetic, structural and pathophysiological data still represents a challenge for computational predictions and their clinical use. We addressed these issues for alpha-1-antitrypsin deficiency, a disease mediated by mutations in the SERPINA1 gene encoding alpha-1-antitrypsin. We compiled a comprehensive database of SERPINA1 coding mutations and assigned them apparent pathological relevance based upon available data. 'Benign' and 'Pathogenic' mutations were used to assess performance of 31 pathogenicity predictors. Well-performing algorithms clustered the subset of variants known to be severely pathogenic with high scores. Eight new mutations identified in the ExAC database and achieving high scores were selected for characterisation in cell models and showed secretory deficiency and polymer formation, supporting the predictive power of our computational approach. The behaviour of the pathogenic new variants and consistent outliers were rationalised by considering the protein structural context and residue conservation. These findings highlight the potential of computational methods to provide meaningful predictions of the pathogenic significance of novel mutations and identify areas for further investigation. This article is protected by copyright. All rights reserved. This article is protected by copyright. All rights reserved.

  5. High-performance computing with quantum processing units

    DOE PAGES

    Britt, Keith A.; Oak Ridge National Lab.; Humble, Travis S.; ...

    2017-03-01

    The prospects of quantum computing have driven efforts to realize fully functional quantum processing units (QPUs). Recent success in developing proof-of-principle QPUs has prompted the question of how to integrate these emerging processors into modern high-performance computing (HPC) systems. We examine how QPUs can be integrated into current and future HPC system architectures by accounting for func- tional and physical design requirements. We identify two integration pathways that are differentiated by infrastructure constraints on the QPU and the use cases expected for the HPC system. This includes a tight integration that assumes infrastructure bottlenecks can be overcome as well asmore » a loose integration that as- sumes they cannot. We find that the performance of both approaches is likely to depend on the quantum interconnect that serves to entangle multiple QPUs. As a result, we also identify several challenges in assessing QPU performance for HPC, and we consider new metrics that capture the interplay between system architecture and the quantum parallelism underlying computational performance.« less

  6. High-performance computing with quantum processing units

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

    Britt, Keith A.; Oak Ridge National Lab.; Humble, Travis S.

    The prospects of quantum computing have driven efforts to realize fully functional quantum processing units (QPUs). Recent success in developing proof-of-principle QPUs has prompted the question of how to integrate these emerging processors into modern high-performance computing (HPC) systems. We examine how QPUs can be integrated into current and future HPC system architectures by accounting for func- tional and physical design requirements. We identify two integration pathways that are differentiated by infrastructure constraints on the QPU and the use cases expected for the HPC system. This includes a tight integration that assumes infrastructure bottlenecks can be overcome as well asmore » a loose integration that as- sumes they cannot. We find that the performance of both approaches is likely to depend on the quantum interconnect that serves to entangle multiple QPUs. As a result, we also identify several challenges in assessing QPU performance for HPC, and we consider new metrics that capture the interplay between system architecture and the quantum parallelism underlying computational performance.« less

  7. Hypothesis testing for differentially correlated features.

    PubMed

    Sheng, Elisa; Witten, Daniela; Zhou, Xiao-Hua

    2016-10-01

    In a multivariate setting, we consider the task of identifying features whose correlations with the other features differ across conditions. Such correlation shifts may occur independently of mean shifts, or differences in the means of the individual features across conditions. Previous approaches for detecting correlation shifts consider features simultaneously, by computing a correlation-based test statistic for each feature. However, since correlations involve two features, such approaches do not lend themselves to identifying which feature is the culprit. In this article, we instead consider a serial testing approach, by comparing columns of the sample correlation matrix across two conditions, and removing one feature at a time. Our method provides a novel perspective and favorable empirical results compared with competing approaches. © The Author 2016. Published by Oxford University Press. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.

  8. Pharmacophore modeling, docking, and principal component analysis based clustering: combined computer-assisted approaches to identify new inhibitors of the human rhinovirus coat protein.

    PubMed

    Steindl, Theodora M; Crump, Carolyn E; Hayden, Frederick G; Langer, Thierry

    2005-10-06

    The development and application of a sophisticated virtual screening and selection protocol to identify potential, novel inhibitors of the human rhinovirus coat protein employing various computer-assisted strategies are described. A large commercially available database of compounds was screened using a highly selective, structure-based pharmacophore model generated with the program Catalyst. A docking study and a principal component analysis were carried out within the software package Cerius and served to validate and further refine the obtained results. These combined efforts led to the selection of six candidate structures, for which in vitro anti-rhinoviral activity could be shown in a biological assay.

  9. USE OF COMPETITIVE DNA HYBRIDIZATION TO IDENTIFY DIFFERENCES IN THE GENOMES OF TWO CLOSELY RELATED FECAL INDICATOR BACTERIA

    EPA Science Inventory

    Although recent technological advances in DNA sequencing and computational biology now allow scientists to compare entire microbial genomes, comparisons of closely related bacterial species and individual isolates by whole-genome sequencing approaches remains prohibitively expens...

  10. Game playing.

    PubMed

    Rosin, Christopher D

    2014-03-01

    Game playing has been a core domain of artificial intelligence research since the beginnings of the field. Game playing provides clearly defined arenas within which computational approaches can be readily compared to human expertise through head-to-head competition and other benchmarks. Game playing research has identified several simple core algorithms that provide successful foundations, with development focused on the challenges of defeating human experts in specific games. Key developments include minimax search in chess, machine learning from self-play in backgammon, and Monte Carlo tree search in Go. These approaches have generalized successfully to additional games. While computers have surpassed human expertise in a wide variety of games, open challenges remain and research focuses on identifying and developing new successful algorithmic foundations. WIREs Cogn Sci 2014, 5:193-205. doi: 10.1002/wcs.1278 CONFLICT OF INTEREST: The author has declared no conflicts of interest for this article. For further resources related to this article, please visit the WIREs website. © 2014 John Wiley & Sons, Ltd.

  11. Investigation of different modeling approaches for computational fluid dynamics simulation of high-pressure rocket combustors

    NASA Astrophysics Data System (ADS)

    Ivancic, B.; Riedmann, H.; Frey, M.; Knab, O.; Karl, S.; Hannemann, K.

    2016-07-01

    The paper summarizes technical results and first highlights of the cooperation between DLR and Airbus Defence and Space (DS) within the work package "CFD Modeling of Combustion Chamber Processes" conducted in the frame of the Propulsion 2020 Project. Within the addressed work package, DLR Göttingen and Airbus DS Ottobrunn have identified several test cases where adequate test data are available and which can be used for proper validation of the computational fluid dynamics (CFD) tools. In this paper, the first test case, the Penn State chamber (RCM1), is discussed. Presenting the simulation results from three different tools, it is shown that the test case can be computed properly with steady-state Reynolds-averaged Navier-Stokes (RANS) approaches. The achieved simulation results reproduce the measured wall heat flux as an important validation parameter very well but also reveal some inconsistencies in the test data which are addressed in this paper.

  12. The impact of pharmacophore modeling in drug design.

    PubMed

    Guner, Osman F

    2005-07-01

    With the reliable use of computer simulations in scientific research, it is possible to achieve significant increases in productivity as well as a reduction in research costs compared with experimental approaches. For example, computer-simulation can substantially enchance productivity by focusing the scientist to better, more informed choices, while also driving the 'fail-early' concept to result in a significant reduction in cost. Pharmacophore modeling is a reliable computer-aided design tool used in the discovery of new classes of compounds for a given therapeutic category. This commentary will briefly review the benefits and applications of this technology in drug discovery and design, and will also highlight its historical evolution. The two most commonly used approaches for pharmacophore model development will be discussed, and several examples of how this technology was successfully applied to identify new potent leads will be provided. The article concludes with a brief outline of the controversial issue of patentability of pharmacophore models.

  13. A community computational challenge to predict the activity of pairs of compounds.

    PubMed

    Bansal, Mukesh; Yang, Jichen; Karan, Charles; Menden, Michael P; Costello, James C; Tang, Hao; Xiao, Guanghua; Li, Yajuan; Allen, Jeffrey; Zhong, Rui; Chen, Beibei; Kim, Minsoo; Wang, Tao; Heiser, Laura M; Realubit, Ronald; Mattioli, Michela; Alvarez, Mariano J; Shen, Yao; Gallahan, Daniel; Singer, Dinah; Saez-Rodriguez, Julio; Xie, Yang; Stolovitzky, Gustavo; Califano, Andrea

    2014-12-01

    Recent therapeutic successes have renewed interest in drug combinations, but experimental screening approaches are costly and often identify only small numbers of synergistic combinations. The DREAM consortium launched an open challenge to foster the development of in silico methods to computationally rank 91 compound pairs, from the most synergistic to the most antagonistic, based on gene-expression profiles of human B cells treated with individual compounds at multiple time points and concentrations. Using scoring metrics based on experimental dose-response curves, we assessed 32 methods (31 community-generated approaches and SynGen), four of which performed significantly better than random guessing. We highlight similarities between the methods. Although the accuracy of predictions was not optimal, we find that computational prediction of compound-pair activity is possible, and that community challenges can be useful to advance the field of in silico compound-synergy prediction.

  14. TSSer: an automated method to identify transcription start sites in prokaryotic genomes from differential RNA sequencing data.

    PubMed

    Jorjani, Hadi; Zavolan, Mihaela

    2014-04-01

    Accurate identification of transcription start sites (TSSs) is an essential step in the analysis of transcription regulatory networks. In higher eukaryotes, the capped analysis of gene expression technology enabled comprehensive annotation of TSSs in genomes such as those of mice and humans. In bacteria, an equivalent approach, termed differential RNA sequencing (dRNA-seq), has recently been proposed, but the application of this approach to a large number of genomes is hindered by the paucity of computational analysis methods. With few exceptions, when the method has been used, annotation of TSSs has been largely done manually. In this work, we present a computational method called 'TSSer' that enables the automatic inference of TSSs from dRNA-seq data. The method rests on a probabilistic framework for identifying both genomic positions that are preferentially enriched in the dRNA-seq data as well as preferentially captured relative to neighboring genomic regions. Evaluating our approach for TSS calling on several publicly available datasets, we find that TSSer achieves high consistency with the curated lists of annotated TSSs, but identifies many additional TSSs. Therefore, TSSer can accelerate genome-wide identification of TSSs in bacterial genomes and can aid in further characterization of bacterial transcription regulatory networks. TSSer is freely available under GPL license at http://www.clipz.unibas.ch/TSSer/index.php

  15. Delamination detection using methods of computational intelligence

    NASA Astrophysics Data System (ADS)

    Ihesiulor, Obinna K.; Shankar, Krishna; Zhang, Zhifang; Ray, Tapabrata

    2012-11-01

    Abstract Reliable delamination prediction scheme is indispensable in order to prevent potential risks of catastrophic failures in composite structures. The existence of delaminations changes the vibration characteristics of composite laminates and hence such indicators can be used to quantify the health characteristics of laminates. An approach for online health monitoring of in-service composite laminates is presented in this paper that relies on methods based on computational intelligence. Typical changes in the observed vibration characteristics (i.e. change in natural frequencies) are considered as inputs to identify the existence, location and magnitude of delaminations. The performance of the proposed approach is demonstrated using numerical models of composite laminates. Since this identification problem essentially involves the solution of an optimization problem, the use of finite element (FE) methods as the underlying tool for analysis turns out to be computationally expensive. A surrogate assisted optimization approach is hence introduced to contain the computational time within affordable limits. An artificial neural network (ANN) model with Bayesian regularization is used as the underlying approximation scheme while an improved rate of convergence is achieved using a memetic algorithm. However, building of ANN surrogate models usually requires large training datasets. K-means clustering is effectively employed to reduce the size of datasets. ANN is also used via inverse modeling to determine the position, size and location of delaminations using changes in measured natural frequencies. The results clearly highlight the efficiency and the robustness of the approach.

  16. Are Technology Interruptions Impacting Your Bottom Line? An Innovative Proposal for Change.

    PubMed

    Ledbetter, Tamera; Shultz, Sarah; Beckham, Roxanne

    2017-10-01

    Nursing interruptions are a costly and dangerous variable in acute care hospitals. Malfunctioning technology equipment interrupts nursing care and prevents full utilization of computer safety systems to prevent patient care errors. This paper identifies an innovative approach to nursing interruptions related to computer and computer cart malfunctions. The impact on human resources is defined and outcome measures were proposed. A multifaceted proposal, based on a literature review, aimed at reducing nursing interruptions is presented. This proposal is expected to increase patient safety, as well as patient and nurse satisfaction. Acute care hospitals utilizing electronic medical records and bar-coded medication administration technology. Nurses, information technology staff, nursing informatics staff, and all leadership teams affected by technology problems and their proposed solutions. Literature from multiple fields was reviewed to evaluate research related to computer/computer cart failures, and the approaches used to resolve these issues. Outcome measured strategic goals related to patient safety, and nurse and patient satisfaction. Specific help desk metrics will demonstrate the effect of interventions. This paper addresses a gap in the literature and proposes practical and innovative solutions. A comprehensive computer and computer cart repair program is essential for patient safety, financial stewardship, and utilization of resources. © 2015 Wiley Periodicals, Inc.

  17. Promoter-enhancer interactions identified from Hi-C data using probabilistic models and hierarchical topological domains.

    PubMed

    Ron, Gil; Globerson, Yuval; Moran, Dror; Kaplan, Tommy

    2017-12-21

    Proximity-ligation methods such as Hi-C allow us to map physical DNA-DNA interactions along the genome, and reveal its organization into topologically associating domains (TADs). As the Hi-C data accumulate, computational methods were developed for identifying domain borders in multiple cell types and organisms. Here, we present PSYCHIC, a computational approach for analyzing Hi-C data and identifying promoter-enhancer interactions. We use a unified probabilistic model to segment the genome into domains, which we then merge hierarchically and fit using a local background model, allowing us to identify over-represented DNA-DNA interactions across the genome. By analyzing the published Hi-C data sets in human and mouse, we identify hundreds of thousands of putative enhancers and their target genes, and compile an extensive genome-wide catalog of gene regulation in human and mouse. As we show, our predictions are highly enriched for ChIP-seq and DNA accessibility data, evolutionary conservation, eQTLs and other DNA-DNA interaction data.

  18. On-line Bayesian model updating for structural health monitoring

    NASA Astrophysics Data System (ADS)

    Rocchetta, Roberto; Broggi, Matteo; Huchet, Quentin; Patelli, Edoardo

    2018-03-01

    Fatigue induced cracks is a dangerous failure mechanism which affects mechanical components subject to alternating load cycles. System health monitoring should be adopted to identify cracks which can jeopardise the structure. Real-time damage detection may fail in the identification of the cracks due to different sources of uncertainty which have been poorly assessed or even fully neglected. In this paper, a novel efficient and robust procedure is used for the detection of cracks locations and lengths in mechanical components. A Bayesian model updating framework is employed, which allows accounting for relevant sources of uncertainty. The idea underpinning the approach is to identify the most probable crack consistent with the experimental measurements. To tackle the computational cost of the Bayesian approach an emulator is adopted for replacing the computationally costly Finite Element model. To improve the overall robustness of the procedure, different numerical likelihoods, measurement noises and imprecision in the value of model parameters are analysed and their effects quantified. The accuracy of the stochastic updating and the efficiency of the numerical procedure are discussed. An experimental aluminium frame and on a numerical model of a typical car suspension arm are used to demonstrate the applicability of the approach.

  19. The framework for simulation of bioinspired security mechanisms against network infrastructure attacks.

    PubMed

    Shorov, Andrey; Kotenko, Igor

    2014-01-01

    The paper outlines a bioinspired approach named "network nervous system" and methods of simulation of infrastructure attacks and protection mechanisms based on this approach. The protection mechanisms based on this approach consist of distributed procedures of information collection and processing, which coordinate the activities of the main devices of a computer network, identify attacks, and determine necessary countermeasures. Attacks and protection mechanisms are specified as structural models using a set-theoretic approach. An environment for simulation of protection mechanisms based on the biological metaphor is considered; the experiments demonstrating the effectiveness of the protection mechanisms are described.

  20. A Disciplined Architectural Approach to Scaling Data Analysis for Massive, Scientific Data

    NASA Astrophysics Data System (ADS)

    Crichton, D. J.; Braverman, A. J.; Cinquini, L.; Turmon, M.; Lee, H.; Law, E.

    2014-12-01

    Data collections across remote sensing and ground-based instruments in astronomy, Earth science, and planetary science are outpacing scientists' ability to analyze them. Furthermore, the distribution, structure, and heterogeneity of the measurements themselves pose challenges that limit the scalability of data analysis using traditional approaches. Methods for developing science data processing pipelines, distribution of scientific datasets, and performing analysis will require innovative approaches that integrate cyber-infrastructure, algorithms, and data into more systematic approaches that can more efficiently compute and reduce data, particularly distributed data. This requires the integration of computer science, machine learning, statistics and domain expertise to identify scalable architectures for data analysis. The size of data returned from Earth Science observing satellites and the magnitude of data from climate model output, is predicted to grow into the tens of petabytes challenging current data analysis paradigms. This same kind of growth is present in astronomy and planetary science data. One of the major challenges in data science and related disciplines defining new approaches to scaling systems and analysis in order to increase scientific productivity and yield. Specific needs include: 1) identification of optimized system architectures for analyzing massive, distributed data sets; 2) algorithms for systematic analysis of massive data sets in distributed environments; and 3) the development of software infrastructures that are capable of performing massive, distributed data analysis across a comprehensive data science framework. NASA/JPL has begun an initiative in data science to address these challenges. Our goal is to evaluate how scientific productivity can be improved through optimized architectural topologies that identify how to deploy and manage the access, distribution, computation, and reduction of massive, distributed data, while managing the uncertainties of scientific conclusions derived from such capabilities. This talk will provide an overview of JPL's efforts in developing a comprehensive architectural approach to data science.

  1. A multi-criteria decision making approach to identify a vaccine formulation.

    PubMed

    Dewé, Walthère; Durand, Christelle; Marion, Sandie; Oostvogels, Lidia; Devaster, Jeanne-Marie; Fourneau, Marc

    2016-01-01

    This article illustrates the use of a multi-criteria decision making approach, based on desirability functions, to identify an appropriate adjuvant composition for an influenza vaccine to be used in elderly. The proposed adjuvant system contained two main elements: monophosphoryl lipid and α-tocopherol with squalene in an oil/water emulsion. The objective was to elicit a stronger immune response while maintaining an acceptable reactogenicity and safety profile. The study design, the statistical models, the choice of the desirability functions, the computation of the overall desirability index, and the assessment of the robustness of the ranking are all detailed in this manuscript.

  2. Connectivity inference from neural recording data: Challenges, mathematical bases and research directions.

    PubMed

    Magrans de Abril, Ildefons; Yoshimoto, Junichiro; Doya, Kenji

    2018-06-01

    This article presents a review of computational methods for connectivity inference from neural activity data derived from multi-electrode recordings or fluorescence imaging. We first identify biophysical and technical challenges in connectivity inference along the data processing pipeline. We then review connectivity inference methods based on two major mathematical foundations, namely, descriptive model-free approaches and generative model-based approaches. We investigate representative studies in both categories and clarify which challenges have been addressed by which method. We further identify critical open issues and possible research directions. Copyright © 2018 The Author(s). Published by Elsevier Ltd.. All rights reserved.

  3. Conduction velocity of the uterine contraction in serial magnetomyogram (MMG) data: event based simulation and validation.

    PubMed

    Furdea, Adrian; Preissl, Hubert; Lowery, Curtis L; Eswaran, Hari; Govindan, Rathinaswamy B

    2011-01-01

    We propose a novel approach to calculate the conduction velocity (CV) of the uterine contraction bursts in magnetomyogram (MMG) signals measured using a multichannel SQUID array. For this purpose, we partition the sensor coordinates into four different quadrants and identify the contractile bursts using a previously proposed Hilbert-wavelet transform approach. If contractile burst is identified in more than one quadrant, we calculate the center of gravity (CoG) in each quadrant for each time point as the sum of the product of the sensor coordinates with the Hilbert amplitude of the MMG signals normalized by the sum of the Hilbert amplitude of the signals over all sensors. Following this we compute the delay between the CoGs of all (six) possible quadrant pairs combinations. As a first step, we validate this approach by simulating a stochastic model based on independent second-order autoregressive processes (AR2) and we divide them into 30 second disjoint windows and insert burst activity at specific time instances in preselected sensors. Also we introduce a lag of 5 ± 1 seconds between different quadrants. Using our approach we calculate the CoG of the signals in a quadrant. To this end, we compute the delay between CoGs obtained from different quadrants and show that our approach is able to reliably capture the delay incorporated in the model. We apply the proposed approach to 19 serial MMG data obtained from two subjects and show an increase in the CV as the subjects approached labor.

  4. Hybrid Quantum-Classical Approach to Quantum Optimal Control.

    PubMed

    Li, Jun; Yang, Xiaodong; Peng, Xinhua; Sun, Chang-Pu

    2017-04-14

    A central challenge in quantum computing is to identify more computational problems for which utilization of quantum resources can offer significant speedup. Here, we propose a hybrid quantum-classical scheme to tackle the quantum optimal control problem. We show that the most computationally demanding part of gradient-based algorithms, namely, computing the fitness function and its gradient for a control input, can be accomplished by the process of evolution and measurement on a quantum simulator. By posing queries to and receiving answers from the quantum simulator, classical computing devices update the control parameters until an optimal control solution is found. To demonstrate the quantum-classical scheme in experiment, we use a seven-qubit nuclear magnetic resonance system, on which we have succeeded in optimizing state preparation without involving classical computation of the large Hilbert space evolution.

  5. Large-Scale Bi-Level Strain Design Approaches and Mixed-Integer Programming Solution Techniques

    PubMed Central

    Kim, Joonhoon; Reed, Jennifer L.; Maravelias, Christos T.

    2011-01-01

    The use of computational models in metabolic engineering has been increasing as more genome-scale metabolic models and computational approaches become available. Various computational approaches have been developed to predict how genetic perturbations affect metabolic behavior at a systems level, and have been successfully used to engineer microbial strains with improved primary or secondary metabolite production. However, identification of metabolic engineering strategies involving a large number of perturbations is currently limited by computational resources due to the size of genome-scale models and the combinatorial nature of the problem. In this study, we present (i) two new bi-level strain design approaches using mixed-integer programming (MIP), and (ii) general solution techniques that improve the performance of MIP-based bi-level approaches. The first approach (SimOptStrain) simultaneously considers gene deletion and non-native reaction addition, while the second approach (BiMOMA) uses minimization of metabolic adjustment to predict knockout behavior in a MIP-based bi-level problem for the first time. Our general MIP solution techniques significantly reduced the CPU times needed to find optimal strategies when applied to an existing strain design approach (OptORF) (e.g., from ∼10 days to ∼5 minutes for metabolic engineering strategies with 4 gene deletions), and identified strategies for producing compounds where previous studies could not (e.g., malate and serine). Additionally, we found novel strategies using SimOptStrain with higher predicted production levels (for succinate and glycerol) than could have been found using an existing approach that considers network additions and deletions in sequential steps rather than simultaneously. Finally, using BiMOMA we found novel strategies involving large numbers of modifications (for pyruvate and glutamate), which sequential search and genetic algorithms were unable to find. The approaches and solution techniques developed here will facilitate the strain design process and extend the scope of its application to metabolic engineering. PMID:21949695

  6. Large-scale bi-level strain design approaches and mixed-integer programming solution techniques.

    PubMed

    Kim, Joonhoon; Reed, Jennifer L; Maravelias, Christos T

    2011-01-01

    The use of computational models in metabolic engineering has been increasing as more genome-scale metabolic models and computational approaches become available. Various computational approaches have been developed to predict how genetic perturbations affect metabolic behavior at a systems level, and have been successfully used to engineer microbial strains with improved primary or secondary metabolite production. However, identification of metabolic engineering strategies involving a large number of perturbations is currently limited by computational resources due to the size of genome-scale models and the combinatorial nature of the problem. In this study, we present (i) two new bi-level strain design approaches using mixed-integer programming (MIP), and (ii) general solution techniques that improve the performance of MIP-based bi-level approaches. The first approach (SimOptStrain) simultaneously considers gene deletion and non-native reaction addition, while the second approach (BiMOMA) uses minimization of metabolic adjustment to predict knockout behavior in a MIP-based bi-level problem for the first time. Our general MIP solution techniques significantly reduced the CPU times needed to find optimal strategies when applied to an existing strain design approach (OptORF) (e.g., from ∼10 days to ∼5 minutes for metabolic engineering strategies with 4 gene deletions), and identified strategies for producing compounds where previous studies could not (e.g., malate and serine). Additionally, we found novel strategies using SimOptStrain with higher predicted production levels (for succinate and glycerol) than could have been found using an existing approach that considers network additions and deletions in sequential steps rather than simultaneously. Finally, using BiMOMA we found novel strategies involving large numbers of modifications (for pyruvate and glutamate), which sequential search and genetic algorithms were unable to find. The approaches and solution techniques developed here will facilitate the strain design process and extend the scope of its application to metabolic engineering.

  7. Do Clouds Compute? A Framework for Estimating the Value of Cloud Computing

    NASA Astrophysics Data System (ADS)

    Klems, Markus; Nimis, Jens; Tai, Stefan

    On-demand provisioning of scalable and reliable compute services, along with a cost model that charges consumers based on actual service usage, has been an objective in distributed computing research and industry for a while. Cloud Computing promises to deliver on this objective: consumers are able to rent infrastructure in the Cloud as needed, deploy applications and store data, and access them via Web protocols on a pay-per-use basis. The acceptance of Cloud Computing, however, depends on the ability for Cloud Computing providers and consumers to implement a model for business value co-creation. Therefore, a systematic approach to measure costs and benefits of Cloud Computing is needed. In this paper, we discuss the need for valuation of Cloud Computing, identify key components, and structure these components in a framework. The framework assists decision makers in estimating Cloud Computing costs and to compare these costs to conventional IT solutions. We demonstrate by means of representative use cases how our framework can be applied to real world scenarios.

  8. Functional requirements of computer systems for the U.S. Geological Survey, Water Resources Division, 1988-97

    USGS Publications Warehouse

    Hathaway, R.M.; McNellis, J.M.

    1989-01-01

    Investigating the occurrence, quantity, quality, distribution, and movement of the Nation 's water resources is the principal mission of the U.S. Geological Survey 's Water Resources Division. Reports of these investigations are published and available to the public. To accomplish this mission, the Division requires substantial computer technology to process, store, and analyze data from more than 57,000 hydrologic sites. The Division 's computer resources are organized through the Distributed Information System Program Office that manages the nationwide network of computers. The contract that provides the major computer components for the Water Resources Division 's Distributed information System expires in 1991. Five work groups were organized to collect the information needed to procure a new generation of computer systems for the U. S. Geological Survey, Water Resources Division. Each group was assigned a major Division activity and asked to describe its functional requirements of computer systems for the next decade. The work groups and major activities are: (1) hydrologic information; (2) hydrologic applications; (3) geographic information systems; (4) reports and electronic publishing; and (5) administrative. The work groups identified 42 functions and described their functional requirements for 1988, 1992, and 1997. A few new functions such as Decision Support Systems and Executive Information Systems, were identified, but most are the same as performed today. Although the number of functions will remain about the same, steady growth in the size, complexity, and frequency of many functions is predicted for the next decade. No compensating increase in the Division 's staff is anticipated during this period. To handle the increased workload and perform these functions, new approaches will be developed that use advanced computer technology. The advanced technology is required in a unified, tightly coupled system that will support all functions simultaneously. The new approaches and expanded use of computers will require substantial increases in the quantity and sophistication of the Division 's computer resources. The requirements presented in this report will be used to develop technical specifications that describe the computer resources needed during the 1990's. (USGS)

  9. Uncertainty Reduction using Bayesian Inference and Sensitivity Analysis: A Sequential Approach to the NASA Langley Uncertainty Quantification Challenge

    NASA Technical Reports Server (NTRS)

    Sankararaman, Shankar

    2016-01-01

    This paper presents a computational framework for uncertainty characterization and propagation, and sensitivity analysis under the presence of aleatory and epistemic un- certainty, and develops a rigorous methodology for efficient refinement of epistemic un- certainty by identifying important epistemic variables that significantly affect the overall performance of an engineering system. The proposed methodology is illustrated using the NASA Langley Uncertainty Quantification Challenge (NASA-LUQC) problem that deals with uncertainty analysis of a generic transport model (GTM). First, Bayesian inference is used to infer subsystem-level epistemic quantities using the subsystem-level model and corresponding data. Second, tools of variance-based global sensitivity analysis are used to identify four important epistemic variables (this limitation specified in the NASA-LUQC is reflective of practical engineering situations where not all epistemic variables can be refined due to time/budget constraints) that significantly affect system-level performance. The most significant contribution of this paper is the development of the sequential refine- ment methodology, where epistemic variables for refinement are not identified all-at-once. Instead, only one variable is first identified, and then, Bayesian inference and global sensi- tivity calculations are repeated to identify the next important variable. This procedure is continued until all 4 variables are identified and the refinement in the system-level perfor- mance is computed. The advantages of the proposed sequential refinement methodology over the all-at-once uncertainty refinement approach are explained, and then applied to the NASA Langley Uncertainty Quantification Challenge problem.

  10. The Added Value of a Single-photon Emission Computed Tomography-Computed Tomography in Sentinel Lymph Node Mapping in Patients with Breast Cancer and Malignant Melanoma.

    PubMed

    Bennie, George; Vorster, Mariza; Buscombe, John; Sathekge, Mike

    2015-01-01

    Single-photon emission computed tomography-computed tomography (SPECT-CT) allows for physiological and anatomical co-registration in sentinel lymph node (SLN) mapping and offers additional benefits over conventional planar imaging. However, the clinical relevance when considering added costs and radiation burden of these reported benefits remains somewhat uncertain. This study aimed to evaluate the possible added value of SPECT-CT and intra-operative gamma-probe use over planar imaging alone in the South African setting. 80 patients with breast cancer or malignant melanoma underwent both planar and SPECT-CT imaging for SLN mapping. We assessed and compared the number of nodes detected on each study, false positive and negative findings, changes in surgical approach and or patient management. In all cases where a sentinel node was identified, SPECT-CT was more accurate anatomically. There was a significant change in surgical approach in 30 cases - breast cancer (n = 13; P 0.001) and malignant melanoma (n = 17; P 0.0002). In 4 cases a node not identified on planar imaging was seen on SPECT-CT. In 16 cases additional echelon nodes were identified. False positives were excluded by SPECT-CT in 12 cases. The addition of SPECT-CT and use of intra-operative gamma-probe to planar imaging offers important benefits in patients who present with breast cancer and melanoma. These benefits include increased nodal detection, elimination of false positives and negatives and improved anatomical localization that ultimately aids and expedites surgical management. This has been demonstrated in the context of industrialized country previously and has now also been confirmed in the setting of a emerging-market nation.

  11. Site-Mutation of Hydrophobic Core Residues Synchronically Poise Super Interleukin 2 for Signaling: Identifying Distant Structural Effects through Affordable Computations.

    PubMed

    Mei, Longcan; Zhou, Yanping; Zhu, Lizhe; Liu, Changlin; Wu, Zhuo; Wang, Fangkui; Hao, Gefei; Yu, Di; Yuan, Hong; Cui, Yanfang

    2018-03-20

    A superkine variant of interleukin-2 with six site mutations away from the binding interface developed from the yeast display technique has been previously characterized as undergoing a distal structure alteration which is responsible for its super-potency and provides an elegant case study with which to get insight about how to utilize allosteric effect to achieve desirable protein functions. By examining the dynamic network and the allosteric pathways related to those mutated residues using various computational approaches, we found that nanosecond time scale all-atom molecular dynamics simulations can identify the dynamic network as efficient as an ensemble algorithm. The differentiated pathways for the six core residues form a dynamic network that outlines the area of structure alteration. The results offer potentials of using affordable computing power to predict allosteric structure of mutants in knowledge-based mutagenesis.

  12. Restricted Authentication and Encryption for Cyber-physical Systems

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

    Kirkpatrick, Michael S; Bertino, Elisa; Sheldon, Frederick T

    2009-01-01

    Cyber-physical systems (CPS) are characterized by the close linkage of computational resources and physical devices. These systems can be deployed in a number of critical infrastructure settings. As a result, the security requirements of CPS are different than traditional computing architectures. For example, critical functions must be identified and isolated from interference by other functions. Similarly, lightweight schemes may be required, as CPS can include devices with limited computing power. One approach that offers promise for CPS security is the use of lightweight, hardware-based authentication. Specifically, we consider the use of Physically Unclonable Functions (PUFs) to bind an access requestmore » to specific hardware with device-specific keys. PUFs are implemented in hardware, such as SRAM, and can be used to uniquely identify the device. This technology could be used in CPS to ensure location-based access control and encryption, both of which would be desirable for CPS implementations.« less

  13. An efficient, large-scale, non-lattice-detection algorithm for exhaustive structural auditing of biomedical ontologies.

    PubMed

    Zhang, Guo-Qiang; Xing, Guangming; Cui, Licong

    2018-04-01

    One of the basic challenges in developing structural methods for systematic audition on the quality of biomedical ontologies is the computational cost usually involved in exhaustive sub-graph analysis. We introduce ANT-LCA, a new algorithm for computing all non-trivial lowest common ancestors (LCA) of each pair of concepts in the hierarchical order induced by an ontology. The computation of LCA is a fundamental step for non-lattice approach for ontology quality assurance. Distinct from existing approaches, ANT-LCA only computes LCAs for non-trivial pairs, those having at least one common ancestor. To skip all trivial pairs that may be of no practical interest, ANT-LCA employs a simple but innovative algorithmic strategy combining topological order and dynamic programming to keep track of non-trivial pairs. We provide correctness proofs and demonstrate a substantial reduction in computational time for two largest biomedical ontologies: SNOMED CT and Gene Ontology (GO). ANT-LCA achieved an average computation time of 30 and 3 sec per version for SNOMED CT and GO, respectively, about 2 orders of magnitude faster than the best known approaches. Our algorithm overcomes a fundamental computational barrier in sub-graph based structural analysis of large ontological systems. It enables the implementation of a new breed of structural auditing methods that not only identifies potential problematic areas, but also automatically suggests changes to fix the issues. Such structural auditing methods can lead to more effective tools supporting ontology quality assurance work. Copyright © 2018 Elsevier Inc. All rights reserved.

  14. Identifying traction-separation behavior of self-adhesive polymeric films from in situ digital images under T-peeling

    NASA Astrophysics Data System (ADS)

    Nase, Michael; Rennert, Mirko; Naumenko, Konstantin; Eremeyev, Victor A.

    2016-06-01

    In this paper procedures are developed to identify traction-separation curves from digital images of the deformed flexible films during peeling. T-peel tests were performed for self-adhesive polymeric films. High quality photographs of the deformed shape within and outside the zone of adhesive interaction were made in situ by the digital light microscope. The deformed line is approximated by a power series with coefficients computed by minimizing a least squares functional. Two approaches to identify the traction-separation curve for the given deformation line are proposed. The first one is based on the energy integral of the non-linear theory of rods and allows the direct evaluation of the adhesion force potential. The second one utilizes the complementary energy type variational equation and the Ritz method to compute the adhesion force. The accuracy of both approaches is analyzed with respect to different approximations for the deformed line and the force of interaction. The obtained traction vs. axial coordinate and the traction-separation curves provide several properties of the adhesive system including the maximum adhesion force, the length of the adhesive zone and the equilibrium position, where the adhesive force is zero while the separation is positive.

  15. The node-weighted Steiner tree approach to identify elements of cancer-related signaling pathways.

    PubMed

    Sun, Yahui; Ma, Chenkai; Halgamuge, Saman

    2017-12-28

    Cancer constitutes a momentous health burden in our society. Critical information on cancer may be hidden in its signaling pathways. However, even though a large amount of money has been spent on cancer research, some critical information on cancer-related signaling pathways still remains elusive. Hence, new works towards a complete understanding of cancer-related signaling pathways will greatly benefit the prevention, diagnosis, and treatment of cancer. We propose the node-weighted Steiner tree approach to identify important elements of cancer-related signaling pathways at the level of proteins. This new approach has advantages over previous approaches since it is fast in processing large protein-protein interaction networks. We apply this new approach to identify important elements of two well-known cancer-related signaling pathways: PI3K/Akt and MAPK. First, we generate a node-weighted protein-protein interaction network using protein and signaling pathway data. Second, we modify and use two preprocessing techniques and a state-of-the-art Steiner tree algorithm to identify a subnetwork in the generated network. Third, we propose two new metrics to select important elements from this subnetwork. On a commonly used personal computer, this new approach takes less than 2 s to identify the important elements of PI3K/Akt and MAPK signaling pathways in a large node-weighted protein-protein interaction network with 16,843 vertices and 1,736,922 edges. We further analyze and demonstrate the significance of these identified elements to cancer signal transduction by exploring previously reported experimental evidences. Our node-weighted Steiner tree approach is shown to be both fast and effective to identify important elements of cancer-related signaling pathways. Furthermore, it may provide new perspectives into the identification of signaling pathways for other human diseases.

  16. Algorithmic design of a noise-resistant and efficient closed-loop deep brain stimulation system: A computational approach

    PubMed Central

    Karamintziou, Sofia D.; Custódio, Ana Luísa; Piallat, Brigitte; Polosan, Mircea; Chabardès, Stéphan; Stathis, Pantelis G.; Tagaris, George A.; Sakas, Damianos E.; Polychronaki, Georgia E.; Tsirogiannis, George L.; David, Olivier; Nikita, Konstantina S.

    2017-01-01

    Advances in the field of closed-loop neuromodulation call for analysis and modeling approaches capable of confronting challenges related to the complex neuronal response to stimulation and the presence of strong internal and measurement noise in neural recordings. Here we elaborate on the algorithmic aspects of a noise-resistant closed-loop subthalamic nucleus deep brain stimulation system for advanced Parkinson’s disease and treatment-refractory obsessive-compulsive disorder, ensuring remarkable performance in terms of both efficiency and selectivity of stimulation, as well as in terms of computational speed. First, we propose an efficient method drawn from dynamical systems theory, for the reliable assessment of significant nonlinear coupling between beta and high-frequency subthalamic neuronal activity, as a biomarker for feedback control. Further, we present a model-based strategy through which optimal parameters of stimulation for minimum energy desynchronizing control of neuronal activity are being identified. The strategy integrates stochastic modeling and derivative-free optimization of neural dynamics based on quadratic modeling. On the basis of numerical simulations, we demonstrate the potential of the presented modeling approach to identify, at a relatively low computational cost, stimulation settings potentially associated with a significantly higher degree of efficiency and selectivity compared with stimulation settings determined post-operatively. Our data reinforce the hypothesis that model-based control strategies are crucial for the design of novel stimulation protocols at the backstage of clinical applications. PMID:28222198

  17. A true minimally invasive approach for cochlear implantation: high accuracy in cranial base navigation through flat-panel-based volume computed tomography.

    PubMed

    Majdani, Omid; Bartling, Soenke H; Leinung, Martin; Stöver, Timo; Lenarz, Minoo; Dullin, Christian; Lenarz, Thomas

    2008-02-01

    High-precision intraoperative navigation using high-resolution flat-panel volume computed tomography makes feasible the possibility of minimally invasive cochlear implant surgery, including cochleostomy. Conventional cochlear implant surgery is typically performed via mastoidectomy with facial recess to identify and avoid damage to vital anatomic landmarks. To accomplish this procedure via a minimally invasive approach--without performing mastoidectomy--in a precise fashion, image-guided technology is necessary. With such an approach, surgical time and expertise may be reduced, and hearing preservation may be improved. Flat-panel volume computed tomography was used to scan 4 human temporal bones. A drilling channel was planned preoperatively from the mastoid surface to the round window niche, providing a margin of safety to all functional important structures (e.g., facial nerve, chorda tympani, incus). Postoperatively, computed tomographic imaging and conventional surgical exploration of the drilled route to the cochlea were performed. All 4 specimens showed a cochleostomy located at the scala tympani anterior inferior to the round window. The chorda tympani was damaged in 1 specimen--this was preoperatively planned as a narrow facial recess was encountered. Using flat-panel volume computed tomography for image-guided surgical navigation, we were able to perform minimally invasive cochlear implant surgery defined as a narrow, single-channel mastoidotomy with cochleostomy. Although this finding is preliminary, it is technologically achievable.

  18. Ab Initio Infrared and Raman Spectra.

    DTIC Science & Technology

    1982-08-01

    equilibrium and non -equilibrium systems. It b pointed out that a similar ab !ni- te QFC molecular dynamic approach could be used to compute other types of...applied to -2- equilibrium and non -equilibrium system. It is pointed out that a similar oh im- ib QFCT molecular dynamic approach could be used to...desire to be able to experimentally identify and understand transient species or states (such as those existing during the course of chemical

  19. Morbidity of Early Spine Surgery in the Multiply Injured Patient

    DTIC Science & Technology

    2014-07-31

    stable’’) based on the following clinical criteria, which were adopted from previously described criteria to indicate a damage control approach to...obtained from primary source documents. Presence of any intra- cranial injury or a chest injury was identified by review of the trauma computed tomography...approach to their injuries [7, 19–21]. ‘‘Damage Control surgery’’ was first adopted by trauma surgeons to describe initial control of penetrating

  20. Non-parallel processing: Gendered attrition in academic computer science

    NASA Astrophysics Data System (ADS)

    Cohoon, Joanne Louise Mcgrath

    2000-10-01

    This dissertation addresses the issue of disproportionate female attrition from computer science as an instance of gender segregation in higher education. By adopting a theoretical framework from organizational sociology, it demonstrates that the characteristics and processes of computer science departments strongly influence female retention. The empirical data identifies conditions under which women are retained in the computer science major at comparable rates to men. The research for this dissertation began with interviews of students, faculty, and chairpersons from five computer science departments. These exploratory interviews led to a survey of faculty and chairpersons at computer science and biology departments in Virginia. The data from these surveys are used in comparisons of the computer science and biology disciplines, and for statistical analyses that identify which departmental characteristics promote equal attrition for male and female undergraduates in computer science. This three-pronged methodological approach of interviews, discipline comparisons, and statistical analyses shows that departmental variation in gendered attrition rates can be explained largely by access to opportunity, relative numbers, and other characteristics of the learning environment. Using these concepts, this research identifies nine factors that affect the differential attrition of women from CS departments. These factors are: (1) The gender composition of enrolled students and faculty; (2) Faculty turnover; (3) Institutional support for the department; (4) Preferential attitudes toward female students; (5) Mentoring and supervising by faculty; (6) The local job market, starting salaries, and competitiveness of graduates; (7) Emphasis on teaching; and (8) Joint efforts for student success. This work contributes to our understanding of the gender segregation process in higher education. In addition, it contributes information that can lead to effective solutions for an economically significant issue in modern American society---gender equality in computer science.

  1. A compendium of computational fluid dynamics at the Langley Research Center

    NASA Technical Reports Server (NTRS)

    1980-01-01

    Through numerous summary examples, the scope and general nature of the computational fluid dynamics (CFD) effort at Langley is identified. These summaries will help inform researchers in CFD and line management at Langley of the overall effort. In addition to the inhouse efforts, out of house CFD work supported by Langley through industrial contracts and university grants are included. Researchers were encouraged to include summaries of work in preliminary and tentative states of development as well as current research approaching definitive results.

  2. Identifying Trustworthiness Deficit in Legacy Systems Using the NFR Approach

    DTIC Science & Technology

    2014-01-01

    trustworthy envi- ronment. These adaptations can be stated in terms of design modifications and/or implementation mechanisms (for example, wrappers) that will...extensions to the VHSIC Hardware Description Language ( VHDL -AMS). He has spent the last 10 years leading research in high performance embedded computing

  3. Identifying Key Events in AOPs for Embryonic Disruption using Computational Toxicology (European Teratology Society - AOP symp.)

    EPA Science Inventory

    Addressing safety aspects of drugs and environmental chemicals relies extensively on animal testing; however, the quantity of chemicals needing assessment and challenges of species extrapolation require alternative approaches to traditional animal studies. Newer in vitro and in s...

  4. Detecting isotopic ratio outliers

    NASA Astrophysics Data System (ADS)

    Bayne, C. K.; Smith, D. H.

    An alternative method is proposed for improving isotopic ratio estimates. This method mathematically models pulse-count data and uses iterative reweighted Poisson regression to estimate model parameters to calculate the isotopic ratios. This computer-oriented approach provides theoretically better methods than conventional techniques to establish error limits and to identify outliers.

  5. NANOSTRUCTURED MATERIAL DESIGN FOR HG, AS, AND SE CAPTURE

    EPA Science Inventory

    The goal of this research project is to identify potential materials that can be used as multipollutant sorbents using a hierarchy of computational modeling approaches. Palladium (Pd) and gold (Au) alloys were investigated and the results show that the addition of a small amou...

  6. A Sparse Reconstruction Approach for Identifying Gene Regulatory Networks Using Steady-State Experiment Data

    PubMed Central

    Zhang, Wanhong; Zhou, Tong

    2015-01-01

    Motivation Identifying gene regulatory networks (GRNs) which consist of a large number of interacting units has become a problem of paramount importance in systems biology. Situations exist extensively in which causal interacting relationships among these units are required to be reconstructed from measured expression data and other a priori information. Though numerous classical methods have been developed to unravel the interactions of GRNs, these methods either have higher computing complexities or have lower estimation accuracies. Note that great similarities exist between identification of genes that directly regulate a specific gene and a sparse vector reconstruction, which often relates to the determination of the number, location and magnitude of nonzero entries of an unknown vector by solving an underdetermined system of linear equations y = Φx. Based on these similarities, we propose a novel framework of sparse reconstruction to identify the structure of a GRN, so as to increase accuracy of causal regulation estimations, as well as to reduce their computational complexity. Results In this paper, a sparse reconstruction framework is proposed on basis of steady-state experiment data to identify GRN structure. Different from traditional methods, this approach is adopted which is well suitable for a large-scale underdetermined problem in inferring a sparse vector. We investigate how to combine the noisy steady-state experiment data and a sparse reconstruction algorithm to identify causal relationships. Efficiency of this method is tested by an artificial linear network, a mitogen-activated protein kinase (MAPK) pathway network and the in silico networks of the DREAM challenges. The performance of the suggested approach is compared with two state-of-the-art algorithms, the widely adopted total least-squares (TLS) method and those available results on the DREAM project. Actual results show that, with a lower computational cost, the proposed method can significantly enhance estimation accuracy and greatly reduce false positive and negative errors. Furthermore, numerical calculations demonstrate that the proposed algorithm may have faster convergence speed and smaller fluctuation than other methods when either estimate error or estimate bias is considered. PMID:26207991

  7. Waveform Fingerprinting for Efficient Seismic Signal Detection

    NASA Astrophysics Data System (ADS)

    Yoon, C. E.; OReilly, O. J.; Beroza, G. C.

    2013-12-01

    Cross-correlating an earthquake waveform template with continuous waveform data has proven a powerful approach for detecting events missing from earthquake catalogs. If templates do not exist, it is possible to divide the waveform data into short overlapping time windows, then identify window pairs with similar waveforms. Applying these approaches to earthquake monitoring in seismic networks has tremendous potential to improve the completeness of earthquake catalogs, but because effort scales quadratically with time, it rapidly becomes computationally infeasible. We develop a fingerprinting technique to identify similar waveforms, using only a few compact features of the original data. The concept is similar to human fingerprints, which utilize key diagnostic features to identify people uniquely. Analogous audio-fingerprinting approaches have accurately and efficiently found similar audio clips within large databases; example applications include identifying songs and finding copyrighted content within YouTube videos. In order to fingerprint waveforms, we compute a spectrogram of the time series, and segment it into multiple overlapping windows (spectral images). For each spectral image, we apply a wavelet transform, and retain only the sign of the maximum magnitude wavelet coefficients. This procedure retains just the large-scale structure of the data, providing both robustness to noise and significant dimensionality reduction. Each fingerprint is a high-dimensional, sparse, binary data object that can be stored in a database without significant storage costs. Similar fingerprints within the database are efficiently searched using locality-sensitive hashing. We test this technique on waveform data from the Northern California Seismic Network that contains events not detected in the catalog. We show that this algorithm successfully identifies similar waveforms and detects uncataloged low magnitude events in addition to cataloged events, while running to completion faster than a comparison waveform autocorrelation code.

  8. Targeted intervention: Computational approaches to elucidate and predict relapse in alcoholism.

    PubMed

    Heinz, Andreas; Deserno, Lorenz; Zimmermann, Ulrich S; Smolka, Michael N; Beck, Anne; Schlagenhauf, Florian

    2017-05-01

    Alcohol use disorder (AUD) and addiction in general is characterized by failures of choice resulting in repeated drug intake despite severe negative consequences. Behavioral change is hard to accomplish and relapse after detoxification is common and can be promoted by consumption of small amounts of alcohol as well as exposure to alcohol-associated cues or stress. While those environmental factors contributing to relapse have long been identified, the underlying psychological and neurobiological mechanism on which those factors act are to date incompletely understood. Based on the reinforcing effects of drugs of abuse, animal experiments showed that drug, cue and stress exposure affect Pavlovian and instrumental learning processes, which can increase salience of drug cues and promote habitual drug intake. In humans, computational approaches can help to quantify changes in key learning mechanisms during the development and maintenance of alcohol dependence, e.g. by using sequential decision making in combination with computational modeling to elucidate individual differences in model-free versus more complex, model-based learning strategies and their neurobiological correlates such as prediction error signaling in fronto-striatal circuits. Computational models can also help to explain how alcohol-associated cues trigger relapse: mechanisms such as Pavlovian-to-Instrumental Transfer can quantify to which degree Pavlovian conditioned stimuli can facilitate approach behavior including alcohol seeking and intake. By using generative models of behavioral and neural data, computational approaches can help to quantify individual differences in psychophysiological mechanisms that underlie the development and maintenance of AUD and thus promote targeted intervention. Copyright © 2016 Elsevier Inc. All rights reserved.

  9. SABRE: a method for assessing the stability of gene modules in complex tissues and subject populations.

    PubMed

    Shannon, Casey P; Chen, Virginia; Takhar, Mandeep; Hollander, Zsuzsanna; Balshaw, Robert; McManus, Bruce M; Tebbutt, Scott J; Sin, Don D; Ng, Raymond T

    2016-11-14

    Gene network inference (GNI) algorithms can be used to identify sets of coordinately expressed genes, termed network modules from whole transcriptome gene expression data. The identification of such modules has become a popular approach to systems biology, with important applications in translational research. Although diverse computational and statistical approaches have been devised to identify such modules, their performance behavior is still not fully understood, particularly in complex human tissues. Given human heterogeneity, one important question is how the outputs of these computational methods are sensitive to the input sample set, or stability. A related question is how this sensitivity depends on the size of the sample set. We describe here the SABRE (Similarity Across Bootstrap RE-sampling) procedure for assessing the stability of gene network modules using a re-sampling strategy, introduce a novel criterion for identifying stable modules, and demonstrate the utility of this approach in a clinically-relevant cohort, using two different gene network module discovery algorithms. The stability of modules increased as sample size increased and stable modules were more likely to be replicated in larger sets of samples. Random modules derived from permutated gene expression data were consistently unstable, as assessed by SABRE, and provide a useful baseline value for our proposed stability criterion. Gene module sets identified by different algorithms varied with respect to their stability, as assessed by SABRE. Finally, stable modules were more readily annotated in various curated gene set databases. The SABRE procedure and proposed stability criterion may provide guidance when designing systems biology studies in complex human disease and tissues.

  10. Identifying the Presence of Prostate Cancer in Individuals with PSA Levels <20 ng ml-1 Using Computational Data Extraction Analysis of High Dimensional Peripheral Blood Flow Cytometric Phenotyping Data.

    PubMed

    Cosma, Georgina; McArdle, Stéphanie E; Reeder, Stephen; Foulds, Gemma A; Hood, Simon; Khan, Masood; Pockley, A Graham

    2017-01-01

    Determining whether an asymptomatic individual with Prostate-Specific Antigen (PSA) levels below 20 ng ml -1 has prostate cancer in the absence of definitive, biopsy-based evidence continues to present a significant challenge to clinicians who must decide whether such individuals with low PSA values have prostate cancer. Herein, we present an advanced computational data extraction approach which can identify the presence of prostate cancer in men with PSA levels <20 ng ml -1 on the basis of peripheral blood immune cell profiles that have been generated using multi-parameter flow cytometry. Statistical analysis of immune phenotyping datasets relating to the presence and prevalence of key leukocyte populations in the peripheral blood, as generated from individuals undergoing routine tests for prostate cancer (including tissue biopsy) using multi-parametric flow cytometric analysis, was unable to identify significant relationships between leukocyte population profiles and the presence of benign disease (no prostate cancer) or prostate cancer. By contrast, a Genetic Algorithm computational approach identified a subset of five flow cytometry features ( CD 8 + CD 45 RA - CD 27 - CD 28 - ( CD 8 + Effector Memory cells); CD 4 + CD 45 RA - CD 27 - CD 28 - ( CD 4 + Terminally Differentiated Effector Memory Cells re-expressing CD45RA); CD 3 - CD 19 + (B cells); CD 3 + CD 56 + CD 8 + CD 4 + (NKT cells)) from a set of twenty features, which could potentially discriminate between benign disease and prostate cancer. These features were used to construct a prostate cancer prediction model using the k-Nearest-Neighbor classification algorithm. The proposed model, which takes as input the set of flow cytometry features, outperformed the predictive model which takes PSA values as input. Specifically, the flow cytometry-based model achieved Accuracy = 83.33%, AUC = 83.40%, and optimal ROC points of FPR = 16.13%, TPR = 82.93%, whereas the PSA-based model achieved Accuracy = 77.78%, AUC = 76.95%, and optimal ROC points of FPR = 29.03%, TPR = 82.93%. Combining PSA and flow cytometry predictors achieved Accuracy = 79.17%, AUC = 78.17% and optimal ROC points of FPR = 29.03%, TPR = 85.37%. The results demonstrate the value of computational intelligence-based approaches for interrogating immunophenotyping datasets and that combining peripheral blood phenotypic profiling with PSA levels improves diagnostic accuracy compared to using PSA test alone. These studies also demonstrate that the presence of cancer is reflected in changes in the peripheral blood immune phenotype profile which can be identified using computational analysis and interpretation of complex flow cytometry datasets.

  11. Knowledge-Assisted Approach to Identify Pathways with Differential Dependencies | Office of Cancer Genomics

    Cancer.gov

    We have previously developed a statistical method to identify gene sets enriched with condition-specific genetic dependencies. The method constructs gene dependency networks from bootstrapped samples in one condition and computes the divergence between distributions of network likelihood scores from different conditions. It was shown to be capable of sensitive and specific identification of pathways with phenotype-specific dysregulation, i.e., rewiring of dependencies between genes in different conditions.

  12. Continuous stacking computational approach based automated microscope slide scanner

    NASA Astrophysics Data System (ADS)

    Murali, Swetha; Adhikari, Jayesh Vasudeva; Jagannadh, Veerendra Kalyan; Gorthi, Sai Siva

    2018-02-01

    Cost-effective and automated acquisition of whole slide images is a bottleneck for wide-scale deployment of digital pathology. In this article, a computation augmented approach for the development of an automated microscope slide scanner is presented. The realization of a prototype device built using inexpensive off-the-shelf optical components and motors is detailed. The applicability of the developed prototype to clinical diagnostic testing is demonstrated by generating good quality digital images of malaria-infected blood smears. Further, the acquired slide images have been processed to identify and count the number of malaria-infected red blood cells and thereby perform quantitative parasitemia level estimation. The presented prototype would enable cost-effective deployment of slide-based cyto-diagnostic testing in endemic areas.

  13. Surface Modeling, Grid Generation, and Related Issues in Computational Fluid Dynamic (CFD) Solutions

    NASA Technical Reports Server (NTRS)

    Choo, Yung K. (Compiler)

    1995-01-01

    The NASA Steering Committee for Surface Modeling and Grid Generation (SMAGG) sponsored a workshop on surface modeling, grid generation, and related issues in Computational Fluid Dynamics (CFD) solutions at Lewis Research Center, Cleveland, Ohio, May 9-11, 1995. The workshop provided a forum to identify industry needs, strengths, and weaknesses of the five grid technologies (patched structured, overset structured, Cartesian, unstructured, and hybrid), and to exchange thoughts about where each technology will be in 2 to 5 years. The workshop also provided opportunities for engineers and scientists to present new methods, approaches, and applications in SMAGG for CFD. This Conference Publication (CP) consists of papers on industry overview, NASA overview, five grid technologies, new methods/ approaches/applications, and software systems.

  14. a Radical Collaborative Approach: Developing a Model for Learning Theory, Human-Based Computation and Participant Motivation in a Rock-Art Heritage Application

    NASA Astrophysics Data System (ADS)

    Haubt, R.

    2016-06-01

    This paper explores a Radical Collaborative Approach in the global and centralized Rock-Art Database project to find new ways to look at rock-art by making information more accessible and more visible through public contributions. It looks at rock-art through the Key Performance Indicator (KPI), identified with the latest Australian State of the Environment Reports to help develop a better understanding of rock-art within a broader Cultural and Indigenous Heritage context. Using a practice-led approach the project develops a conceptual collaborative model that is deployed within the RADB Management System. Exploring learning theory, human-based computation and participant motivation the paper develops a procedure for deploying collaborative functions within the interface design of the RADB Management System. The paper presents the results of the collaborative model implementation and discusses considerations for the next iteration of the RADB Universe within an Agile Development Approach.

  15. An Integrative Bioinformatics Approach for Knowledge Discovery

    NASA Astrophysics Data System (ADS)

    Peña-Castillo, Lourdes; Phan, Sieu; Famili, Fazel

    The vast amount of data being generated by large scale omics projects and the computational approaches developed to deal with this data have the potential to accelerate the advancement of our understanding of the molecular basis of genetic diseases. This better understanding may have profound clinical implications and transform the medical practice; for instance, therapeutic management could be prescribed based on the patient’s genetic profile instead of being based on aggregate data. Current efforts have established the feasibility and utility of integrating and analysing heterogeneous genomic data to identify molecular associations to pathogenesis. However, since these initiatives are data-centric, they either restrict the research community to specific data sets or to a certain application domain, or force researchers to develop their own analysis tools. To fully exploit the potential of omics technologies, robust computational approaches need to be developed and made available to the community. This research addresses such challenge and proposes an integrative approach to facilitate knowledge discovery from diverse datasets and contribute to the advancement of genomic medicine.

  16. Computational Social Creativity.

    PubMed

    Saunders, Rob; Bown, Oliver

    2015-01-01

    This article reviews the development of computational models of creativity where social interactions are central. We refer to this area as computational social creativity. Its context is described, including the broader study of creativity, the computational modeling of other social phenomena, and computational models of individual creativity. Computational modeling has been applied to a number of areas of social creativity and has the potential to contribute to our understanding of creativity. A number of requirements for computational models of social creativity are common in artificial life and computational social science simulations. Three key themes are identified: (1) computational social creativity research has a critical role to play in understanding creativity as a social phenomenon and advancing computational creativity by making clear epistemological contributions in ways that would be challenging for other approaches; (2) the methodologies developed in artificial life and computational social science carry over directly to computational social creativity; and (3) the combination of computational social creativity with individual models of creativity presents significant opportunities and poses interesting challenges for the development of integrated models of creativity that have yet to be realized.

  17. Microarray-based cancer prediction using soft computing approach.

    PubMed

    Wang, Xiaosheng; Gotoh, Osamu

    2009-05-26

    One of the difficulties in using gene expression profiles to predict cancer is how to effectively select a few informative genes to construct accurate prediction models from thousands or ten thousands of genes. We screen highly discriminative genes and gene pairs to create simple prediction models involved in single genes or gene pairs on the basis of soft computing approach and rough set theory. Accurate cancerous prediction is obtained when we apply the simple prediction models for four cancerous gene expression datasets: CNS tumor, colon tumor, lung cancer and DLBCL. Some genes closely correlated with the pathogenesis of specific or general cancers are identified. In contrast with other models, our models are simple, effective and robust. Meanwhile, our models are interpretable for they are based on decision rules. Our results demonstrate that very simple models may perform well on cancerous molecular prediction and important gene markers of cancer can be detected if the gene selection approach is chosen reasonably.

  18. Computational approach for deriving cancer progression roadmaps from static sample data

    PubMed Central

    Yao, Jin; Yang, Le; Chen, Runpu; Nowak, Norma J.

    2017-01-01

    Abstract As with any biological process, cancer development is inherently dynamic. While major efforts continue to catalog the genomic events associated with human cancer, it remains difficult to interpret and extrapolate the accumulating data to provide insights into the dynamic aspects of the disease. Here, we present a computational strategy that enables the construction of a cancer progression model using static tumor sample data. The developed approach overcame many technical limitations of existing methods. Application of the approach to breast cancer data revealed a linear, branching model with two distinct trajectories for malignant progression. The validity of the constructed model was demonstrated in 27 independent breast cancer data sets, and through visualization of the data in the context of disease progression we were able to identify a number of potentially key molecular events in the advance of breast cancer to malignancy. PMID:28108658

  19. Segmentation-assisted detection of dirt impairments in archived film sequences.

    PubMed

    Ren, Jinchang; Vlachos, Theodore

    2007-04-01

    In this correspondence, a novel segmentation-assisted method for film-dirt detection is proposed. We exploit the fact that film dirt manifests in the spatial domain as a cluster of connected pixels whose intensity differs substantially from that of its neighborhood, and we employ a segmentation-based approach to identify this type of structure. A key feature of our approach is the computation of a measure of confidence attached to detected dirt regions, which can be utilized for performance fine tuning. Another important feature of our algorithm is the avoidance of the computational complexity associated with motion estimation. Our experimental framework benefits from the availability of manually derived as well as objective ground-truth data obtained using infrared scanning. Our results demonstrate that the proposed method compares favorably with standard spatial, temporal, and multistage median-filtering approaches and provides efficient and robust detection for a wide variety of test materials.

  20. Algorithm For Optimal Control Of Large Structures

    NASA Technical Reports Server (NTRS)

    Salama, Moktar A.; Garba, John A..; Utku, Senol

    1989-01-01

    Cost of computation appears competitive with other methods. Problem to compute optimal control of forced response of structure with n degrees of freedom identified in terms of smaller number, r, of vibrational modes. Article begins with Hamilton-Jacobi formulation of mechanics and use of quadratic cost functional. Complexity reduced by alternative approach in which quadratic cost functional expressed in terms of control variables only. Leads to iterative solution of second-order time-integral matrix Volterra equation of second kind containing optimal control vector. Cost of algorithm, measured in terms of number of computations required, is of order of, or less than, cost of prior algoritms applied to similar problems.

  1. Statistical Model Applied to NetFlow for Network Intrusion Detection

    NASA Astrophysics Data System (ADS)

    Proto, André; Alexandre, Leandro A.; Batista, Maira L.; Oliveira, Isabela L.; Cansian, Adriano M.

    The computers and network services became presence guaranteed in several places. These characteristics resulted in the growth of illicit events and therefore the computers and networks security has become an essential point in any computing environment. Many methodologies were created to identify these events; however, with increasing of users and services on the Internet, many difficulties are found in trying to monitor a large network environment. This paper proposes a methodology for events detection in large-scale networks. The proposal approaches the anomaly detection using the NetFlow protocol, statistical methods and monitoring the environment in a best time for the application.

  2. Mining Consumer Health Vocabulary from Community-Generated Text

    PubMed Central

    Vydiswaran, V.G. Vinod; Mei, Qiaozhu; Hanauer, David A.; Zheng, Kai

    2014-01-01

    Community-generated text corpora can be a valuable resource to extract consumer health vocabulary (CHV) and link them to professional terminologies and alternative variants. In this research, we propose a pattern-based text-mining approach to identify pairs of CHV and professional terms from Wikipedia, a large text corpus created and maintained by the community. A novel measure, leveraging the ratio of frequency of occurrence, was used to differentiate consumer terms from professional terms. We empirically evaluated the applicability of this approach using a large data sample consisting of MedLine abstracts and all posts from an online health forum, MedHelp. The results show that the proposed approach is able to identify synonymous pairs and label the terms as either consumer or professional term with high accuracy. We conclude that the proposed approach provides great potential to produce a high quality CHV to improve the performance of computational applications in processing consumer-generated health text. PMID:25954426

  3. Computer Software Training and HRD: What Are the Critical Issues?

    ERIC Educational Resources Information Center

    Altemeyer, Brad

    2005-01-01

    The paper explores critical issues for HRD practice from a parsonian framework across the HRD legs of organizational development, adult learning, and training and development. Insights into the critical issues emerge from this approach. Identifying successful transfer of training to be critical for organizational, group, and individual success.…

  4. Computational Immunology for the Defense of Large Scale Systems

    DTIC Science & Technology

    2002-07-01

    or unusual activity (e.g., multiple login attempts, possibly in order to guess a password). We can summarize our results as follows: • Our...such as those used in SRI’s Emerald project. There are two important characteristics of the approach introduced in [5]. First, it identifies a simple

  5. Retrenchment in Education: Hierarchical Decision Models for Instructional Program Termination, District Consolidations and School Closures.

    ERIC Educational Resources Information Center

    Wholeben, Brent Edward

    A number of key issues facing elementary, secondary, and postsecondary educational administrators during retrenchment require a hierarchical decision-modeling approach. This paper identifies and discusses the use of a hierarchical multiple-alternatives modeling formulation (computer-based) that compares and evaluates a group of solution…

  6. Intersectional Computer-Supported Collaboration in Business Writing: Learning through Challenged Performance

    ERIC Educational Resources Information Center

    Remley, Dirk

    2009-01-01

    Carter (2007) identifies four meta-genres associated with writing activities that can help students learn discipline-specific writing skills relative to standards within a given field: these include problem solving, empirical approaches to analysis, selection of sources to use within research, and production of materials that meet accepted…

  7. A Low-Tech, Hands-On Approach To Teaching Sorting Algorithms to Working Students.

    ERIC Educational Resources Information Center

    Dios, R.; Geller, J.

    1998-01-01

    Focuses on identifying the educational effects of "activity oriented" instructional techniques. Examines which instructional methods produce enhanced learning and comprehension. Discusses the problem of learning "sorting algorithms," a major topic in every Computer Science curriculum. Presents a low-tech, hands-on teaching method for sorting…

  8. Three Essays on the Economics of Information Systems

    ERIC Educational Resources Information Center

    Jian, Lian

    2010-01-01

    My dissertation contains three studies centering on the question: how to motivate people to share high quality information on online information aggregation systems, also known as social computing systems? I take a social scientific approach to "identify" the strategic behavior of individuals in information systems, and "analyze" how non-monetary…

  9. The Instructional Cost Index. A Simplified Approach to Interinstitutional Cost Comparison.

    ERIC Educational Resources Information Center

    Beatty, George, Jr.; And Others

    The paper describes a simple, yet effective method of computing a comparative index of instructional costs. The Instructional Cost Index identifies direct cost differentials among instructional programs. Cost differentials are described in terms of differences among numerical values of variables that reflect fundamental academic and resource…

  10. Combining Genome-Scale Experimental and Computational Methods To Identify Essential Genes in Rhodobacter sphaeroides

    DOE PAGES

    Burger, Brian T.; Imam, Saheed; Scarborough, Matthew J.; ...

    2017-06-06

    Rhodobacter sphaeroides is one of the best-studied alphaproteobacteria from biochemical, genetic, and genomic perspectives. To gain a better systems-level understanding of this organism, we generated a large transposon mutant library and used transposon sequencing (Tn-seq) to identify genes that are essential under several growth conditions. Using newly developed Tn-seq analysis software (TSAS), we identified 493 genes as essential for aerobic growth on a rich medium. We then used the mutant library to identify conditionally essential genes under two laboratory growth conditions, identifying 85 additional genes required for aerobic growth in a minimal medium and 31 additional genes required for photosyntheticmore » growth. In all instances, our analyses confirmed essentiality for many known genes and identified genes not previously considered to be essential. We used the resulting Tn-seq data to refine and improve a genome-scale metabolic network model (GEM) for R. sphaeroides. Together, we demonstrate how genetic, genomic, and computational approaches can be combined to obtain a systems-level understanding of the genetic framework underlying metabolic diversity in bacterial species.« less

  11. Systems Pharmacology-Based Approach of Connecting Disease Genes in Genome-Wide Association Studies with Traditional Chinese Medicine.

    PubMed

    Kim, Jihye; Yoo, Minjae; Shin, Jimin; Kim, Hyunmin; Kang, Jaewoo; Tan, Aik Choon

    2018-01-01

    Traditional Chinese medicine (TCM) originated in ancient China has been practiced over thousands of years for treating various symptoms and diseases. However, the molecular mechanisms of TCM in treating these diseases remain unknown. In this study, we employ a systems pharmacology-based approach for connecting GWAS diseases with TCM for potential drug repurposing and repositioning. We studied 102 TCM components and their target genes by analyzing microarray gene expression experiments. We constructed disease-gene networks from 2558 GWAS studies. We applied a systems pharmacology approach to prioritize disease-target genes. Using this bioinformatics approach, we analyzed 14,713 GWAS disease-TCM-target gene pairs and identified 115 disease-gene pairs with q value < 0.2. We validated several of these GWAS disease-TCM-target gene pairs with literature evidence, demonstrating that this computational approach could reveal novel indications for TCM. We also develop TCM-Disease web application to facilitate the traditional Chinese medicine drug repurposing efforts. Systems pharmacology is a promising approach for connecting GWAS diseases with TCM for potential drug repurposing and repositioning. The computational approaches described in this study could be easily expandable to other disease-gene network analysis.

  12. Systems Pharmacology-Based Approach of Connecting Disease Genes in Genome-Wide Association Studies with Traditional Chinese Medicine

    PubMed Central

    Kim, Jihye; Yoo, Minjae; Shin, Jimin; Kim, Hyunmin; Kang, Jaewoo

    2018-01-01

    Traditional Chinese medicine (TCM) originated in ancient China has been practiced over thousands of years for treating various symptoms and diseases. However, the molecular mechanisms of TCM in treating these diseases remain unknown. In this study, we employ a systems pharmacology-based approach for connecting GWAS diseases with TCM for potential drug repurposing and repositioning. We studied 102 TCM components and their target genes by analyzing microarray gene expression experiments. We constructed disease-gene networks from 2558 GWAS studies. We applied a systems pharmacology approach to prioritize disease-target genes. Using this bioinformatics approach, we analyzed 14,713 GWAS disease-TCM-target gene pairs and identified 115 disease-gene pairs with q value < 0.2. We validated several of these GWAS disease-TCM-target gene pairs with literature evidence, demonstrating that this computational approach could reveal novel indications for TCM. We also develop TCM-Disease web application to facilitate the traditional Chinese medicine drug repurposing efforts. Systems pharmacology is a promising approach for connecting GWAS diseases with TCM for potential drug repurposing and repositioning. The computational approaches described in this study could be easily expandable to other disease-gene network analysis. PMID:29765977

  13. Parallel approach to identifying the well-test interpretation model using a neurocomputer

    NASA Astrophysics Data System (ADS)

    May, Edward A., Jr.; Dagli, Cihan H.

    1996-03-01

    The well test is one of the primary diagnostic and predictive tools used in the analysis of oil and gas wells. In these tests, a pressure recording device is placed in the well and the pressure response is recorded over time under controlled flow conditions. The interpreted results are indicators of the well's ability to flow and the damage done to the formation surrounding the wellbore during drilling and completion. The results are used for many purposes, including reservoir modeling (simulation) and economic forecasting. The first step in the analysis is the identification of the Well-Test Interpretation (WTI) model, which determines the appropriate solution method. Mis-identification of the WTI model occurs due to noise and non-ideal reservoir conditions. Previous studies have shown that a feed-forward neural network using the backpropagation algorithm can be used to identify the WTI model. One of the drawbacks to this approach is, however, training time, which can run into days of CPU time on personal computers. In this paper a similar neural network is applied using both a personal computer and a neurocomputer. Input data processing, network design, and performance are discussed and compared. The results show that the neurocomputer greatly eases the burden of training and allows the network to outperform a similar network running on a personal computer.

  14. Global investigation of protein-protein interactions in yeast Saccharomyces cerevisiae using re-occurring short polypeptide sequences.

    PubMed

    Pitre, S; North, C; Alamgir, M; Jessulat, M; Chan, A; Luo, X; Green, J R; Dumontier, M; Dehne, F; Golshani, A

    2008-08-01

    Protein-protein interaction (PPI) maps provide insight into cellular biology and have received considerable attention in the post-genomic era. While large-scale experimental approaches have generated large collections of experimentally determined PPIs, technical limitations preclude certain PPIs from detection. Recently, we demonstrated that yeast PPIs can be computationally predicted using re-occurring short polypeptide sequences between known interacting protein pairs. However, the computational requirements and low specificity made this method unsuitable for large-scale investigations. Here, we report an improved approach, which exhibits a specificity of approximately 99.95% and executes 16,000 times faster. Importantly, we report the first all-to-all sequence-based computational screen of PPIs in yeast, Saccharomyces cerevisiae in which we identify 29,589 high confidence interactions of approximately 2 x 10(7) possible pairs. Of these, 14,438 PPIs have not been previously reported and may represent novel interactions. In particular, these results reveal a richer set of membrane protein interactions, not readily amenable to experimental investigations. From the novel PPIs, a novel putative protein complex comprised largely of membrane proteins was revealed. In addition, two novel gene functions were predicted and experimentally confirmed to affect the efficiency of non-homologous end-joining, providing further support for the usefulness of the identified PPIs in biological investigations.

  15. Next generation of network medicine: interdisciplinary signaling approaches.

    PubMed

    Korcsmaros, Tamas; Schneider, Maria Victoria; Superti-Furga, Giulio

    2017-02-20

    In the last decade, network approaches have transformed our understanding of biological systems. Network analyses and visualizations have allowed us to identify essential molecules and modules in biological systems, and improved our understanding of how changes in cellular processes can lead to complex diseases, such as cancer, infectious and neurodegenerative diseases. "Network medicine" involves unbiased large-scale network-based analyses of diverse data describing interactions between genes, diseases, phenotypes, drug targets, drug transport, drug side-effects, disease trajectories and more. In terms of drug discovery, network medicine exploits our understanding of the network connectivity and signaling system dynamics to help identify optimal, often novel, drug targets. Contrary to initial expectations, however, network approaches have not yet delivered a revolution in molecular medicine. In this review, we propose that a key reason for the limited impact, so far, of network medicine is a lack of quantitative multi-disciplinary studies involving scientists from different backgrounds. To support this argument, we present existing approaches from structural biology, 'omics' technologies (e.g., genomics, proteomics, lipidomics) and computational modeling that point towards how multi-disciplinary efforts allow for important new insights. We also highlight some breakthrough studies as examples of the potential of these approaches, and suggest ways to make greater use of the power of interdisciplinarity. This review reflects discussions held at an interdisciplinary signaling workshop which facilitated knowledge exchange from experts from several different fields, including in silico modelers, computational biologists, biochemists, geneticists, molecular and cell biologists as well as cancer biologists and pharmacologists.

  16. Characterization and Computational Modeling of Minor Phases in Alloy LSHR

    NASA Technical Reports Server (NTRS)

    Jou, Herng-Jeng; Olson, Gregory; Gabb, Timothy; Garg, Anita; Miller, Derek

    2012-01-01

    The minor phases of powder metallurgy disk superalloy LSHR were studied. Samples were consistently heat treated at three different temperatures for long times to approach equilibrium. Additional heat treatments were also performed for shorter times, to assess minor phase kinetics in non-equilibrium conditions. Minor phases including MC carbides, M23C6 carbides, M3B2 borides, and sigma were identified. Their average sizes and total area fractions were determined. CALPHAD thermodynamics databases and PrecipiCalc(TradeMark), a computational precipitation modeling tool, were employed with Ni-base thermodynamics and diffusion databases to model and simulate the phase microstructural evolution observed in the experiments with an objective to identify the model limitations and the directions of model enhancement.

  17. Signal design study for shuttle/TDRSS Ku-band uplink

    NASA Technical Reports Server (NTRS)

    1976-01-01

    The adequacy of the signal design approach chosen for the TDRSS/orbiter uplink was evaluated. Critical functions and/or components associated with the baseline design were identified, and design alternatives were developed for those areas considered high risk. A detailed set of RF and signal processing performance specifications for the orbiter hardware associated with the TDRSS/orbiter Ku band uplink was analyzed. Performances of a detailed design of the PN despreader, the PSK carrier synchronization loop, and the symbol synchronizer are identified. The performance of the downlink signal by means of computer simulation to obtain a realistic determination of bit error rate degradations was studied. The three channel PM downlink signal was detailed by means of analysis and computer simulation.

  18. groHMM: a computational tool for identifying unannotated and cell type-specific transcription units from global run-on sequencing data.

    PubMed

    Chae, Minho; Danko, Charles G; Kraus, W Lee

    2015-07-16

    Global run-on coupled with deep sequencing (GRO-seq) provides extensive information on the location and function of coding and non-coding transcripts, including primary microRNAs (miRNAs), long non-coding RNAs (lncRNAs), and enhancer RNAs (eRNAs), as well as yet undiscovered classes of transcripts. However, few computational tools tailored toward this new type of sequencing data are available, limiting the applicability of GRO-seq data for identifying novel transcription units. Here, we present groHMM, a computational tool in R, which defines the boundaries of transcription units de novo using a two state hidden-Markov model (HMM). A systematic comparison of the performance between groHMM and two existing peak-calling methods tuned to identify broad regions (SICER and HOMER) favorably supports our approach on existing GRO-seq data from MCF-7 breast cancer cells. To demonstrate the broader utility of our approach, we have used groHMM to annotate a diverse array of transcription units (i.e., primary transcripts) from four GRO-seq data sets derived from cells representing a variety of different human tissue types, including non-transformed cells (cardiomyocytes and lung fibroblasts) and transformed cells (LNCaP and MCF-7 cancer cells), as well as non-mammalian cells (from flies and worms). As an example of the utility of groHMM and its application to questions about the transcriptome, we show how groHMM can be used to analyze cell type-specific enhancers as defined by newly annotated enhancer transcripts. Our results show that groHMM can reveal new insights into cell type-specific transcription by identifying novel transcription units, and serve as a complete and useful tool for evaluating functional genomic elements in cells.

  19. Imaging genetics approach to predict progression of Parkinson's diseases.

    PubMed

    Mansu Kim; Seong-Jin Son; Hyunjin Park

    2017-07-01

    Imaging genetics is a tool to extract genetic variants associated with both clinical phenotypes and imaging information. The approach can extract additional genetic variants compared to conventional approaches to better investigate various diseased conditions. Here, we applied imaging genetics to study Parkinson's disease (PD). We aimed to extract significant features derived from imaging genetics and neuroimaging. We built a regression model based on extracted significant features combining genetics and neuroimaging to better predict clinical scores of PD progression (i.e. MDS-UPDRS). Our model yielded high correlation (r = 0.697, p <; 0.001) and low root mean squared error (8.36) between predicted and actual MDS-UPDRS scores. Neuroimaging (from 123 I-Ioflupane SPECT) predictors of regression model were computed from independent component analysis approach. Genetic features were computed using image genetics approach based on identified neuroimaging features as intermediate phenotypes. Joint modeling of neuroimaging and genetics could provide complementary information and thus have the potential to provide further insight into the pathophysiology of PD. Our model included newly found neuroimaging features and genetic variants which need further investigation.

  20. Comprehensive efficiency analysis of supercomputer resource usage based on system monitoring data

    NASA Astrophysics Data System (ADS)

    Mamaeva, A. A.; Shaykhislamov, D. I.; Voevodin, Vad V.; Zhumatiy, S. A.

    2018-03-01

    One of the main problems of modern supercomputers is the low efficiency of their usage, which leads to the significant idle time of computational resources, and, in turn, to the decrease in speed of scientific research. This paper presents three approaches to study the efficiency of supercomputer resource usage based on monitoring data analysis. The first approach performs an analysis of computing resource utilization statistics, which allows to identify different typical classes of programs, to explore the structure of the supercomputer job flow and to track overall trends in the supercomputer behavior. The second approach is aimed specifically at analyzing off-the-shelf software packages and libraries installed on the supercomputer, since efficiency of their usage is becoming an increasingly important factor for the efficient functioning of the entire supercomputer. Within the third approach, abnormal jobs – jobs with abnormally inefficient behavior that differs significantly from the standard behavior of the overall supercomputer job flow – are being detected. For each approach, the results obtained in practice in the Supercomputer Center of Moscow State University are demonstrated.

  1. A Virtual Screening Approach For Identifying Plants with Anti H5N1 Neuraminidase Activity

    PubMed Central

    2016-01-01

    Recent outbreaks of highly pathogenic and occasional drug-resistant influenza strains have highlighted the need to develop novel anti-influenza therapeutics. Here, we report computational and experimental efforts to identify influenza neuraminidase inhibitors from among the 3000 natural compounds in the Malaysian-Plants Natural-Product (NADI) database. These 3000 compounds were first docked into the neuraminidase active site. The five plants with the largest number of top predicted ligands were selected for experimental evaluation. Twelve specific compounds isolated from these five plants were shown to inhibit neuraminidase, including two compounds with IC50 values less than 92 μM. Furthermore, four of the 12 isolated compounds had also been identified in the top 100 compounds from the virtual screen. Together, these results suggest an effective new approach for identifying bioactive plant species that will further the identification of new pharmacologically active compounds from diverse natural-product resources. PMID:25555059

  2. Merging K-means with hierarchical clustering for identifying general-shaped groups.

    PubMed

    Peterson, Anna D; Ghosh, Arka P; Maitra, Ranjan

    2018-01-01

    Clustering partitions a dataset such that observations placed together in a group are similar but different from those in other groups. Hierarchical and K -means clustering are two approaches but have different strengths and weaknesses. For instance, hierarchical clustering identifies groups in a tree-like structure but suffers from computational complexity in large datasets while K -means clustering is efficient but designed to identify homogeneous spherically-shaped clusters. We present a hybrid non-parametric clustering approach that amalgamates the two methods to identify general-shaped clusters and that can be applied to larger datasets. Specifically, we first partition the dataset into spherical groups using K -means. We next merge these groups using hierarchical methods with a data-driven distance measure as a stopping criterion. Our proposal has the potential to reveal groups with general shapes and structure in a dataset. We demonstrate good performance on several simulated and real datasets.

  3. A correlational approach to predicting operator status

    NASA Technical Reports Server (NTRS)

    Shingledecker, Clark A.

    1988-01-01

    This paper discusses a research approach for identifying and validating candidate physiological and behavioral parameters which can be used to predict the performance capabilities of aircrew and other system operators. In this methodology, concurrent and advance correlations are computed between predictor values and criterion performance measures. Continuous performance and sleep loss are used as stressors to promote performance variation. Preliminary data are presented which suggest dependence of prediction capability on the resource allocation policy of the operator.

  4. Automatic Reconstruction of Spacecraft 3D Shape from Imagery

    NASA Astrophysics Data System (ADS)

    Poelman, C.; Radtke, R.; Voorhees, H.

    We describe a system that computes the three-dimensional (3D) shape of a spacecraft from a sequence of uncalibrated, two-dimensional images. While the mathematics of multi-view geometry is well understood, building a system that accurately recovers 3D shape from real imagery remains an art. A novel aspect of our approach is the combination of algorithms from computer vision, photogrammetry, and computer graphics. We demonstrate our system by computing spacecraft models from imagery taken by the Air Force Research Laboratory's XSS-10 satellite and DARPA's Orbital Express satellite. Using feature tie points (each identified in two or more images), we compute the relative motion of each frame and the 3D location of each feature using iterative linear factorization followed by non-linear bundle adjustment. The "point cloud" that results from this traditional shape-from-motion approach is typically too sparse to generate a detailed 3D model. Therefore, we use the computed motion solution as input to a volumetric silhouette-carving algorithm, which constructs a solid 3D model based on viewpoint consistency with the image frames. The resulting voxel model is then converted to a facet-based surface representation and is texture-mapped, yielding realistic images from arbitrary viewpoints. We also illustrate other applications of the algorithm, including 3D mensuration and stereoscopic 3D movie generation.

  5. Survey data collection using Audio Computer Assisted Self-Interview.

    PubMed

    Jones, Rachel

    2003-04-01

    The Audio Computer Assisted Self-Interview (ACASI) is a computer application that allows a research participant to hear survey interview items over a computer headset and read the corresponding items on a computer monitor. The ACASI automates progression from one item to the next, skipping irrelevant items. The research participant responds by pressing a number keypad, sending the data directly into a database. The ACASI was used to enhance participants' sense of privacy. A convenience sample of 257 young urban women, ages 18 to 29 years, were interviewed in neighborhood settings concerning human immune deficiency virus (HIV) sexual risk behaviors. Notebook computers were used to facilitate mobility. The overwhelming majority rated their experience with ACASI as easy to use. This article will focus on the use of ACASI in HIV behavioral research, its benefits, and approaches to resolve some identified problems with this method of data collection.

  6. Calculating orthologs in bacteria and Archaea: a divide and conquer approach.

    PubMed

    Halachev, Mihail R; Loman, Nicholas J; Pallen, Mark J

    2011-01-01

    Among proteins, orthologs are defined as those that are derived by vertical descent from a single progenitor in the last common ancestor of their host organisms. Our goal is to compute a complete set of protein orthologs derived from all currently available complete bacterial and archaeal genomes. Traditional approaches typically rely on all-against-all BLAST searching which is prohibitively expensive in terms of hardware requirements or computational time (requiring an estimated 18 months or more on a typical server). Here, we present xBASE-Orth, a system for ongoing ortholog annotation, which applies a "divide and conquer" approach and adopts a pragmatic scheme that trades accuracy for speed. Starting at species level, xBASE-Orth carefully constructs and uses pan-genomes as proxies for the full collections of coding sequences at each level as it progressively climbs the taxonomic tree using the previously computed data. This leads to a significant decrease in the number of alignments that need to be performed, which translates into faster computation, making ortholog computation possible on a global scale. Using xBASE-Orth, we analyzed an NCBI collection of 1,288 bacterial and 94 archaeal complete genomes with more than 4 million coding sequences in 5 weeks and predicted more than 700 million ortholog pairs, clustered in 175,531 orthologous groups. We have also identified sets of highly conserved bacterial and archaeal orthologs and in so doing have highlighted anomalies in genome annotation and in the proposed composition of the minimal bacterial genome. In summary, our approach allows for scalable and efficient computation of the bacterial and archaeal ortholog annotations. In addition, due to its hierarchical nature, it is suitable for incorporating novel complete genomes and alternative genome annotations. The computed ortholog data and a continuously evolving set of applications based on it are integrated in the xBASE database, available at http://www.xbase.ac.uk/.

  7. Independent component analysis (ICA) and self-organizing map (SOM) approach to multidetection system for network intruders

    NASA Astrophysics Data System (ADS)

    Abdi, Abdi M.; Szu, Harold H.

    2003-04-01

    With the growing rate of interconnection among computer systems, network security is becoming a real challenge. Intrusion Detection System (IDS) is designed to protect the availability, confidentiality and integrity of critical network information systems. Today"s approach to network intrusion detection involves the use of rule-based expert systems to identify an indication of known attack or anomalies. However, these techniques are less successful in identifying today"s attacks. Hackers are perpetually inventing new and previously unanticipated techniques to compromise information infrastructure. This paper proposes a dynamic way of detecting network intruders on time serious data. The proposed approach consists of a two-step process. Firstly, obtaining an efficient multi-user detection method, employing the recently introduced complexity minimization approach as a generalization of a standard ICA. Secondly, we identified unsupervised learning neural network architecture based on Kohonen"s Self-Organizing Map for potential functional clustering. These two steps working together adaptively will provide a pseudo-real time novelty detection attribute to supplement the current intrusion detection statistical methodology.

  8. Inference of sigma factor controlled networks by using numerical modeling applied to microarray time series data of the germinating prokaryote.

    PubMed

    Strakova, Eva; Zikova, Alice; Vohradsky, Jiri

    2014-01-01

    A computational model of gene expression was applied to a novel test set of microarray time series measurements to reveal regulatory interactions between transcriptional regulators represented by 45 sigma factors and the genes expressed during germination of a prokaryote Streptomyces coelicolor. Using microarrays, the first 5.5 h of the process was recorded in 13 time points, which provided a database of gene expression time series on genome-wide scale. The computational modeling of the kinetic relations between the sigma factors, individual genes and genes clustered according to the similarity of their expression kinetics identified kinetically plausible sigma factor-controlled networks. Using genome sequence annotations, functional groups of genes that were predominantly controlled by specific sigma factors were identified. Using external binding data complementing the modeling approach, specific genes involved in the control of the studied process were identified and their function suggested.

  9. An efficient algorithm for computing fixed length attractors based on bounded model checking in synchronous Boolean networks with biochemical applications.

    PubMed

    Li, X Y; Yang, G W; Zheng, D S; Guo, W S; Hung, W N N

    2015-04-28

    Genetic regulatory networks are the key to understanding biochemical systems. One condition of the genetic regulatory network under different living environments can be modeled as a synchronous Boolean network. The attractors of these Boolean networks will help biologists to identify determinant and stable factors. Existing methods identify attractors based on a random initial state or the entire state simultaneously. They cannot identify the fixed length attractors directly. The complexity of including time increases exponentially with respect to the attractor number and length of attractors. This study used the bounded model checking to quickly locate fixed length attractors. Based on the SAT solver, we propose a new algorithm for efficiently computing the fixed length attractors, which is more suitable for large Boolean networks and numerous attractors' networks. After comparison using the tool BooleNet, empirical experiments involving biochemical systems demonstrated the feasibility and efficiency of our approach.

  10. Identifying seizure onset zone from electrocorticographic recordings: A machine learning approach based on phase locking value.

    PubMed

    Elahian, Bahareh; Yeasin, Mohammed; Mudigoudar, Basanagoud; Wheless, James W; Babajani-Feremi, Abbas

    2017-10-01

    Using a novel technique based on phase locking value (PLV), we investigated the potential for features extracted from electrocorticographic (ECoG) recordings to serve as biomarkers to identify the seizure onset zone (SOZ). We computed the PLV between the phase of the amplitude of high gamma activity (80-150Hz) and the phase of lower frequency rhythms (4-30Hz) from ECoG recordings obtained from 10 patients with epilepsy (21 seizures). We extracted five features from the PLV and used a machine learning approach based on logistic regression to build a model that classifies electrodes as SOZ or non-SOZ. More than 96% of electrodes identified as the SOZ by our algorithm were within the resected area in six seizure-free patients. In four non-seizure-free patients, more than 31% of the identified SOZ electrodes by our algorithm were outside the resected area. In addition, we observed that the seizure outcome in non-seizure-free patients correlated with the number of non-resected SOZ electrodes identified by our algorithm. This machine learning approach, based on features extracted from the PLV, effectively identified electrodes within the SOZ. The approach has the potential to assist clinicians in surgical decision-making when pre-surgical intracranial recordings are utilized. Copyright © 2017 British Epilepsy Association. Published by Elsevier Ltd. All rights reserved.

  11. The Framework for Simulation of Bioinspired Security Mechanisms against Network Infrastructure Attacks

    PubMed Central

    Kotenko, Igor

    2014-01-01

    The paper outlines a bioinspired approach named “network nervous system" and methods of simulation of infrastructure attacks and protection mechanisms based on this approach. The protection mechanisms based on this approach consist of distributed prosedures of information collection and processing, which coordinate the activities of the main devices of a computer network, identify attacks, and determine nessesary countermeasures. Attacks and protection mechanisms are specified as structural models using a set-theoretic approach. An environment for simulation of protection mechanisms based on the biological metaphor is considered; the experiments demonstrating the effectiveness of the protection mechanisms are described. PMID:25254229

  12. DAFi: A directed recursive data filtering and clustering approach for improving and interpreting data clustering identification of cell populations from polychromatic flow cytometry data.

    PubMed

    Lee, Alexandra J; Chang, Ivan; Burel, Julie G; Lindestam Arlehamn, Cecilia S; Mandava, Aishwarya; Weiskopf, Daniela; Peters, Bjoern; Sette, Alessandro; Scheuermann, Richard H; Qian, Yu

    2018-04-17

    Computational methods for identification of cell populations from polychromatic flow cytometry data are changing the paradigm of cytometry bioinformatics. Data clustering is the most common computational approach to unsupervised identification of cell populations from multidimensional cytometry data. However, interpretation of the identified data clusters is labor-intensive. Certain types of user-defined cell populations are also difficult to identify by fully automated data clustering analysis. Both are roadblocks before a cytometry lab can adopt the data clustering approach for cell population identification in routine use. We found that combining recursive data filtering and clustering with constraints converted from the user manual gating strategy can effectively address these two issues. We named this new approach DAFi: Directed Automated Filtering and Identification of cell populations. Design of DAFi preserves the data-driven characteristics of unsupervised clustering for identifying novel cell subsets, but also makes the results interpretable to experimental scientists through mapping and merging the multidimensional data clusters into the user-defined two-dimensional gating hierarchy. The recursive data filtering process in DAFi helped identify small data clusters which are otherwise difficult to resolve by a single run of the data clustering method due to the statistical interference of the irrelevant major clusters. Our experiment results showed that the proportions of the cell populations identified by DAFi, while being consistent with those by expert centralized manual gating, have smaller technical variances across samples than those from individual manual gating analysis and the nonrecursive data clustering analysis. Compared with manual gating segregation, DAFi-identified cell populations avoided the abrupt cut-offs on the boundaries. DAFi has been implemented to be used with multiple data clustering methods including K-means, FLOCK, FlowSOM, and the ClusterR package. For cell population identification, DAFi supports multiple options including clustering, bisecting, slope-based gating, and reversed filtering to meet various autogating needs from different scientific use cases. © 2018 International Society for Advancement of Cytometry. © 2018 International Society for Advancement of Cytometry.

  13. Dynamic isoperimetry and the geometry of Lagrangian coherent structures

    NASA Astrophysics Data System (ADS)

    Froyland, Gary

    2015-10-01

    The study of transport and mixing processes in dynamical systems is particularly important for the analysis of mathematical models of physical systems. We propose a novel, direct geometric method to identify subsets of phase space that remain strongly coherent over a finite time duration. This new method is based on a dynamic extension of classical (static) isoperimetric problems; the latter are concerned with identifying submanifolds with the smallest boundary size relative to their volume. The present work introduces dynamic isoperimetric problems; the study of sets with small boundary size relative to volume as they are evolved by a general dynamical system. We formulate and prove dynamic versions of the fundamental (static) isoperimetric (in)equalities; a dynamic Federer-Fleming theorem and a dynamic Cheeger inequality. We introduce a new dynamic Laplace operator and describe a computational method to identify coherent sets based on eigenfunctions of the dynamic Laplacian. Our results include formal mathematical statements concerning geometric properties of finite-time coherent sets, whose boundaries can be regarded as Lagrangian coherent structures. The computational advantages of our new approach are a well-separated spectrum for the dynamic Laplacian, and flexibility in appropriate numerical approximation methods. Finally, we demonstrate that the dynamic Laplace operator can be realised as a zero-diffusion limit of a newly advanced probabilistic transfer operator method [9] for finding coherent sets, which is based on small diffusion. Thus, the present approach sits naturally alongside the probabilistic approach [9], and adds a formal geometric interpretation.

  14. Maximal Predictability Approach for Identifying the Right Descriptors for Electrocatalytic Reactions.

    PubMed

    Krishnamurthy, Dilip; Sumaria, Vaidish; Viswanathan, Venkatasubramanian

    2018-02-01

    Density functional theory (DFT) calculations are being routinely used to identify new material candidates that approach activity near fundamental limits imposed by thermodynamics or scaling relations. DFT calculations are associated with inherent uncertainty, which limits the ability to delineate materials (distinguishability) that possess high activity. Development of error-estimation capabilities in DFT has enabled uncertainty propagation through activity-prediction models. In this work, we demonstrate an approach to propagating uncertainty through thermodynamic activity models leading to a probability distribution of the computed activity and thereby its expectation value. A new metric, prediction efficiency, is defined, which provides a quantitative measure of the ability to distinguish activity of materials and can be used to identify the optimal descriptor(s) ΔG opt . We demonstrate the framework for four important electrochemical reactions: hydrogen evolution, chlorine evolution, oxygen reduction and oxygen evolution. Future studies could utilize expected activity and prediction efficiency to significantly improve the prediction accuracy of highly active material candidates.

  15. A robust automated left ventricle region of interest localization technique using a cardiac cine MRI atlas

    NASA Astrophysics Data System (ADS)

    Ben-Zikri, Yehuda Kfir; Linte, Cristian A.

    2016-03-01

    Region of interest detection is a precursor to many medical image processing and analysis applications, including segmentation, registration and other image manipulation techniques. The optimal region of interest is often selected manually, based on empirical knowledge and features of the image dataset. However, if inconsistently identified, the selected region of interest may greatly affect the subsequent image analysis or interpretation steps, in turn leading to incomplete assessment during computer-aided diagnosis or incomplete visualization or identification of the surgical targets, if employed in the context of pre-procedural planning or image-guided interventions. Therefore, the need for robust, accurate and computationally efficient region of interest localization techniques is prevalent in many modern computer-assisted diagnosis and therapy applications. Here we propose a fully automated, robust, a priori learning-based approach that provides reliable estimates of the left and right ventricle features from cine cardiac MR images. The proposed approach leverages the temporal frame-to-frame motion extracted across a range of short axis left ventricle slice images with small training set generated from les than 10% of the population. This approach is based on histogram of oriented gradients features weighted by local intensities to first identify an initial region of interest depicting the left and right ventricles that exhibits the greatest extent of cardiac motion. This region is correlated with the homologous region that belongs to the training dataset that best matches the test image using feature vector correlation techniques. Lastly, the optimal left ventricle region of interest of the test image is identified based on the correlation of known ground truth segmentations associated with the training dataset deemed closest to the test image. The proposed approach was tested on a population of 100 patient datasets and was validated against the ground truth region of interest of the test images manually annotated by experts. This tool successfully identified a mask around the LV and RV and furthermore the minimal region of interest around the LV that fully enclosed the left ventricle from all testing datasets, yielding a 98% overlap with their corresponding ground truth. The achieved mean absolute distance error between the two contours that normalized by the radius of the ground truth is 0.20 +/- 0.09.

  16. A Socio-Technical Approach to Preventing, Mitigating, and Recovering from Ransomware Attacks.

    PubMed

    Sittig, Dean F; Singh, Hardeep

    2016-01-01

    Recently there have been several high-profile ransomware attacks involving hospitals around the world. Ransomware is intended to damage or disable a user's computer unless the user makes a payment. Once the attack has been launched, users have three options: 1) try to restore their data from backup; 2) pay the ransom; or 3) lose their data. In this manuscript, we discuss a socio-technical approach to address ransomware and outline four overarching steps that organizations can undertake to secure an electronic health record (EHR) system and the underlying computing infrastructure. First, health IT professionals need to ensure adequate system protection by correctly installing and configuring computers and networks that connect them. Next, the health care organizations need to ensure more reliable system defense by implementing user-focused strategies, including simulation and training on correct and complete use of computers and network applications. Concomitantly, the organization needs to monitor computer and application use continuously in an effort to detect suspicious activities and identify and address security problems before they cause harm. Finally, organizations need to respond adequately to and recover quickly from ransomware attacks and take actions to prevent them in future. We also elaborate on recommendations from other authoritative sources, including the National Institute of Standards and Technology (NIST). Similar to approaches to address other complex socio-technical health IT challenges, the responsibility of preventing, mitigating, and recovering from these attacks is shared between health IT professionals and end-users.

  17. A Socio-Technical Approach to Preventing, Mitigating, and Recovering from Ransomware Attacks

    PubMed Central

    Singh, Hardeep

    2016-01-01

    Summary Recently there have been several high-profile ransomware attacks involving hospitals around the world. Ransomware is intended to damage or disable a user’s computer unless the user makes a payment. Once the attack has been launched, users have three options: 1) try to restore their data from backup; 2) pay the ransom; or 3) lose their data. In this manuscript, we discuss a socio-technical approach to address ransomware and outline four overarching steps that organizations can undertake to secure an electronic health record (EHR) system and the underlying computing infrastructure. First, health IT professionals need to ensure adequate system protection by correctly installing and configuring computers and networks that connect them. Next, the health care organizations need to ensure more reliable system defense by implementing user-focused strategies, including simulation and training on correct and complete use of computers and network applications. Concomitantly, the organization needs to monitor computer and application use continuously in an effort to detect suspicious activities and identify and address security problems before they cause harm. Finally, organizations need to respond adequately to and recover quickly from ransomware attacks and take actions to prevent them in future. We also elaborate on recommendations from other authoritative sources, including the National Institute of Standards and Technology (NIST). Similar to approaches to address other complex socio-technical health IT challenges, the responsibility of preventing, mitigating, and recovering from these attacks is shared between health IT professionals and end-users. PMID:27437066

  18. Visualizing complex processes using a cognitive-mapping tool to support the learning of clinical reasoning.

    PubMed

    Wu, Bian; Wang, Minhong; Grotzer, Tina A; Liu, Jun; Johnson, Janice M

    2016-08-22

    Practical experience with clinical cases has played an important role in supporting the learning of clinical reasoning. However, learning through practical experience involves complex processes difficult to be captured by students. This study aimed to examine the effects of a computer-based cognitive-mapping approach that helps students to externalize the reasoning process and the knowledge underlying the reasoning process when they work with clinical cases. A comparison between the cognitive-mapping approach and the verbal-text approach was made by analyzing their effects on learning outcomes. Fifty-two third-year or higher students from two medical schools participated in the study. Students in the experimental group used the computer-base cognitive-mapping approach, while the control group used the verbal-text approach, to make sense of their thinking and actions when they worked with four simulated cases over 4 weeks. For each case, students in both groups reported their reasoning process (involving data capture, hypotheses formulation, and reasoning with justifications) and the underlying knowledge (involving identified concepts and the relationships between the concepts) using the given approach. The learning products (cognitive maps or verbal text) revealed that students in the cognitive-mapping group outperformed those in the verbal-text group in the reasoning process, but not in making sense of the knowledge underlying the reasoning process. No significant differences were found in a knowledge posttest between the two groups. The computer-based cognitive-mapping approach has shown a promising advantage over the verbal-text approach in improving students' reasoning performance. Further studies are needed to examine the effects of the cognitive-mapping approach in improving the construction of subject-matter knowledge on the basis of practical experience.

  19. Recent trends in digital human modeling and the concurrent issues that face human modeling approach

    NASA Technical Reports Server (NTRS)

    Rajulu, Sudhakar; Gonzalez, L. Javier; Margerum, Sarah; Clowers, Kurt; Moreny, Richard; Abercomby, Andrew; Velasquez, Luis

    2006-01-01

    Tremendous strides have been made in the recent years to digitally represent human beings in computer simulation models ranging from assembly plant maintenance operations to occupants getting in and out of vehicles to action movie scenarios. While some of these tools are being actively pursued by the engineering communities, there is still a lot of work that remains to be done for the newly planned planetary exploration missions. For example, certain unique and several common challenges are seen in developing computer generated suited human models for designing the next generation space vehicle. The purpose of this presentation is to discuss NASA s potential needs for better human models and to show also many of the inherent yet not too obvious pitfalls that still are left unresolved in this new arena of digital human modeling. As part of NASA s Habitability and Human Factors Branch, the Anthropometry and Biomechanics Facility has been engaged in studying the various facets of computer generated human physical performance models; for instance, it has been engaged in utilizing three-dimensional laser scan data along with three dimensional video based motion and reach data to gather suited anthropometric and shape and size information that are not available yet in the form of computer mannequins. Our goal is to bring in new approaches to deal with heavily clothed humans (such as, suited astronauts) and to overcome the current limitations of wrongly identifying humans (either real or virtual) as univariate percentiles. We are looking at whole-body posture based anthropometric models as a means to identify humans of significantly different shapes and sizes to arrive at mathematically sound computer models for analytical purposes.

  20. A biomarker-based screen of a gene expression compendium reveals regulation of Nrf2 by CAR and STAT5b

    EPA Science Inventory

    Computational approaches were developed to identify factors that regulate Nrf2 in a large gene expression compendium of microarray profiles including >2000 comparisons which queried the effects of chemicals, genes, diets, and infectious agents on gene expression in the mouse l...

  1. A Risk-Analysis Approach to Implementing Web-Based Assessment

    ERIC Educational Resources Information Center

    Ricketts, Chris; Zakrzewski, Stan

    2005-01-01

    Computer-Based Assessment is a risky business. This paper proposes the use of a model for web-based assessment systems that identifies pedagogic, operational, technical (non web-based), web-based and financial risks. The strategies and procedures for risk elimination or reduction arise from risk analysis and management and are the means by which…

  2. An Achievement Degree Analysis Approach to Identifying Learning Problems in Object-Oriented Programming

    ERIC Educational Resources Information Center

    Allinjawi, Arwa A.; Al-Nuaim, Hana A.; Krause, Paul

    2014-01-01

    Students often face difficulties while learning object-oriented programming (OOP) concepts. Many papers have presented various assessment methods for diagnosing learning problems to improve the teaching of programming in computer science (CS) higher education. The research presented in this article illustrates that although max-min composition is…

  3. Five C Framework: A Student-Centered Approach for Teaching Programming Courses to Students with Diverse Disciplinary Background

    ERIC Educational Resources Information Center

    Tom, Mary

    2015-01-01

    The already existing complexities of teaching and learning computer programming are increased where students are diverse in their disciplinary backgrounds, language skills, and culture. Learners experience emotional issues of anxiety, fear or boredom. Identifying opportunities for improvement and applying theoretical and empirical evidence found…

  4. Correlating Trainee Attributes to Performance in 3D CAD Training

    ERIC Educational Resources Information Center

    Hamade, Ramsey F.; Artail, Hassan A.; Sikstrom, Sverker

    2007-01-01

    Purpose: The purpose of this exploratory study is to identify trainee attributes relevant for development of skills in 3D computer-aided design (CAD). Design/methodology/approach: Participants were trained to perform cognitive tasks of comparable complexity over time. Performance data were collected on the time needed to construct test models, and…

  5. Log Defect Recognition Using CT-images and Neural Net Classifiers

    Treesearch

    Daniel L. Schmoldt; Pei Li; A. Lynn Abbott

    1995-01-01

    Although several approaches have been introduced to automatically identify internal log defects using computed tomography (CT) imagery, most of these have been feasibility efforts and consequently have had several limitations: (1) reports of classification accuracy are largely subjective, not statistical, (2) there has been no attempt to achieve real-time operation,...

  6. Unsupervised Approaches for Post-Processing in Computationally Efficient Waveform-Similarity-Based Earthquake Detection

    NASA Astrophysics Data System (ADS)

    Bergen, K.; Yoon, C. E.; OReilly, O. J.; Beroza, G. C.

    2015-12-01

    Recent improvements in computational efficiency for waveform correlation-based detections achieved by new methods such as Fingerprint and Similarity Thresholding (FAST) promise to allow large-scale blind search for similar waveforms in long-duration continuous seismic data. Waveform similarity search applied to datasets of months to years of continuous seismic data will identify significantly more events than traditional detection methods. With the anticipated increase in number of detections and associated increase in false positives, manual inspection of the detection results will become infeasible. This motivates the need for new approaches to process the output of similarity-based detection. We explore data mining techniques for improved detection post-processing. We approach this by considering similarity-detector output as a sparse similarity graph with candidate events as vertices and similarities as weighted edges. Image processing techniques are leveraged to define candidate events and combine results individually processed at multiple stations. Clustering and graph analysis methods are used to identify groups of similar waveforms and assign a confidence score to candidate detections. Anomaly detection and classification are applied to waveform data for additional false detection removal. A comparison of methods will be presented and their performance will be demonstrated on a suspected induced and non-induced earthquake sequence.

  7. Identification of sequence-structure RNA binding motifs for SELEX-derived aptamers.

    PubMed

    Hoinka, Jan; Zotenko, Elena; Friedman, Adam; Sauna, Zuben E; Przytycka, Teresa M

    2012-06-15

    Systematic Evolution of Ligands by EXponential Enrichment (SELEX) represents a state-of-the-art technology to isolate single-stranded (ribo)nucleic acid fragments, named aptamers, which bind to a molecule (or molecules) of interest via specific structural regions induced by their sequence-dependent fold. This powerful method has applications in designing protein inhibitors, molecular detection systems, therapeutic drugs and antibody replacement among others. However, full understanding and consequently optimal utilization of the process has lagged behind its wide application due to the lack of dedicated computational approaches. At the same time, the combination of SELEX with novel sequencing technologies is beginning to provide the data that will allow the examination of a variety of properties of the selection process. To close this gap we developed, Aptamotif, a computational method for the identification of sequence-structure motifs in SELEX-derived aptamers. To increase the chances of identifying functional motifs, Aptamotif uses an ensemble-based approach. We validated the method using two published aptamer datasets containing experimentally determined motifs of increasing complexity. We were able to recreate the author's findings to a high degree, thus proving the capability of our approach to identify binding motifs in SELEX data. Additionally, using our new experimental dataset, we illustrate the application of Aptamotif to elucidate several properties of the selection process.

  8. Hybrid CFD/CAA Modeling for Liftoff Acoustic Predictions

    NASA Technical Reports Server (NTRS)

    Strutzenberg, Louise L.; Liever, Peter A.

    2011-01-01

    This paper presents development efforts at the NASA Marshall Space flight Center to establish a hybrid Computational Fluid Dynamics and Computational Aero-Acoustics (CFD/CAA) simulation system for launch vehicle liftoff acoustics environment analysis. Acoustic prediction engineering tools based on empirical jet acoustic strength and directivity models or scaled historical measurements are of limited value in efforts to proactively design and optimize launch vehicles and launch facility configurations for liftoff acoustics. CFD based modeling approaches are now able to capture the important details of vehicle specific plume flow environment, identifY the noise generation sources, and allow assessment of the influence of launch pad geometric details and sound mitigation measures such as water injection. However, CFD methodologies are numerically too dissipative to accurately capture the propagation of the acoustic waves in the large CFD models. The hybrid CFD/CAA approach combines the high-fidelity CFD analysis capable of identifYing the acoustic sources with a fast and efficient Boundary Element Method (BEM) that accurately propagates the acoustic field from the source locations. The BEM approach was chosen for its ability to properly account for reflections and scattering of acoustic waves from launch pad structures. The paper will present an overview of the technology components of the CFD/CAA framework and discuss plans for demonstration and validation against test data.

  9. Integrative Computational Network Analysis Reveals Site-Specific Mediators of Inflammation in Alzheimer's Disease

    PubMed Central

    Ravichandran, Srikanth; Michelucci, Alessandro; del Sol, Antonio

    2018-01-01

    Alzheimer's disease (AD) is a major neurodegenerative disease and is one of the most common cause of dementia in older adults. Among several factors, neuroinflammation is known to play a critical role in the pathogenesis of chronic neurodegenerative diseases. In particular, studies of brains affected by AD show a clear involvement of several inflammatory pathways. Furthermore, depending on the brain regions affected by the disease, the nature and the effect of inflammation can vary. Here, in order to shed more light on distinct and common features of inflammation in different brain regions affected by AD, we employed a computational approach to analyze gene expression data of six site-specific neuronal populations from AD patients. Our network based computational approach is driven by the concept that a sustained inflammatory environment could result in neurotoxicity leading to the disease. Thus, our method aims to infer intracellular signaling pathways/networks that are likely to be constantly activated or inhibited due to persistent inflammatory conditions. The computational analysis identified several inflammatory mediators, such as tumor necrosis factor alpha (TNF-a)-associated pathway, as key upstream receptors/ligands that are likely to transmit sustained inflammatory signals. Further, the analysis revealed that several inflammatory mediators were mainly region specific with few commonalities across different brain regions. Taken together, our results show that our integrative approach aids identification of inflammation-related signaling pathways that could be responsible for the onset or the progression of AD and can be applied to study other neurodegenerative diseases. Furthermore, such computational approaches can enable the translation of clinical omics data toward the development of novel therapeutic strategies for neurodegenerative diseases. PMID:29551980

  10. Integrative Computational Network Analysis Reveals Site-Specific Mediators of Inflammation in Alzheimer's Disease.

    PubMed

    Ravichandran, Srikanth; Michelucci, Alessandro; Del Sol, Antonio

    2018-01-01

    Alzheimer's disease (AD) is a major neurodegenerative disease and is one of the most common cause of dementia in older adults. Among several factors, neuroinflammation is known to play a critical role in the pathogenesis of chronic neurodegenerative diseases. In particular, studies of brains affected by AD show a clear involvement of several inflammatory pathways. Furthermore, depending on the brain regions affected by the disease, the nature and the effect of inflammation can vary. Here, in order to shed more light on distinct and common features of inflammation in different brain regions affected by AD, we employed a computational approach to analyze gene expression data of six site-specific neuronal populations from AD patients. Our network based computational approach is driven by the concept that a sustained inflammatory environment could result in neurotoxicity leading to the disease. Thus, our method aims to infer intracellular signaling pathways/networks that are likely to be constantly activated or inhibited due to persistent inflammatory conditions. The computational analysis identified several inflammatory mediators, such as tumor necrosis factor alpha (TNF-a)-associated pathway, as key upstream receptors/ligands that are likely to transmit sustained inflammatory signals. Further, the analysis revealed that several inflammatory mediators were mainly region specific with few commonalities across different brain regions. Taken together, our results show that our integrative approach aids identification of inflammation-related signaling pathways that could be responsible for the onset or the progression of AD and can be applied to study other neurodegenerative diseases. Furthermore, such computational approaches can enable the translation of clinical omics data toward the development of novel therapeutic strategies for neurodegenerative diseases.

  11. Machine learning bandgaps of double perovskites

    PubMed Central

    Pilania, G.; Mannodi-Kanakkithodi, A.; Uberuaga, B. P.; Ramprasad, R.; Gubernatis, J. E.; Lookman, T.

    2016-01-01

    The ability to make rapid and accurate predictions on bandgaps of double perovskites is of much practical interest for a range of applications. While quantum mechanical computations for high-fidelity bandgaps are enormously computation-time intensive and thus impractical in high throughput studies, informatics-based statistical learning approaches can be a promising alternative. Here we demonstrate a systematic feature-engineering approach and a robust learning framework for efficient and accurate predictions of electronic bandgaps of double perovskites. After evaluating a set of more than 1.2 million features, we identify lowest occupied Kohn-Sham levels and elemental electronegativities of the constituent atomic species as the most crucial and relevant predictors. The developed models are validated and tested using the best practices of data science and further analyzed to rationalize their prediction performance. PMID:26783247

  12. Recent developments in structural proteomics for protein structure determination.

    PubMed

    Liu, Hsuan-Liang; Hsu, Jyh-Ping

    2005-05-01

    The major challenges in structural proteomics include identifying all the proteins on the genome-wide scale, determining their structure-function relationships, and outlining the precise three-dimensional structures of the proteins. Protein structures are typically determined by experimental approaches such as X-ray crystallography or nuclear magnetic resonance (NMR) spectroscopy. However, the knowledge of three-dimensional space by these techniques is still limited. Thus, computational methods such as comparative and de novo approaches and molecular dynamic simulations are intensively used as alternative tools to predict the three-dimensional structures and dynamic behavior of proteins. This review summarizes recent developments in structural proteomics for protein structure determination; including instrumental methods such as X-ray crystallography and NMR spectroscopy, and computational methods such as comparative and de novo structure prediction and molecular dynamics simulations.

  13. A Deep Learning Approach to Neuroanatomical Characterisation of Alzheimer's Disease.

    PubMed

    Ambastha, Abhinit Kumar; Leong, Tze-Yun

    2017-01-01

    Alzheimer's disease (AD) is a neurological degenerative disorder that leads to progressive mental deterioration. This work introduces a computational approach to improve our understanding of the progression of AD. We use ensemble learning methods and deep neural networks to identify salient structural correlations among brain regions that degenerate together in AD; this provides an understanding of how AD progresses in the brain. The proposed technique has a classification accuracy of 81.79% for AD against healthy subjects using a single modality imaging dataset.

  14. Mechanotransduction and the Cytoskeleton

    NASA Technical Reports Server (NTRS)

    Pickard, Barbara G.

    1996-01-01

    Two intellectual developments in the lab suggested a more powerful approach to the problem defined in the original statement. These were discussed with the "Program Chief', who agreed that an alteration of approach was desirable. Second expansion of our work was on the computational optical sectioning microscope (COSM). Additionally, we hoped to visualize the channels with respect to cytoskeletal entities. We have identified major here-to-fore unknown cytoskeletal structure in our representative experimental system, the onion epidermal cell, and have named this structure the endomembrane grant.

  15. Quantifiable and objective approach to organizational performance enhancement.

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

    Scholand, Andrew Joseph; Tausczik, Yla R.

    This report describes a new methodology, social language network analysis (SLNA), that combines tools from social language processing and network analysis to identify socially situated relationships between individuals which, though subtle, are highly influential. Specifically, SLNA aims to identify and characterize the nature of working relationships by processing artifacts generated with computer-mediated communication systems, such as instant message texts or emails. Because social language processing is able to identify psychological, social, and emotional processes that individuals are not able to fully mask, social language network analysis can clarify and highlight complex interdependencies between group members, even when these relationships aremore » latent or unrecognized. This report outlines the philosophical antecedents of SLNA, the mechanics of preprocessing, processing, and post-processing stages, and some example results obtained by applying this approach to a 15-month corporate discussion archive.« less

  16. Computer-Aided Drug Discovery Approaches against the Tropical Infectious Diseases Malaria, Tuberculosis, Trypanosomiasis, and Leishmaniasis.

    PubMed

    Njogu, Peter M; Guantai, Eric M; Pavadai, Elumalai; Chibale, Kelly

    2016-01-08

    Despite the tremendous improvement in overall global health heralded by the adoption of the Millennium Declaration in the year 2000, tropical infections remain a major health problem in the developing world. Recent estimates indicate that the major tropical infectious diseases, namely, malaria, tuberculosis, trypanosomiasis, and leishmaniasis, account for more than 2.2 million deaths and a loss of approximately 85 million disability-adjusted life years annually. The crucial role of chemotherapy in curtailing the deleterious health and economic impacts of these infections has invigorated the search for new drugs against tropical infectious diseases. The research efforts have involved increased application of computational technologies in mainstream drug discovery programs at the hit identification, hit-to-lead, and lead optimization stages. This review highlights various computer-aided drug discovery approaches that have been utilized in efforts to identify novel antimalarial, antitubercular, antitrypanosomal, and antileishmanial agents. The focus is largely on developments over the past 5 years (2010-2014).

  17. Applying Mathematical Optimization Methods to an ACT-R Instance-Based Learning Model.

    PubMed

    Said, Nadia; Engelhart, Michael; Kirches, Christian; Körkel, Stefan; Holt, Daniel V

    2016-01-01

    Computational models of cognition provide an interface to connect advanced mathematical tools and methods to empirically supported theories of behavior in psychology, cognitive science, and neuroscience. In this article, we consider a computational model of instance-based learning, implemented in the ACT-R cognitive architecture. We propose an approach for obtaining mathematical reformulations of such cognitive models that improve their computational tractability. For the well-established Sugar Factory dynamic decision making task, we conduct a simulation study to analyze central model parameters. We show how mathematical optimization techniques can be applied to efficiently identify optimal parameter values with respect to different optimization goals. Beyond these methodological contributions, our analysis reveals the sensitivity of this particular task with respect to initial settings and yields new insights into how average human performance deviates from potential optimal performance. We conclude by discussing possible extensions of our approach as well as future steps towards applying more powerful derivative-based optimization methods.

  18. Big Data: Next-Generation Machines for Big Science

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

    Hack, James J.; Papka, Michael E.

    Addressing the scientific grand challenges identified by the US Department of Energy’s (DOE’s) Office of Science’s programs alone demands a total leadership-class computing capability of 150 to 400 Pflops by the end of this decade. The successors to three of the DOE’s most powerful leadership-class machines are set to arrive in 2017 and 2018—the products of the Collaboration Oak Ridge Argonne Livermore (CORAL) initiative, a national laboratory–industry design/build approach to engineering nextgeneration petascale computers for grand challenge science. These mission-critical machines will enable discoveries in key scientific fields such as energy, biotechnology, nanotechnology, materials science, and high-performance computing, and servemore » as a milestone on the path to deploying exascale computing capabilities.« less

  19. Pivotal role of computers and software in mass spectrometry - SEQUEST and 20 years of tandem MS database searching.

    PubMed

    Yates, John R

    2015-11-01

    Advances in computer technology and software have driven developments in mass spectrometry over the last 50 years. Computers and software have been impactful in three areas: the automation of difficult calculations to aid interpretation, the collection of data and control of instruments, and data interpretation. As the power of computers has grown, so too has the utility and impact on mass spectrometers and their capabilities. This has been particularly evident in the use of tandem mass spectrometry data to search protein and nucleotide sequence databases to identify peptide and protein sequences. This capability has driven the development of many new approaches to study biological systems, including the use of "bottom-up shotgun proteomics" to directly analyze protein mixtures. Graphical Abstract ᅟ.

  20. Combining on-chip synthesis of a focused combinatorial library with computational target prediction reveals imidazopyridine GPCR ligands.

    PubMed

    Reutlinger, Michael; Rodrigues, Tiago; Schneider, Petra; Schneider, Gisbert

    2014-01-07

    Using the example of the Ugi three-component reaction we report a fast and efficient microfluidic-assisted entry into the imidazopyridine scaffold, where building block prioritization was coupled to a new computational method for predicting ligand-target associations. We identified an innovative GPCR-modulating combinatorial chemotype featuring ligand-efficient adenosine A1/2B and adrenergic α1A/B receptor antagonists. Our results suggest the tight integration of microfluidics-assisted synthesis with computer-based target prediction as a viable approach to rapidly generate bioactivity-focused combinatorial compound libraries with high success rates. Copyright © 2014 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

  1. Space-time programming.

    PubMed

    Beal, Jacob; Viroli, Mirko

    2015-07-28

    Computation increasingly takes place not on an individual device, but distributed throughout a material or environment, whether it be a silicon surface, a network of wireless devices, a collection of biological cells or a programmable material. Emerging programming models embrace this reality and provide abstractions inspired by physics, such as computational fields, that allow such systems to be programmed holistically, rather than in terms of individual devices. This paper aims to provide a unified approach for the investigation and engineering of computations programmed with the aid of space-time abstractions, by bringing together a number of recent results, as well as to identify critical open problems. © 2015 The Author(s) Published by the Royal Society. All rights reserved.

  2. ANTLR Tree Grammar Generator and Extensions

    NASA Technical Reports Server (NTRS)

    Craymer, Loring

    2005-01-01

    A computer program implements two extensions of ANTLR (Another Tool for Language Recognition), which is a set of software tools for translating source codes between different computing languages. ANTLR supports predicated- LL(k) lexer and parser grammars, a notation for annotating parser grammars to direct tree construction, and predicated tree grammars. [ LL(k) signifies left-right, leftmost derivation with k tokens of look-ahead, referring to certain characteristics of a grammar.] One of the extensions is a syntax for tree transformations. The other extension is the generation of tree grammars from annotated parser or input tree grammars. These extensions can simplify the process of generating source-to-source language translators and they make possible an approach, called "polyphase parsing," to translation between computing languages. The typical approach to translator development is to identify high-level semantic constructs such as "expressions," "declarations," and "definitions" as fundamental building blocks in the grammar specification used for language recognition. The polyphase approach is to lump ambiguous syntactic constructs during parsing and then disambiguate the alternatives in subsequent tree transformation passes. Polyphase parsing is believed to be useful for generating efficient recognizers for C++ and other languages that, like C++, have significant ambiguities.

  3. Benchmarking Brain-Computer Interfaces Outside the Laboratory: The Cybathlon 2016

    PubMed Central

    Novak, Domen; Sigrist, Roland; Gerig, Nicolas J.; Wyss, Dario; Bauer, René; Götz, Ulrich; Riener, Robert

    2018-01-01

    This paper presents a new approach to benchmarking brain-computer interfaces (BCIs) outside the lab. A computer game was created that mimics a real-world application of assistive BCIs, with the main outcome metric being the time needed to complete the game. This approach was used at the Cybathlon 2016, a competition for people with disabilities who use assistive technology to achieve tasks. The paper summarizes the technical challenges of BCIs, describes the design of the benchmarking game, then describes the rules for acceptable hardware, software and inclusion of human pilots in the BCI competition at the Cybathlon. The 11 participating teams, their approaches, and their results at the Cybathlon are presented. Though the benchmarking procedure has some limitations (for instance, we were unable to identify any factors that clearly contribute to BCI performance), it can be successfully used to analyze BCI performance in realistic, less structured conditions. In the future, the parameters of the benchmarking game could be modified to better mimic different applications (e.g., the need to use some commands more frequently than others). Furthermore, the Cybathlon has the potential to showcase such devices to the general public. PMID:29375294

  4. Dynamically Reconfigurable Approach to Multidisciplinary Problems

    NASA Technical Reports Server (NTRS)

    Alexandrov, Natalie M.; Lewis, Robert Michael

    2003-01-01

    The complexity and autonomy of the constituent disciplines and the diversity of the disciplinary data formats make the task of integrating simulations into a multidisciplinary design optimization problem extremely time-consuming and difficult. We propose a dynamically reconfigurable approach to MDO problem formulation wherein an appropriate implementation of the disciplinary information results in basic computational components that can be combined into different MDO problem formulations and solution algorithms, including hybrid strategies, with relative ease. The ability to re-use the computational components is due to the special structure of the MDO problem. We believe that this structure can and should be used to formulate and solve optimization problems in the multidisciplinary context. The present work identifies the basic computational components in several MDO problem formulations and examines the dynamically reconfigurable approach in the context of a popular class of optimization methods. We show that if the disciplinary sensitivity information is implemented in a modular fashion, the transfer of sensitivity information among the formulations under study is straightforward. This enables not only experimentation with a variety of problem formations in a research environment, but also the flexible use of formulations in a production design environment.

  5. 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

  6. Structural identifiability of cyclic graphical models of biological networks with latent variables.

    PubMed

    Wang, Yulin; Lu, Na; Miao, Hongyu

    2016-06-13

    Graphical models have long been used to describe biological networks for a variety of important tasks such as the determination of key biological parameters, and the structure of graphical model ultimately determines whether such unknown parameters can be unambiguously obtained from experimental observations (i.e., the identifiability problem). Limited by resources or technical capacities, complex biological networks are usually partially observed in experiment, which thus introduces latent variables into the corresponding graphical models. A number of previous studies have tackled the parameter identifiability problem for graphical models such as linear structural equation models (SEMs) with or without latent variables. However, the limited resolution and efficiency of existing approaches necessarily calls for further development of novel structural identifiability analysis algorithms. An efficient structural identifiability analysis algorithm is developed in this study for a broad range of network structures. The proposed method adopts the Wright's path coefficient method to generate identifiability equations in forms of symbolic polynomials, and then converts these symbolic equations to binary matrices (called identifiability matrix). Several matrix operations are introduced for identifiability matrix reduction with system equivalency maintained. Based on the reduced identifiability matrices, the structural identifiability of each parameter is determined. A number of benchmark models are used to verify the validity of the proposed approach. Finally, the network module for influenza A virus replication is employed as a real example to illustrate the application of the proposed approach in practice. The proposed approach can deal with cyclic networks with latent variables. The key advantage is that it intentionally avoids symbolic computation and is thus highly efficient. Also, this method is capable of determining the identifiability of each single parameter and is thus of higher resolution in comparison with many existing approaches. Overall, this study provides a basis for systematic examination and refinement of graphical models of biological networks from the identifiability point of view, and it has a significant potential to be extended to more complex network structures or high-dimensional systems.

  7. Developing an evidence-based curriculum designed to help psychiatric nurses learn to use computers and the Internet.

    PubMed

    Koivunen, Marita; Välimäki, Maritta; Jakobsson, Tiina; Pitkänen, Anneli

    2008-01-01

    This article describes the systematic process in which an evidence-based approach was used to develop a curriculum designed to support the computer and Internet skills of nurses in psychiatric hospitals in Finland. The pressure on organizations to have skilled and motivated nurses who use modern information and communication technology in health care organizations has increased due to rapid technology development at the international and national levels. However, less frequently has the development of those computer education curricula been based on evidence-based knowledge. First, we identified psychiatric nurses' learning experiences and barriers to computer use by examining written essays. Second, nurses' computer skills were surveyed. Last, evidence from the literature was scrutinized to find effective methods that can be used to teach and learn computer use in health care. This information was integrated and used for the development process of an education curriculum designed to support nurses' computer and Internet skills.

  8. Simulation of Detecting Damage in Composite Stiffened Panel Using Lamb Waves

    NASA Technical Reports Server (NTRS)

    Wang, John T.; Ross, Richard W.; Huang, Guo L.; Yuan, Fuh G.

    2013-01-01

    Lamb wave damage detection in a composite stiffened panel is simulated by performing explicit transient dynamic finite element analyses and using signal imaging techniques. This virtual test process does not need to use real structures, actuators/sensors, or laboratory equipment. Quasi-isotropic laminates are used for the stiffened panels. Two types of damage are studied. One type is a damage in the skin bay and the other type is a debond between the stiffener flange and the skin. Innovative approaches for identifying the damage location and imaging the damage were developed. The damage location is identified by finding the intersection of the damage locus and the path of the time reversal wave packet re-emitted from the sensor nodes. The damage locus is a circle that envelops the potential damage locations. Its center is at the actuator location and its radius is computed by multiplying the group velocity by the time of flight to damage. To create a damage image for estimating the size of damage, a group of nodes in the neighborhood of the damage location is identified for applying an image condition. The image condition, computed at a finite element node, is the zero-lag cross-correlation (ZLCC) of the time-reversed incident wave signal and the time reversal wave signal from the sensor nodes. This damage imaging process is computationally efficient since only the ZLCC values of a small amount of nodes in the neighborhood of the identified damage location are computed instead of those of the full model.

  9. Identification of key ancestors of modern germplasm in a breeding program of maize.

    PubMed

    Technow, F; Schrag, T A; Schipprack, W; Melchinger, A E

    2014-12-01

    Probabilities of gene origin computed from the genomic kinships matrix can accurately identify key ancestors of modern germplasms Identifying the key ancestors of modern plant breeding populations can provide valuable insights into the history of a breeding program and provide reference genomes for next generation whole genome sequencing. In an animal breeding context, a method was developed that employs probabilities of gene origin, computed from the pedigree-based additive kinship matrix, for identifying key ancestors. Because reliable and complete pedigree information is often not available in plant breeding, we replaced the additive kinship matrix with the genomic kinship matrix. As a proof-of-concept, we applied this approach to simulated data sets with known ancestries. The relative contribution of the ancestral lines to later generations could be determined with high accuracy, with and without selection. Our method was subsequently used for identifying the key ancestors of the modern Dent germplasm of the public maize breeding program of the University of Hohenheim. We found that the modern germplasm can be traced back to six or seven key ancestors, with one or two of them having a disproportionately large contribution. These results largely corroborated conjectures based on early records of the breeding program. We conclude that probabilities of gene origin computed from the genomic kinships matrix can be used for identifying key ancestors in breeding programs and estimating the proportion of genes contributed by them.

  10. All Paths Lead to TRIM25.

    PubMed

    Zhou, Hengbo; Costello, James C

    2017-10-01

    Identifying key factors that regulate the transition from primary to metastatic cancer is a fundamental challenge. Walsh et al. took a systems biology approach integrating computational, in vitro, and in vivo experiments to identify TRIM25 (tripartite motif containing 25) as a key factor that regulates metastatic gene signatures both at the transcriptional and post-transcriptional level in breast cancer. Targeting TRIM25 therapeutically is attractive because it governs a broad set of coordinated transcriptional modules that dictate metastatic progression. Copyright © 2017 Elsevier Inc. All rights reserved.

  11. Novel Codon Insert in HIV Type 1 Clade B Reverse Transcriptase Associated with Low-Level Viremia During Antiretroviral Therapy

    PubMed Central

    Gianella, Sara; Vazquez, Homero; Ignacio, Caroline; Zweig, Adam C.; Richman, Douglas D.; Smith, Davey M.

    2014-01-01

    Abstract We investigated the pol genotype in two phylogenetically and epidemiologically linked partners, who were both experiencing persistent low-level viremia during antiretroviral therapy. In one partner we identified a new residue insertion between codon 248 and 249 of the HIV-1 RNA reverse transcriptase (RT) coding region (HXB2 numbering). We then investigated the potential impact of identified mutations in RT and antiretroviral binding affinity using a novel computational approach. PMID:24020934

  12. Knowledge-driven computational modeling in Alzheimer's disease research: Current state and future trends.

    PubMed

    Geerts, Hugo; Hofmann-Apitius, Martin; Anastasio, Thomas J

    2017-11-01

    Neurodegenerative diseases such as Alzheimer's disease (AD) follow a slowly progressing dysfunctional trajectory, with a large presymptomatic component and many comorbidities. Using preclinical models and large-scale omics studies ranging from genetics to imaging, a large number of processes that might be involved in AD pathology at different stages and levels have been identified. The sheer number of putative hypotheses makes it almost impossible to estimate their contribution to the clinical outcome and to develop a comprehensive view on the pathological processes driving the clinical phenotype. Traditionally, bioinformatics approaches have provided correlations and associations between processes and phenotypes. Focusing on causality, a new breed of advanced and more quantitative modeling approaches that use formalized domain expertise offer new opportunities to integrate these different modalities and outline possible paths toward new therapeutic interventions. This article reviews three different computational approaches and their possible complementarities. Process algebras, implemented using declarative programming languages such as Maude, facilitate simulation and analysis of complicated biological processes on a comprehensive but coarse-grained level. A model-driven Integration of Data and Knowledge, based on the OpenBEL platform and using reverse causative reasoning and network jump analysis, can generate mechanistic knowledge and a new, mechanism-based taxonomy of disease. Finally, Quantitative Systems Pharmacology is based on formalized implementation of domain expertise in a more fine-grained, mechanism-driven, quantitative, and predictive humanized computer model. We propose a strategy to combine the strengths of these individual approaches for developing powerful modeling methodologies that can provide actionable knowledge for rational development of preventive and therapeutic interventions. Development of these computational approaches is likely to be required for further progress in understanding and treating AD. Copyright © 2017 the Alzheimer's Association. Published by Elsevier Inc. All rights reserved.

  13. Computational neuroscience approach to biomarkers and treatments for mental disorders.

    PubMed

    Yahata, Noriaki; Kasai, Kiyoto; Kawato, Mitsuo

    2017-04-01

    Psychiatry research has long experienced a stagnation stemming from a lack of understanding of the neurobiological underpinnings of phenomenologically defined mental disorders. Recently, the application of computational neuroscience to psychiatry research has shown great promise in establishing a link between phenomenological and pathophysiological aspects of mental disorders, thereby recasting current nosology in more biologically meaningful dimensions. In this review, we highlight recent investigations into computational neuroscience that have undertaken either theory- or data-driven approaches to quantitatively delineate the mechanisms of mental disorders. The theory-driven approach, including reinforcement learning models, plays an integrative role in this process by enabling correspondence between behavior and disorder-specific alterations at multiple levels of brain organization, ranging from molecules to cells to circuits. Previous studies have explicated a plethora of defining symptoms of mental disorders, including anhedonia, inattention, and poor executive function. The data-driven approach, on the other hand, is an emerging field in computational neuroscience seeking to identify disorder-specific features among high-dimensional big data. Remarkably, various machine-learning techniques have been applied to neuroimaging data, and the extracted disorder-specific features have been used for automatic case-control classification. For many disorders, the reported accuracies have reached 90% or more. However, we note that rigorous tests on independent cohorts are critically required to translate this research into clinical applications. Finally, we discuss the utility of the disorder-specific features found by the data-driven approach to psychiatric therapies, including neurofeedback. Such developments will allow simultaneous diagnosis and treatment of mental disorders using neuroimaging, thereby establishing 'theranostics' for the first time in clinical psychiatry. © 2016 The Authors. Psychiatry and Clinical Neurosciences © 2016 Japanese Society of Psychiatry and Neurology.

  14. Evaluation of Semantic Web Technologies for Storing Computable Definitions of Electronic Health Records Phenotyping Algorithms.

    PubMed

    Papež, Václav; Denaxas, Spiros; Hemingway, Harry

    2017-01-01

    Electronic Health Records are electronic data generated during or as a byproduct of routine patient care. Structured, semi-structured and unstructured EHR offer researchers unprecedented phenotypic breadth and depth and have the potential to accelerate the development of precision medicine approaches at scale. A main EHR use-case is defining phenotyping algorithms that identify disease status, onset and severity. Phenotyping algorithms utilize diagnoses, prescriptions, laboratory tests, symptoms and other elements in order to identify patients with or without a specific trait. No common standardized, structured, computable format exists for storing phenotyping algorithms. The majority of algorithms are stored as human-readable descriptive text documents making their translation to code challenging due to their inherent complexity and hinders their sharing and re-use across the community. In this paper, we evaluate the two key Semantic Web Technologies, the Web Ontology Language and the Resource Description Framework, for enabling computable representations of EHR-driven phenotyping algorithms.

  15. Objective definition of rosette shape variation using a combined computer vision and data mining approach.

    PubMed

    Camargo, Anyela; Papadopoulou, Dimitra; Spyropoulou, Zoi; Vlachonasios, Konstantinos; Doonan, John H; Gay, Alan P

    2014-01-01

    Computer-vision based measurements of phenotypic variation have implications for crop improvement and food security because they are intrinsically objective. It should be possible therefore to use such approaches to select robust genotypes. However, plants are morphologically complex and identification of meaningful traits from automatically acquired image data is not straightforward. Bespoke algorithms can be designed to capture and/or quantitate specific features but this approach is inflexible and is not generally applicable to a wide range of traits. In this paper, we have used industry-standard computer vision techniques to extract a wide range of features from images of genetically diverse Arabidopsis rosettes growing under non-stimulated conditions, and then used statistical analysis to identify those features that provide good discrimination between ecotypes. This analysis indicates that almost all the observed shape variation can be described by 5 principal components. We describe an easily implemented pipeline including image segmentation, feature extraction and statistical analysis. This pipeline provides a cost-effective and inherently scalable method to parameterise and analyse variation in rosette shape. The acquisition of images does not require any specialised equipment and the computer routines for image processing and data analysis have been implemented using open source software. Source code for data analysis is written using the R package. The equations to calculate image descriptors have been also provided.

  16. Advanced Architectures for Astrophysical Supercomputing

    NASA Astrophysics Data System (ADS)

    Barsdell, B. R.; Barnes, D. G.; Fluke, C. J.

    2010-12-01

    Astronomers have come to rely on the increasing performance of computers to reduce, analyze, simulate and visualize their data. In this environment, faster computation can mean more science outcomes or the opening up of new parameter spaces for investigation. If we are to avoid major issues when implementing codes on advanced architectures, it is important that we have a solid understanding of our algorithms. A recent addition to the high-performance computing scene that highlights this point is the graphics processing unit (GPU). The hardware originally designed for speeding-up graphics rendering in video games is now achieving speed-ups of O(100×) in general-purpose computation - performance that cannot be ignored. We are using a generalized approach, based on the analysis of astronomy algorithms, to identify the optimal problem-types and techniques for taking advantage of both current GPU hardware and future developments in computing architectures.

  17. Cross-species extrapolation of toxicity information using the ...

    EPA Pesticide Factsheets

    In the United States, the Endocrine Disruptor Screening Program (EDSP) was established to identify chemicals that may lead to adverse effects via perturbation of the endocrine system (i.e., estrogen, androgen, and thyroid hormone systems). In the mid-1990s the EDSP adopted a two tiered approach for screening chemicals that applied standardized in vitro and in vivo toxicity tests. The Tier 1 screening assays were designed to identify substances that have the potential of interacting with the endocrine system and Tier 2 testing was developed to identify adverse effects caused by the chemical, with documentation of dose-response relationships. While this tiered approach was effective in identifying possible endocrine disrupting chemicals, the cost and time to screen a single chemical was significant. Therefore, in 2012 the EDSP proposed a transition to make greater use of computational approaches (in silico) and high-throughput screening (HTS; in vitro) assays to more rapidly and cost-efficiently screen chemicals for endocrine activity. This transition from resource intensive, primarily in vivo, screening methods to more pathway-based approaches aligns with the simultaneously occurring transformation in toxicity testing termed “Toxicity Testing in the 21st Century” which shifts the focus to the disturbance of the biological pathway predictive of the observable toxic effects. An example of such screening tools include the US Environmental Protection Agency’s

  18. Combining H/D exchange mass spectroscopy and computational docking reveals extended DNA-binding surface on uracil-DNA glycosylase

    PubMed Central

    Roberts, Victoria A.; Pique, Michael E.; Hsu, Simon; Li, Sheng; Slupphaug, Geir; Rambo, Robert P.; Jamison, Jonathan W.; Liu, Tong; Lee, Jun H.; Tainer, John A.; Ten Eyck, Lynn F.; Woods, Virgil L.

    2012-01-01

    X-ray crystallography provides excellent structural data on protein–DNA interfaces, but crystallographic complexes typically contain only small fragments of large DNA molecules. We present a new approach that can use longer DNA substrates and reveal new protein–DNA interactions even in extensively studied systems. Our approach combines rigid-body computational docking with hydrogen/deuterium exchange mass spectrometry (DXMS). DXMS identifies solvent-exposed protein surfaces; docking is used to create a 3-dimensional model of the protein–DNA interaction. We investigated the enzyme uracil-DNA glycosylase (UNG), which detects and cleaves uracil from DNA. UNG was incubated with a 30 bp DNA fragment containing a single uracil, giving the complex with the abasic DNA product. Compared with free UNG, the UNG–DNA complex showed increased solvent protection at the UNG active site and at two regions outside the active site: residues 210–220 and 251–264. Computational docking also identified these two DNA-binding surfaces, but neither shows DNA contact in UNG–DNA crystallographic structures. Our results can be explained by separation of the two DNA strands on one side of the active site. These non-sequence-specific DNA-binding surfaces may aid local uracil search, contribute to binding the abasic DNA product and help present the DNA product to APE-1, the next enzyme on the DNA-repair pathway. PMID:22492624

  19. Holliday Triangle Hunter (HolT Hunter): Efficient Software for Identifying Low Strain DNA Triangular Configurations

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

    Sherman, W.B.

    2012-04-16

    Synthetic DNA nanostructures are typically held together primarily by Holliday junctions. One of the most basic types of structures possible to assemble with only DNA and Holliday junctions is the triangle. To date, however, only equilateral triangles have been assembled in this manner - primarily because it is difficult to figure out what configurations of Holliday triangles have low strain. Early attempts at identifying such configurations relied upon calculations that followed the strained helical paths of DNA. Those methods, however, were computationally expensive, and failed to find many of the possible solutions. I have developed a new approach to identifyingmore » Holliday triangles that is computationally faster, and finds well over 95% of the possible solutions. The new approach is based on splitting the problem into two parts. The first part involves figuring out all the different ways that three featureless rods of the appropriate length and diameter can weave over and under one another to form a triangle. The second part of the computation entails seeing whether double helical DNA backbones can fit into the shape dictated by the rods in such a manner that the strands can cross over from one domain to the other at the appropriate spots. Structures with low strain (that is, good fit between the rods and the helices) on all three edges are recorded as promising for assembly.« less

  20. Aeroelastic Model Structure Computation for Envelope Expansion

    NASA Technical Reports Server (NTRS)

    Kukreja, Sunil L.

    2007-01-01

    Structure detection is a procedure for selecting a subset of candidate terms, from a full model description, that best describes the observed output. This is a necessary procedure to compute an efficient system description which may afford greater insight into the functionality of the system or a simpler controller design. Structure computation as a tool for black-box modelling may be of critical importance in the development of robust, parsimonious models for the flight-test community. Moreover, this approach may lead to efficient strategies for rapid envelope expansion which may save significant development time and costs. In this study, a least absolute shrinkage and selection operator (LASSO) technique is investigated for computing efficient model descriptions of nonlinear aeroelastic systems. The LASSO minimises the residual sum of squares by the addition of an l(sub 1) penalty term on the parameter vector of the traditional 2 minimisation problem. Its use for structure detection is a natural extension of this constrained minimisation approach to pseudolinear regression problems which produces some model parameters that are exactly zero and, therefore, yields a parsimonious system description. Applicability of this technique for model structure computation for the F/A-18 Active Aeroelastic Wing using flight test data is shown for several flight conditions (Mach numbers) by identifying a parsimonious system description with a high percent fit for cross-validated data.

  1. Environmental computing compendium - background and motivation

    NASA Astrophysics Data System (ADS)

    Heikkurinen, Matti; Kranzlmüller, Dieter

    2017-04-01

    The emerging discipline of environmental computing brings together experts in applied, advanced environmental modelling. The application domains address several fundamental societal challenges, ranging from disaster risk reduction to sustainability issues (such as food security on the global scale). The community has used an Intuitive, pragmatic approach when determining which initiatives are considered to "belong to the discipline". The community's growth is based on sharing of experiences and tools provides opportunities for reusing solutions or applying knowledge in new settings. Thus, limiting possible synergies by applying an arbitrary, formal definition to exclude some of the sources of solutions and knowledge would be counterproductive. However, the number of individuals and initiatives involved has grown to the level where a survey of initiatives and sub-themes they focus on is of interest. By surveying the project landscape and identifying common themes and building a shared vocabulary to describe them we can both communicate the relevance of the new discipline to the general public more easily and make it easier for the new members of the community to find the most promising collaboration partners. This talk presents the methodology and initial findings of the initial survey of the environmental computing initiatives and organisations, as well as approaches that could lead to an environmental computing compendium that would be a collaborative maintained shared resource of the environmental computing community.

  2. How to Assess Vulnerabilities of Water Policies to Global Change?

    NASA Astrophysics Data System (ADS)

    Kumar, A.; Haasnoot, M.; Weijs, S.

    2017-12-01

    Water managers are confronted with uncertainties arising from hydrological, societal, economical and political drivers. To manage these uncertainties two paradigms have been identified: top-down and bottom-up approaches. Top-down or prediction-based approaches use socio-economic scenarios together with a discrete set of GCM projections (often downscaled) to assess the expected impact of drivers and policies on water resource system through various hydrological and social systems models. Adaptation strategies to alleviate these impacts are then identified and tested against the scenarios. To address GCM and downscaling uncertainties, these approaches put more focus on climate predictions, rather than the decision problem itself. Triggered by the wish to have a more scenario-neutral approach and address downscaling uncertainties, recent analyses have been shifted towards vulnerability-based (bottom-up or decision-centric) approaches. They begin at the local scale by addressing socio-economic responses to climate, often involving stakeholder's input; identify vulnerabilities under a larger sample of plausible futures and evaluate sensitivity and robustness of possible adaptation options. Several bottom-up approaches have emerged so far and are increasingly recommended. Fundamentally they share several core ideas, however, subtle differences exist in vulnerability assessment, visualization tools for exploring vulnerabilities and computational methods used for identifying robust water policies. Through this study, we try to identify how these approaches are progressing, how the climate and non-climate uncertainties are being confronted and how to integrate existing and new tools. We find that choice of a method may depend on the number of vulnerability drivers identified and type of threshold levels (environmental conditions or policy objectives) defined. Certain approaches are suited well for assessing adaptive capacities, tipping points and sequencing of decisions. However, visualization of the vulnerability domain is still challenging if multiple drivers are present. New emerging tools are focused on generating synthetic scenarios, addressing multiple objectives, linking decision-making frameworks to adaptation pathways and communicating risks to the stakeholders.

  3. Visual Analysis of Cloud Computing Performance Using Behavioral Lines.

    PubMed

    Muelder, Chris; Zhu, Biao; Chen, Wei; Zhang, Hongxin; Ma, Kwan-Liu

    2016-02-29

    Cloud computing is an essential technology to Big Data analytics and services. A cloud computing system is often comprised of a large number of parallel computing and storage devices. Monitoring the usage and performance of such a system is important for efficient operations, maintenance, and security. Tracing every application on a large cloud system is untenable due to scale and privacy issues. But profile data can be collected relatively efficiently by regularly sampling the state of the system, including properties such as CPU load, memory usage, network usage, and others, creating a set of multivariate time series for each system. Adequate tools for studying such large-scale, multidimensional data are lacking. In this paper, we present a visual based analysis approach to understanding and analyzing the performance and behavior of cloud computing systems. Our design is based on similarity measures and a layout method to portray the behavior of each compute node over time. When visualizing a large number of behavioral lines together, distinct patterns often appear suggesting particular types of performance bottleneck. The resulting system provides multiple linked views, which allow the user to interactively explore the data by examining the data or a selected subset at different levels of detail. Our case studies, which use datasets collected from two different cloud systems, show that this visual based approach is effective in identifying trends and anomalies of the systems.

  4. Reducing false-positive detections by combining two stage-1 computer-aided mass detection algorithms

    NASA Astrophysics Data System (ADS)

    Bedard, Noah D.; Sampat, Mehul P.; Stokes, Patrick A.; Markey, Mia K.

    2006-03-01

    In this paper we present a strategy for reducing the number of false-positives in computer-aided mass detection. Our approach is to only mark "consensus" detections from among the suspicious sites identified by different "stage-1" detection algorithms. By "stage-1" we mean that each of the Computer-aided Detection (CADe) algorithms is designed to operate with high sensitivity, allowing for a large number of false positives. In this study, two mass detection methods were used: (1) Heath and Bowyer's algorithm based on the average fraction under the minimum filter (AFUM) and (2) a low-threshold bi-lateral subtraction algorithm. The two methods were applied separately to a set of images from the Digital Database for Screening Mammography (DDSM) to obtain paired sets of mass candidates. The consensus mass candidates for each image were identified by a logical "and" operation of the two CADe algorithms so as to eliminate regions of suspicion that were not independently identified by both techniques. It was shown that by combining the evidence from the AFUM filter method with that obtained from bi-lateral subtraction, the same sensitivity could be reached with fewer false-positives per image relative to using the AFUM filter alone.

  5. Combining self-organizing mapping and supervised affinity propagation clustering approach to investigate functional brain networks involved in motor imagery and execution with fMRI measurements.

    PubMed

    Zhang, Jiang; Liu, Qi; Chen, Huafu; Yuan, Zhen; Huang, Jin; Deng, Lihua; Lu, Fengmei; Zhang, Junpeng; Wang, Yuqing; Wang, Mingwen; Chen, Liangyin

    2015-01-01

    Clustering analysis methods have been widely applied to identifying the functional brain networks of a multitask paradigm. However, the previously used clustering analysis techniques are computationally expensive and thus impractical for clinical applications. In this study a novel method, called SOM-SAPC that combines self-organizing mapping (SOM) and supervised affinity propagation clustering (SAPC), is proposed and implemented to identify the motor execution (ME) and motor imagery (MI) networks. In SOM-SAPC, SOM was first performed to process fMRI data and SAPC is further utilized for clustering the patterns of functional networks. As a result, SOM-SAPC is able to significantly reduce the computational cost for brain network analysis. Simulation and clinical tests involving ME and MI were conducted based on SOM-SAPC, and the analysis results indicated that functional brain networks were clearly identified with different response patterns and reduced computational cost. In particular, three activation clusters were clearly revealed, which include parts of the visual, ME and MI functional networks. These findings validated that SOM-SAPC is an effective and robust method to analyze the fMRI data with multitasks.

  6. Adaptive Gaussian mixture models for pre-screening in GPR data

    NASA Astrophysics Data System (ADS)

    Torrione, Peter; Morton, Kenneth, Jr.; Besaw, Lance E.

    2011-06-01

    Due to the large amount of data generated by vehicle-mounted ground penetrating radar (GPR) antennae arrays, advanced feature extraction and classification can only be performed on a small subset of data during real-time operation. As a result, most GPR based landmine detection systems implement "pre-screening" algorithms to processes all of the data generated by the antennae array and identify locations with anomalous signatures for more advanced processing. These pre-screening algorithms must be computationally efficient and obtain high probability of detection, but can permit a false alarm rate which might be higher than the total system requirements. Many approaches to prescreening have previously been proposed, including linear prediction coefficients, the LMS algorithm, and CFAR-based approaches. Similar pre-screening techniques have also been developed in the field of video processing to identify anomalous behavior or anomalous objects. One such algorithm, an online k-means approximation to an adaptive Gaussian mixture model (GMM), is particularly well-suited to application for pre-screening in GPR data due to its computational efficiency, non-linear nature, and relevance of the logic underlying the algorithm to GPR processing. In this work we explore the application of an adaptive GMM-based approach for anomaly detection from the video processing literature to pre-screening in GPR data. Results with the ARA Nemesis landmine detection system demonstrate significant pre-screening performance improvements compared to alternative approaches, and indicate that the proposed algorithm is a complimentary technique to existing methods.

  7. A machine-learning graph-based approach for 3D segmentation of Bruch's membrane opening from glaucomatous SD-OCT volumes.

    PubMed

    Miri, Mohammad Saleh; Abràmoff, Michael D; Kwon, Young H; Sonka, Milan; Garvin, Mona K

    2017-07-01

    Bruch's membrane opening-minimum rim width (BMO-MRW) is a recently proposed structural parameter which estimates the remaining nerve fiber bundles in the retina and is superior to other conventional structural parameters for diagnosing glaucoma. Measuring this structural parameter requires identification of BMO locations within spectral domain-optical coherence tomography (SD-OCT) volumes. While most automated approaches for segmentation of the BMO either segment the 2D projection of BMO points or identify BMO points in individual B-scans, in this work, we propose a machine-learning graph-based approach for true 3D segmentation of BMO from glaucomatous SD-OCT volumes. The problem is formulated as an optimization problem for finding a 3D path within the SD-OCT volume. In particular, the SD-OCT volumes are transferred to the radial domain where the closed loop BMO points in the original volume form a path within the radial volume. The estimated location of BMO points in 3D are identified by finding the projected location of BMO points using a graph-theoretic approach and mapping the projected locations onto the Bruch's membrane (BM) surface. Dynamic programming is employed in order to find the 3D BMO locations as the minimum-cost path within the volume. In order to compute the cost function needed for finding the minimum-cost path, a random forest classifier is utilized to learn a BMO model, obtained by extracting intensity features from the volumes in the training set, and computing the required 3D cost function. The proposed method is tested on 44 glaucoma patients and evaluated using manual delineations. Results show that the proposed method successfully identifies the 3D BMO locations and has significantly smaller errors compared to the existing 3D BMO identification approaches. Published by Elsevier B.V.

  8. Candidate gene prioritization by network analysis of differential expression using machine learning approaches

    PubMed Central

    2010-01-01

    Background Discovering novel disease genes is still challenging for diseases for which no prior knowledge - such as known disease genes or disease-related pathways - is available. Performing genetic studies frequently results in large lists of candidate genes of which only few can be followed up for further investigation. We have recently developed a computational method for constitutional genetic disorders that identifies the most promising candidate genes by replacing prior knowledge by experimental data of differential gene expression between affected and healthy individuals. To improve the performance of our prioritization strategy, we have extended our previous work by applying different machine learning approaches that identify promising candidate genes by determining whether a gene is surrounded by highly differentially expressed genes in a functional association or protein-protein interaction network. Results We have proposed three strategies scoring disease candidate genes relying on network-based machine learning approaches, such as kernel ridge regression, heat kernel, and Arnoldi kernel approximation. For comparison purposes, a local measure based on the expression of the direct neighbors is also computed. We have benchmarked these strategies on 40 publicly available knockout experiments in mice, and performance was assessed against results obtained using a standard procedure in genetics that ranks candidate genes based solely on their differential expression levels (Simple Expression Ranking). Our results showed that our four strategies could outperform this standard procedure and that the best results were obtained using the Heat Kernel Diffusion Ranking leading to an average ranking position of 8 out of 100 genes, an AUC value of 92.3% and an error reduction of 52.8% relative to the standard procedure approach which ranked the knockout gene on average at position 17 with an AUC value of 83.7%. Conclusion In this study we could identify promising candidate genes using network based machine learning approaches even if no knowledge is available about the disease or phenotype. PMID:20840752

  9. Joint Model and Parameter Dimension Reduction for Bayesian Inversion Applied to an Ice Sheet Flow Problem

    NASA Astrophysics Data System (ADS)

    Ghattas, O.; Petra, N.; Cui, T.; Marzouk, Y.; Benjamin, P.; Willcox, K.

    2016-12-01

    Model-based projections of the dynamics of the polar ice sheets play a central role in anticipating future sea level rise. However, a number of mathematical and computational challenges place significant barriers on improving predictability of these models. One such challenge is caused by the unknown model parameters (e.g., in the basal boundary conditions) that must be inferred from heterogeneous observational data, leading to an ill-posed inverse problem and the need to quantify uncertainties in its solution. In this talk we discuss the problem of estimating the uncertainty in the solution of (large-scale) ice sheet inverse problems within the framework of Bayesian inference. Computing the general solution of the inverse problem--i.e., the posterior probability density--is intractable with current methods on today's computers, due to the expense of solving the forward model (3D full Stokes flow with nonlinear rheology) and the high dimensionality of the uncertain parameters (which are discretizations of the basal sliding coefficient field). To overcome these twin computational challenges, it is essential to exploit problem structure (e.g., sensitivity of the data to parameters, the smoothing property of the forward model, and correlations in the prior). To this end, we present a data-informed approach that identifies low-dimensional structure in both parameter space and the forward model state space. This approach exploits the fact that the observations inform only a low-dimensional parameter space and allows us to construct a parameter-reduced posterior. Sampling this parameter-reduced posterior still requires multiple evaluations of the forward problem, therefore we also aim to identify a low dimensional state space to reduce the computational cost. To this end, we apply a proper orthogonal decomposition (POD) approach to approximate the state using a low-dimensional manifold constructed using ``snapshots'' from the parameter reduced posterior, and the discrete empirical interpolation method (DEIM) to approximate the nonlinearity in the forward problem. We show that using only a limited number of forward solves, the resulting subspaces lead to an efficient method to explore the high-dimensional posterior.

  10. Introducing a "Means-End" Approach to Human-Computer Interaction: Why Users Choose Particular Web Sites Over Others.

    ERIC Educational Resources Information Center

    Subramony, Deepak Prem

    Gutman's means-end theory, widely used in market research, identifies three levels of abstraction: attributes, consequences, and values--associated with the use of products, representing the process by which physical attributes of products gain personal meaning for users. The primary methodological manifestation of means-end theory is the…

  11. Parents as Educators of Sex and Relationship Education: The Role for Effective Communication in British Families

    ERIC Educational Resources Information Center

    Turnbull, Triece; van Wersch, Anna; van Schaik, Paul

    2011-01-01

    Objective: To identify how British families discuss sexual matters and to determine if the use of a multimedia computer program increases knowledge to promote communication about sexual topics. Design: A mixed-method approach in which families were interviewed within the family environment. Method: Twenty families from the north east of England…

  12. Transitional Employment Aspirations for Bridging Retirement: Implications for Training and Development

    ERIC Educational Resources Information Center

    Pillay, Hitendra; Kelly, Kathy; Tones, Megan

    2010-01-01

    Purpose: The purpose of this paper is to identify the transitional employment (TE) aspirations and training and development needs of older and younger workers at risk of early retirement due to limited education and/or employment in blue-collar (BC) occupations. Design/methodology/approach: A computer-based methodology is used to evaluate the…

  13. An integrative, translational approach to understanding rare and orphan genetically based diseases

    PubMed Central

    Hoehndorf, Robert; Schofield, Paul N.; Gkoutos, Georgios V.

    2013-01-01

    PhenomeNet is an approach for integrating phenotypes across species and identifying candidate genes for genetic diseases based on the similarity between a disease and animal model phenotypes. In contrast to ‘guilt-by-association’ approaches, PhenomeNet relies exclusively on the comparison of phenotypes to suggest candidate genes, and can, therefore, be applied to study the molecular basis of rare and orphan diseases for which the molecular basis is unknown. In addition to disease phenotypes from the Online Mendelian Inheritance in Man (OMIM) database, we have now integrated the clinical signs from Orphanet into PhenomeNet. We demonstrate that our approach can efficiently identify known candidate genes for genetic diseases in Orphanet and OMIM. Furthermore, we find evidence that mutations in the HIP1 gene might cause Bassoe syndrome, a rare disorder with unknown genetic aetiology. Our results demonstrate that integration and computational analysis of human disease and animal model phenotypes using PhenomeNet has the potential to reveal novel insights into the pathobiology underlying genetic diseases. PMID:23853703

  14. Mirroring co-evolving trees in the light of their topologies.

    PubMed

    Hajirasouliha, Iman; Schönhuth, Alexander; de Juan, David; Valencia, Alfonso; Sahinalp, S Cenk

    2012-05-01

    Determining the interaction partners among protein/domain families poses hard computational problems, in particular in the presence of paralogous proteins. Available approaches aim to identify interaction partners among protein/domain families through maximizing the similarity between trimmed versions of their phylogenetic trees. Since maximization of any natural similarity score is computationally difficult, many approaches employ heuristics to evaluate the distance matrices corresponding to the tree topologies in question. In this article, we devise an efficient deterministic algorithm which directly maximizes the similarity between two leaf labeled trees with edge lengths, obtaining a score-optimal alignment of the two trees in question. Our algorithm is significantly faster than those methods based on distance matrix comparison: 1 min on a single processor versus 730 h on a supercomputer. Furthermore, we outperform the current state-of-the-art exhaustive search approach in terms of precision, while incurring acceptable losses in recall. A C implementation of the method demonstrated in this article is available at http://compbio.cs.sfu.ca/mirrort.htm

  15. Action and language integration: from humans to cognitive robots.

    PubMed

    Borghi, Anna M; Cangelosi, Angelo

    2014-07-01

    The topic is characterized by a highly interdisciplinary approach to the issue of action and language integration. Such an approach, combining computational models and cognitive robotics experiments with neuroscience, psychology, philosophy, and linguistic approaches, can be a powerful means that can help researchers disentangle ambiguous issues, provide better and clearer definitions, and formulate clearer predictions on the links between action and language. In the introduction we briefly describe the papers and discuss the challenges they pose to future research. We identify four important phenomena the papers address and discuss in light of empirical and computational evidence: (a) the role played not only by sensorimotor and emotional information but also of natural language in conceptual representation; (b) the contextual dependency and high flexibility of the interaction between action, concepts, and language; (c) the involvement of the mirror neuron system in action and language processing; (d) the way in which the integration between action and language can be addressed by developmental robotics and Human-Robot Interaction. Copyright © 2014 Cognitive Science Society, Inc.

  16. Elucidating Mechanisms of Molecular Recognition Between Human Argonaute and miRNA Using Computational Approaches.

    PubMed

    Jiang, Hanlun; Zhu, Lizhe; Héliou, Amélie; Gao, Xin; Bernauer, Julie; Huang, Xuhui

    2017-01-01

    MicroRNA (miRNA) and Argonaute (AGO) protein together form the RNA-induced silencing complex (RISC) that plays an essential role in the regulation of gene expression. Elucidating the underlying mechanism of AGO-miRNA recognition is thus of great importance not only for the in-depth understanding of miRNA function but also for inspiring new drugs targeting miRNAs. In this chapter we introduce a combined computational approach of molecular dynamics (MD) simulations, Markov state models (MSMs), and protein-RNA docking to investigate AGO-miRNA recognition. Constructed from MD simulations, MSMs can elucidate the conformational dynamics of AGO at biologically relevant timescales. Protein-RNA docking can then efficiently identify the AGO conformations that are geometrically accessible to miRNA. Using our recent work on human AGO2 as an example, we explain the rationale and the workflow of our method in details. This combined approach holds great promise to complement experiments in unraveling the mechanisms of molecular recognition between large, flexible, and complex biomolecules.

  17. Optimal subinterval selection approach for power system transient stability simulation

    DOE PAGES

    Kim, Soobae; Overbye, Thomas J.

    2015-10-21

    Power system transient stability analysis requires an appropriate integration time step to avoid numerical instability as well as to reduce computational demands. For fast system dynamics, which vary more rapidly than what the time step covers, a fraction of the time step, called a subinterval, is used. However, the optimal value of this subinterval is not easily determined because the analysis of the system dynamics might be required. This selection is usually made from engineering experiences, and perhaps trial and error. This paper proposes an optimal subinterval selection approach for power system transient stability analysis, which is based on modalmore » analysis using a single machine infinite bus (SMIB) system. Fast system dynamics are identified with the modal analysis and the SMIB system is used focusing on fast local modes. An appropriate subinterval time step from the proposed approach can reduce computational burden and achieve accurate simulation responses as well. As a result, the performance of the proposed method is demonstrated with the GSO 37-bus system.« less

  18. Fan Noise Prediction with Applications to Aircraft System Noise Assessment

    NASA Technical Reports Server (NTRS)

    Nark, Douglas M.; Envia, Edmane; Burley, Casey L.

    2009-01-01

    This paper describes an assessment of current fan noise prediction tools by comparing measured and predicted sideline acoustic levels from a benchmark fan noise wind tunnel test. Specifically, an empirical method and newly developed coupled computational approach are utilized to predict aft fan noise for a benchmark test configuration. Comparisons with sideline noise measurements are performed to assess the relative merits of the two approaches. The study identifies issues entailed in coupling the source and propagation codes, as well as provides insight into the capabilities of the tools in predicting the fan noise source and subsequent propagation and radiation. In contrast to the empirical method, the new coupled computational approach provides the ability to investigate acoustic near-field effects. The potential benefits/costs of these new methods are also compared with the existing capabilities in a current aircraft noise system prediction tool. The knowledge gained in this work provides a basis for improved fan source specification in overall aircraft system noise studies.

  19. Foreign Object Damage Identification in Turbine Engines

    NASA Technical Reports Server (NTRS)

    Strack, William; Zhang, Desheng; Turso, James; Pavlik, William; Lopez, Isaac

    2005-01-01

    This report summarizes the collective work of a five-person team from different organizations examining the problem of detecting foreign object damage (FOD) events in turbofan engines from gas path thermodynamic and bearing accelerometer sensors, and determining the severity of damage to each component (diagnosis). Several detection and diagnostic approaches were investigated and a software tool (FODID) was developed to assist researchers detect/diagnose FOD events. These approaches include (1) fan efficiency deviation computed from upstream and downstream temperature/ pressure measurements, (2) gas path weighted least squares estimation of component health parameter deficiencies, (3) Kalman filter estimation of component health parameters, and (4) use of structural vibration signal processing to detect both large and small FOD events. The last three of these approaches require a significant amount of computation in conjunction with a physics-based analytic model of the underlying phenomenon the NPSS thermodynamic cycle code for approaches 1 to 3 and the DyRoBeS reduced-order rotor dynamics code for approach 4. A potential application of the FODID software tool, in addition to its detection/diagnosis role, is using its sensitivity results to help identify the best types of sensors and their optimum locations within the gas path, and similarly for bearing accelerometers.

  20. Binding-Site Assessment by Virtual Fragment Screening

    PubMed Central

    Huang, Niu; Jacobson, Matthew P.

    2010-01-01

    The accurate prediction of protein druggability (propensity to bind high-affinity drug-like small molecules) would greatly benefit the fields of chemical genomics and drug discovery. We have developed a novel approach to quantitatively assess protein druggability by computationally screening a fragment-like compound library. In analogy to NMR-based fragment screening, we dock ∼11000 fragments against a given binding site and compute a computational hit rate based on the fraction of molecules that exceed an empirically chosen score cutoff. We perform a large-scale evaluation of the approach on four datasets, totaling 152 binding sites. We demonstrate that computed hit rates correlate with hit rates measured experimentally in a previously published NMR-based screening method. Secondly, we show that the in silico fragment screening method can be used to distinguish known druggable and non-druggable targets, including both enzymes and protein-protein interaction sites. Finally, we explore the sensitivity of the results to different receptor conformations, including flexible protein-protein interaction sites. Besides its original aim to assess druggability of different protein targets, this method could be used to identifying druggable conformations of flexible binding site for lead discovery, and suggesting strategies for growing or joining initial fragment hits to obtain more potent inhibitors. PMID:20404926

  1. A new framework for comprehensive, robust, and efficient global sensitivity analysis: 1. Theory

    NASA Astrophysics Data System (ADS)

    Razavi, Saman; Gupta, Hoshin V.

    2016-01-01

    Computer simulation models are continually growing in complexity with increasingly more factors to be identified. Sensitivity Analysis (SA) provides an essential means for understanding the role and importance of these factors in producing model responses. However, conventional approaches to SA suffer from (1) an ambiguous characterization of sensitivity, and (2) poor computational efficiency, particularly as the problem dimension grows. Here, we present a new and general sensitivity analysis framework (called VARS), based on an analogy to "variogram analysis," that provides an intuitive and comprehensive characterization of sensitivity across the full spectrum of scales in the factor space. We prove, theoretically, that Morris (derivative-based) and Sobol (variance-based) methods and their extensions are special cases of VARS, and that their SA indices can be computed as by-products of the VARS framework. Synthetic functions that resemble actual model response surfaces are used to illustrate the concepts, and show VARS to be as much as two orders of magnitude more computationally efficient than the state-of-the-art Sobol approach. In a companion paper, we propose a practical implementation strategy, and demonstrate the effectiveness, efficiency, and reliability (robustness) of the VARS framework on real-data case studies.

  2. DMRfinder: efficiently identifying differentially methylated regions from MethylC-seq data.

    PubMed

    Gaspar, John M; Hart, Ronald P

    2017-11-29

    DNA methylation is an epigenetic modification that is studied at a single-base resolution with bisulfite treatment followed by high-throughput sequencing. After alignment of the sequence reads to a reference genome, methylation counts are analyzed to determine genomic regions that are differentially methylated between two or more biological conditions. Even though a variety of software packages is available for different aspects of the bioinformatics analysis, they often produce results that are biased or require excessive computational requirements. DMRfinder is a novel computational pipeline that identifies differentially methylated regions efficiently. Following alignment, DMRfinder extracts methylation counts and performs a modified single-linkage clustering of methylation sites into genomic regions. It then compares methylation levels using beta-binomial hierarchical modeling and Wald tests. Among its innovative attributes are the analyses of novel methylation sites and methylation linkage, as well as the simultaneous statistical analysis of multiple sample groups. To demonstrate its efficiency, DMRfinder is benchmarked against other computational approaches using a large published dataset. Contrasting two replicates of the same sample yielded minimal genomic regions with DMRfinder, whereas two alternative software packages reported a substantial number of false positives. Further analyses of biological samples revealed fundamental differences between DMRfinder and another software package, despite the fact that they utilize the same underlying statistical basis. For each step, DMRfinder completed the analysis in a fraction of the time required by other software. Among the computational approaches for identifying differentially methylated regions from high-throughput bisulfite sequencing datasets, DMRfinder is the first that integrates all the post-alignment steps in a single package. Compared to other software, DMRfinder is extremely efficient and unbiased in this process. DMRfinder is free and open-source software, available on GitHub ( github.com/jsh58/DMRfinder ); it is written in Python and R, and is supported on Linux.

  3. Deciphering molecular circuits from genetic variation underlying transcriptional responsiveness to stimuli

    PubMed Central

    Gat-Viks, Irit; Chevrier, Nicolas; Wilentzik, Roni; Eisenhaure, Thomas; Raychowdhury, Raktima; Steuerman, Yael; Shalek, Alex; Hacohen, Nir; Amit, Ido; Regev, Aviv

    2013-01-01

    Individual genetic variation affects gene expression in response to stimuli, often by influencing complex molecular circuits. Here we combine genomic and intermediate-scale transcriptional profiling with computational methods to identify variants that affect the responsiveness of genes to stimuli (responsiveness QTLs; reQTLs) and to position these variants in molecular circuit diagrams. We apply this approach to study variation in transcriptional responsiveness to pathogen components in dendritic cells from recombinant inbred mouse strains. We identify reQTLs that correlate with particular stimuli and position them in known pathways. For example, in response to a virus-like stimulus, a trans-acting variant acts as an activator of the antiviral response; using RNAi, we identify Rgs16 as the likely causal gene. Our approach charts an experimental and analytic path to decipher the mechanisms underlying genetic variation in circuits that control responses to stimuli. PMID:23503680

  4. Identifying regions of interest in medical images using self-organizing maps.

    PubMed

    Teng, Wei-Guang; Chang, Ping-Lin

    2012-10-01

    Advances in data acquisition, processing and visualization techniques have had a tremendous impact on medical imaging in recent years. However, the interpretation of medical images is still almost always performed by radiologists. Developments in artificial intelligence and image processing have shown the increasingly great potential of computer-aided diagnosis (CAD). Nevertheless, it has remained challenging to develop a general approach to process various commonly used types of medical images (e.g., X-ray, MRI, and ultrasound images). To facilitate diagnosis, we recommend the use of image segmentation to discover regions of interest (ROI) using self-organizing maps (SOM). We devise a two-stage SOM approach that can be used to precisely identify the dominant colors of a medical image and then segment it into several small regions. In addition, by appropriately conducting the recursive merging steps to merge smaller regions into larger ones, radiologists can usually identify one or more ROIs within a medical image.

  5. Deciphering molecular circuits from genetic variation underlying transcriptional responsiveness to stimuli.

    PubMed

    Gat-Viks, Irit; Chevrier, Nicolas; Wilentzik, Roni; Eisenhaure, Thomas; Raychowdhury, Raktima; Steuerman, Yael; Shalek, Alex K; Hacohen, Nir; Amit, Ido; Regev, Aviv

    2013-04-01

    Individual genetic variation affects gene responsiveness to stimuli, often by influencing complex molecular circuits. Here we combine genomic and intermediate-scale transcriptional profiling with computational methods to identify variants that affect the responsiveness of genes to stimuli (responsiveness quantitative trait loci or reQTLs) and to position these variants in molecular circuit diagrams. We apply this approach to study variation in transcriptional responsiveness to pathogen components in dendritic cells from recombinant inbred mouse strains. We identify reQTLs that correlate with particular stimuli and position them in known pathways. For example, in response to a virus-like stimulus, a trans-acting variant responds as an activator of the antiviral response; using RNA interference, we identify Rgs16 as the likely causal gene. Our approach charts an experimental and analytic path to decipher the mechanisms underlying genetic variation in circuits that control responses to stimuli.

  6. Identification of internal properties of fibres and micro-swimmers

    NASA Astrophysics Data System (ADS)

    Plouraboué, Franck; Thiam, E. Ibrahima; Delmotte, Blaise; Climent, Eric

    2017-01-01

    In this paper, we address the identifiability of constitutive parameters of passive or active micro-swimmers. We first present a general framework for describing fibres or micro-swimmers using a bead-model description. Using a kinematic constraint formulation to describe fibres, flagellum or cilia, we find explicit linear relationship between elastic constitutive parameters and generalized velocities from computing contact forces. This linear formulation then permits one to address explicitly identifiability conditions and solve for parameter identification. We show that both active forcing and passive parameters are both identifiable independently but not simultaneously. We also provide unbiased estimators for generalized elastic parameters in the presence of Langevin-like forcing with Gaussian noise using a Bayesian approach. These theoretical results are illustrated in various configurations showing the efficiency of the proposed approach for direct parameter identification. The convergence of the proposed estimators is successfully tested numerically.

  7. A genetic epidemiology approach to cyber-security.

    PubMed

    Gil, Santiago; Kott, Alexander; Barabási, Albert-László

    2014-07-16

    While much attention has been paid to the vulnerability of computer networks to node and link failure, there is limited systematic understanding of the factors that determine the likelihood that a node (computer) is compromised. We therefore collect threat log data in a university network to study the patterns of threat activity for individual hosts. We relate this information to the properties of each host as observed through network-wide scans, establishing associations between the network services a host is running and the kinds of threats to which it is susceptible. We propose a methodology to associate services to threats inspired by the tools used in genetics to identify statistical associations between mutations and diseases. The proposed approach allows us to determine probabilities of infection directly from observation, offering an automated high-throughput strategy to develop comprehensive metrics for cyber-security.

  8. Analysis of Uncertainty and Variability in Finite Element Computational Models for Biomedical Engineering: Characterization and Propagation

    PubMed Central

    Mangado, Nerea; Piella, Gemma; Noailly, Jérôme; Pons-Prats, Jordi; Ballester, Miguel Ángel González

    2016-01-01

    Computational modeling has become a powerful tool in biomedical engineering thanks to its potential to simulate coupled systems. However, real parameters are usually not accurately known, and variability is inherent in living organisms. To cope with this, probabilistic tools, statistical analysis and stochastic approaches have been used. This article aims to review the analysis of uncertainty and variability in the context of finite element modeling in biomedical engineering. Characterization techniques and propagation methods are presented, as well as examples of their applications in biomedical finite element simulations. Uncertainty propagation methods, both non-intrusive and intrusive, are described. Finally, pros and cons of the different approaches and their use in the scientific community are presented. This leads us to identify future directions for research and methodological development of uncertainty modeling in biomedical engineering. PMID:27872840

  9. Analysis of Uncertainty and Variability in Finite Element Computational Models for Biomedical Engineering: Characterization and Propagation.

    PubMed

    Mangado, Nerea; Piella, Gemma; Noailly, Jérôme; Pons-Prats, Jordi; Ballester, Miguel Ángel González

    2016-01-01

    Computational modeling has become a powerful tool in biomedical engineering thanks to its potential to simulate coupled systems. However, real parameters are usually not accurately known, and variability is inherent in living organisms. To cope with this, probabilistic tools, statistical analysis and stochastic approaches have been used. This article aims to review the analysis of uncertainty and variability in the context of finite element modeling in biomedical engineering. Characterization techniques and propagation methods are presented, as well as examples of their applications in biomedical finite element simulations. Uncertainty propagation methods, both non-intrusive and intrusive, are described. Finally, pros and cons of the different approaches and their use in the scientific community are presented. This leads us to identify future directions for research and methodological development of uncertainty modeling in biomedical engineering.

  10. A genetic epidemiology approach to cyber-security

    PubMed Central

    Gil, Santiago; Kott, Alexander; Barabási, Albert-László

    2014-01-01

    While much attention has been paid to the vulnerability of computer networks to node and link failure, there is limited systematic understanding of the factors that determine the likelihood that a node (computer) is compromised. We therefore collect threat log data in a university network to study the patterns of threat activity for individual hosts. We relate this information to the properties of each host as observed through network-wide scans, establishing associations between the network services a host is running and the kinds of threats to which it is susceptible. We propose a methodology to associate services to threats inspired by the tools used in genetics to identify statistical associations between mutations and diseases. The proposed approach allows us to determine probabilities of infection directly from observation, offering an automated high-throughput strategy to develop comprehensive metrics for cyber-security. PMID:25028059

  11. Screening of photosynthetic pigments for herbicidal activity with a new computational molecular approach.

    PubMed

    Krishnaraj, R Navanietha; Chandran, Saravanan; Pal, Parimal; Berchmans, Sheela

    2013-12-01

    There is an immense interest among the researchers to identify new herbicides which are effective against the herbs without affecting the environment. In this work, photosynthetic pigments are used as the ligands to predict their herbicidal activity. The enzyme 5-enolpyruvylshikimate-3-phosphate (EPSP) synthase is a good target for the herbicides. Homology modeling of the target enzyme is done using Modeler 9.11 and the model is validated. Docking studies were performed with AutoDock Vina algorithm to predict the binding of the natural pigments such as β-carotene, chlorophyll a, chlorophyll b, phycoerythrin and phycocyanin to the target. β-carotene, phycoerythrin and phycocyanin have higher binding energies indicating the herbicidal activity of the pigments. This work reports a procedure to screen herbicides with computational molecular approach. These pigments will serve as potential bioherbicides in the future.

  12. Conceptual design of distillation-based hybrid separation processes.

    PubMed

    Skiborowski, Mirko; Harwardt, Andreas; Marquardt, Wolfgang

    2013-01-01

    Hybrid separation processes combine different separation principles and constitute a promising design option for the separation of complex mixtures. Particularly, the integration of distillation with other unit operations can significantly improve the separation of close-boiling or azeotropic mixtures. Although the design of single-unit operations is well understood and supported by computational methods, the optimal design of flowsheets of hybrid separation processes is still a challenging task. The large number of operational and design degrees of freedom requires a systematic and optimization-based design approach. To this end, a structured approach, the so-called process synthesis framework, is proposed. This article reviews available computational methods for the conceptual design of distillation-based hybrid processes for the separation of liquid mixtures. Open problems are identified that must be addressed to finally establish a structured process synthesis framework for such processes.

  13. A Parametric Geometry Computational Fluid Dynamics (CFD) Study Utilizing Design of Experiments (DOE)

    NASA Technical Reports Server (NTRS)

    Rhew, Ray D.; Parker, Peter A.

    2007-01-01

    Design of Experiments (DOE) was applied to the LAS geometric parameter study to efficiently identify and rank primary contributors to integrated drag over the vehicles ascent trajectory in an order of magnitude fewer CFD configurations thereby reducing computational resources and solution time. SME s were able to gain a better understanding on the underlying flowphysics of different geometric parameter configurations through the identification of interaction effects. An interaction effect, which describes how the effect of one factor changes with respect to the levels of other factors, is often the key to product optimization. A DOE approach emphasizes a sequential approach to learning through successive experimentation to continuously build on previous knowledge. These studies represent a starting point for expanded experimental activities that will eventually cover the entire design space of the vehicle and flight trajectory.

  14. Machine learning bandgaps of double perovskites

    DOE PAGES

    Pilania, G.; Mannodi-Kanakkithodi, A.; Uberuaga, B. P.; ...

    2016-01-19

    The ability to make rapid and accurate predictions on bandgaps of double perovskites is of much practical interest for a range of applications. While quantum mechanical computations for high-fidelity bandgaps are enormously computation-time intensive and thus impractical in high throughput studies, informatics-based statistical learning approaches can be a promising alternative. Here we demonstrate a systematic feature-engineering approach and a robust learning framework for efficient and accurate predictions of electronic bandgaps of double perovskites. After evaluating a set of more than 1.2 million features, we identify lowest occupied Kohn-Sham levels and elemental electronegativities of the constituent atomic species as the mostmore » crucial and relevant predictors. As a result, the developed models are validated and tested using the best practices of data science and further analyzed to rationalize their prediction performance.« less

  15. Experimental Identification of Non-Abelian Topological Orders on a Quantum Simulator.

    PubMed

    Li, Keren; Wan, Yidun; Hung, Ling-Yan; Lan, Tian; Long, Guilu; Lu, Dawei; Zeng, Bei; Laflamme, Raymond

    2017-02-24

    Topological orders can be used as media for topological quantum computing-a promising quantum computation model due to its invulnerability against local errors. Conversely, a quantum simulator, often regarded as a quantum computing device for special purposes, also offers a way of characterizing topological orders. Here, we show how to identify distinct topological orders via measuring their modular S and T matrices. In particular, we employ a nuclear magnetic resonance quantum simulator to study the properties of three topologically ordered matter phases described by the string-net model with two string types, including the Z_{2} toric code, doubled semion, and doubled Fibonacci. The third one, non-Abelian Fibonacci order is notably expected to be the simplest candidate for universal topological quantum computing. Our experiment serves as the basic module, built on which one can simulate braiding of non-Abelian anyons and ultimately, topological quantum computation via the braiding, and thus provides a new approach of investigating topological orders using quantum computers.

  16. Reliable Viscosity Calculation from Equilibrium Molecular Dynamics Simulations: A Time Decomposition Method.

    PubMed

    Zhang, Yong; Otani, Akihito; Maginn, Edward J

    2015-08-11

    Equilibrium molecular dynamics is often used in conjunction with a Green-Kubo integral of the pressure tensor autocorrelation function to compute the shear viscosity of fluids. This approach is computationally expensive and is subject to a large amount of variability because the plateau region of the Green-Kubo integral is difficult to identify unambiguously. Here, we propose a time decomposition approach for computing the shear viscosity using the Green-Kubo formalism. Instead of one long trajectory, multiple independent trajectories are run and the Green-Kubo relation is applied to each trajectory. The averaged running integral as a function of time is fit to a double-exponential function with a weighting function derived from the standard deviation of the running integrals. Such a weighting function minimizes the uncertainty of the estimated shear viscosity and provides an objective means of estimating the viscosity. While the formal Green-Kubo integral requires an integration to infinite time, we suggest an integration cutoff time tcut, which can be determined by the relative values of the running integral and the corresponding standard deviation. This approach for computing the shear viscosity can be easily automated and used in computational screening studies where human judgment and intervention in the data analysis are impractical. The method has been applied to the calculation of the shear viscosity of a relatively low-viscosity liquid, ethanol, and relatively high-viscosity ionic liquid, 1-n-butyl-3-methylimidazolium bis(trifluoromethane-sulfonyl)imide ([BMIM][Tf2N]), over a range of temperatures. These test cases show that the method is robust and yields reproducible and reliable shear viscosity values.

  17. A Tool for Interactive Data Visualization: Application to Over 10,000 Brain Imaging and Phantom MRI Data Sets.

    PubMed

    Panta, Sandeep R; Wang, Runtang; Fries, Jill; Kalyanam, Ravi; Speer, Nicole; Banich, Marie; Kiehl, Kent; King, Margaret; Milham, Michael; Wager, Tor D; Turner, Jessica A; Plis, Sergey M; Calhoun, Vince D

    2016-01-01

    In this paper we propose a web-based approach for quick visualization of big data from brain magnetic resonance imaging (MRI) scans using a combination of an automated image capture and processing system, nonlinear embedding, and interactive data visualization tools. We draw upon thousands of MRI scans captured via the COllaborative Imaging and Neuroinformatics Suite (COINS). We then interface the output of several analysis pipelines based on structural and functional data to a t-distributed stochastic neighbor embedding (t-SNE) algorithm which reduces the number of dimensions for each scan in the input data set to two dimensions while preserving the local structure of data sets. Finally, we interactively display the output of this approach via a web-page, based on data driven documents (D3) JavaScript library. Two distinct approaches were used to visualize the data. In the first approach, we computed multiple quality control (QC) values from pre-processed data, which were used as inputs to the t-SNE algorithm. This approach helps in assessing the quality of each data set relative to others. In the second case, computed variables of interest (e.g., brain volume or voxel values from segmented gray matter images) were used as inputs to the t-SNE algorithm. This approach helps in identifying interesting patterns in the data sets. We demonstrate these approaches using multiple examples from over 10,000 data sets including (1) quality control measures calculated from phantom data over time, (2) quality control data from human functional MRI data across various studies, scanners, sites, (3) volumetric and density measures from human structural MRI data across various studies, scanners and sites. Results from (1) and (2) show the potential of our approach to combine t-SNE data reduction with interactive color coding of variables of interest to quickly identify visually unique clusters of data (i.e., data sets with poor QC, clustering of data by site) quickly. Results from (3) demonstrate interesting patterns of gray matter and volume, and evaluate how they map onto variables including scanners, age, and gender. In sum, the proposed approach allows researchers to rapidly identify and extract meaningful information from big data sets. Such tools are becoming increasingly important as datasets grow larger.

  18. Design of strength characteristics on the example of a mining support

    NASA Astrophysics Data System (ADS)

    Gwiazda, A.; Sękala, A.; Banaś, W.; Topolska, S.; Foit, K.; Monica, Z.

    2017-08-01

    It is a special group of particular design aproches that could be characterized as “design for X”. All areas of specific these design methodology, taking into account the requirements of the life cycle are described with the acronym DfX. It means an integrated computing platform approach to design binding together both the area of design knowledge and area of computer systems. In this perspective, computer systems are responsible for the link between design requirements with the subject of the project and to filter the information being circulated throughout the operation of the project. The DfX methodologies together form an approach integrating to different functional areas of industrial organization. Among the internal elements it can distinguish the structure of the project team, the people making it, the same process design, control system design and implementation of the action tools to assist this process. Among the elements that are obtained in the framework of this approach should be distinguished: higher operating efficiency, professionalism, the ability to create innovation, incremental progress of the project and the appropriate focus of the project team. It have been done attempts to integrate identified specific areas for action in the field of design methodology. They have already taken place earlier in the design due to the Economic Design for Manufacture. This approach was characteristic for European industry. In this case, an approach was developed in methodology, which can be defined as the Design to/for Cost. The article presents the idea of an integrated design approach related with the DfX approach. The results are described on the base of a virtual 3D model of a mining support. This model was elaborated in the advanced engineering platform like Siemens PLM NX.

  19. The Potential of Automatic Word Comparison for Historical Linguistics.

    PubMed

    List, Johann-Mattis; Greenhill, Simon J; Gray, Russell D

    2017-01-01

    The amount of data from languages spoken all over the world is rapidly increasing. Traditional manual methods in historical linguistics need to face the challenges brought by this influx of data. Automatic approaches to word comparison could provide invaluable help to pre-analyze data which can be later enhanced by experts. In this way, computational approaches can take care of the repetitive and schematic tasks leaving experts to concentrate on answering interesting questions. Here we test the potential of automatic methods to detect etymologically related words (cognates) in cross-linguistic data. Using a newly compiled database of expert cognate judgments across five different language families, we compare how well different automatic approaches distinguish related from unrelated words. Our results show that automatic methods can identify cognates with a very high degree of accuracy, reaching 89% for the best-performing method Infomap. We identify the specific strengths and weaknesses of these different methods and point to major challenges for future approaches. Current automatic approaches for cognate detection-although not perfect-could become an important component of future research in historical linguistics.

  20. The Potential of Automatic Word Comparison for Historical Linguistics

    PubMed Central

    Greenhill, Simon J.; Gray, Russell D.

    2017-01-01

    The amount of data from languages spoken all over the world is rapidly increasing. Traditional manual methods in historical linguistics need to face the challenges brought by this influx of data. Automatic approaches to word comparison could provide invaluable help to pre-analyze data which can be later enhanced by experts. In this way, computational approaches can take care of the repetitive and schematic tasks leaving experts to concentrate on answering interesting questions. Here we test the potential of automatic methods to detect etymologically related words (cognates) in cross-linguistic data. Using a newly compiled database of expert cognate judgments across five different language families, we compare how well different automatic approaches distinguish related from unrelated words. Our results show that automatic methods can identify cognates with a very high degree of accuracy, reaching 89% for the best-performing method Infomap. We identify the specific strengths and weaknesses of these different methods and point to major challenges for future approaches. Current automatic approaches for cognate detection—although not perfect—could become an important component of future research in historical linguistics. PMID:28129337

  1. Computational models of airway branching morphogenesis.

    PubMed

    Varner, Victor D; Nelson, Celeste M

    2017-07-01

    The bronchial network of the mammalian lung consists of millions of dichotomous branches arranged in a highly complex, space-filling tree. Recent computational models of branching morphogenesis in the lung have helped uncover the biological mechanisms that construct this ramified architecture. In this review, we focus on three different theoretical approaches - geometric modeling, reaction-diffusion modeling, and continuum mechanical modeling - and discuss how, taken together, these models have identified the geometric principles necessary to build an efficient bronchial network, as well as the patterning mechanisms that specify airway geometry in the developing embryo. We emphasize models that are integrated with biological experiments and suggest how recent progress in computational modeling has advanced our understanding of airway branching morphogenesis. Copyright © 2016 Elsevier Ltd. All rights reserved.

  2. Feasibility study of an Integrated Program for Aerospace vehicle Design (IPAD). Volume 1B: Concise review

    NASA Technical Reports Server (NTRS)

    Miller, R. E., Jr.; Southall, J. W.; Kawaguchi, A. S.; Redhed, D. D.

    1973-01-01

    Reports on the design process, support of the design process, IPAD System design catalog of IPAD technical program elements, IPAD System development and operation, and IPAD benefits and impact are concisely reviewed. The approach used to define the design is described. Major activities performed during the product development cycle are identified. The computer system requirements necessary to support the design process are given as computational requirements of the host system, technical program elements and system features. The IPAD computer system design is presented as concepts, a functional description and an organizational diagram of its major components. The cost and schedules and a three phase plan for IPAD implementation are presented. The benefits and impact of IPAD technology are discussed.

  3. Utility of Computational Methods to Identify the Apoptosis Machinery in Unicellular Eukaryotes

    PubMed Central

    Durand, Pierre Marcel; Coetzer, Theresa Louise

    2008-01-01

    Apoptosis is the phenotypic result of an active, regulated process of self-destruction. Following various cellular insults, apoptosis has been demonstrated in numerous unicellular eukaryotes, but very little is known about the genes and proteins that initiate and execute this process in this group of organisms. A bioinformatic approach presents an array of powerful methods to direct investigators in the identification of the apoptosis machinery in protozoans. In this review, we discuss some of the available computational methods and illustrate how they may be applied using the identification of a Plasmodium falciparum metacaspase gene as an example. PMID:19812769

  4. SUMC fault tolerant computer system

    NASA Technical Reports Server (NTRS)

    1980-01-01

    The results of the trade studies are presented. These trades cover: establishing the basic configuration, establishing the CPU/memory configuration, establishing an approach to crosstrapping interfaces, defining the requirements of the redundancy management unit (RMU), establishing a spare plane switching strategy for the fault-tolerant memory (FTM), and identifying the most cost effective way of extending the memory addressing capability beyond the 64 K-bytes (K=1024) of SUMC-II B. The results of the design are compiled in Contract End Item (CEI) Specification for the NASA Standard Spacecraft Computer II (NSSC-II), IBM 7934507. The implementation of the FTM and memory address expansion.

  5. Project Photofly: New 3d Modeling Online Web Service (case Studies and Assessments)

    NASA Astrophysics Data System (ADS)

    Abate, D.; Furini, G.; Migliori, S.; Pierattini, S.

    2011-09-01

    During summer 2010, Autodesk has released a still ongoing project called Project Photofly, freely downloadable from AutodeskLab web site until August 1 2011. Project Photofly based on computer-vision and photogrammetric principles, exploiting the power of cloud computing, is a web service able to convert collections of photographs into 3D models. Aim of our research was to evaluate the Project Photofly, through different case studies, for 3D modeling of cultural heritage monuments and objects, mostly to identify for which goals and objects it is suitable. The automatic approach will be mainly analyzed.

  6. A Patient Record-Filing System for Family Practice

    PubMed Central

    Levitt, Cheryl

    1988-01-01

    The efficient storage and easy retrieval of quality records are a central concern of good family practice. Many physicians starting out in practice have difficulty choosing a practical and lasting system for storing their records. Some who have established practices are installing computers in their offices and finding that their filing systems are worn, outdated, and incompatible with computerized systems. This article describes a new filing system installed simultaneously with a new computer system in a family-practice teaching centre. The approach adopted solved all identifiable problems and is applicable in family practices of all sizes.

  7. Local deformation for soft tissue simulation

    PubMed Central

    Omar, Nadzeri; Zhong, Yongmin; Smith, Julian; Gu, Chengfan

    2016-01-01

    ABSTRACT This paper presents a new methodology to localize the deformation range to improve the computational efficiency for soft tissue simulation. This methodology identifies the local deformation range from the stress distribution in soft tissues due to an external force. A stress estimation method is used based on elastic theory to estimate the stress in soft tissues according to a depth from the contact surface. The proposed methodology can be used with both mass-spring and finite element modeling approaches for soft tissue deformation. Experimental results show that the proposed methodology can improve the computational efficiency while maintaining the modeling realism. PMID:27286482

  8. Neptune: a bioinformatics tool for rapid discovery of genomic variation in bacterial populations

    PubMed Central

    Marinier, Eric; Zaheer, Rahat; Berry, Chrystal; Weedmark, Kelly A.; Domaratzki, Michael; Mabon, Philip; Knox, Natalie C.; Reimer, Aleisha R.; Graham, Morag R.; Chui, Linda; Patterson-Fortin, Laura; Zhang, Jian; Pagotto, Franco; Farber, Jeff; Mahony, Jim; Seyer, Karine; Bekal, Sadjia; Tremblay, Cécile; Isaac-Renton, Judy; Prystajecky, Natalie; Chen, Jessica; Slade, Peter

    2017-01-01

    Abstract The ready availability of vast amounts of genomic sequence data has created the need to rethink comparative genomics algorithms using ‘big data’ approaches. Neptune is an efficient system for rapidly locating differentially abundant genomic content in bacterial populations using an exact k-mer matching strategy, while accommodating k-mer mismatches. Neptune’s loci discovery process identifies sequences that are sufficiently common to a group of target sequences and sufficiently absent from non-targets using probabilistic models. Neptune uses parallel computing to efficiently identify and extract these loci from draft genome assemblies without requiring multiple sequence alignments or other computationally expensive comparative sequence analyses. Tests on simulated and real datasets showed that Neptune rapidly identifies regions that are both sensitive and specific. We demonstrate that this system can identify trait-specific loci from different bacterial lineages. Neptune is broadly applicable for comparative bacterial analyses, yet will particularly benefit pathogenomic applications, owing to efficient and sensitive discovery of differentially abundant genomic loci. The software is available for download at: http://github.com/phac-nml/neptune. PMID:29048594

  9. Literature mining, gene-set enrichment and pathway analysis for target identification in Behçet's disease.

    PubMed

    Wilson, Paul; Larminie, Christopher; Smith, Rona

    2016-01-01

    To use literature mining to catalogue Behçet's associated genes, and advanced computational methods to improve the understanding of the pathways and signalling mechanisms that lead to the typical clinical characteristics of Behçet's patients. To extend this technique to identify potential treatment targets for further experimental validation. Text mining methods combined with gene enrichment tools, pathway analysis and causal analysis algorithms. This approach identified 247 human genes associated with Behçet's disease and the resulting disease map, comprising 644 nodes and 19220 edges, captured important details of the relationships between these genes and their associated pathways, as described in diverse data repositories. Pathway analysis has identified how Behçet's associated genes are likely to participate in innate and adaptive immune responses. Causal analysis algorithms have identified a number of potential therapeutic strategies for further investigation. Computational methods have captured pertinent features of the prominent disease characteristics presented in Behçet's disease and have highlighted NOD2, ICOS and IL18 signalling as potential therapeutic strategies.

  10. A computational approach to candidate gene prioritization for X-linked mental retardation using annotation-based binary filtering and motif-based linear discriminatory analysis

    PubMed Central

    2011-01-01

    Background Several computational candidate gene selection and prioritization methods have recently been developed. These in silico selection and prioritization techniques are usually based on two central approaches - the examination of similarities to known disease genes and/or the evaluation of functional annotation of genes. Each of these approaches has its own caveats. Here we employ a previously described method of candidate gene prioritization based mainly on gene annotation, in accompaniment with a technique based on the evaluation of pertinent sequence motifs or signatures, in an attempt to refine the gene prioritization approach. We apply this approach to X-linked mental retardation (XLMR), a group of heterogeneous disorders for which some of the underlying genetics is known. Results The gene annotation-based binary filtering method yielded a ranked list of putative XLMR candidate genes with good plausibility of being associated with the development of mental retardation. In parallel, a motif finding approach based on linear discriminatory analysis (LDA) was employed to identify short sequence patterns that may discriminate XLMR from non-XLMR genes. High rates (>80%) of correct classification was achieved, suggesting that the identification of these motifs effectively captures genomic signals associated with XLMR vs. non-XLMR genes. The computational tools developed for the motif-based LDA is integrated into the freely available genomic analysis portal Galaxy (http://main.g2.bx.psu.edu/). Nine genes (APLN, ZC4H2, MAGED4, MAGED4B, RAP2C, FAM156A, FAM156B, TBL1X, and UXT) were highlighted as highly-ranked XLMR methods. Conclusions The combination of gene annotation information and sequence motif-orientated computational candidate gene prediction methods highlight an added benefit in generating a list of plausible candidate genes, as has been demonstrated for XLMR. Reviewers: This article was reviewed by Dr Barbara Bardoni (nominated by Prof Juergen Brosius); Prof Neil Smalheiser and Dr Dustin Holloway (nominated by Prof Charles DeLisi). PMID:21668950

  11. Inference of cancer-specific gene regulatory networks using soft computing rules.

    PubMed

    Wang, Xiaosheng; Gotoh, Osamu

    2010-03-24

    Perturbations of gene regulatory networks are essentially responsible for oncogenesis. Therefore, inferring the gene regulatory networks is a key step to overcoming cancer. In this work, we propose a method for inferring directed gene regulatory networks based on soft computing rules, which can identify important cause-effect regulatory relations of gene expression. First, we identify important genes associated with a specific cancer (colon cancer) using a supervised learning approach. Next, we reconstruct the gene regulatory networks by inferring the regulatory relations among the identified genes, and their regulated relations by other genes within the genome. We obtain two meaningful findings. One is that upregulated genes are regulated by more genes than downregulated ones, while downregulated genes regulate more genes than upregulated ones. The other one is that tumor suppressors suppress tumor activators and activate other tumor suppressors strongly, while tumor activators activate other tumor activators and suppress tumor suppressors weakly, indicating the robustness of biological systems. These findings provide valuable insights into the pathogenesis of cancer.

  12. Building a computer program to support children, parents, and distraction during healthcare procedures.

    PubMed

    Hanrahan, Kirsten; McCarthy, Ann Marie; Kleiber, Charmaine; Ataman, Kaan; Street, W Nick; Zimmerman, M Bridget; Ersig, Anne L

    2012-10-01

    This secondary data analysis used data mining methods to develop predictive models of child risk for distress during a healthcare procedure. Data used came from a study that predicted factors associated with children's responses to an intravenous catheter insertion while parents provided distraction coaching. From the 255 items used in the primary study, 44 predictive items were identified through automatic feature selection and used to build support vector machine regression models. Models were validated using multiple cross-validation tests and by comparing variables identified as explanatory in the traditional versus support vector machine regression. Rule-based approaches were applied to the model outputs to identify overall risk for distress. A decision tree was then applied to evidence-based instructions for tailoring distraction to characteristics and preferences of the parent and child. The resulting decision support computer application, titled Children, Parents and Distraction, is being used in research. Future use will support practitioners in deciding the level and type of distraction intervention needed by a child undergoing a healthcare procedure.

  13. Study of launch site processing and facilities for future launch vehicles

    NASA Astrophysics Data System (ADS)

    Shaffer, Rex

    1995-03-01

    The purpose of this research is to provide innovative and creative approaches to assess the impact to the Kennedy Space Center and other launch sites for a range of candidate manned and unmanned space transportation systems. The general scope of the research includes the engineering activities, analyses, and evaluations defined in the four tasks below: (1) development of innovative approaches and computer aided tools; (2) operations analyses of launch vehicle concepts and designs; (3) assessment of ground operations impacts; and (4) development of methodologies to identify promising technologies.

  14. Informational approach to the analysis of acoustic signals

    NASA Astrophysics Data System (ADS)

    Senkevich, Yuriy; Dyuk, Vyacheslav; Mishchenko, Mikhail; Solodchuk, Alexandra

    2017-10-01

    The example of linguistic processing of acoustic signals of a seismic event would be an information approach to the processing of non-stationary signals. The method for converting an acoustic signal into an information message is described by identifying repetitive self-similar patterns. The definitions of the event selection indicators in the symbolic recording of the acoustic signal are given. The results of processing an acoustic signal by a computer program realizing the processing of linguistic data are shown. Advantages and disadvantages of using software algorithms are indicated.

  15. Hit discovery and hit-to-lead approaches.

    PubMed

    Keseru, György M; Makara, Gergely M

    2006-08-01

    Hit discovery technologies range from traditional high-throughput screening to affinity selection of large libraries, fragment-based techniques and computer-aided de novo design, many of which have been extensively reviewed. Development of quality leads using hit confirmation and hit-to-lead approaches present their own challenges, depending on the hit discovery method used to identify the initial hits. In this paper, we summarize common industry practices adopted to tackle hit-to-lead challenges and review how the advantages and drawbacks of different hit discovery techniques could affect the various issues hit-to-lead groups face.

  16. Study of launch site processing and facilities for future launch vehicles

    NASA Technical Reports Server (NTRS)

    Shaffer, Rex

    1995-01-01

    The purpose of this research is to provide innovative and creative approaches to assess the impact to the Kennedy Space Center and other launch sites for a range of candidate manned and unmanned space transportation systems. The general scope of the research includes the engineering activities, analyses, and evaluations defined in the four tasks below: (1) development of innovative approaches and computer aided tools; (2) operations analyses of launch vehicle concepts and designs; (3) assessment of ground operations impacts; and (4) development of methodologies to identify promising technologies.

  17. Validation of learning style measures: implications for medical education practice.

    PubMed

    Chapman, Dane M; Calhoun, Judith G

    2006-06-01

    It is unclear which learners would most benefit from the more individualised, student-structured, interactive approaches characteristic of problem-based and computer-assisted learning. The validity of learning style measures is uncertain, and there is no unifying learning style construct identified to predict such learners. This study was conducted to validate learning style constructs and to identify the learners most likely to benefit from problem-based and computer-assisted curricula. Using a cross-sectional design, 3 established learning style inventories were administered to 97 post-Year 2 medical students. Cognitive personality was measured by the Group Embedded Figures Test, information processing by the Learning Styles Inventory, and instructional preference by the Learning Preference Inventory. The 11 subscales from the 3 inventories were factor-analysed to identify common learning constructs and to verify construct validity. Concurrent validity was determined by intercorrelations of the 11 subscales. A total of 94 pre-clinical medical students completed all 3 inventories. Five meaningful learning style constructs were derived from the 11 subscales: student- versus teacher-structured learning; concrete versus abstract learning; passive versus active learning; individual versus group learning, and field-dependence versus field-independence. The concurrent validity of 10 of 11 subscales was supported by correlation analysis. Medical students most likely to thrive in a problem-based or computer-assisted learning environment would be expected to score highly on abstract, active and individual learning constructs and would be more field-independent. Learning style measures were validated in a medical student population and learning constructs were established for identifying learners who would most likely benefit from a problem-based or computer-assisted curriculum.

  18. Identification of sequence–structure RNA binding motifs for SELEX-derived aptamers

    PubMed Central

    Hoinka, Jan; Zotenko, Elena; Friedman, Adam; Sauna, Zuben E.; Przytycka, Teresa M.

    2012-01-01

    Motivation: Systematic Evolution of Ligands by EXponential Enrichment (SELEX) represents a state-of-the-art technology to isolate single-stranded (ribo)nucleic acid fragments, named aptamers, which bind to a molecule (or molecules) of interest via specific structural regions induced by their sequence-dependent fold. This powerful method has applications in designing protein inhibitors, molecular detection systems, therapeutic drugs and antibody replacement among others. However, full understanding and consequently optimal utilization of the process has lagged behind its wide application due to the lack of dedicated computational approaches. At the same time, the combination of SELEX with novel sequencing technologies is beginning to provide the data that will allow the examination of a variety of properties of the selection process. Results: To close this gap we developed, Aptamotif, a computational method for the identification of sequence–structure motifs in SELEX-derived aptamers. To increase the chances of identifying functional motifs, Aptamotif uses an ensemble-based approach. We validated the method using two published aptamer datasets containing experimentally determined motifs of increasing complexity. We were able to recreate the author's findings to a high degree, thus proving the capability of our approach to identify binding motifs in SELEX data. Additionally, using our new experimental dataset, we illustrate the application of Aptamotif to elucidate several properties of the selection process. Contact: przytyck@ncbi.nlm.nih.gov, Zuben.Sauna@fda.hhs.gov PMID:22689764

  19. Computational identification of epitopes in the glycoproteins of novel bunyavirus (SFTS virus) recognized by a human monoclonal antibody (MAb 4-5)

    NASA Astrophysics Data System (ADS)

    Zhang, Wenshuai; Zeng, Xiaoyan; Zhang, Li; Peng, Haiyan; Jiao, Yongjun; Zeng, Jun; Treutlein, Herbert R.

    2013-06-01

    In this work, we have developed a new approach to predict the epitopes of antigens that are recognized by a specific antibody. Our method is based on the "multiple copy simultaneous search" (MCSS) approach which identifies optimal locations of small chemical functional groups on the surfaces of the antibody, and identifying sequence patterns of peptides that can bind to the surface of the antibody. The identified sequence patterns are then used to search the amino-acid sequence of the antigen protein. The approach was validated by reproducing the binding epitope of HIV gp120 envelop glycoprotein for the human neutralizing antibody as revealed in the available crystal structure. Our method was then applied to predict the epitopes of two glycoproteins of a newly discovered bunyavirus recognized by an antibody named MAb 4-5. These predicted epitopes can be verified by experimental methods. We also discuss the involvement of different amino acids in the antigen-antibody recognition based on the distributions of MCSS minima of different functional groups.

  20. A simplified approach to characterizing a kilovoltage source spectrum for accurate dose computation.

    PubMed

    Poirier, Yannick; Kouznetsov, Alexei; Tambasco, Mauro

    2012-06-01

    To investigate and validate the clinical feasibility of using half-value layer (HVL) and peak tube potential (kVp) for characterizing a kilovoltage (kV) source spectrum for the purpose of computing kV x-ray dose accrued from imaging procedures. To use this approach to characterize a Varian® On-Board Imager® (OBI) source and perform experimental validation of a novel in-house hybrid dose computation algorithm for kV x-rays. We characterized the spectrum of an imaging kV x-ray source using the HVL and the kVp as the sole beam quality identifiers using third-party freeware Spektr to generate the spectra. We studied the sensitivity of our dose computation algorithm to uncertainties in the beam's HVL and kVp by systematically varying these spectral parameters. To validate our approach experimentally, we characterized the spectrum of a Varian® OBI system by measuring the HVL using a Farmer-type Capintec ion chamber (0.06 cc) in air and compared dose calculations using our computationally validated in-house kV dose calculation code to measured percent depth-dose and transverse dose profiles for 80, 100, and 125 kVp open beams in a homogeneous phantom and a heterogeneous phantom comprising tissue, lung, and bone equivalent materials. The sensitivity analysis of the beam quality parameters (i.e., HVL, kVp, and field size) on dose computation accuracy shows that typical measurement uncertainties in the HVL and kVp (±0.2 mm Al and ±2 kVp, respectively) source characterization parameters lead to dose computation errors of less than 2%. Furthermore, for an open beam with no added filtration, HVL variations affect dose computation accuracy by less than 1% for a 125 kVp beam when field size is varied from 5 × 5 cm(2) to 40 × 40 cm(2). The central axis depth dose calculations and experimental measurements for the 80, 100, and 125 kVp energies agreed within 2% for the homogeneous and heterogeneous block phantoms, and agreement for the transverse dose profiles was within 6%. The HVL and kVp are sufficient for characterizing a kV x-ray source spectrum for accurate dose computation. As these parameters can be easily and accurately measured, they provide for a clinically feasible approach to characterizing a kV energy spectrum to be used for patient specific x-ray dose computations. Furthermore, these results provide experimental validation of our novel hybrid dose computation algorithm. © 2012 American Association of Physicists in Medicine.

  1. Quo vadis: Hydrologic inverse analyses using high-performance computing and a D-Wave quantum annealer

    NASA Astrophysics Data System (ADS)

    O'Malley, D.; Vesselinov, V. V.

    2017-12-01

    Classical microprocessors have had a dramatic impact on hydrology for decades, due largely to the exponential growth in computing power predicted by Moore's law. However, this growth is not expected to continue indefinitely and has already begun to slow. Quantum computing is an emerging alternative to classical microprocessors. Here, we demonstrated cutting edge inverse model analyses utilizing some of the best available resources in both worlds: high-performance classical computing and a D-Wave quantum annealer. The classical high-performance computing resources are utilized to build an advanced numerical model that assimilates data from O(10^5) observations, including water levels, drawdowns, and contaminant concentrations. The developed model accurately reproduces the hydrologic conditions at a Los Alamos National Laboratory contamination site, and can be leveraged to inform decision-making about site remediation. We demonstrate the use of a D-Wave 2X quantum annealer to solve hydrologic inverse problems. This work can be seen as an early step in quantum-computational hydrology. We compare and contrast our results with an early inverse approach in classical-computational hydrology that is comparable to the approach we use with quantum annealing. Our results show that quantum annealing can be useful for identifying regions of high and low permeability within an aquifer. While the problems we consider are small-scale compared to the problems that can be solved with modern classical computers, they are large compared to the problems that could be solved with early classical CPUs. Further, the binary nature of the high/low permeability problem makes it well-suited to quantum annealing, but challenging for classical computers.

  2. Enumerating all maximal frequent subtrees in collections of phylogenetic trees

    PubMed Central

    2014-01-01

    Background A common problem in phylogenetic analysis is to identify frequent patterns in a collection of phylogenetic trees. The goal is, roughly, to find a subset of the species (taxa) on which all or some significant subset of the trees agree. One popular method to do so is through maximum agreement subtrees (MASTs). MASTs are also used, among other things, as a metric for comparing phylogenetic trees, computing congruence indices and to identify horizontal gene transfer events. Results We give algorithms and experimental results for two approaches to identify common patterns in a collection of phylogenetic trees, one based on agreement subtrees, called maximal agreement subtrees, the other on frequent subtrees, called maximal frequent subtrees. These approaches can return subtrees on larger sets of taxa than MASTs, and can reveal new common phylogenetic relationships not present in either MASTs or the majority rule tree (a popular consensus method). Our current implementation is available on the web at https://code.google.com/p/mfst-miner/. Conclusions Our computational results confirm that maximal agreement subtrees and all maximal frequent subtrees can reveal a more complete phylogenetic picture of the common patterns in collections of phylogenetic trees than maximum agreement subtrees; they are also often more resolved than the majority rule tree. Further, our experiments show that enumerating maximal frequent subtrees is considerably more practical than enumerating ordinary (not necessarily maximal) frequent subtrees. PMID:25061474

  3. Enumerating all maximal frequent subtrees in collections of phylogenetic trees.

    PubMed

    Deepak, Akshay; Fernández-Baca, David

    2014-01-01

    A common problem in phylogenetic analysis is to identify frequent patterns in a collection of phylogenetic trees. The goal is, roughly, to find a subset of the species (taxa) on which all or some significant subset of the trees agree. One popular method to do so is through maximum agreement subtrees (MASTs). MASTs are also used, among other things, as a metric for comparing phylogenetic trees, computing congruence indices and to identify horizontal gene transfer events. We give algorithms and experimental results for two approaches to identify common patterns in a collection of phylogenetic trees, one based on agreement subtrees, called maximal agreement subtrees, the other on frequent subtrees, called maximal frequent subtrees. These approaches can return subtrees on larger sets of taxa than MASTs, and can reveal new common phylogenetic relationships not present in either MASTs or the majority rule tree (a popular consensus method). Our current implementation is available on the web at https://code.google.com/p/mfst-miner/. Our computational results confirm that maximal agreement subtrees and all maximal frequent subtrees can reveal a more complete phylogenetic picture of the common patterns in collections of phylogenetic trees than maximum agreement subtrees; they are also often more resolved than the majority rule tree. Further, our experiments show that enumerating maximal frequent subtrees is considerably more practical than enumerating ordinary (not necessarily maximal) frequent subtrees.

  4. Rapid discovery of inhibitors of Toxoplasma gondii using hybrid structure-based computational approach

    NASA Astrophysics Data System (ADS)

    Kortagere, Sandhya; Mui, Ernest; McLeod, Rima; Welsh, William J.

    2011-05-01

    Toxoplasma (T.) gondii, the causative agent of toxoplasmosis, is a ubiquitous opportunistic pathogen that infects individuals worldwide, and is a leading cause of severe congenital neurologic and ocular disease in humans. No vaccine to protect humans is available, and hypersensitivity and toxicity limit the use of the few available medicines. Therefore, safer and more effective medicines to treat toxoplasmosis are urgently needed. Using the Hybrid Structure Based (HSB) method, we have previously identified small molecule inhibitors of P. falciparum that seem to target a novel protein-protein interaction between the Myosin tail interacting protein and myosin light chain. This pathway has been hypothesized to be involved in invasion of host erythrocytes by the parasite and is broadly conserved among the apicomplexans. Guided by similar computational drug design approaches, we investigated this series of small molecules as potential inhibitors of T. gondii. Compound C3-21, identified as the most active inhibitor in this series, exhibited an IC50 value 500 nM against T. gondii. Among the 16 structural analogs of C3-21 tested thus far, nine additional compounds were identified with IC50 values <10.0 μM. In vitro assays have revealed that C3-21 markedly limits intracellular growth of T. gondii tachyzoites, but has no effect on host cell human foreskin fibroblasts (HFF) at concentrations more than a log greater than the concentration that inhibits the parasites.

  5. Science & Technology Review January/February 2010

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

    Bearinger, J P

    2009-11-30

    This month's issue has the following articles: (1) Innovative Materials Rise to the Radiation Challenge - Commentary by Bruce Warner; (2) The Hunt for Better Radiation Detection - New materials will help radiation detectors pick up weak signals and accurately identify illicit radioactive sources; (3) Time-Critical Technology Identifies Deadly Bloodborne Pathogens - A portable device can simultaneously distinguish up to five bloodborne pathogens in just minutes; (4) Defending Computer Networks against Attack - A Laboratory effort takes a new approach to detecting increasingly sophisticated cyber attacks; and (5) Imaging Cargo's Inner Secrets - Livermore-University of California collaborators are modeling amore » new radiographic technique for identifying nuclear materials concealed inside cargo containers.« less

  6. Summary and Findings of the ARL Dynamic Failure Forum

    DTIC Science & Technology

    2016-09-29

    short beam shear, quasi -static indentation, depth of penetration, and V50 limit velocity. o Experimental technique suggestions for improvement included...art in experimental , theoretical, and computational studies of dynamic failure. The forum also focused on identifying technologies and approaches...Army-specific problems. Experimental exploration of material behavior and an improved ability to parameterize material models is essential to improving

  7. Patterns of Auditory Perception Skills in Children with Learning Disabilities: A Computer-Assisted Approach.

    ERIC Educational Resources Information Center

    Pressman, E.; And Others

    1986-01-01

    The auditory receptive language skills of 40 learning disabled (LD) and 40 non-disabled boys (all 7 - 11 years old) were assessed via computerized versions of subtests of the Goldman-Fristoe-Woodcock Auditory Skills Test Battery. The computerized assessment correctly identified 92.5% of the LD group and 65% of the normal control children. (DB)

  8. Labeling Defects in CT Images of Hardwood Logs with Species-Dependent and Species-Independent Classifiers

    Treesearch

    Pei Li; Jing He; A. Lynn Abbott; Daniel L. Schmoldt

    1996-01-01

    This paper analyses computed tomography (CT) images of hardwood logs, with the goal of locating internal defects. The ability to detect and identify defects automatically is a critical component of efficiency improvements for future sawmills and veneer mills. This paper describes an approach in which 1) histogram equalization is used during preprocessing to normalize...

  9. Pedagogy and Interactive White Board Technology Integration in Higher Education Institutions: Computer-Based Teaching Scenario Protoypes

    ERIC Educational Resources Information Center

    Al-Qirim, Nabeel; Mesmari, Ahlam; Mazroeei, Khawlah; Khatri, Shamma; Kaabi, Zuwainah

    2017-01-01

    This research is part of a research trilogy that investigated issues impacting Interactive White Board Technology (IWBT) adoption and use in a higher education institution. The other two research studies in this trilogy used (1) focus group approach and (2) survey research to identify such impacting factors. One major insight from these two…

  10. Nutritional Systems Biology Modeling: From Molecular Mechanisms to Physiology

    PubMed Central

    de Graaf, Albert A.; Freidig, Andreas P.; De Roos, Baukje; Jamshidi, Neema; Heinemann, Matthias; Rullmann, Johan A.C.; Hall, Kevin D.; Adiels, Martin; van Ommen, Ben

    2009-01-01

    The use of computational modeling and simulation has increased in many biological fields, but despite their potential these techniques are only marginally applied in nutritional sciences. Nevertheless, recent applications of modeling have been instrumental in answering important nutritional questions from the cellular up to the physiological levels. Capturing the complexity of today's important nutritional research questions poses a challenge for modeling to become truly integrative in the consideration and interpretation of experimental data at widely differing scales of space and time. In this review, we discuss a selection of available modeling approaches and applications relevant for nutrition. We then put these models into perspective by categorizing them according to their space and time domain. Through this categorization process, we identified a dearth of models that consider processes occurring between the microscopic and macroscopic scale. We propose a “middle-out” strategy to develop the required full-scale, multilevel computational models. Exhaustive and accurate phenotyping, the use of the virtual patient concept, and the development of biomarkers from “-omics” signatures are identified as key elements of a successful systems biology modeling approach in nutrition research—one that integrates physiological mechanisms and data at multiple space and time scales. PMID:19956660

  11. Integrating Candida albicans metabolism with biofilm heterogeneity by transcriptome mapping

    NASA Astrophysics Data System (ADS)

    Rajendran, Ranjith; May, Ali; Sherry, Leighann; Kean, Ryan; Williams, Craig; Jones, Brian L.; Burgess, Karl V.; Heringa, Jaap; Abeln, Sanne; Brandt, Bernd W.; Munro, Carol A.; Ramage, Gordon

    2016-10-01

    Candida albicans biofilm formation is an important virulence factor in the pathogenesis of disease, a characteristic which has been shown to be heterogeneous in clinical isolates. Using an unbiased computational approach we investigated the central metabolic pathways driving biofilm heterogeneity. Transcripts from high (HBF) and low (LBF) biofilm forming isolates were analysed by RNA sequencing, with 6312 genes identified to be expressed in these two phenotypes. With a dedicated computational approach we identified and validated a significantly differentially expressed subnetwork of genes associated with these biofilm phenotypes. Our analysis revealed amino acid metabolism, such as arginine, proline, aspartate and glutamate metabolism, were predominantly upregulated in the HBF phenotype. On the contrary, purine, starch and sucrose metabolism was generally upregulated in the LBF phenotype. The aspartate aminotransferase gene AAT1 was found to be a common member of these amino acid pathways and significantly upregulated in the HBF phenotype. Pharmacological inhibition of AAT1 enzyme activity significantly reduced biofilm formation in a dose-dependent manner. Collectively, these findings provide evidence that biofilm phenotype is associated with differential regulation of metabolic pathways. Understanding and targeting such pathways, such as amino acid metabolism, is potentially useful for developing diagnostics and new antifungals to treat biofilm-based infections.

  12. Biologically inspired EM image alignment and neural reconstruction.

    PubMed

    Knowles-Barley, Seymour; Butcher, Nancy J; Meinertzhagen, Ian A; Armstrong, J Douglas

    2011-08-15

    Three-dimensional reconstruction of consecutive serial-section transmission electron microscopy (ssTEM) images of neural tissue currently requires many hours of manual tracing and annotation. Several computational techniques have already been applied to ssTEM images to facilitate 3D reconstruction and ease this burden. Here, we present an alternative computational approach for ssTEM image analysis. We have used biologically inspired receptive fields as a basis for a ridge detection algorithm to identify cell membranes, synaptic contacts and mitochondria. Detected line segments are used to improve alignment between consecutive images and we have joined small segments of membrane into cell surfaces using a dynamic programming algorithm similar to the Needleman-Wunsch and Smith-Waterman DNA sequence alignment procedures. A shortest path-based approach has been used to close edges and achieve image segmentation. Partial reconstructions were automatically generated and used as a basis for semi-automatic reconstruction of neural tissue. The accuracy of partial reconstructions was evaluated and 96% of membrane could be identified at the cost of 13% false positive detections. An open-source reference implementation is available in the Supplementary information. seymour.kb@ed.ac.uk; douglas.armstrong@ed.ac.uk Supplementary data are available at Bioinformatics online.

  13. Systems Approach to Understanding Electromechanical Activity in the Human Heart

    PubMed Central

    Rudy, Yoram; Ackerman, Michael J.; Bers, Donald M.; Clancy, Colleen E.; Houser, Steven R.; London, Barry; McCulloch, Andrew D.; Przywara, Dennis A.; Rasmusson, Randall L.; Solaro, R. John; Trayanova, Natalia A.; Van Wagoner, David R.; Varró, András; Weiss, James N.; Lathrop, David A.

    2010-01-01

    The National Heart, Lung, and Blood Institute (NHLBI) convened a workshop of cardiologists, cardiac electrophysiologists, cell biophysicists, and computational modelers on August 20 and 21, 2007, in Washington, DC, to advise the NHLBI on new research directions needed to develop integrative approaches to elucidate human cardiac function. The workshop strove to identify limitations in the use of data from nonhuman animal species for elucidation of human electromechanical function/activity and to identify what specific information on ion channel kinetics, calcium handling, and dynamic changes in the intracellular/extracellular milieu is needed from human cardiac tissues to develop more robust computational models of human cardiac electromechanical activity. This article summarizes the workshop discussions and recommendations on the following topics: (1) limitations of animal models and differences from human electrophysiology, (2) modeling ion channel structure/function in the context of whole-cell electrophysiology, (3) excitation–contraction coupling and regulatory pathways, (4) whole-heart simulations of human electromechanical activity, and (5) what human data are currently needed and how to obtain them. The recommendations can be found on the NHLBI Web site at http://www.nhlbi.nih.gov/meetings/workshops/electro.htm. PMID:18779456

  14. Lumen-based detection of prostate cancer via convolutional neural networks

    NASA Astrophysics Data System (ADS)

    Kwak, Jin Tae; Hewitt, Stephen M.

    2017-03-01

    We present a deep learning approach for detecting prostate cancers. The approach consists of two steps. In the first step, we perform tissue segmentation that identifies lumens within digitized prostate tissue specimen images. Intensity- and texture-based image features are computed at five different scales, and a multiview boosting method is adopted to cooperatively combine the image features from differing scales and to identify lumens. In the second step, we utilize convolutional neural networks (CNN) to automatically extract high-level image features of lumens and to predict cancers. The segmented lumens are rescaled to reduce computational complexity and data augmentation by scaling, rotating, and flipping the rescaled image is applied to avoid overfitting. We evaluate the proposed method using two tissue microarrays (TMA) - TMA1 includes 162 tissue specimens (73 Benign and 89 Cancer) and TMA2 comprises 185 tissue specimens (70 Benign and 115 Cancer). In cross-validation on TMA1, the proposed method achieved an AUC of 0.95 (CI: 0.93-0.98). Trained on TMA1 and tested on TMA2, CNN obtained an AUC of 0.95 (CI: 0.92-0.98). This demonstrates that the proposed method can potentially improve prostate cancer pathology.

  15. Deep sequencing and in silico analysis of small RNA library reveals novel miRNA from leaf Persicaria minor transcriptome.

    PubMed

    Samad, Abdul Fatah A; Nazaruddin, Nazaruddin; Murad, Abdul Munir Abdul; Jani, Jaeyres; Zainal, Zamri; Ismail, Ismanizan

    2018-03-01

    In current era, majority of microRNA (miRNA) are being discovered through computational approaches which are more confined towards model plants. Here, for the first time, we have described the identification and characterization of novel miRNA in a non-model plant, Persicaria minor ( P . minor ) using computational approach. Unannotated sequences from deep sequencing were analyzed based on previous well-established parameters. Around 24 putative novel miRNAs were identified from 6,417,780 reads of the unannotated sequence which represented 11 unique putative miRNA sequences. PsRobot target prediction tool was deployed to identify the target transcripts of putative novel miRNAs. Most of the predicted target transcripts (mRNAs) were known to be involved in plant development and stress responses. Gene ontology showed that majority of the putative novel miRNA targets involved in cellular component (69.07%), followed by molecular function (30.08%) and biological process (0.85%). Out of 11 unique putative miRNAs, 7 miRNAs were validated through semi-quantitative PCR. These novel miRNAs discoveries in P . minor may develop and update the current public miRNA database.

  16. A Model of Differential Growth-Guided Apical Hook Formation in Plants

    PubMed Central

    Žádníková, Petra; Wabnik, Krzysztof; Abuzeineh, Anas; Prusinkiewicz, Przemysław

    2016-01-01

    Differential cell growth enables flexible organ bending in the presence of environmental signals such as light or gravity. A prominent example of the developmental processes based on differential cell growth is the formation of the apical hook that protects the fragile shoot apical meristem when it breaks through the soil during germination. Here, we combined in silico and in vivo approaches to identify a minimal mechanism producing auxin gradient-guided differential growth during the establishment of the apical hook in the model plant Arabidopsis thaliana. Computer simulation models based on experimental data demonstrate that asymmetric expression of the PIN-FORMED auxin efflux carrier at the concave (inner) versus convex (outer) side of the hook suffices to establish an auxin maximum in the epidermis at the concave side of the apical hook. Furthermore, we propose a mechanism that translates this maximum into differential growth, and thus curvature, of the apical hook. Through a combination of experimental and in silico computational approaches, we have identified the individual contributions of differential cell elongation and proliferation to defining the apical hook and reveal the role of auxin-ethylene crosstalk in balancing these two processes. PMID:27754878

  17. A theoretical study on predicted protein targets of apple polyphenols and possible mechanisms of chemoprevention in colorectal cancer

    PubMed Central

    Scafuri, Bernardina; Marabotti, Anna; Carbone, Virginia; Minasi, Paola; Dotolo, Serena; Facchiano, Angelo

    2016-01-01

    We investigated the potential role of apple phenolic compounds in human pathologies by integrating chemical characterization of phenolic compounds in three apple varieties, computational approaches to identify potential protein targets of the compounds, bioinformatics analyses on data from public archive of gene expression data, and functional analyses to hypothesize the effects of the selected compounds in molecular pathways. Starting by the analytic characterization of phenolic compounds in three apple varieties, i.e. Annurca, Red Delicious, and Golden Delicious, we used computational approaches to verify by reverse docking the potential protein targets of the identified compounds. Direct docking validation of the potential protein-ligand interactions has generated a short list of human proteins potentially bound by the apple phenolic compounds. By considering the known chemo-preventive role of apple antioxidants’ extracts against some human pathologies, we performed a functional analysis by comparison with experimental gene expression data and interaction networks, obtained from public repositories. The results suggest the hypothesis that chemo-preventive effects of apple extracts in human pathologies, in particular for colorectal cancer, may be the interference with the activity of nucleotide metabolism and methylation enzymes, similarly to some classes of anticancer drugs. PMID:27587238

  18. Crowdsourced estimation of cognitive decline and resilience in Alzheimer’s disease

    PubMed Central

    Allen, Genevera I; Amoroso, Nicola; Anghel, Catalina; Balagurusamy, Venkat; Bare, Christopher J; Beaton, Derek; Bellotti, Roberto; Bennett, David A; Boehme, Kevin; Boutros, Paul C; Caberlotto, Laura; Caloian, Cristian; Campbell, Frederick; Neto, Elias Chaibub; Chang, Yu-Chuan; Chen, Beibei; Chen, Chien-Yu; Chien, Ting-Ying; Clark, Tim; Das, Sudeshna; Davatzikos, Christos; Deng, Jieyao; Dillenberger, Donna; Dobson, Richard JB; Dong, Qilin; Doshi, Jimit; Duma, Denise; Errico, Rosangela; Erus, Guray; Everett, Evan; Fardo, David W; Friend, Stephen H; Fröhlich, Holger; Gan, Jessica; St George-Hyslop, Peter; Ghosh, Satrajit S; Glaab, Enrico; Green, Robert C; Guan, Yuanfang; Hong, Ming-Yi; Huang, Chao; Hwang, Jinseub; Ibrahim, Joseph; Inglese, Paolo; Jiang, Qijia; Katsumata, Yuriko; Kong, Dehan; Krause, Roland; Lalonde, Emilie; Lauria, Mario; Lee, Eunjee; Lin, Xihui; Liu, Zhandong; Livingstone, Julie; Logsdon, Benjamin A; Lovestone, Simon; Lyappan, Anandhi; Ma, Michelle; Malhotra, Ashutosh; Maxwell, Taylor J; Merrill, Emily; Nagorski, John; Namasivayam, Aishwarya; Narayan, Manjari; Naz, Mufassra; Newhouse, Stephen J; Norman, Thea C; Nurtdinov, Ramil N; Oyang, Yen-Jen; Pawitan, Yudi; Peng, Shengwen; Piccolo, Stephen R; Praveen, Paurush; Priami, Corrado; Sabelnykova, Veronica Y; Senger, Philipp; Shen, Xia; Simmons, Andrew; Sotiras, Aristeidis; Stolovitzky, Gustavo; Tangaro, Sabina; Tateo, Andrea; Tung, Yi-An; Tustison, Nicholas J; Varol, Erdem; Vradenburg, George; Weiner, Michael W; Xiao, Guanghua; Xie, Lei; Xie, Yang; Xu, Jia; Yang, Hojin; Zhan, Xiaowei; Zhou, Yunyun; Zhu, Fan; Zhu, Hongtu; Zhu, Shanfeng

    2017-01-01

    Identifying accurate biomarkers of cognitive decline is essential for advancing early diagnosis and prevention therapies in Alzheimer’s Disease. The Alzheimer’s Disease DREAM Challenge was designed as a computational crowdsourced project to benchmark the current state-of-the-art in predicting cognitive outcomes in Alzheimer’s Disease based on high-dimensional, publicly available genetic and structural imaging data. This meta-analysis failed to identify a meaningful predictor developed from either data modality, suggesting that alternate approaches should be considered for to prediction of cognitive performance. PMID:27079753

  19. Computer-aided detection of renal calculi from noncontrast CT images using TV-flow and MSER features.

    PubMed

    Liu, Jianfei; Wang, Shijun; Turkbey, Evrim B; Linguraru, Marius George; Yao, Jianhua; Summers, Ronald M

    2015-01-01

    Renal calculi are common extracolonic incidental findings on computed tomographic colonography (CTC). This work aims to develop a fully automated computer-aided diagnosis system to accurately detect renal calculi on CTC images. The authors developed a total variation (TV) flow method to reduce image noise within the kidneys while maintaining the characteristic appearance of renal calculi. Maximally stable extremal region (MSER) features were then calculated to robustly identify calculi candidates. Finally, the authors computed texture and shape features that were imported to support vector machines for calculus classification. The method was validated on a dataset of 192 patients and compared to a baseline approach that detects calculi by thresholding. The authors also compared their method with the detection approaches using anisotropic diffusion and nonsmoothing. At a false positive rate of 8 per patient, the sensitivities of the new method and the baseline thresholding approach were 69% and 35% (p < 1e - 3) on all calculi from 1 to 433 mm(3) in the testing dataset. The sensitivities of the detection methods using anisotropic diffusion and nonsmoothing were 36% and 0%, respectively. The sensitivity of the new method increased to 90% if only larger and more clinically relevant calculi were considered. Experimental results demonstrated that TV-flow and MSER features are efficient means to robustly and accurately detect renal calculi on low-dose, high noise CTC images. Thus, the proposed method can potentially improve diagnosis.

  20. Computer-aided detection of renal calculi from noncontrast CT images using TV-flow and MSER features

    PubMed Central

    Liu, Jianfei; Wang, Shijun; Turkbey, Evrim B.; Linguraru, Marius George; Yao, Jianhua; Summers, Ronald M.

    2015-01-01

    Purpose: Renal calculi are common extracolonic incidental findings on computed tomographic colonography (CTC). This work aims to develop a fully automated computer-aided diagnosis system to accurately detect renal calculi on CTC images. Methods: The authors developed a total variation (TV) flow method to reduce image noise within the kidneys while maintaining the characteristic appearance of renal calculi. Maximally stable extremal region (MSER) features were then calculated to robustly identify calculi candidates. Finally, the authors computed texture and shape features that were imported to support vector machines for calculus classification. The method was validated on a dataset of 192 patients and compared to a baseline approach that detects calculi by thresholding. The authors also compared their method with the detection approaches using anisotropic diffusion and nonsmoothing. Results: At a false positive rate of 8 per patient, the sensitivities of the new method and the baseline thresholding approach were 69% and 35% (p < 1e − 3) on all calculi from 1 to 433 mm3 in the testing dataset. The sensitivities of the detection methods using anisotropic diffusion and nonsmoothing were 36% and 0%, respectively. The sensitivity of the new method increased to 90% if only larger and more clinically relevant calculi were considered. Conclusions: Experimental results demonstrated that TV-flow and MSER features are efficient means to robustly and accurately detect renal calculi on low-dose, high noise CTC images. Thus, the proposed method can potentially improve diagnosis. PMID:25563255

  1. Reduction of numerical diffusion in three-dimensional vortical flows using a coupled Eulerian/Lagrangian solution procedure

    NASA Technical Reports Server (NTRS)

    Felici, Helene M.; Drela, Mark

    1993-01-01

    A new approach based on the coupling of an Eulerian and a Lagrangian solver, aimed at reducing the numerical diffusion errors of standard Eulerian time-marching finite-volume solvers, is presented. The approach is applied to the computation of the secondary flow in two bent pipes and the flow around a 3D wing. Using convective point markers the Lagrangian approach provides a correction of the basic Eulerian solution. The Eulerian flow in turn integrates in time the Lagrangian state-vector. A comparison of coarse and fine grid Eulerian solutions makes it possible to identify numerical diffusion. It is shown that the Eulerian/Lagrangian approach is an effective method for reducing numerical diffusion errors.

  2. An information-theoretic approach to assess practical identifiability of parametric dynamical systems.

    PubMed

    Pant, Sanjay; Lombardi, Damiano

    2015-10-01

    A new approach for assessing parameter identifiability of dynamical systems in a Bayesian setting is presented. The concept of Shannon entropy is employed to measure the inherent uncertainty in the parameters. The expected reduction in this uncertainty is seen as the amount of information one expects to gain about the parameters due to the availability of noisy measurements of the dynamical system. Such expected information gain is interpreted in terms of the variance of a hypothetical measurement device that can measure the parameters directly, and is related to practical identifiability of the parameters. If the individual parameters are unidentifiable, correlation between parameter combinations is assessed through conditional mutual information to determine which sets of parameters can be identified together. The information theoretic quantities of entropy and information are evaluated numerically through a combination of Monte Carlo and k-nearest neighbour methods in a non-parametric fashion. Unlike many methods to evaluate identifiability proposed in the literature, the proposed approach takes the measurement-noise into account and is not restricted to any particular noise-structure. Whilst computationally intensive for large dynamical systems, it is easily parallelisable and is non-intrusive as it does not necessitate re-writing of the numerical solvers of the dynamical system. The application of such an approach is presented for a variety of dynamical systems--ranging from systems governed by ordinary differential equations to partial differential equations--and, where possible, validated against results previously published in the literature. Copyright © 2015 Elsevier Inc. All rights reserved.

  3. On Target Localization Using Combined RSS and AoA Measurements

    PubMed Central

    Beko, Marko; Dinis, Rui

    2018-01-01

    This work revises existing solutions for a problem of target localization in wireless sensor networks (WSNs), utilizing integrated measurements, namely received signal strength (RSS) and angle of arrival (AoA). The problem of RSS/AoA-based target localization became very popular in the research community recently, owing to its great applicability potential and relatively low implementation cost. Therefore, here, a comprehensive study of the state-of-the-art (SoA) solutions and their detailed analysis is presented. The beginning of this work starts by considering the SoA approaches based on convex relaxation techniques (more computationally complex in general), and it goes through other (less computationally complex) approaches, as well, such as the ones based on the generalized trust region sub-problems framework and linear least squares. Furthermore, a detailed analysis of the computational complexity of each solution is reviewed. Furthermore, an extensive set of simulation results is presented. Finally, the main conclusions are summarized, and a set of future aspects and trends that might be interesting for future research in this area is identified. PMID:29671832

  4. Computer-Aided Drug Design Methods.

    PubMed

    Yu, Wenbo; MacKerell, Alexander D

    2017-01-01

    Computational approaches are useful tools to interpret and guide experiments to expedite the antibiotic drug design process. Structure-based drug design (SBDD) and ligand-based drug design (LBDD) are the two general types of computer-aided drug design (CADD) approaches in existence. SBDD methods analyze macromolecular target 3-dimensional structural information, typically of proteins or RNA, to identify key sites and interactions that are important for their respective biological functions. Such information can then be utilized to design antibiotic drugs that can compete with essential interactions involving the target and thus interrupt the biological pathways essential for survival of the microorganism(s). LBDD methods focus on known antibiotic ligands for a target to establish a relationship between their physiochemical properties and antibiotic activities, referred to as a structure-activity relationship (SAR), information that can be used for optimization of known drugs or guide the design of new drugs with improved activity. In this chapter, standard CADD protocols for both SBDD and LBDD will be presented with a special focus on methodologies and targets routinely studied in our laboratory for antibiotic drug discoveries.

  5. Is Model-Based Development a Favorable Approach for Complex and Safety-Critical Computer Systems on Commercial Aircraft?

    NASA Technical Reports Server (NTRS)

    Torres-Pomales, Wilfredo

    2014-01-01

    A system is safety-critical if its failure can endanger human life or cause significant damage to property or the environment. State-of-the-art computer systems on commercial aircraft are highly complex, software-intensive, functionally integrated, and network-centric systems of systems. Ensuring that such systems are safe and comply with existing safety regulations is costly and time-consuming as the level of rigor in the development process, especially the validation and verification activities, is determined by considerations of system complexity and safety criticality. A significant degree of care and deep insight into the operational principles of these systems is required to ensure adequate coverage of all design implications relevant to system safety. Model-based development methodologies, methods, tools, and techniques facilitate collaboration and enable the use of common design artifacts among groups dealing with different aspects of the development of a system. This paper examines the application of model-based development to complex and safety-critical aircraft computer systems. Benefits and detriments are identified and an overall assessment of the approach is given.

  6. Computer-Aided Molecular Design of Bis-phosphine Oxide Lanthanide Extractants

    DOE PAGES

    McCann, Billy W.; Silva, Nuwan De; Windus, Theresa L.; ...

    2016-02-17

    Computer-aided molecular design and high-throughput screening of viable host architectures can significantly reduce the efforts in the design of novel ligands for efficient extraction of rare earth elements. This paper presents a computational approach to the deliberate design of bis-phosphine oxide host architectures that are structurally organized for complexation of trivalent lanthanides. Molecule building software, HostDesigner, was interfaced with molecular mechanics software, PCModel, providing a tool for generating and screening millions of potential R 2(O)P-link-P(O)R 2 ligand geometries. The molecular mechanics ranking of ligand structures is consistent with both the solution-phase free energies of complexation obtained with density functional theorymore » and the performance of known bis-phosphine oxide extractants. For the case where link is -CH 2-, evaluation of the ligand geometry provides the first characterization of a steric origin for the ‘anomalous aryl strengthening’ effect. The design approach has identified a number of novel bis-phosphine oxide ligands that are better organized for lanthanide complexation than previously studied examples.« less

  7. Applying a Bayesian Approach to Identification of Orthotropic Elastic Constants from Full Field Displacement Measurements

    NASA Astrophysics Data System (ADS)

    Gogu, C.; Yin, W.; Haftka, R.; Ifju, P.; Molimard, J.; Le Riche, R.; Vautrin, A.

    2010-06-01

    A major challenge in the identification of material properties is handling different sources of uncertainty in the experiment and the modelling of the experiment for estimating the resulting uncertainty in the identified properties. Numerous improvements in identification methods have provided increasingly accurate estimates of various material properties. However, characterizing the uncertainty in the identified properties is still relatively crude. Different material properties obtained from a single test are not obtained with the same confidence. Typically the highest uncertainty is associated with respect to properties to which the experiment is the most insensitive. In addition, the uncertainty in different properties can be strongly correlated, so that obtaining only variance estimates may be misleading. A possible approach for handling the different sources of uncertainty and estimating the uncertainty in the identified properties is the Bayesian method. This method was introduced in the late 1970s in the context of identification [1] and has been applied since to different problems, notably identification of elastic constants from plate vibration experiments [2]-[4]. The applications of the method to these classical pointwise tests involved only a small number of measurements (typically ten natural frequencies in the previously cited vibration test) which facilitated the application of the Bayesian approach. For identifying elastic constants, full field strain or displacement measurements provide a high number of measured quantities (one measurement per image pixel) and hence a promise of smaller uncertainties in the properties. However, the high number of measurements represents also a major computational challenge in applying the Bayesian approach to full field measurements. To address this challenge we propose an approach based on the proper orthogonal decomposition (POD) of the full fields in order to drastically reduce their dimensionality. POD is based on projecting the full field images on a modal basis, constructed from sample simulations, and which can account for the variations of the full field as the elastic constants and other parameters of interest are varied. The fidelity of the decomposition depends on the number of basis vectors used. Typically even complex fields can be accurately represented with no more than a few dozen modes and for our problem we showed that only four or five modes are sufficient [5]. To further reduce the computational cost of the Bayesian approach we use response surface approximations of the POD coefficients of the fields. We show that 3rd degree polynomial response surface approximations provide a satisfying accuracy. The combination of POD decomposition and response surface methodology allows to bring down the computational time of the Bayesian identification to a few days. The proposed approach is applied to Moiré interferometry full field displacement measurements from a traction experiment on a plate with a hole. The laminate with a layup of [45,- 45,0]s is made out of a Toray® T800/3631 graphite/epoxy prepreg. The measured displacement maps are provided in Figure 1. The mean values of the identified properties joint probability density function are in agreement with previous identifications carried out on the same material. Furthermore the probability density function also provides the coefficient of variation with which the properties are identified as well as the correlations between the various properties. We find that while the longitudinal Young’s modulus is identified with good accuracy (low standard deviation), the Poisson’s ration is identified with much higher uncertainty. Several of the properties are also found to be correlated. The identified uncertainty structure of the elastic constants (i.e. variance co-variance matrix) has potential benefits to reliability analyses, by allowing a more accurate description of the input uncertainty. An additional advantage of the Bayesian approach is that it provides a natural way (in the form of the prior probability density function) for accounting for prior information that may be available on the material properties thought. This is of great interest for reducing the uncertainty on properties that can only be determined with low confidence from the plate with a hole experiment, such as Poisson’s ratio or transverse Young’s modulus in our case.

  8. Next Generation Sequence Analysis and Computational Genomics Using Graphical Pipeline Workflows

    PubMed Central

    Torri, Federica; Dinov, Ivo D.; Zamanyan, Alen; Hobel, Sam; Genco, Alex; Petrosyan, Petros; Clark, Andrew P.; Liu, Zhizhong; Eggert, Paul; Pierce, Jonathan; Knowles, James A.; Ames, Joseph; Kesselman, Carl; Toga, Arthur W.; Potkin, Steven G.; Vawter, Marquis P.; Macciardi, Fabio

    2012-01-01

    Whole-genome and exome sequencing have already proven to be essential and powerful methods to identify genes responsible for simple Mendelian inherited disorders. These methods can be applied to complex disorders as well, and have been adopted as one of the current mainstream approaches in population genetics. These achievements have been made possible by next generation sequencing (NGS) technologies, which require substantial bioinformatics resources to analyze the dense and complex sequence data. The huge analytical burden of data from genome sequencing might be seen as a bottleneck slowing the publication of NGS papers at this time, especially in psychiatric genetics. We review the existing methods for processing NGS data, to place into context the rationale for the design of a computational resource. We describe our method, the Graphical Pipeline for Computational Genomics (GPCG), to perform the computational steps required to analyze NGS data. The GPCG implements flexible workflows for basic sequence alignment, sequence data quality control, single nucleotide polymorphism analysis, copy number variant identification, annotation, and visualization of results. These workflows cover all the analytical steps required for NGS data, from processing the raw reads to variant calling and annotation. The current version of the pipeline is freely available at http://pipeline.loni.ucla.edu. These applications of NGS analysis may gain clinical utility in the near future (e.g., identifying miRNA signatures in diseases) when the bioinformatics approach is made feasible. Taken together, the annotation tools and strategies that have been developed to retrieve information and test hypotheses about the functional role of variants present in the human genome will help to pinpoint the genetic risk factors for psychiatric disorders. PMID:23139896

  9. Computing Optimal Stochastic Portfolio Execution Strategies: A Parametric Approach Using Simulations

    NASA Astrophysics Data System (ADS)

    Moazeni, Somayeh; Coleman, Thomas F.; Li, Yuying

    2010-09-01

    Computing optimal stochastic portfolio execution strategies under appropriate risk consideration presents great computational challenge. We investigate a parametric approach for computing optimal stochastic strategies using Monte Carlo simulations. This approach allows reduction in computational complexity by computing coefficients for a parametric representation of a stochastic dynamic strategy based on static optimization. Using this technique, constraints can be similarly handled using appropriate penalty functions. We illustrate the proposed approach to minimize the expected execution cost and Conditional Value-at-Risk (CVaR).

  10. Methodological flaws introduce strong bias into molecular analysis of microbial populations.

    PubMed

    Krakat, N; Anjum, R; Demirel, B; Schröder, P

    2017-02-01

    In this study, we report how different cell disruption methods, PCR primers and in silico analyses can seriously bias results from microbial population studies, with consequences for the credibility and reproducibility of the findings. Our results emphasize the pitfalls of commonly used experimental methods that can seriously weaken the interpretation of results. Four different cell lysis methods, three commonly used primer pairs and various computer-based analyses were applied to investigate the microbial diversity of a fermentation sample composed of chicken dung. The fault-prone, but still frequently used, amplified rRNA gene restriction analysis was chosen to identify common weaknesses. In contrast to other studies, we focused on the complete analytical process, from cell disruption to in silico analysis, and identified potential error rates. This identified a wide disagreement of results between applied experimental approaches leading to very different community structures depending on the chosen approach. The interpretation of microbial diversity data remains a challenge. In order to accurately investigate the taxonomic diversity and structure of prokaryotic communities, we suggest a multi-level approach combining DNA-based and DNA-independent techniques. The identified weaknesses of commonly used methods to study microbial diversity can be overcome by a multi-level approach, which produces more reliable data about the fate and behaviour of microbial communities of engineered habitats such as biogas plants, so that the best performance can be ensured. © 2016 The Society for Applied Microbiology.

  11. Computational Modeling in Liver Surgery

    PubMed Central

    Christ, Bruno; Dahmen, Uta; Herrmann, Karl-Heinz; König, Matthias; Reichenbach, Jürgen R.; Ricken, Tim; Schleicher, Jana; Ole Schwen, Lars; Vlaic, Sebastian; Waschinsky, Navina

    2017-01-01

    The need for extended liver resection is increasing due to the growing incidence of liver tumors in aging societies. Individualized surgical planning is the key for identifying the optimal resection strategy and to minimize the risk of postoperative liver failure and tumor recurrence. Current computational tools provide virtual planning of liver resection by taking into account the spatial relationship between the tumor and the hepatic vascular trees, as well as the size of the future liver remnant. However, size and function of the liver are not necessarily equivalent. Hence, determining the future liver volume might misestimate the future liver function, especially in cases of hepatic comorbidities such as hepatic steatosis. A systems medicine approach could be applied, including biological, medical, and surgical aspects, by integrating all available anatomical and functional information of the individual patient. Such an approach holds promise for better prediction of postoperative liver function and hence improved risk assessment. This review provides an overview of mathematical models related to the liver and its function and explores their potential relevance for computational liver surgery. We first summarize key facts of hepatic anatomy, physiology, and pathology relevant for hepatic surgery, followed by a description of the computational tools currently used in liver surgical planning. Then we present selected state-of-the-art computational liver models potentially useful to support liver surgery. Finally, we discuss the main challenges that will need to be addressed when developing advanced computational planning tools in the context of liver surgery. PMID:29249974

  12. A computational framework for the study of confidence in humans and animals

    PubMed Central

    Kepecs, Adam; Mainen, Zachary F.

    2012-01-01

    Confidence judgements, self-assessments about the quality of a subject's knowledge, are considered a central example of metacognition. Prima facie, introspection and self-report appear the only way to access the subjective sense of confidence or uncertainty. Contrary to this notion, overt behavioural measures can be used to study confidence judgements by animals trained in decision-making tasks with perceptual or mnemonic uncertainty. Here, we suggest that a computational approach can clarify the issues involved in interpreting these tasks and provide a much needed springboard for advancing the scientific understanding of confidence. We first review relevant theories of probabilistic inference and decision-making. We then critically discuss behavioural tasks employed to measure confidence in animals and show how quantitative models can help to constrain the computational strategies underlying confidence-reporting behaviours. In our view, post-decision wagering tasks with continuous measures of confidence appear to offer the best available metrics of confidence. Since behavioural reports alone provide a limited window into mechanism, we argue that progress calls for measuring the neural representations and identifying the computations underlying confidence reports. We present a case study using such a computational approach to study the neural correlates of decision confidence in rats. This work shows that confidence assessments may be considered higher order, but can be generated using elementary neural computations that are available to a wide range of species. Finally, we discuss the relationship of confidence judgements to the wider behavioural uses of confidence and uncertainty. PMID:22492750

  13. A cost-utility analysis of the use of preoperative computed tomographic angiography in abdomen-based perforator flap breast reconstruction.

    PubMed

    Offodile, Anaeze C; Chatterjee, Abhishek; Vallejo, Sergio; Fisher, Carla S; Tchou, Julia C; Guo, Lifei

    2015-04-01

    Computed tomographic angiography is a diagnostic tool increasingly used for preoperative vascular mapping in abdomen-based perforator flap breast reconstruction. This study compared the use of computed tomographic angiography and the conventional practice of Doppler ultrasonography only in postmastectomy reconstruction using a cost-utility model. Following a comprehensive literature review, a decision analytic model was created using the three most clinically relevant health outcomes in free autologous breast reconstruction with computed tomographic angiography versus Doppler ultrasonography only. Cost and utility estimates for each health outcome were used to derive the quality-adjusted life-years and incremental cost-utility ratio. One-way sensitivity analysis was performed to scrutinize the robustness of the authors' results. Six studies and 782 patients were identified. Cost-utility analysis revealed a baseline cost savings of $3179, a gain in quality-adjusted life-years of 0.25. This yielded an incremental cost-utility ratio of -$12,716, implying a dominant choice favoring preoperative computed tomographic angiography. Sensitivity analysis revealed that computed tomographic angiography was costlier when the operative time difference between the two techniques was less than 21.3 minutes. However, the clinical advantage of computed tomographic angiography over Doppler ultrasonography only showed that computed tomographic angiography would still remain the cost-effective option even if it offered no additional operating time advantage. The authors' results show that computed tomographic angiography is a cost-effective technology for identifying lower abdominal perforators for autologous breast reconstruction. Although the perfect study would be a randomized controlled trial of the two approaches with true cost accrual, the authors' results represent the best available evidence.

  14. Aeroelastic Model Structure Computation for Envelope Expansion

    NASA Technical Reports Server (NTRS)

    Kukreja, Sunil L.

    2007-01-01

    Structure detection is a procedure for selecting a subset of candidate terms, from a full model description, that best describes the observed output. This is a necessary procedure to compute an efficient system description which may afford greater insight into the functionality of the system or a simpler controller design. Structure computation as a tool for black-box modeling may be of critical importance in the development of robust, parsimonious models for the flight-test community. Moreover, this approach may lead to efficient strategies for rapid envelope expansion that may save significant development time and costs. In this study, a least absolute shrinkage and selection operator (LASSO) technique is investigated for computing efficient model descriptions of non-linear aeroelastic systems. The LASSO minimises the residual sum of squares with the addition of an l(Sub 1) penalty term on the parameter vector of the traditional l(sub 2) minimisation problem. Its use for structure detection is a natural extension of this constrained minimisation approach to pseudo-linear regression problems which produces some model parameters that are exactly zero and, therefore, yields a parsimonious system description. Applicability of this technique for model structure computation for the F/A-18 (McDonnell Douglas, now The Boeing Company, Chicago, Illinois) Active Aeroelastic Wing project using flight test data is shown for several flight conditions (Mach numbers) by identifying a parsimonious system description with a high percent fit for cross-validated data.

  15. Applications of computational models to better understand microvascular remodelling: a focus on biomechanical integration across scales

    PubMed Central

    Murfee, Walter L.; Sweat, Richard S.; Tsubota, Ken-ichi; Gabhann, Feilim Mac; Khismatullin, Damir; Peirce, Shayn M.

    2015-01-01

    Microvascular network remodelling is a common denominator for multiple pathologies and involves both angiogenesis, defined as the sprouting of new capillaries, and network patterning associated with the organization and connectivity of existing vessels. Much of what we know about microvascular remodelling at the network, cellular and molecular scales has been derived from reductionist biological experiments, yet what happens when the experiments provide incomplete (or only qualitative) information? This review will emphasize the value of applying computational approaches to advance our understanding of the underlying mechanisms and effects of microvascular remodelling. Examples of individual computational models applied to each of the scales will highlight the potential of answering specific questions that cannot be answered using typical biological experimentation alone. Looking into the future, we will also identify the needs and challenges associated with integrating computational models across scales. PMID:25844149

  16. Psychological Issues in Online Adaptive Task Allocation

    NASA Technical Reports Server (NTRS)

    Morris, N. M.; Rouse, W. B.; Ward, S. L.; Frey, P. R.

    1984-01-01

    Adaptive aiding is an idea that offers potential for improvement over many current approaches to aiding in human-computer systems. The expected return of tailoring the system to fit the user could be in the form of improved system performance and/or increased user satisfaction. Issues such as the manner in which information is shared between human and computer, the appropriate division of labor between them, and the level of autonomy of the aid are explored. A simulated visual search task was developed. Subjects are required to identify targets in a moving display while performing a compensatory sub-critical tracking task. By manipulating characteristics of the situation such as imposed task-related workload and effort required to communicate with the computer, it is possible to create conditions in which interaction with the computer would be more or less desirable. The results of preliminary research using this experimental scenario are presented, and future directions for this research effort are discussed.

  17. Computer vision-based classification of hand grip variations in neurorehabilitation.

    PubMed

    Zariffa, José; Steeves, John D

    2011-01-01

    The complexity of hand function is such that most existing upper limb rehabilitation robotic devices use only simplified hand interfaces. This is in contrast to the importance of the hand in regaining function after neurological injury. Computer vision technology has been used to identify hand posture in the field of Human Computer Interaction, but this approach has not been translated to the rehabilitation context. We describe a computer vision-based classifier that can be used to discriminate rehabilitation-relevant hand postures, and could be integrated into a virtual reality-based upper limb rehabilitation system. The proposed system was tested on a set of video recordings from able-bodied individuals performing cylindrical grasps, lateral key grips, and tip-to-tip pinches. The overall classification success rate was 91.2%, and was above 98% for 6 out of the 10 subjects. © 2011 IEEE

  18. Is systems pharmacology ready to impact upon therapy development? A study on the cholesterol biosynthesis pathway

    PubMed Central

    Benson, Helen E; Sharman, Joanna L; Mpamhanga, Chido P; Parton, Andrew; Southan, Christopher; Harmar, Anthony J; Ghazal, Peter

    2017-01-01

    Background and Purpose An ever‐growing wealth of information on current drugs and their pharmacological effects is available from online databases. As our understanding of systems biology increases, we have the opportunity to predict, model and quantify how drug combinations can be introduced that outperform conventional single‐drug therapies. Here, we explore the feasibility of such systems pharmacology approaches with an analysis of the mevalonate branch of the cholesterol biosynthesis pathway. Experimental Approach Using open online resources, we assembled a computational model of the mevalonate pathway and compiled a set of inhibitors directed against targets in this pathway. We used computational optimization to identify combination and dose options that show not only maximal efficacy of inhibition on the cholesterol producing branch but also minimal impact on the geranylation branch, known to mediate the side effects of pharmaceutical treatment. Key Results We describe serious impediments to systems pharmacology studies arising from limitations in the data, incomplete coverage and inconsistent reporting. By curating a more complete dataset, we demonstrate the utility of computational optimization for identifying multi‐drug treatments with high efficacy and minimal off‐target effects. Conclusion and Implications We suggest solutions that facilitate systems pharmacology studies, based on the introduction of standards for data capture that increase the power of experimental data. We propose a systems pharmacology workflow for the refinement of data and the generation of future therapeutic hypotheses. PMID:28910500

  19. First-principles data-driven discovery of transition metal oxides for artificial photosynthesis

    NASA Astrophysics Data System (ADS)

    Yan, Qimin

    We develop a first-principles data-driven approach for rapid identification of transition metal oxide (TMO) light absorbers and photocatalysts for artificial photosynthesis using the Materials Project. Initially focusing on Cr, V, and Mn-based ternary TMOs in the database, we design a broadly-applicable multiple-layer screening workflow automating density functional theory (DFT) and hybrid functional calculations of bulk and surface electronic and magnetic structures. We further assess the electrochemical stability of TMOs in aqueous environments from computed Pourbaix diagrams. Several promising earth-abundant low band-gap TMO compounds with desirable band edge energies and electrochemical stability are identified by our computational efforts and then synergistically evaluated using high-throughput synthesis and photoelectrochemical screening techniques by our experimental collaborators at Caltech. Our joint theory-experiment effort has successfully identified new earth-abundant copper and manganese vanadate complex oxides that meet highly demanding requirements for photoanodes, substantially expanding the known space of such materials. By integrating theory and experiment, we validate our approach and develop important new insights into structure-property relationships for TMOs for oxygen evolution photocatalysts, paving the way for use of first-principles data-driven techniques in future applications. This work is supported by the Materials Project Predictive Modeling Center and the Joint Center for Artificial Photosynthesis through the U.S. Department of Energy, Office of Basic Energy Sciences, Materials Sciences and Engineering Division, under Contract No. DE-AC02-05CH11231. Computational resources also provided by the Department of Energy through the National Energy Supercomputing Center.

  20. In silico polypharmacology of natural products.

    PubMed

    Fang, Jiansong; Liu, Chuang; Wang, Qi; Lin, Ping; Cheng, Feixiong

    2017-04-27

    Natural products with polypharmacological profiles have demonstrated promise as novel therapeutics for various complex diseases, including cancer. Currently, many gaps exist in our knowledge of which compounds interact with which targets, and experimentally testing all possible interactions is infeasible. Recent advances and developments of systems pharmacology and computational (in silico) approaches provide powerful tools for exploring the polypharmacological profiles of natural products. In this review, we introduce recent progresses and advances of computational tools and systems pharmacology approaches for identifying drug targets of natural products by focusing on the development of targeted cancer therapy. We survey the polypharmacological and systems immunology profiles of five representative natural products that are being considered as cancer therapies. We summarize various chemoinformatics, bioinformatics and systems biology resources for reconstructing drug-target networks of natural products. We then review currently available computational approaches and tools for prediction of drug-target interactions by focusing on five domains: target-based, ligand-based, chemogenomics-based, network-based and omics-based systems biology approaches. In addition, we describe a practical example of the application of systems pharmacology approaches by integrating the polypharmacology of natural products and large-scale cancer genomics data for the development of precision oncology under the systems biology framework. Finally, we highlight the promise of cancer immunotherapies and combination therapies that target tumor ecosystems (e.g. clones or 'selfish' sub-clones) via exploiting the immunological and inflammatory 'side' effects of natural products in the cancer post-genomics era. © The Author 2017. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oup.com.

  1. Low cost paths to binary optics

    NASA Technical Reports Server (NTRS)

    Nelson, Arthur; Domash, Lawrence

    1993-01-01

    Application of binary optics has been limited to a few major laboratories because of the limited availability of fabrication facilities such as e-beam machines and the lack of standardized design software. Foster-Miller has attempted to identify low cost approaches to medium-resolution binary optics using readily available computer and fabrication tools, primarily for the use of students and experimenters in optical computing. An early version of our system, MacBEEP, made use of an optimized laser film recorder from the commercial typesetting industry with 10 micron resolution. This report is an update on our current efforts to design and build a second generation MacBEEP, which aims at 1 micron resolution and multiple phase levels. Trails included a low cost scanning electron microscope in microlithography mode, and alternative laser inscribers or photomask generators. Our current software approach is based on Mathematica and PostScript compatibility.

  2. An on-line equivalent system identification scheme for adaptive control. Ph.D. Thesis - Stanford Univ.

    NASA Technical Reports Server (NTRS)

    Sliwa, S. M.

    1984-01-01

    A prime obstacle to the widespread use of adaptive control is the degradation of performance and possible instability resulting from the presence of unmodeled dynamics. The approach taken is to explicitly include the unstructured model uncertainty in the output error identification algorithm. The order of the compensator is successively increased by including identified modes. During this model building stage, heuristic rules are used to test for convergence prior to designing compensators. Additionally, the recursive identification algorithm as extended to multi-input, multi-output systems. Enhancements were also made to reduce the computational burden of an algorithm for obtaining minimal state space realizations from the inexact, multivariate transfer functions which result from the identification process. A number of potential adaptive control applications for this approach are illustrated using computer simulations. Results indicated that when speed of adaptation and plant stability are not critical, the proposed schemes converge to enhance system performance.

  3. Security Approaches in Using Tablet Computers for Primary Data Collection in Clinical Research

    PubMed Central

    Wilcox, Adam B.; Gallagher, Kathleen; Bakken, Suzanne

    2013-01-01

    Next-generation tablets (iPads and Android tablets) may potentially improve the collection and management of clinical research data. The widespread adoption of tablets, coupled with decreased software and hardware costs, has led to increased consideration of tablets for primary research data collection. When using tablets for the Washington Heights/Inwood Infrastructure for Comparative Effectiveness Research (WICER) project, we found that the devices give rise to inherent security issues associated with the potential use of cloud-based data storage approaches. This paper identifies and describes major security considerations for primary data collection with tablets; proposes a set of architectural strategies for implementing data collection forms with tablet computers; and discusses the security, cost, and workflow of each strategy. The paper briefly reviews the strategies with respect to their implementation for three primary data collection activities for the WICER project. PMID:25848559

  4. Security approaches in using tablet computers for primary data collection in clinical research.

    PubMed

    Wilcox, Adam B; Gallagher, Kathleen; Bakken, Suzanne

    2013-01-01

    Next-generation tablets (iPads and Android tablets) may potentially improve the collection and management of clinical research data. The widespread adoption of tablets, coupled with decreased software and hardware costs, has led to increased consideration of tablets for primary research data collection. When using tablets for the Washington Heights/Inwood Infrastructure for Comparative Effectiveness Research (WICER) project, we found that the devices give rise to inherent security issues associated with the potential use of cloud-based data storage approaches. This paper identifies and describes major security considerations for primary data collection with tablets; proposes a set of architectural strategies for implementing data collection forms with tablet computers; and discusses the security, cost, and workflow of each strategy. The paper briefly reviews the strategies with respect to their implementation for three primary data collection activities for the WICER project.

  5. Computer Modeling of Violent Intent: A Content Analysis Approach

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

    Sanfilippo, Antonio P.; Mcgrath, Liam R.; Bell, Eric B.

    We present a computational approach to modeling the intent of a communication source representing a group or an individual to engage in violent behavior. Our aim is to identify and rank aspects of radical rhetoric that are endogenously related to violent intent to predict the potential for violence as encoded in written or spoken language. We use correlations between contentious rhetoric and the propensity for violent behavior found in documents from radical terrorist and non-terrorist groups and individuals to train and evaluate models of violent intent. We then apply these models to unseen instances of linguistic behavior to detect signsmore » of contention that have a positive correlation with violent intent factors. Of particular interest is the application of violent intent models to social media, such as Twitter, that have proved to serve as effective channels in furthering sociopolitical change.« less

  6. Computational Wear Simulation of Patellofemoral Articular Cartilage during In Vitro Testing

    PubMed Central

    Li, Lingmin; Patil, Shantanu; Steklov, Nick; Bae, Won; Temple-Wong, Michele; D'Lima, Darryl D.; Sah, Robert L.; Fregly, Benjamin J.

    2011-01-01

    Though changes in normal joint motions and loads (e.g., following anterior cruciate ligament injury) contribute to the development of knee osteoarthritis, the precise mechanism by which these changes induce osteoarthritis remains unknown. As a first step toward identifying this mechanism, this study evaluates computational wear simulations of a patellofemoral joint specimen wear tested on a knee simulator machine. A multi-body dynamic model of the specimen mounted in the simulator machine was constructed in commercial computer-aided engineering software. A custom elastic foundation contact model was used to calculate contact pressures and wear on the femoral and patellar articular surfaces using geometry created from laser scan and MR data. Two different wear simulation approaches were investigated – one that wore the surface geometries gradually over a sequence of 10 one-cycle dynamic simulations (termed the “progressive” approach), and one that wore the surface geometries abruptly using results from a single one-cycle dynamic simulation (termed the “non-progressive” approach). The progressive approach with laser scan geometry reproduced the experimentally measured wear depths and areas for both the femur and patella. The less costly non-progressive approach predicted deeper wear depths, especially on the patella, but had little influence on predicted wear areas. Use of MR data for creating the articular and subchondral bone geometry altered wear depth and area predictions by at most 13%. These results suggest that MR-derived geometry may be sufficient for simulating articular cartilage wear in vivo and that a progressive simulation approach may be needed for the patella and tibia since both remain in continuous contact with the femur. PMID:21453922

  7. Computational wear simulation of patellofemoral articular cartilage during in vitro testing.

    PubMed

    Li, Lingmin; Patil, Shantanu; Steklov, Nick; Bae, Won; Temple-Wong, Michele; D'Lima, Darryl D; Sah, Robert L; Fregly, Benjamin J

    2011-05-17

    Though changes in normal joint motions and loads (e.g., following anterior cruciate ligament injury) contribute to the development of knee osteoarthritis, the precise mechanism by which these changes induce osteoarthritis remains unknown. As a first step toward identifying this mechanism, this study evaluates computational wear simulations of a patellofemoral joint specimen wear tested on a knee simulator machine. A multibody dynamic model of the specimen mounted in the simulator machine was constructed in commercial computer-aided engineering software. A custom elastic foundation contact model was used to calculate contact pressures and wear on the femoral and patellar articular surfaces using geometry created from laser scan and MR data. Two different wear simulation approaches were investigated--one that wore the surface geometries gradually over a sequence of 10 one-cycle dynamic simulations (termed the "progressive" approach), and one that wore the surface geometries abruptly using results from a single one-cycle dynamic simulation (termed the "non-progressive" approach). The progressive approach with laser scan geometry reproduced the experimentally measured wear depths and areas for both the femur and patella. The less costly non-progressive approach predicted deeper wear depths, especially on the patella, but had little influence on predicted wear areas. Use of MR data for creating the articular and subchondral bone geometry altered wear depth and area predictions by at most 13%. These results suggest that MR-derived geometry may be sufficient for simulating articular cartilage wear in vivo and that a progressive simulation approach may be needed for the patella and tibia since both remain in continuous contact with the femur. Copyright © 2011 Elsevier Ltd. All rights reserved.

  8. AA9int: SNP Interaction Pattern Search Using Non-Hierarchical Additive Model Set.

    PubMed

    Lin, Hui-Yi; Huang, Po-Yu; Chen, Dung-Tsa; Tung, Heng-Yuan; Sellers, Thomas A; Pow-Sang, Julio; Eeles, Rosalind; Easton, Doug; Kote-Jarai, Zsofia; Amin Al Olama, Ali; Benlloch, Sara; Muir, Kenneth; Giles, Graham G; Wiklund, Fredrik; Gronberg, Henrik; Haiman, Christopher A; Schleutker, Johanna; Nordestgaard, Børge G; Travis, Ruth C; Hamdy, Freddie; Neal, David E; Pashayan, Nora; Khaw, Kay-Tee; Stanford, Janet L; Blot, William J; Thibodeau, Stephen N; Maier, Christiane; Kibel, Adam S; Cybulski, Cezary; Cannon-Albright, Lisa; Brenner, Hermann; Kaneva, Radka; Batra, Jyotsna; Teixeira, Manuel R; Pandha, Hardev; Lu, Yong-Jie; Park, Jong Y

    2018-06-07

    The use of single nucleotide polymorphism (SNP) interactions to predict complex diseases is getting more attention during the past decade, but related statistical methods are still immature. We previously proposed the SNP Interaction Pattern Identifier (SIPI) approach to evaluate 45 SNP interaction patterns/patterns. SIPI is statistically powerful but suffers from a large computation burden. For large-scale studies, it is necessary to use a powerful and computation-efficient method. The objective of this study is to develop an evidence-based mini-version of SIPI as the screening tool or solitary use and to evaluate the impact of inheritance mode and model structure on detecting SNP-SNP interactions. We tested two candidate approaches: the 'Five-Full' and 'AA9int' method. The Five-Full approach is composed of the five full interaction models considering three inheritance modes (additive, dominant and recessive). The AA9int approach is composed of nine interaction models by considering non-hierarchical model structure and the additive mode. Our simulation results show that AA9int has similar statistical power compared to SIPI and is superior to the Five-Full approach, and the impact of the non-hierarchical model structure is greater than that of the inheritance mode in detecting SNP-SNP interactions. In summary, it is recommended that AA9int is a powerful tool to be used either alone or as the screening stage of a two-stage approach (AA9int+SIPI) for detecting SNP-SNP interactions in large-scale studies. The 'AA9int' and 'parAA9int' functions (standard and parallel computing version) are added in the SIPI R package, which is freely available at https://linhuiyi.github.io/LinHY_Software/. hlin1@lsuhsc.edu. Supplementary data are available at Bioinformatics online.

  9. Vibrational Spectroscopy and Astrobiology

    NASA Technical Reports Server (NTRS)

    Chaban, Galina M.; Kwak, D. (Technical Monitor)

    2001-01-01

    Role of vibrational spectroscopy in solving problems related to astrobiology will be discussed. Vibrational (infrared) spectroscopy is a very sensitive tool for identifying molecules. Theoretical approach used in this work is based on direct computation of anharmonic vibrational frequencies and intensities from electronic structure codes. One of the applications of this computational technique is possible identification of biological building blocks (amino acids, small peptides, DNA bases) in the interstellar medium (ISM). Identifying small biological molecules in the ISM is very important from the point of view of origin of life. Hybrid (quantum mechanics/molecular mechanics) theoretical techniques will be discussed that may allow to obtain accurate vibrational spectra of biomolecular building blocks and to create a database of spectroscopic signatures that can assist observations of these molecules in space. Another application of the direct computational spectroscopy technique is to help to design and analyze experimental observations of ice surfaces of one of the Jupiter's moons, Europa, that possibly contains hydrated salts. The presence of hydrated salts on the surface can be an indication of a subsurface ocean and the possible existence of life forms inhabiting such an ocean.

  10. Computational modeling identifies key gene regulatory interactions underlying phenobarbital-mediated tumor promotion

    PubMed Central

    Luisier, Raphaëlle; Unterberger, Elif B.; Goodman, Jay I.; Schwarz, Michael; Moggs, Jonathan; Terranova, Rémi; van Nimwegen, Erik

    2014-01-01

    Gene regulatory interactions underlying the early stages of non-genotoxic carcinogenesis are poorly understood. Here, we have identified key candidate regulators of phenobarbital (PB)-mediated mouse liver tumorigenesis, a well-characterized model of non-genotoxic carcinogenesis, by applying a new computational modeling approach to a comprehensive collection of in vivo gene expression studies. We have combined our previously developed motif activity response analysis (MARA), which models gene expression patterns in terms of computationally predicted transcription factor binding sites with singular value decomposition (SVD) of the inferred motif activities, to disentangle the roles that different transcriptional regulators play in specific biological pathways of tumor promotion. Furthermore, transgenic mouse models enabled us to identify which of these regulatory activities was downstream of constitutive androstane receptor and β-catenin signaling, both crucial components of PB-mediated liver tumorigenesis. We propose novel roles for E2F and ZFP161 in PB-mediated hepatocyte proliferation and suggest that PB-mediated suppression of ESR1 activity contributes to the development of a tumor-prone environment. Our study shows that combining MARA with SVD allows for automated identification of independent transcription regulatory programs within a complex in vivo tissue environment and provides novel mechanistic insights into PB-mediated hepatocarcinogenesis. PMID:24464994

  11. Meeting the security requirements of electronic medical records in the ERA of high-speed computing.

    PubMed

    Alanazi, H O; Zaidan, A A; Zaidan, B B; Kiah, M L Mat; Al-Bakri, S H

    2015-01-01

    This study has two objectives. First, it aims to develop a system with a highly secured approach to transmitting electronic medical records (EMRs), and second, it aims to identify entities that transmit private patient information without permission. The NTRU and the Advanced Encryption Standard (AES) cryptosystems are secured encryption methods. The AES is a tested technology that has already been utilized in several systems to secure sensitive data. The United States government has been using AES since June 2003 to protect sensitive and essential information. Meanwhile, NTRU protects sensitive data against attacks through the use of quantum computers, which can break the RSA cryptosystem and elliptic curve cryptography algorithms. A hybrid of AES and NTRU is developed in this work to improve EMR security. The proposed hybrid cryptography technique is implemented to secure the data transmission process of EMRs. The proposed security solution can provide protection for over 40 years and is resistant to quantum computers. Moreover, the technique provides the necessary evidence required by law to identify disclosure or misuse of patient records. The proposed solution can effectively secure EMR transmission and protect patient rights. It also identifies the source responsible for disclosing confidential patient records. The proposed hybrid technique for securing data managed by institutional websites must be improved in the future.

  12. Computer-aided decision support systems for endoscopy in the gastrointestinal tract: a review.

    PubMed

    Liedlgruber, Michael; Uhl, Andreas

    2011-01-01

    Today, medical endoscopy is a widely used procedure to inspect the inner cavities of the human body. The advent of endoscopic imaging techniques-allowing the acquisition of images or videos-created the possibility for the development of the whole new branch of computer-aided decision support systems. Such systems aim at helping physicians to identify possibly malignant abnormalities more accurately. At the beginning of this paper, we give a brief introduction to the history of endoscopy, followed by introducing the main types of endoscopes which emerged so far (flexible endoscope, wireless capsule endoscope, and confocal laser endomicroscope). We then give a brief introduction to computer-aided decision support systems specifically targeted at endoscopy in the gastrointestinal tract. Then we present general facts and figures concerning computer-aided decision support systems and summarize work specifically targeted at computer-aided decision support in the gastrointestinal tract. This summary is followed by a discussion of some common issues concerning the approaches reviewed and suggestions of possible ways to resolve them.

  13. An autonomous molecular computer for logical control of gene expression.

    PubMed

    Benenson, Yaakov; Gil, Binyamin; Ben-Dor, Uri; Adar, Rivka; Shapiro, Ehud

    2004-05-27

    Early biomolecular computer research focused on laboratory-scale, human-operated computers for complex computational problems. Recently, simple molecular-scale autonomous programmable computers were demonstrated allowing both input and output information to be in molecular form. Such computers, using biological molecules as input data and biologically active molecules as outputs, could produce a system for 'logical' control of biological processes. Here we describe an autonomous biomolecular computer that, at least in vitro, logically analyses the levels of messenger RNA species, and in response produces a molecule capable of affecting levels of gene expression. The computer operates at a concentration of close to a trillion computers per microlitre and consists of three programmable modules: a computation module, that is, a stochastic molecular automaton; an input module, by which specific mRNA levels or point mutations regulate software molecule concentrations, and hence automaton transition probabilities; and an output module, capable of controlled release of a short single-stranded DNA molecule. This approach might be applied in vivo to biochemical sensing, genetic engineering and even medical diagnosis and treatment. As a proof of principle we programmed the computer to identify and analyse mRNA of disease-related genes associated with models of small-cell lung cancer and prostate cancer, and to produce a single-stranded DNA molecule modelled after an anticancer drug.

  14. Incorporating gene-environment interaction in testing for association with rare genetic variants.

    PubMed

    Chen, Han; Meigs, James B; Dupuis, Josée

    2014-01-01

    The incorporation of gene-environment interactions could improve the ability to detect genetic associations with complex traits. For common genetic variants, single-marker interaction tests and joint tests of genetic main effects and gene-environment interaction have been well-established and used to identify novel association loci for complex diseases and continuous traits. For rare genetic variants, however, single-marker tests are severely underpowered due to the low minor allele frequency, and only a few gene-environment interaction tests have been developed. We aimed at developing powerful and computationally efficient tests for gene-environment interaction with rare variants. In this paper, we propose interaction and joint tests for testing gene-environment interaction of rare genetic variants. Our approach is a generalization of existing gene-environment interaction tests for multiple genetic variants under certain conditions. We show in our simulation studies that our interaction and joint tests have correct type I errors, and that the joint test is a powerful approach for testing genetic association, allowing for gene-environment interaction. We also illustrate our approach in a real data example from the Framingham Heart Study. Our approach can be applied to both binary and continuous traits, it is powerful and computationally efficient.

  15. Automated surface inspection for steel products using computer vision approach.

    PubMed

    Xi, Jiaqi; Shentu, Lifeng; Hu, Jikang; Li, Mian

    2017-01-10

    Surface inspection is a critical step in ensuring the product quality in the steel-making industry. In order to relieve inspectors of laborious work and improve the consistency of inspection, much effort has been dedicated to the automated inspection using computer vision approaches over the past decades. However, due to non-uniform illumination conditions and similarity between the surface textures and defects, the present methods are usually applicable to very specific cases. In this paper a new framework for surface inspection has been proposed to overcome these limitations. By investigating the image formation process, a quantitative model characterizing the impact of illumination on the image quality is developed, based on which the non-uniform brightness in the image can be effectively removed. Then a simple classifier is designed to identify the defects among the surface textures. The significance of this approach lies in its robustness to illumination changes and wide applicability to different inspection scenarios. The proposed approach has been successfully applied to the real-time surface inspection of round billets in real manufacturing. Implemented on a conventional industrial PC, the algorithm can proceed at 12.5 frames per second with the successful detection rate being over 90% for turned and skinned billets.

  16. Dynamic Analyses of Result Quality in Energy-Aware Approximate Programs

    NASA Astrophysics Data System (ADS)

    RIngenburg, Michael F.

    Energy efficiency is a key concern in the design of modern computer systems. One promising approach to energy-efficient computation, approximate computing, trades off output precision for energy efficiency. However, this tradeoff can have unexpected effects on computation quality. This thesis presents dynamic analysis tools to study, debug, and monitor the quality and energy efficiency of approximate computations. We propose three styles of tools: prototyping tools that allow developers to experiment with approximation in their applications, online tools that instrument code to determine the key sources of error, and online tools that monitor the quality of deployed applications in real time. Our prototyping tool is based on an extension to the functional language OCaml. We add approximation constructs to the language, an approximation simulator to the runtime, and profiling and auto-tuning tools for studying and experimenting with energy-quality tradeoffs. We also present two online debugging tools and three online monitoring tools. The first online tool identifies correlations between output quality and the total number of executions of, and errors in, individual approximate operations. The second tracks the number of approximate operations that flow into a particular value. Our online tools comprise three low-cost approaches to dynamic quality monitoring. They are designed to monitor quality in deployed applications without spending more energy than is saved by approximation. Online monitors can be used to perform real time adjustments to energy usage in order to meet specific quality goals. We present prototype implementations of all of these tools and describe their usage with several applications. Our prototyping, profiling, and autotuning tools allow us to experiment with approximation strategies and identify new strategies, our online tools succeed in providing new insights into the effects of approximation on output quality, and our monitors succeed in controlling output quality while still maintaining significant energy efficiency gains.

  17. HIGH-FIDELITY SIMULATION-DRIVEN MODEL DEVELOPMENT FOR COARSE-GRAINED COMPUTATIONAL FLUID DYNAMICS

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

    Hanna, Botros N.; Dinh, Nam T.; Bolotnov, Igor A.

    Nuclear reactor safety analysis requires identifying various credible accident scenarios and determining their consequences. For a full-scale nuclear power plant system behavior, it is impossible to obtain sufficient experimental data for a broad range of risk-significant accident scenarios. In single-phase flow convective problems, Direct Numerical Simulation (DNS) and Large Eddy Simulation (LES) can provide us with high fidelity results when physical data are unavailable. However, these methods are computationally expensive and cannot be afforded for simulation of long transient scenarios in nuclear accidents despite extraordinary advances in high performance scientific computing over the past decades. The major issue is themore » inability to make the transient computation parallel, thus making number of time steps required in high-fidelity methods unaffordable for long transients. In this work, we propose to apply a high fidelity simulation-driven approach to model sub-grid scale (SGS) effect in Coarse Grained Computational Fluid Dynamics CG-CFD. This approach aims to develop a statistical surrogate model instead of the deterministic SGS model. We chose to start with a turbulent natural convection case with volumetric heating in a horizontal fluid layer with a rigid, insulated lower boundary and isothermal (cold) upper boundary. This scenario of unstable stratification is relevant to turbulent natural convection in a molten corium pool during a severe nuclear reactor accident, as well as in containment mixing and passive cooling. The presented approach demonstrates how to create a correction for the CG-CFD solution by modifying the energy balance equation. A global correction for the temperature equation proves to achieve a significant improvement to the prediction of steady state temperature distribution through the fluid layer.« less

  18. Identification of new stress-induced microRNA and their targets in wheat using computational approach.

    PubMed

    Pandey, Bharati; Gupta, Om Prakash; Pandey, Dev Mani; Sharma, Indu; Sharma, Pradeep

    2013-05-01

    MicroRNAs (miRNAs) are a class of short endogenous non-coding small RNA molecules of about 18-22 nucleotides in length. Their main function is to downregulate gene expression in different manners like translational repression, mRNA cleavage and epigenetic modification. Computational predictions have raised the number of miRNAs in wheat significantly using an EST based approach. Hence, a combinatorial approach which is amalgamation of bioinformatics software and perl script was used to identify new miRNA to add to the growing database of wheat miRNA. Identification of miRNAs was initiated by mining the EST (Expressed Sequence Tags) database available at National Center for Biotechnology Information. In this investigation, 4677 mature microRNA sequences belonging to 50 miRNA families from different plant species were used to predict miRNA in wheat. A total of five abiotic stress-responsive new miRNAs were predicted and named Ta-miR5653, Ta-miR855, Ta-miR819k, Ta-miR3708 and Ta-miR5156. In addition, four previously identified miRNA, i.e., Ta-miR1122, miR1117, Ta-miR1134 and Ta-miR1133 were predicted in newly identified EST sequence and 14 potential target genes were subsequently predicted, most of which seems to encode ubiquitin carrier protein, serine/threonine protein kinase, 40S ribosomal protein, F-box/kelch-repeat protein, BTB/POZ domain-containing protein, transcription factors which are involved in growth, development, metabolism and stress response. Our result has increased the number of miRNAs in wheat, which should be useful for further investigation into the biological functions and evolution of miRNAs in wheat and other plant species.

  19. Discovering transcription factor binding sites in highly repetitive regions of genomes with multi-read analysis of ChIP-Seq data.

    PubMed

    Chung, Dongjun; Kuan, Pei Fen; Li, Bo; Sanalkumar, Rajendran; Liang, Kun; Bresnick, Emery H; Dewey, Colin; Keleş, Sündüz

    2011-07-01

    Chromatin immunoprecipitation followed by high-throughput sequencing (ChIP-seq) is rapidly replacing chromatin immunoprecipitation combined with genome-wide tiling array analysis (ChIP-chip) as the preferred approach for mapping transcription-factor binding sites and chromatin modifications. The state of the art for analyzing ChIP-seq data relies on using only reads that map uniquely to a relevant reference genome (uni-reads). This can lead to the omission of up to 30% of alignable reads. We describe a general approach for utilizing reads that map to multiple locations on the reference genome (multi-reads). Our approach is based on allocating multi-reads as fractional counts using a weighted alignment scheme. Using human STAT1 and mouse GATA1 ChIP-seq datasets, we illustrate that incorporation of multi-reads significantly increases sequencing depths, leads to detection of novel peaks that are not otherwise identifiable with uni-reads, and improves detection of peaks in mappable regions. We investigate various genome-wide characteristics of peaks detected only by utilization of multi-reads via computational experiments. Overall, peaks from multi-read analysis have similar characteristics to peaks that are identified by uni-reads except that the majority of them reside in segmental duplications. We further validate a number of GATA1 multi-read only peaks by independent quantitative real-time ChIP analysis and identify novel target genes of GATA1. These computational and experimental results establish that multi-reads can be of critical importance for studying transcription factor binding in highly repetitive regions of genomes with ChIP-seq experiments.

  20. Integrative Analysis of Many Weighted Co-Expression Networks Using Tensor Computation

    PubMed Central

    Li, Wenyuan; Liu, Chun-Chi; Zhang, Tong; Li, Haifeng; Waterman, Michael S.; Zhou, Xianghong Jasmine

    2011-01-01

    The rapid accumulation of biological networks poses new challenges and calls for powerful integrative analysis tools. Most existing methods capable of simultaneously analyzing a large number of networks were primarily designed for unweighted networks, and cannot easily be extended to weighted networks. However, it is known that transforming weighted into unweighted networks by dichotomizing the edges of weighted networks with a threshold generally leads to information loss. We have developed a novel, tensor-based computational framework for mining recurrent heavy subgraphs in a large set of massive weighted networks. Specifically, we formulate the recurrent heavy subgraph identification problem as a heavy 3D subtensor discovery problem with sparse constraints. We describe an effective approach to solving this problem by designing a multi-stage, convex relaxation protocol, and a non-uniform edge sampling technique. We applied our method to 130 co-expression networks, and identified 11,394 recurrent heavy subgraphs, grouped into 2,810 families. We demonstrated that the identified subgraphs represent meaningful biological modules by validating against a large set of compiled biological knowledge bases. We also showed that the likelihood for a heavy subgraph to be meaningful increases significantly with its recurrence in multiple networks, highlighting the importance of the integrative approach to biological network analysis. Moreover, our approach based on weighted graphs detects many patterns that would be overlooked using unweighted graphs. In addition, we identified a large number of modules that occur predominately under specific phenotypes. This analysis resulted in a genome-wide mapping of gene network modules onto the phenome. Finally, by comparing module activities across many datasets, we discovered high-order dynamic cooperativeness in protein complex networks and transcriptional regulatory networks. PMID:21698123

  1. Space Station Simulation Computer System (SCS) study for NASA/MSFC. Volume 2: Baseline architecture report

    NASA Technical Reports Server (NTRS)

    1990-01-01

    NASA's Space Station Freedom Program (SSFP) planning efforts have identified a need for a payload training simulator system to serve as both a training facility and as a demonstrator to validate operational concepts. The envisioned MSFC Payload Training Complex (PTC) required to meet this need will train the Space Station payload scientists, station scientists, and ground controllers to operate the wide variety of experiments that will be onboard the Space Station Freedom. The Simulation Computer System (SCS) is the computer hardware, software, and workstations that will support the Payload Training Complex at MSFC. The purpose of this SCS Study is to investigate issues related to the SCS, alternative requirements, simulator approaches, and state-of-the-art technologies to develop candidate concepts and designs.

  2. Space Station Simulation Computer System (SCS) study for NASA/MSFC. Phased development plan

    NASA Technical Reports Server (NTRS)

    1990-01-01

    NASA's Space Station Freedom Program (SSFP) planning efforts have identified a need for a payload training simulator system to serve as both a training facility and as a demonstrator to validate operational concepts. The envisioned MSFC Payload Training Complex (PTC) required to meet this need will train the Space Station payload scientists, station scientists and ground controllers to operate the wide variety of experiments that will be onboard the Space Station Freedom. The Simulation Computer System (SCS) is made up of computer hardware, software, and workstations that will support the Payload Training Complex at MSFC. The purpose of this SCS Study is to investigate issues related to the SCS, alternative requirements, simulator approaches, and state-of-the-art technologies to develop candidate concepts and designs.

  3. Space Station Simulation Computer System (SCS) study for NASA/MSFC. Volume 1: Baseline architecture report

    NASA Technical Reports Server (NTRS)

    1990-01-01

    NASA's Space Station Freedom Program (SSFP) planning efforts have identified a need for a payload training simulator system to serve as both a training facility and as a demonstrator to validate operational concepts. The envisioned MSFC Payload Training Complex (PTC) required to meet this need will train the Space Station payload scientists, station scientists, and ground controllers to operate the wide variety of experiments that will be onboard the Space Station Freedom. The Simulation Computer System (SCS) is made up of the computer hardware, software, and workstations that will support the Payload Training Complex at MSFC. The purpose of this SCS Study is to investigate issues related to the SCS, alternative requirements, simulator approaches, and state-of-the-art technologies to develop candidate concepts and designs.

  4. Diversity of Streptomyces spp. in Eastern Himalayan region – computational RNomics approach to phylogeny

    PubMed Central

    Bhattacharjee, Kaushik; Banerjee, Subhro; Joshi, Santa Ram

    2012-01-01

    Isolation and characterization of actinomycetes from soil samples from altitudinal gradient of North-East India were investigated for computational RNomics based phylogeny. A total of 52 diverse isolates of Streptomyces from the soil samples were isolated on four different media and from these 6 isolates were selected on the basis of cultural characteristics, microscopic and biochemical studies. Sequencing of 16S rDNA of the selected isolates identified them to belong to six different species of Streptomyces. The molecular morphometric and physico-kinetic analysis of 16S rRNA sequences were performed to predict the diversity of the genus. The computational RNomics study revealed the significance of the structural RNA based phylogenetic analysis in a relatively diverse group of Streptomyces. PMID:22829729

  5. Space Station Simulation Computer System (SCS) study for NASA/MSFC. Operations concept report

    NASA Technical Reports Server (NTRS)

    1990-01-01

    NASA's Space Station Freedom Program (SSFP) planning efforts have identified a need for a payload training simulator system to serve as both a training facility and as a demonstrator to validate operational concepts. The envisioned MSFC Payload Training Complex (PTC) required to meet this need will train the Space Station payload scientists, station scientists, and ground controllers to operate the wide variety of experiments that will be onboard the Space Station Freedom. The Simulation Computer System (SCS) is made up of computer hardware, software, and workstations that will support the Payload Training Complex at MSFC. The purpose of this SCS Study is to investigate issues related to the SCS, alternative requirements, simulator approaches, and state-of-the-art technologies to develop candidate concepts and designs.

  6. Computational modeling of an endovascular approach to deep brain stimulation

    NASA Astrophysics Data System (ADS)

    Teplitzky, Benjamin A.; Connolly, Allison T.; Bajwa, Jawad A.; Johnson, Matthew D.

    2014-04-01

    Objective. Deep brain stimulation (DBS) therapy currently relies on a transcranial neurosurgical technique to implant one or more electrode leads into the brain parenchyma. In this study, we used computational modeling to investigate the feasibility of using an endovascular approach to target DBS therapy. Approach. Image-based anatomical reconstructions of the human brain and vasculature were used to identify 17 established and hypothesized anatomical targets of DBS, of which five were found adjacent to a vein or artery with intraluminal diameter ≥1 mm. Two of these targets, the fornix and subgenual cingulate white matter (SgCwm) tracts, were further investigated using a computational modeling framework that combined segmented volumes of the vascularized brain, finite element models of the tissue voltage during DBS, and multi-compartment axon models to predict the direct electrophysiological effects of endovascular DBS. Main results. The models showed that: (1) a ring-electrode conforming to the vessel wall was more efficient at neural activation than a guidewire design, (2) increasing the length of a ring-electrode had minimal effect on neural activation thresholds, (3) large variability in neural activation occurred with suboptimal placement of a ring-electrode along the targeted vessel, and (4) activation thresholds for the fornix and SgCwm tracts were comparable for endovascular and stereotactic DBS, though endovascular DBS was able to produce significantly larger contralateral activation for a unilateral implantation. Significance. Together, these results suggest that endovascular DBS can serve as a complementary approach to stereotactic DBS in select cases.

  7. A mixed-integer linear programming approach to the reduction of genome-scale metabolic networks.

    PubMed

    Röhl, Annika; Bockmayr, Alexander

    2017-01-03

    Constraint-based analysis has become a widely used method to study metabolic networks. While some of the associated algorithms can be applied to genome-scale network reconstructions with several thousands of reactions, others are limited to small or medium-sized models. In 2015, Erdrich et al. introduced a method called NetworkReducer, which reduces large metabolic networks to smaller subnetworks, while preserving a set of biological requirements that can be specified by the user. Already in 2001, Burgard et al. developed a mixed-integer linear programming (MILP) approach for computing minimal reaction sets under a given growth requirement. Here we present an MILP approach for computing minimum subnetworks with the given properties. The minimality (with respect to the number of active reactions) is not guaranteed by NetworkReducer, while the method by Burgard et al. does not allow specifying the different biological requirements. Our procedure is about 5-10 times faster than NetworkReducer and can enumerate all minimum subnetworks in case there exist several ones. This allows identifying common reactions that are present in all subnetworks, and reactions appearing in alternative pathways. Applying complex analysis methods to genome-scale metabolic networks is often not possible in practice. Thus it may become necessary to reduce the size of the network while keeping important functionalities. We propose a MILP solution to this problem. Compared to previous work, our approach is more efficient and allows computing not only one, but even all minimum subnetworks satisfying the required properties.

  8. PSEA-Quant: a protein set enrichment analysis on label-free and label-based protein quantification data.

    PubMed

    Lavallée-Adam, Mathieu; Rauniyar, Navin; McClatchy, Daniel B; Yates, John R

    2014-12-05

    The majority of large-scale proteomics quantification methods yield long lists of quantified proteins that are often difficult to interpret and poorly reproduced. Computational approaches are required to analyze such intricate quantitative proteomics data sets. We propose a statistical approach to computationally identify protein sets (e.g., Gene Ontology (GO) terms) that are significantly enriched with abundant proteins with reproducible quantification measurements across a set of replicates. To this end, we developed PSEA-Quant, a protein set enrichment analysis algorithm for label-free and label-based protein quantification data sets. It offers an alternative approach to classic GO analyses, models protein annotation biases, and allows the analysis of samples originating from a single condition, unlike analogous approaches such as GSEA and PSEA. We demonstrate that PSEA-Quant produces results complementary to GO analyses. We also show that PSEA-Quant provides valuable information about the biological processes involved in cystic fibrosis using label-free protein quantification of a cell line expressing a CFTR mutant. Finally, PSEA-Quant highlights the differences in the mechanisms taking place in the human, rat, and mouse brain frontal cortices based on tandem mass tag quantification. Our approach, which is available online, will thus improve the analysis of proteomics quantification data sets by providing meaningful biological insights.

  9. PSEA-Quant: A Protein Set Enrichment Analysis on Label-Free and Label-Based Protein Quantification Data

    PubMed Central

    2015-01-01

    The majority of large-scale proteomics quantification methods yield long lists of quantified proteins that are often difficult to interpret and poorly reproduced. Computational approaches are required to analyze such intricate quantitative proteomics data sets. We propose a statistical approach to computationally identify protein sets (e.g., Gene Ontology (GO) terms) that are significantly enriched with abundant proteins with reproducible quantification measurements across a set of replicates. To this end, we developed PSEA-Quant, a protein set enrichment analysis algorithm for label-free and label-based protein quantification data sets. It offers an alternative approach to classic GO analyses, models protein annotation biases, and allows the analysis of samples originating from a single condition, unlike analogous approaches such as GSEA and PSEA. We demonstrate that PSEA-Quant produces results complementary to GO analyses. We also show that PSEA-Quant provides valuable information about the biological processes involved in cystic fibrosis using label-free protein quantification of a cell line expressing a CFTR mutant. Finally, PSEA-Quant highlights the differences in the mechanisms taking place in the human, rat, and mouse brain frontal cortices based on tandem mass tag quantification. Our approach, which is available online, will thus improve the analysis of proteomics quantification data sets by providing meaningful biological insights. PMID:25177766

  10. A template-based approach for parallel hexahedral two-refinement

    DOE PAGES

    Owen, Steven J.; Shih, Ryan M.; Ernst, Corey D.

    2016-10-17

    Here, we provide a template-based approach for generating locally refined all-hex meshes. We focus specifically on refinement of initially structured grids utilizing a 2-refinement approach where uniformly refined hexes are subdivided into eight child elements. The refinement algorithm consists of identifying marked nodes that are used as the basis for a set of four simple refinement templates. The target application for 2-refinement is a parallel grid-based all-hex meshing tool for high performance computing in a distributed environment. The result is a parallel consistent locally refined mesh requiring minimal communication and where minimum mesh quality is greater than scaled Jacobian 0.3more » prior to smoothing.« less

  11. A template-based approach for parallel hexahedral two-refinement

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

    Owen, Steven J.; Shih, Ryan M.; Ernst, Corey D.

    Here, we provide a template-based approach for generating locally refined all-hex meshes. We focus specifically on refinement of initially structured grids utilizing a 2-refinement approach where uniformly refined hexes are subdivided into eight child elements. The refinement algorithm consists of identifying marked nodes that are used as the basis for a set of four simple refinement templates. The target application for 2-refinement is a parallel grid-based all-hex meshing tool for high performance computing in a distributed environment. The result is a parallel consistent locally refined mesh requiring minimal communication and where minimum mesh quality is greater than scaled Jacobian 0.3more » prior to smoothing.« less

  12. A compositional approach to building applications in a computational environment

    NASA Astrophysics Data System (ADS)

    Roslovtsev, V. V.; Shumsky, L. D.; Wolfengagen, V. E.

    2014-04-01

    The paper presents an approach to creating an applicative computational environment to feature computational processes and data decomposition, and a compositional approach to application building. The approach in question is based on the notion of combinator - both in systems with variable binding (such as λ-calculi) and those allowing programming without variables (combinatory logic style). We present a computation decomposition technique based on objects' structural decomposition, with the focus on computation decomposition. The computational environment's architecture is based on a network with nodes playing several roles simultaneously.

  13. Targeted proteomics identifies liquid-biopsy signatures for extracapsular prostate cancer

    PubMed Central

    Kim, Yunee; Jeon, Jouhyun; Mejia, Salvador; Yao, Cindy Q; Ignatchenko, Vladimir; Nyalwidhe, Julius O; Gramolini, Anthony O; Lance, Raymond S; Troyer, Dean A; Drake, Richard R; Boutros, Paul C; Semmes, O. John; Kislinger, Thomas

    2016-01-01

    Biomarkers are rapidly gaining importance in personalized medicine. Although numerous molecular signatures have been developed over the past decade, there is a lack of overlap and many biomarkers fail to validate in independent patient cohorts and hence are not useful for clinical application. For these reasons, identification of novel and robust biomarkers remains a formidable challenge. We combine targeted proteomics with computational biology to discover robust proteomic signatures for prostate cancer. Quantitative proteomics conducted in expressed prostatic secretions from men with extraprostatic and organ-confined prostate cancers identified 133 differentially expressed proteins. Using synthetic peptides, we evaluate them by targeted proteomics in a 74-patient cohort of expressed prostatic secretions in urine. We quantify a panel of 34 candidates in an independent 207-patient cohort. We apply machine-learning approaches to develop clinical predictive models for prostate cancer diagnosis and prognosis. Our results demonstrate that computationally guided proteomics can discover highly accurate non-invasive biomarkers. PMID:27350604

  14. Identification of reduced-order thermal therapy models using thermal MR images: theory and validation.

    PubMed

    Niu, Ran; Skliar, Mikhail

    2012-07-01

    In this paper, we develop and validate a method to identify computationally efficient site- and patient-specific models of ultrasound thermal therapies from MR thermal images. The models of the specific absorption rate of the transduced energy and the temperature response of the therapy target are identified in the reduced basis of proper orthogonal decomposition of thermal images, acquired in response to a mild thermal test excitation. The method permits dynamic reidentification of the treatment models during the therapy by recursively utilizing newly acquired images. Such adaptation is particularly important during high-temperature therapies, which are known to substantially and rapidly change tissue properties and blood perfusion. The developed theory was validated for the case of focused ultrasound heating of a tissue phantom. The experimental and computational results indicate that the developed approach produces accurate low-dimensional treatment models despite temporal and spatial noises in MR images and slow image acquisition rate.

  15. A systems biology approach to predict and characterize human gut microbial metabolites in colorectal cancer.

    PubMed

    Wang, QuanQiu; Li, Li; Xu, Rong

    2018-04-18

    Colorectal cancer (CRC) is the second leading cause of cancer-related deaths. It is estimated that about half the cases of CRC occurring today are preventable. Recent studies showed that human gut microbiota and their collective metabolic outputs play important roles in CRC. However, the mechanisms by which human gut microbial metabolites interact with host genetics in contributing CRC remain largely unknown. We hypothesize that computational approaches that integrate and analyze vast amounts of publicly available biomedical data have great potential in better understanding how human gut microbial metabolites are mechanistically involved in CRC. Leveraging vast amount of publicly available data, we developed a computational algorithm to predict human gut microbial metabolites for CRC. We validated the prediction algorithm by showing that previously known CRC-associated gut microbial metabolites ranked highly (mean ranking: top 10.52%; median ranking: 6.29%; p-value: 3.85E-16). Moreover, we identified new gut microbial metabolites likely associated with CRC. Through computational analysis, we propose potential roles for tartaric acid, the top one ranked metabolite, in CRC etiology. In summary, our data-driven computation-based study generated a large amount of associations that could serve as a starting point for further experiments to refute or validate these microbial metabolite associations in CRC cancer.

  16. The Linear Interaction Energy Method for the Prediction of Protein Stability Changes Upon Mutation

    PubMed Central

    Wickstrom, Lauren; Gallicchio, Emilio; Levy, Ronald M.

    2011-01-01

    The coupling of protein energetics and sequence changes is a critical aspect of computational protein design, as well as for the understanding of protein evolution, human disease, and drug resistance. In order to study the molecular basis for this coupling, computational tools must be sufficiently accurate and computationally inexpensive enough to handle large amounts of sequence data. We have developed a computational approach based on the linear interaction energy (LIE) approximation to predict the changes in the free energy of the native state induced by a single mutation. This approach was applied to a set of 822 mutations in 10 proteins which resulted in an average unsigned error of 0.82 kcal/mol and a correlation coefficient of 0.72 between the calculated and experimental ΔΔG values. The method is able to accurately identify destabilizing hot spot mutations however it has difficulty in distinguishing between stabilizing and destabilizing mutations due to the distribution of stability changes for the set of mutations used to parameterize the model. In addition, the model also performs quite well in initial tests on a small set of double mutations. Based on these promising results, we can begin to examine the relationship between protein stability and fitness, correlated mutations, and drug resistance. PMID:22038697

  17. The DYNAMO Simulation Language--An Alternate Approach to Computer Science Education.

    ERIC Educational Resources Information Center

    Bronson, Richard

    1986-01-01

    Suggests the use of computer simulation of continuous systems as a problem solving approach to computer languages. Outlines the procedures that the system dynamics approach employs in computer simulations. Explains the advantages of the special purpose language, DYNAMO. (ML)

  18. Computing the sensitivity of drag and lift in flow past a circular cylinder: Time-stepping versus self-consistent analysis

    NASA Astrophysics Data System (ADS)

    Meliga, Philippe

    2017-07-01

    We provide in-depth scrutiny of two methods making use of adjoint-based gradients to compute the sensitivity of drag in the two-dimensional, periodic flow past a circular cylinder (Re≲189 ): first, the time-stepping analysis used in Meliga et al. [Phys. Fluids 26, 104101 (2014), 10.1063/1.4896941] that relies on classical Navier-Stokes modeling and determines the sensitivity to any generic control force from time-dependent adjoint equations marched backwards in time; and, second, a self-consistent approach building on the model of Mantič-Lugo et al. [Phys. Rev. Lett. 113, 084501 (2014), 10.1103/PhysRevLett.113.084501] to compute semilinear approximations of the sensitivity to the mean and fluctuating components of the force. Both approaches are applied to open-loop control by a small secondary cylinder and allow identifying the sensitive regions without knowledge of the controlled states. The theoretical predictions obtained by time-stepping analysis reproduce well the results obtained by direct numerical simulation of the two-cylinder system. So do the predictions obtained by self-consistent analysis, which corroborates the relevance of the approach as a guideline for efficient and systematic control design in the attempt to reduce drag, even though the Reynolds number is not close to the instability threshold and the oscillation amplitude is not small. This is because, unlike simpler approaches relying on linear stability analysis to predict the main features of the flow unsteadiness, the semilinear framework encompasses rigorously the effect of the control on the mean flow, as well as on the finite-amplitude fluctuation that feeds back nonlinearly onto the mean flow via the formation of Reynolds stresses. Such results are especially promising as the self-consistent approach determines the sensitivity from time-independent equations that can be solved iteratively, which makes it generally less computationally demanding. We ultimately discuss the extent to which relevant information can be gained from a hybrid modeling computing self-consistent sensitivities from the postprocessing of DNS data. Application to alternative control objectives such as increasing the lift and alleviating the fluctuating drag and lift is also discussed.

  19. CT Image Sequence Analysis for Object Recognition - A Rule-Based 3-D Computer Vision System

    Treesearch

    Dongping Zhu; Richard W. Conners; Daniel L. Schmoldt; Philip A. Araman

    1991-01-01

    Research is now underway to create a vision system for hardwood log inspection using a knowledge-based approach. In this paper, we present a rule-based, 3-D vision system for locating and identifying wood defects using topological, geometric, and statistical attributes. A number of different features can be derived from the 3-D input scenes. These features and evidence...

  20. Conventional and reciprocal approaches to the inverse dipole localization problem for N(20)-P (20) somatosensory evoked potentials.

    PubMed

    Finke, Stefan; Gulrajani, Ramesh M; Gotman, Jean; Savard, Pierre

    2013-01-01

    The non-invasive localization of the primary sensory hand area can be achieved by solving the inverse problem of electroencephalography (EEG) for N(20)-P(20) somatosensory evoked potentials (SEPs). This study compares two different mathematical approaches for the computation of transfer matrices used to solve the EEG inverse problem. Forward transfer matrices relating dipole sources to scalp potentials are determined via conventional and reciprocal approaches using individual, realistically shaped head models. The reciprocal approach entails calculating the electric field at the dipole position when scalp electrodes are reciprocally energized with unit current-scalp potentials are obtained from the scalar product of this electric field and the dipole moment. Median nerve stimulation is performed on three healthy subjects and single-dipole inverse solutions for the N(20)-P(20) SEPs are then obtained by simplex minimization and validated against the primary sensory hand area identified on magnetic resonance images. Solutions are presented for different time points, filtering strategies, boundary-element method discretizations, and skull conductivity values. Both approaches produce similarly small position errors for the N(20)-P(20) SEP. Position error for single-dipole inverse solutions is inherently robust to inaccuracies in forward transfer matrices but dependent on the overlapping activity of other neural sources. Significantly smaller time and storage requirements are the principal advantages of the reciprocal approach. Reduced computational requirements and similar dipole position accuracy support the use of reciprocal approaches over conventional approaches for N(20)-P(20) SEP source localization.

  1. Evaluation of an Inverse Molecular Design Algorithm in a Model Binding Site

    PubMed Central

    Huggins, David J.; Altman, Michael D.; Tidor, Bruce

    2008-01-01

    Computational molecular design is a useful tool in modern drug discovery. Virtual screening is an approach that docks and then scores individual members of compound libraries. In contrast to this forward approach, inverse approaches construct compounds from fragments, such that the computed affinity, or a combination of relevant properties, is optimized. We have recently developed a new inverse approach to drug design based on the dead-end elimination and A* algorithms employing a physical potential function. This approach has been applied to combinatorially constructed libraries of small-molecule ligands to design high-affinity HIV-1 protease inhibitors [M. D. Altman et al. J. Am. Chem. Soc. 130: 6099–6013, 2008]. Here we have evaluated the new method using the well studied W191G mutant of cytochrome c peroxidase. This mutant possesses a charged binding pocket and has been used to evaluate other design approaches. The results show that overall the new inverse approach does an excellent job of separating binders from non-binders. For a few individual cases, scoring inaccuracies led to false positives. The majority of these involve erroneous solvation energy estimation for charged amines, anilinium ions and phenols, which has been observed previously for a variety of scoring algorithms. Interestingly, although inverse approaches are generally expected to identify some but not all binders in a library, due to limited conformational searching, these results show excellent coverage of the known binders while still showing strong discrimination of the non-binders. PMID:18831031

  2. Evaluation of an inverse molecular design algorithm in a model binding site.

    PubMed

    Huggins, David J; Altman, Michael D; Tidor, Bruce

    2009-04-01

    Computational molecular design is a useful tool in modern drug discovery. Virtual screening is an approach that docks and then scores individual members of compound libraries. In contrast to this forward approach, inverse approaches construct compounds from fragments, such that the computed affinity, or a combination of relevant properties, is optimized. We have recently developed a new inverse approach to drug design based on the dead-end elimination and A* algorithms employing a physical potential function. This approach has been applied to combinatorially constructed libraries of small-molecule ligands to design high-affinity HIV-1 protease inhibitors (Altman et al., J Am Chem Soc 2008;130:6099-6013). Here we have evaluated the new method using the well-studied W191G mutant of cytochrome c peroxidase. This mutant possesses a charged binding pocket and has been used to evaluate other design approaches. The results show that overall the new inverse approach does an excellent job of separating binders from nonbinders. For a few individual cases, scoring inaccuracies led to false positives. The majority of these involve erroneous solvation energy estimation for charged amines, anilinium ions, and phenols, which has been observed previously for a variety of scoring algorithms. Interestingly, although inverse approaches are generally expected to identify some but not all binders in a library, due to limited conformational searching, these results show excellent coverage of the known binders while still showing strong discrimination of the nonbinders. (c) 2008 Wiley-Liss, Inc.

  3. Development of a computer-simulation model for a plant-nematode system.

    PubMed

    Ferris, H

    1976-07-01

    A computer-simulation model (MELSIM) of a Meloidogyne-grapevine system is developed. The objective is to attempt a holistic approach to the study of nematode population dynamics by using experimental data from controlled environmental conditions. A simulator with predictive ability would be useful in considering pest management alternatives and in teaching. Rates of flow and interaction between the components of the system are governed by environmental conditions. Equations for these rates are determined by fitting curves to data from controlled environment studies. Development of the model and trial simulations have revealed deficiencies in understanding of the system and identified areas where further research is necessary.

  4. Advances in natural language processing.

    PubMed

    Hirschberg, Julia; Manning, Christopher D

    2015-07-17

    Natural language processing employs computational techniques for the purpose of learning, understanding, and producing human language content. Early computational approaches to language research focused on automating the analysis of the linguistic structure of language and developing basic technologies such as machine translation, speech recognition, and speech synthesis. Today's researchers refine and make use of such tools in real-world applications, creating spoken dialogue systems and speech-to-speech translation engines, mining social media for information about health or finance, and identifying sentiment and emotion toward products and services. We describe successes and challenges in this rapidly advancing area. Copyright © 2015, American Association for the Advancement of Science.

  5. On a categorial aspect of knowledge representation

    NASA Astrophysics Data System (ADS)

    Tataj, Emanuel; Mulawka, Jan; Nieznański, Edward

    Adequate representation of data is crucial for modeling any type of data. To faithfully present and describe the relevant section of the world it is necessary to select the method that can easily be implemented on a computer system which will help in further description allowing reasoning. The main objective of this contribution is to present methods of knowledge representation using categorial approach. Next to identify the main advantages for computer implementation. Categorical aspect of knowledge representation is considered in semantic networks realisation. Such method borrows already known metaphysics properties for data modeling process. The potential topics of further development of categorical semantic networks implementations are also underlined.

  6. Personal Health Records: A Systematic Literature Review

    PubMed Central

    2017-01-01

    Background Information and communication technology (ICT) has transformed the health care field worldwide. One of the main drivers of this change is the electronic health record (EHR). However, there are still open issues and challenges because the EHR usually reflects the partial view of a health care provider without the ability for patients to control or interact with their data. Furthermore, with the growth of mobile and ubiquitous computing, the number of records regarding personal health is increasing exponentially. This movement has been characterized as the Internet of Things (IoT), including the widespread development of wearable computing technology and assorted types of health-related sensors. This leads to the need for an integrated method of storing health-related data, defined as the personal health record (PHR), which could be used by health care providers and patients. This approach could combine EHRs with data gathered from sensors or other wearable computing devices. This unified view of patients’ health could be shared with providers, who may not only use previous health-related records but also expand them with data resulting from their interactions. Another PHR advantage is that patients can interact with their health data, making decisions that may positively affect their health. Objective This work aimed to explore the recent literature related to PHRs by defining the taxonomy and identifying challenges and open questions. In addition, this study specifically sought to identify data types, standards, profiles, goals, methods, functions, and architecture with regard to PHRs. Methods The method to achieve these objectives consists of using the systematic literature review approach, which is guided by research questions using the population, intervention, comparison, outcome, and context (PICOC) criteria. Results As a result, we reviewed more than 5000 scientific studies published in the last 10 years, selected the most significant approaches, and thoroughly surveyed the health care field related to PHRs. We developed an updated taxonomy and identified challenges, open questions, and current data types, related standards, main profiles, input strategies, goals, functions, and architectures of the PHR. Conclusions All of these results contribute to the achievement of a significant degree of coverage regarding the technology related to PHRs. PMID:28062391

  7. Functional genomics platform for pooled screening and mammalian genetic interaction maps

    PubMed Central

    Kampmann, Martin; Bassik, Michael C.; Weissman, Jonathan S.

    2014-01-01

    Systematic genetic interaction maps in microorganisms are powerful tools for identifying functional relationships between genes and defining the function of uncharacterized genes. We have recently implemented this strategy in mammalian cells as a two-stage approach. First, genes of interest are robustly identified in a pooled genome-wide screen using complex shRNA libraries. Second, phenotypes for all pairwise combinations of hit genes are measured in a double-shRNA screen and used to construct a genetic interaction map. Our protocol allows for rapid pooled screening under various conditions without a requirement for robotics, in contrast to arrayed approaches. Each stage of the protocol can be implemented in ~2 weeks, with additional time for analysis and generation of reagents. We discuss considerations for screen design, and present complete experimental procedures as well as a full computational analysis suite for identification of hits in pooled screens and generation of genetic interaction maps. While the protocols outlined here were developed for our original shRNA-based approach, they can be applied more generally, including to CRISPR-based approaches. PMID:24992097

  8. Three-Dimensional Innervation Zone Imaging from Multi-Channel Surface EMG Recordings.

    PubMed

    Liu, Yang; Ning, Yong; Li, Sheng; Zhou, Ping; Rymer, William Z; Zhang, Yingchun

    2015-09-01

    There is an unmet need to accurately identify the locations of innervation zones (IZs) of spastic muscles, so as to guide botulinum toxin (BTX) injections for the best clinical outcome. A novel 3D IZ imaging (3DIZI) approach was developed by combining the bioelectrical source imaging and surface electromyogram (EMG) decomposition methods to image the 3D distribution of IZs in the target muscles. Surface IZ locations of motor units (MUs), identified from the bipolar map of their MU action potentials (MUAPs) were employed as a prior knowledge in the 3DIZI approach to improve its imaging accuracy. The performance of the 3DIZI approach was first optimized and evaluated via a series of designed computer simulations, and then validated with the intramuscular EMG data, together with simultaneously recorded 128-channel surface EMG data from the biceps of two subjects. Both simulation and experimental validation results demonstrate the high performance of the 3DIZI approach in accurately reconstructing the distributions of IZs and the dynamic propagation of internal muscle activities in the biceps from high-density surface EMG recordings.

  9. THREE-DIMENSIONAL INNERVATION ZONE IMAGING FROM MULTI-CHANNEL SURFACE EMG RECORDINGS

    PubMed Central

    LIU, YANG; NING, YONG; LI, SHENG; ZHOU, PING; RYMER, WILLIAM Z.; ZHANG, YINGCHUN

    2017-01-01

    There is an unmet need to accurately identify the locations of innervation zones (IZs) of spastic muscles, so as to guide botulinum toxin (BTX) injections for the best clinical outcome. A novel 3-dimensional IZ imaging (3DIZI) approach was developed by combining the bioelectrical source imaging and surface electromyogram (EMG) decomposition methods to image the 3D distribution of IZs in the target muscles. Surface IZ locations of motor units (MUs), identified from the bipolar map of their motor unit action potentials (MUAPs) were employed as a prior knowledge in the 3DIZI approach to improve its imaging accuracy. The performance of the 3DIZI approach was first optimized and evaluated via a series of designed computer simulations, and then validated with the intramuscular EMG data, together with simultaneously recorded 128-channel surface EMG data from the biceps of two subjects. Both simulation and experimental validation results demonstrate the high performance of the 3DIZI approach in accurately reconstructing the distributions of IZs and the dynamic propagation of internal muscle activities in the biceps from high-density surface EMG recordings. PMID:26160432

  10. MIR@NT@N: a framework integrating transcription factors, microRNAs and their targets to identify sub-network motifs in a meta-regulation network model

    PubMed Central

    2011-01-01

    Background To understand biological processes and diseases, it is crucial to unravel the concerted interplay of transcription factors (TFs), microRNAs (miRNAs) and their targets within regulatory networks and fundamental sub-networks. An integrative computational resource generating a comprehensive view of these regulatory molecular interactions at a genome-wide scale would be of great interest to biologists, but is not available to date. Results To identify and analyze molecular interaction networks, we developed MIR@NT@N, an integrative approach based on a meta-regulation network model and a large-scale database. MIR@NT@N uses a graph-based approach to predict novel molecular actors across multiple regulatory processes (i.e. TFs acting on protein-coding or miRNA genes, or miRNAs acting on messenger RNAs). Exploiting these predictions, the user can generate networks and further analyze them to identify sub-networks, including motifs such as feedback and feedforward loops (FBL and FFL). In addition, networks can be built from lists of molecular actors with an a priori role in a given biological process to predict novel and unanticipated interactions. Analyses can be contextualized and filtered by integrating additional information such as microarray expression data. All results, including generated graphs, can be visualized, saved and exported into various formats. MIR@NT@N performances have been evaluated using published data and then applied to the regulatory program underlying epithelium to mesenchyme transition (EMT), an evolutionary-conserved process which is implicated in embryonic development and disease. Conclusions MIR@NT@N is an effective computational approach to identify novel molecular regulations and to predict gene regulatory networks and sub-networks including conserved motifs within a given biological context. Taking advantage of the M@IA environment, MIR@NT@N is a user-friendly web resource freely available at http://mironton.uni.lu which will be updated on a regular basis. PMID:21375730

  11. Accelerating the design of solar thermal fuel materials through high throughput simulations.

    PubMed

    Liu, Yun; Grossman, Jeffrey C

    2014-12-10

    Solar thermal fuels (STF) store the energy of sunlight, which can then be released later in the form of heat, offering an emission-free and renewable solution for both solar energy conversion and storage. However, this approach is currently limited by the lack of low-cost materials with high energy density and high stability. In this Letter, we present an ab initio high-throughput computational approach to accelerate the design process and allow for searches over a broad class of materials. The high-throughput screening platform we have developed can run through large numbers of molecules composed of earth-abundant elements and identifies possible metastable structures of a given material. Corresponding isomerization enthalpies associated with the metastable structures are then computed. Using this high-throughput simulation approach, we have discovered molecular structures with high isomerization enthalpies that have the potential to be new candidates for high-energy density STF. We have also discovered physical principles to guide further STF materials design through structural analysis. More broadly, our results illustrate the potential of using high-throughput ab initio simulations to design materials that undergo targeted structural transitions.

  12. DOE Advanced Scientific Computing Advisory Subcommittee (ASCAC) Report: Top Ten Exascale Research Challenges

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

    Lucas, Robert; Ang, James; Bergman, Keren

    2014-02-10

    Exascale computing systems are essential for the scientific fields that will transform the 21st century global economy, including energy, biotechnology, nanotechnology, and materials science. Progress in these fields is predicated on the ability to perform advanced scientific and engineering simulations, and analyze the deluge of data. On July 29, 2013, ASCAC was charged by Patricia Dehmer, the Acting Director of the Office of Science, to assemble a subcommittee to provide advice on exascale computing. This subcommittee was directed to return a list of no more than ten technical approaches (hardware and software) that will enable the development of a systemmore » that achieves the Department's goals for exascale computing. Numerous reports over the past few years have documented the technical challenges and the non¬-viability of simply scaling existing computer designs to reach exascale. The technical challenges revolve around energy consumption, memory performance, resilience, extreme concurrency, and big data. Drawing from these reports and more recent experience, this ASCAC subcommittee has identified the top ten computing technology advancements that are critical to making a capable, economically viable, exascale system.« less

  13. Assessment of Computer Literacy of Nurses in Lesotho.

    PubMed

    Mugomeri, Eltony; Chatanga, Peter; Maibvise, Charles; Masitha, Matseliso

    2016-11-01

    Health systems worldwide are moving toward use of information technology to improve healthcare delivery. However, this requires basic computer skills. This study assessed the computer literacy of nurses in Lesotho using a cross-sectional quantitative approach. A structured questionnaire with 32 standardized computer skills was distributed to 290 randomly selected nurses in Maseru District. Univariate and multivariate logistic regression analyses in Stata 13 were performed to identify factors associated with having inadequate computer skills. Overall, 177 (61%) nurses scored below 16 of the 32 skills assessed. Finding hyperlinks on Web pages (63%), use of advanced search parameters (60.2%), and downloading new software (60.1%) proved to be challenging to the highest proportions of nurses. Age, sex, year of obtaining latest qualification, computer experience, and work experience were significantly (P < .05) associated with inadequate computer skills in univariate analysis. However, in multivariate analyses, sex (P = .001), year of obtaining latest qualification (P = .011), and computer experience (P < .001) emerged as significant factors. The majority of nurses in Lesotho have inadequate computer skills, and this is significantly associated with having many years since obtaining their latest qualification, being female, and lack of exposure to computers. These factors should be considered during planning of training curriculum for nurses in Lesotho.

  14. Combinatorial Approach for Large-scale Identification of Linked Peptides from Tandem Mass Spectrometry Spectra*

    PubMed Central

    Wang, Jian; Anania, Veronica G.; Knott, Jeff; Rush, John; Lill, Jennie R.; Bourne, Philip E.; Bandeira, Nuno

    2014-01-01

    The combination of chemical cross-linking and mass spectrometry has recently been shown to constitute a powerful tool for studying protein–protein interactions and elucidating the structure of large protein complexes. However, computational methods for interpreting the complex MS/MS spectra from linked peptides are still in their infancy, making the high-throughput application of this approach largely impractical. Because of the lack of large annotated datasets, most current approaches do not capture the specific fragmentation patterns of linked peptides and therefore are not optimal for the identification of cross-linked peptides. Here we propose a generic approach to address this problem and demonstrate it using disulfide-bridged peptide libraries to (i) efficiently generate large mass spectral reference data for linked peptides at a low cost and (ii) automatically train an algorithm that can efficiently and accurately identify linked peptides from MS/MS spectra. We show that using this approach we were able to identify thousands of MS/MS spectra from disulfide-bridged peptides through comparison with proteome-scale sequence databases and significantly improve the sensitivity of cross-linked peptide identification. This allowed us to identify 60% more direct pairwise interactions between the protein subunits in the 20S proteasome complex than existing tools on cross-linking studies of the proteasome complexes. The basic framework of this approach and the MS/MS reference dataset generated should be valuable resources for the future development of new tools for the identification of linked peptides. PMID:24493012

  15. Multidisciplinary optimization in aircraft design using analytic technology models

    NASA Technical Reports Server (NTRS)

    Malone, Brett; Mason, W. H.

    1991-01-01

    An approach to multidisciplinary optimization is presented which combines the Global Sensitivity Equation method, parametric optimization, and analytic technology models. The result is a powerful yet simple procedure for identifying key design issues. It can be used both to investigate technology integration issues very early in the design cycle, and to establish the information flow framework between disciplines for use in multidisciplinary optimization projects using much more computational intense representations of each technology. To illustrate the approach, an examination of the optimization of a short takeoff heavy transport aircraft is presented for numerous combinations of performance and technology constraints.

  16. A computational neural approach to support the discovery of gene function and classes of cancer.

    PubMed

    Azuaje, F

    2001-03-01

    Advances in molecular classification of tumours may play a central role in cancer treatment. Here, a novel approach to genome expression pattern interpretation is described and applied to the recognition of B-cell malignancies as a test set. Using cDNA microarrays data generated by a previous study, a neural network model known as simplified fuzzy ARTMAP is able to identify normal and diffuse large B-cell lymphoma (DLBCL) patients. Furthermore, it discovers the distinction between patients with molecularly distinct forms of DLBCL without previous knowledge of those subtypes.

  17. Imaging approach to mechanistic study of nanoparticle interactions with the blood-brain barrier.

    PubMed

    Bramini, Mattia; Ye, Dong; Hallerbach, Anna; Nic Raghnaill, Michelle; Salvati, Anna; Aberg, Christoffer; Dawson, Kenneth A

    2014-05-27

    Understanding nanoparticle interactions with the central nervous system, in particular the blood-brain barrier, is key to advances in therapeutics, as well as assessing the safety of nanoparticles. Challenges in achieving insights have been significant, even for relatively simple models. Here we use a combination of live cell imaging and computational analysis to directly study nanoparticle translocation across a human in vitro blood-brain barrier model. This approach allows us to identify and avoid problems in more conventional inferential in vitro measurements by identifying the catalogue of events of barrier internalization and translocation as they occur. Potentially this approach opens up the window of applicability of in vitro models, thereby enabling in depth mechanistic studies in the future. Model nanoparticles are used to illustrate the method. For those, we find that translocation, though rare, appears to take place. On the other hand, barrier uptake is efficient, and since barrier export is small, there is significant accumulation within the barrier.

  18. Brain-computer interface using wavelet transformation and naïve bayes classifier.

    PubMed

    Bassani, Thiago; Nievola, Julio Cesar

    2010-01-01

    The main purpose of this work is to establish an exploratory approach using electroencephalographic (EEG) signal, analyzing the patterns in the time-frequency plane. This work also aims to optimize the EEG signal analysis through the improvement of classifiers and, eventually, of the BCI performance. In this paper a novel exploratory approach for data mining of EEG signal based on continuous wavelet transformation (CWT) and wavelet coherence (WC) statistical analysis is introduced and applied. The CWT allows the representation of time-frequency patterns of the signal's information content by WC qualiatative analysis. Results suggest that the proposed methodology is capable of identifying regions in time-frequency spectrum during the specified task of BCI. Furthermore, an example of a region is identified, and the patterns are classified using a Naïve Bayes Classifier (NBC). This innovative characteristic of the process justifies the feasibility of the proposed approach to other data mining applications. It can open new physiologic researches in this field and on non stationary time series analysis.

  19. Combining Machine Learning Systems and Multiple Docking Simulation Packages to Improve Docking Prediction Reliability for Network Pharmacology

    PubMed Central

    Hsin, Kun-Yi; Ghosh, Samik; Kitano, Hiroaki

    2013-01-01

    Increased availability of bioinformatics resources is creating opportunities for the application of network pharmacology to predict drug effects and toxicity resulting from multi-target interactions. Here we present a high-precision computational prediction approach that combines two elaborately built machine learning systems and multiple molecular docking tools to assess binding potentials of a test compound against proteins involved in a complex molecular network. One of the two machine learning systems is a re-scoring function to evaluate binding modes generated by docking tools. The second is a binding mode selection function to identify the most predictive binding mode. Results from a series of benchmark validations and a case study show that this approach surpasses the prediction reliability of other techniques and that it also identifies either primary or off-targets of kinase inhibitors. Integrating this approach with molecular network maps makes it possible to address drug safety issues by comprehensively investigating network-dependent effects of a drug or drug candidate. PMID:24391846

  20. Integrating publicly-available data to generate computationally ...

    EPA Pesticide Factsheets

    The adverse outcome pathway (AOP) framework provides a way of organizing knowledge related to the key biological events that result in a particular health outcome. For the majority of environmental chemicals, the availability of curated pathways characterizing potential toxicity is limited. Methods are needed to assimilate large amounts of available molecular data and quickly generate putative AOPs for further testing and use in hazard assessment. A graph-based workflow was used to facilitate the integration of multiple data types to generate computationally-predicted (cp) AOPs. Edges between graph entities were identified through direct experimental or literature information or computationally inferred using frequent itemset mining. Data from the TG-GATEs and ToxCast programs were used to channel large-scale toxicogenomics information into a cpAOP network (cpAOPnet) of over 20,000 relationships describing connections between chemical treatments, phenotypes, and perturbed pathways measured by differential gene expression and high-throughput screening targets. Sub-networks of cpAOPs for a reference chemical (carbon tetrachloride, CCl4) and outcome (hepatic steatosis) were extracted using the network topology. Comparison of the cpAOP subnetworks to published mechanistic descriptions for both CCl4 toxicity and hepatic steatosis demonstrate that computational approaches can be used to replicate manually curated AOPs and identify pathway targets that lack genomic mar

  1. Identification of fidgety movements and prediction of CP by the use of computer-based video analysis is more accurate when based on two video recordings.

    PubMed

    Adde, Lars; Helbostad, Jorunn; Jensenius, Alexander R; Langaas, Mette; Støen, Ragnhild

    2013-08-01

    This study evaluates the role of postterm age at assessment and the use of one or two video recordings for the detection of fidgety movements (FMs) and prediction of cerebral palsy (CP) using computer vision software. Recordings between 9 and 17 weeks postterm age from 52 preterm and term infants (24 boys, 28 girls; 26 born preterm) were used. Recordings were analyzed using computer vision software. Movement variables, derived from differences between subsequent video frames, were used for quantitative analysis. Sensitivities, specificities, and area under curve were estimated for the first and second recording, or a mean of both. FMs were classified based on the Prechtl approach of general movement assessment. CP status was reported at 2 years. Nine children developed CP of whom all recordings had absent FMs. The mean variability of the centroid of motion (CSD) from two recordings was more accurate than using only one recording, and identified all children who were diagnosed with CP at 2 years. Age at assessment did not influence the detection of FMs or prediction of CP. The accuracy of computer vision techniques in identifying FMs and predicting CP based on two recordings should be confirmed in future studies.

  2. Computational strategy for quantifying human pesticide exposure based upon a saliva measurement

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

    Timchalk, Charles; Weber, Thomas J.; Smith, Jordan N.

    The National Research Council of the National Academies report, Toxicity Testing in the 21st Century: A Vision and Strategy, highlighted the importance of quantitative exposure data for evaluating human toxicity risk and noted that biomonitoring is a critical tool for quantitatively evaluating exposure from both environmental and occupational settings. Direct measurement of chemical exposures using personal monitoring provides the most accurate estimation of a subject’s true exposure, and non-invasive methods have also been advocated for quantifying the pharmacokinetics and bioavailability of drugs and xenobiotics. In this regard, there is a need to identify chemicals that are readily cleared in salivamore » at concentrations that can be quantified to support the implementation of this approach.. The current manuscript describes the use of computational modeling approaches that are closely coupled to in vivo and in vitro experiments to predict salivary uptake and clearance of xenobiotics. The primary mechanism by which xenobiotics leave the blood and enter saliva is thought to involve paracellular transport, passive transcellular diffusion, or trancellular active transport with the majority of drugs and xenobiotics cleared from plasma into saliva by passive diffusion. The transcellular or paracellular diffusion of unbound chemicals in plasma to saliva has been computational modeled using a combination of compartmental and physiologically based approaches. Of key importance for determining the plasma:saliva partitioning was the utilization of a modified Schmitt algorithm that calculates partitioning based upon the tissue composition, pH, chemical pKa and plasma protein-binding. Sensitivity analysis of key model parameters specifically identified that both protein-binding and pKa (for weak acids and bases) had the most significant impact on the determination of partitioning and that there were clear species dependent differences based upon physiological variance between rats and humans. Ongoing efforts are focused on extending this modeling strategy to an in vitro salivary acinar cell based system that will be utilized to experimentally determine and computationally predict salivary gland uptake and clearance for a broad range of xenobiotics. Hence, it is envisioned that a combination of salivary biomonitoring and computational modeling will enable the non-invasive measurement of both environmental and occupational exposure in human populations using saliva.« less

  3. FLOCK cluster analysis of mast cell event clustering by high-sensitivity flow cytometry predicts systemic mastocytosis.

    PubMed

    Dorfman, David M; LaPlante, Charlotte D; Pozdnyakova, Olga; Li, Betty

    2015-11-01

    In our high-sensitivity flow cytometric approach for systemic mastocytosis (SM), we identified mast cell event clustering as a new diagnostic criterion for the disease. To objectively characterize mast cell gated event distributions, we performed cluster analysis using FLOCK, a computational approach to identify cell subsets in multidimensional flow cytometry data in an unbiased, automated fashion. FLOCK identified discrete mast cell populations in most cases of SM (56/75 [75%]) but only a minority of non-SM cases (17/124 [14%]). FLOCK-identified mast cell populations accounted for 2.46% of total cells on average in SM cases and 0.09% of total cells on average in non-SM cases (P < .0001) and were predictive of SM, with a sensitivity of 75%, a specificity of 86%, a positive predictive value of 76%, and a negative predictive value of 85%. FLOCK analysis provides useful diagnostic information for evaluating patients with suspected SM, and may be useful for the analysis of other hematopoietic neoplasms. Copyright© by the American Society for Clinical Pathology.

  4. Identifying a largest logical plane from a plurality of logical planes formed of compute nodes of a subcommunicator in a parallel computer

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

    Davis, Kristan D.; Faraj, Daniel A.

    In a parallel computer, a largest logical plane from a plurality of logical planes formed of compute nodes of a subcommunicator may be identified by: identifying, by each compute node of the subcommunicator, all logical planes that include the compute node; calculating, by each compute node for each identified logical plane that includes the compute node, an area of the identified logical plane; initiating, by a root node of the subcommunicator, a gather operation; receiving, by the root node from each compute node of the subcommunicator, each node's calculated areas as contribution data to the gather operation; and identifying, bymore » the root node in dependence upon the received calculated areas, a logical plane of the subcommunicator having the greatest area.« less

  5. Computationally identified novel agonists for GPRC6A

    PubMed Central

    Ye, Ruisong; Hwang, Dong-Jin; Miller, Duane D.; Smith, Jeremy C.; Baudry, Jerome; Quarles, L. Darryl

    2018-01-01

    New insights into G protein coupled receptor regulation of glucose metabolism by β-cells, skeletal muscle and liver hepatocytes identify GPRC6A as a potential therapeutic target for treating type 2 diabetes mellitus (T2D). Activating GPRC6A with a small molecule drug represents a potential paradigm-shifting opportunity to make significant strides in regulating glucose homeostasis by simultaneously correcting multiple metabolic derangements that underlie T2D, including abnormalities in β-cell proliferation and insulin secretion and peripheral insulin resistance. Using a computational, structure-based high-throughput screening approach, we identified novel tri-phenyl compounds predicted to bind to the venus fly trap (VFT) and 7-transmembrane (7-TM) domains of GPRC6A. Experimental testing found that these compounds dose-dependently stimulated GPRC6A signaling in a heterologous cell expression system. Additional chemical modifications and functional analysis identified one tri-phenyl lead compound, DJ-V-159 that demonstrated the greatest potency in stimulating insulin secretion in β-cells and lowering serum glucose in wild-type mice. Collectively, these studies show that GPRC6A is a “druggable” target for developing chemical probes to treat T2DM. PMID:29684031

  6. Peptide Identification by Database Search of Mixture Tandem Mass Spectra*

    PubMed Central

    Wang, Jian; Bourne, Philip E.; Bandeira, Nuno

    2011-01-01

    In high-throughput proteomics the development of computational methods and novel experimental strategies often rely on each other. In certain areas, mass spectrometry methods for data acquisition are ahead of computational methods to interpret the resulting tandem mass spectra. Particularly, although there are numerous situations in which a mixture tandem mass spectrum can contain fragment ions from two or more peptides, nearly all database search tools still make the assumption that each tandem mass spectrum comes from one peptide. Common examples include mixture spectra from co-eluting peptides in complex samples, spectra generated from data-independent acquisition methods, and spectra from peptides with complex post-translational modifications. We propose a new database search tool (MixDB) that is able to identify mixture tandem mass spectra from more than one peptide. We show that peptides can be reliably identified with up to 95% accuracy from mixture spectra while considering only a 0.01% of all possible peptide pairs (four orders of magnitude speedup). Comparison with current database search methods indicates that our approach has better or comparable sensitivity and precision at identifying single-peptide spectra while simultaneously being able to identify 38% more peptides from mixture spectra at significantly higher precision. PMID:21862760

  7. Helicopter Flight Procedures for Community Noise Reduction

    NASA Technical Reports Server (NTRS)

    Greenwood, Eric

    2017-01-01

    A computationally efficient, semiempirical noise model suitable for maneuvering flight noise prediction is used to evaluate the community noise impact of practical variations on several helicopter flight procedures typical of normal operations. Turns, "quick-stops," approaches, climbs, and combinations of these maneuvers are assessed. Relatively small variations in flight procedures are shown to cause significant changes to Sound Exposure Levels over a wide area. Guidelines are developed for helicopter pilots intended to provide effective strategies for reducing the negative effects of helicopter noise on the community. Finally, direct optimization of flight trajectories is conducted to identify low noise optimal flight procedures and quantify the magnitude of community noise reductions that can be obtained through tailored helicopter flight procedures. Physically realizable optimal turns and approaches are identified that achieve global noise reductions of as much as 10 dBA Sound Exposure Level.

  8. Trajectory analysis via a geometric feature space approach

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

    Rintoul, Mark D.; Wilson, Andrew T.

    This study aimed to organize a body of trajectories in order to identify, search for and classify both common and uncommon behaviors among objects such as aircraft and ships. Existing comparison functions such as the Fréchet distance are computationally expensive and yield counterintuitive results in some cases. We propose an approach using feature vectors whose components represent succinctly the salient information in trajectories. These features incorporate basic information such as the total distance traveled and the distance between start/stop points as well as geometric features related to the properties of the convex hull, trajectory curvature and general distance geometry. Additionally,more » these features can generally be mapped easily to behaviors of interest to humans who are searching large databases. Most of these geometric features are invariant under rigid transformation. Furthermore, we demonstrate the use of different subsets of these features to identify trajectories similar to an exemplar, cluster a database of several hundred thousand trajectories and identify outliers.« less

  9. Trajectory analysis via a geometric feature space approach

    DOE PAGES

    Rintoul, Mark D.; Wilson, Andrew T.

    2015-10-05

    This study aimed to organize a body of trajectories in order to identify, search for and classify both common and uncommon behaviors among objects such as aircraft and ships. Existing comparison functions such as the Fréchet distance are computationally expensive and yield counterintuitive results in some cases. We propose an approach using feature vectors whose components represent succinctly the salient information in trajectories. These features incorporate basic information such as the total distance traveled and the distance between start/stop points as well as geometric features related to the properties of the convex hull, trajectory curvature and general distance geometry. Additionally,more » these features can generally be mapped easily to behaviors of interest to humans who are searching large databases. Most of these geometric features are invariant under rigid transformation. Furthermore, we demonstrate the use of different subsets of these features to identify trajectories similar to an exemplar, cluster a database of several hundred thousand trajectories and identify outliers.« less

  10. Parameters Identification of Interface Friction Model for Ceramic Matrix Composites Based on Stress-Strain Response

    NASA Astrophysics Data System (ADS)

    Han, Xiao; Gao, Xiguang; Song, Yingdong

    2017-10-01

    An approach to identify parameters of interface friction model for Ceramic Matrix composites based on stress-strain response was developed. The stress distribution of fibers in the interface slip region and intact region of the damaged composite was determined by adopting the interface friction model. The relation between maximum strain, secant moduli of hysteresis loop and interface shear stress, interface de-bonding stress was established respectively with the method of symbolic-graphic combination. By comparing the experimental strain, secant moduli of hysteresis loop with computation values, the interface shear stress and interface de-bonding stress corresponding to first cycle were identified. Substituting the identification of parameters into interface friction model, the stress-strain curves were predicted and the predicted results fit experiments well. Besides, the influence of number of data points on identifying the value of interface parameters was discussed. And the approach was compared with the method based on the area of hysteresis loop.

  11. Real-time multiple human perception with color-depth cameras on a mobile robot.

    PubMed

    Zhang, Hao; Reardon, Christopher; Parker, Lynne E

    2013-10-01

    The ability to perceive humans is an essential requirement for safe and efficient human-robot interaction. In real-world applications, the need for a robot to interact in real time with multiple humans in a dynamic, 3-D environment presents a significant challenge. The recent availability of commercial color-depth cameras allow for the creation of a system that makes use of the depth dimension, thus enabling a robot to observe its environment and perceive in the 3-D space. Here we present a system for 3-D multiple human perception in real time from a moving robot equipped with a color-depth camera and a consumer-grade computer. Our approach reduces computation time to achieve real-time performance through a unique combination of new ideas and established techniques. We remove the ground and ceiling planes from the 3-D point cloud input to separate candidate point clusters. We introduce the novel information concept, depth of interest, which we use to identify candidates for detection, and that avoids the computationally expensive scanning-window methods of other approaches. We utilize a cascade of detectors to distinguish humans from objects, in which we make intelligent reuse of intermediary features in successive detectors to improve computation. Because of the high computational cost of some methods, we represent our candidate tracking algorithm with a decision directed acyclic graph, which allows us to use the most computationally intense techniques only where necessary. We detail the successful implementation of our novel approach on a mobile robot and examine its performance in scenarios with real-world challenges, including occlusion, robot motion, nonupright humans, humans leaving and reentering the field of view (i.e., the reidentification challenge), human-object and human-human interaction. We conclude with the observation that the incorporation of the depth information, together with the use of modern techniques in new ways, we are able to create an accurate system for real-time 3-D perception of humans by a mobile robot.

  12. Computing and visualizing time-varying merge trees for high-dimensional data

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

    Oesterling, Patrick; Heine, Christian; Weber, Gunther H.

    2017-06-03

    We introduce a new method that identifies and tracks features in arbitrary dimensions using the merge tree -- a structure for identifying topological features based on thresholding in scalar fields. This method analyzes the evolution of features of the function by tracking changes in the merge tree and relates features by matching subtrees between consecutive time steps. Using the time-varying merge tree, we present a structural visualization of the changing function that illustrates both features and their temporal evolution. We demonstrate the utility of our approach by applying it to temporal cluster analysis of high-dimensional point clouds.

  13. Crowdsourced estimation of cognitive decline and resilience in Alzheimer's disease.

    PubMed

    Allen, Genevera I; Amoroso, Nicola; Anghel, Catalina; Balagurusamy, Venkat; Bare, Christopher J; Beaton, Derek; Bellotti, Roberto; Bennett, David A; Boehme, Kevin L; Boutros, Paul C; Caberlotto, Laura; Caloian, Cristian; Campbell, Frederick; Chaibub Neto, Elias; Chang, Yu-Chuan; Chen, Beibei; Chen, Chien-Yu; Chien, Ting-Ying; Clark, Tim; Das, Sudeshna; Davatzikos, Christos; Deng, Jieyao; Dillenberger, Donna; Dobson, Richard J B; Dong, Qilin; Doshi, Jimit; Duma, Denise; Errico, Rosangela; Erus, Guray; Everett, Evan; Fardo, David W; Friend, Stephen H; Fröhlich, Holger; Gan, Jessica; St George-Hyslop, Peter; Ghosh, Satrajit S; Glaab, Enrico; Green, Robert C; Guan, Yuanfang; Hong, Ming-Yi; Huang, Chao; Hwang, Jinseub; Ibrahim, Joseph; Inglese, Paolo; Iyappan, Anandhi; Jiang, Qijia; Katsumata, Yuriko; Kauwe, John S K; Klein, Arno; Kong, Dehan; Krause, Roland; Lalonde, Emilie; Lauria, Mario; Lee, Eunjee; Lin, Xihui; Liu, Zhandong; Livingstone, Julie; Logsdon, Benjamin A; Lovestone, Simon; Ma, Tsung-Wei; Malhotra, Ashutosh; Mangravite, Lara M; Maxwell, Taylor J; Merrill, Emily; Nagorski, John; Namasivayam, Aishwarya; Narayan, Manjari; Naz, Mufassra; Newhouse, Stephen J; Norman, Thea C; Nurtdinov, Ramil N; Oyang, Yen-Jen; Pawitan, Yudi; Peng, Shengwen; Peters, Mette A; Piccolo, Stephen R; Praveen, Paurush; Priami, Corrado; Sabelnykova, Veronica Y; Senger, Philipp; Shen, Xia; Simmons, Andrew; Sotiras, Aristeidis; Stolovitzky, Gustavo; Tangaro, Sabina; Tateo, Andrea; Tung, Yi-An; Tustison, Nicholas J; Varol, Erdem; Vradenburg, George; Weiner, Michael W; Xiao, Guanghua; Xie, Lei; Xie, Yang; Xu, Jia; Yang, Hojin; Zhan, Xiaowei; Zhou, Yunyun; Zhu, Fan; Zhu, Hongtu; Zhu, Shanfeng

    2016-06-01

    Identifying accurate biomarkers of cognitive decline is essential for advancing early diagnosis and prevention therapies in Alzheimer's disease. The Alzheimer's disease DREAM Challenge was designed as a computational crowdsourced project to benchmark the current state-of-the-art in predicting cognitive outcomes in Alzheimer's disease based on high dimensional, publicly available genetic and structural imaging data. This meta-analysis failed to identify a meaningful predictor developed from either data modality, suggesting that alternate approaches should be considered for prediction of cognitive performance. Copyright © 2016 The Authors. Published by Elsevier Inc. All rights reserved.

  14. Coevolution analysis of Hepatitis C virus genome to identify the structural and functional dependency network of viral proteins

    NASA Astrophysics Data System (ADS)

    Champeimont, Raphaël; Laine, Elodie; Hu, Shuang-Wei; Penin, Francois; Carbone, Alessandra

    2016-05-01

    A novel computational approach of coevolution analysis allowed us to reconstruct the protein-protein interaction network of the Hepatitis C Virus (HCV) at the residue resolution. For the first time, coevolution analysis of an entire viral genome was realized, based on a limited set of protein sequences with high sequence identity within genotypes. The identified coevolving residues constitute highly relevant predictions of protein-protein interactions for further experimental identification of HCV protein complexes. The method can be used to analyse other viral genomes and to predict the associated protein interaction networks.

  15. Evolutionary and Neural Computing Based Decision Support System for Disease Diagnosis from Clinical Data Sets in Medical Practice.

    PubMed

    Sudha, M

    2017-09-27

    As a recent trend, various computational intelligence and machine learning approaches have been used for mining inferences hidden in the large clinical databases to assist the clinician in strategic decision making. In any target data the irrelevant information may be detrimental, causing confusion for the mining algorithm and degrades the prediction outcome. To address this issue, this study attempts to identify an intelligent approach to assist disease diagnostic procedure using an optimal set of attributes instead of all attributes present in the clinical data set. In this proposed Application Specific Intelligent Computing (ASIC) decision support system, a rough set based genetic algorithm is employed in pre-processing phase and a back propagation neural network is applied in training and testing phase. ASIC has two phases, the first phase handles outliers, noisy data, and missing values to obtain a qualitative target data to generate appropriate attribute reduct sets from the input data using rough computing based genetic algorithm centred on a relative fitness function measure. The succeeding phase of this system involves both training and testing of back propagation neural network classifier on the selected reducts. The model performance is evaluated with widely adopted existing classifiers. The proposed ASIC system for clinical decision support has been tested with breast cancer, fertility diagnosis and heart disease data set from the University of California at Irvine (UCI) machine learning repository. The proposed system outperformed the existing approaches attaining the accuracy rate of 95.33%, 97.61%, and 93.04% for breast cancer, fertility issue and heart disease diagnosis.

  16. A Dictionary Approach to Electron Backscatter Diffraction Indexing.

    PubMed

    Chen, Yu H; Park, Se Un; Wei, Dennis; Newstadt, Greg; Jackson, Michael A; Simmons, Jeff P; De Graef, Marc; Hero, Alfred O

    2015-06-01

    We propose a framework for indexing of grain and subgrain structures in electron backscatter diffraction patterns of polycrystalline materials. We discretize the domain of a dynamical forward model onto a dense grid of orientations, producing a dictionary of patterns. For each measured pattern, we identify the most similar patterns in the dictionary, and identify boundaries, detect anomalies, and index crystal orientations. The statistical distribution of these closest matches is used in an unsupervised binary decision tree (DT) classifier to identify grain boundaries and anomalous regions. The DT classifies a pattern as an anomaly if it has an abnormally low similarity to any pattern in the dictionary. It classifies a pixel as being near a grain boundary if the highly ranked patterns in the dictionary differ significantly over the pixel's neighborhood. Indexing is accomplished by computing the mean orientation of the closest matches to each pattern. The mean orientation is estimated using a maximum likelihood approach that models the orientation distribution as a mixture of Von Mises-Fisher distributions over the quaternionic three sphere. The proposed dictionary matching approach permits segmentation, anomaly detection, and indexing to be performed in a unified manner with the additional benefit of uncertainty quantification.

  17. A Market-Basket Approach to Predict the Acute Aquatic Toxicity of Munitions and Energetic Materials.

    PubMed

    Burgoon, Lyle D

    2016-06-01

    An ongoing challenge in chemical production, including the production of insensitive munitions and energetics, is the ability to make predictions about potential environmental hazards early in the process. To address this challenge, a quantitative structure activity relationship model was developed to predict acute fathead minnow toxicity of insensitive munitions and energetic materials. Computational predictive toxicology models like this one may be used to identify and prioritize environmentally safer materials early in their development. The developed model is based on the Apriori market-basket/frequent itemset mining approach to identify probabilistic prediction rules using chemical atom-pairs and the lethality data for 57 compounds from a fathead minnow acute toxicity assay. Lethality data were discretized into four categories based on the Globally Harmonized System of Classification and Labelling of Chemicals. Apriori identified toxicophores for categories two and three. The model classified 32 of the 57 compounds correctly, with a fivefold cross-validation classification rate of 74 %. A structure-based surrogate approach classified the remaining 25 chemicals correctly at 48 %. This result is unsurprising as these 25 chemicals were fairly unique within the larger set.

  18. Active Job Monitoring in Pilots

    NASA Astrophysics Data System (ADS)

    Kuehn, Eileen; Fischer, Max; Giffels, Manuel; Jung, Christopher; Petzold, Andreas

    2015-12-01

    Recent developments in high energy physics (HEP) including multi-core jobs and multi-core pilots require data centres to gain a deep understanding of the system to monitor, design, and upgrade computing clusters. Networking is a critical component. Especially the increased usage of data federations, for example in diskless computing centres or as a fallback solution, relies on WAN connectivity and availability. The specific demands of different experiments and communities, but also the need for identification of misbehaving batch jobs, requires an active monitoring. Existing monitoring tools are not capable of measuring fine-grained information at batch job level. This complicates network-aware scheduling and optimisations. In addition, pilots add another layer of abstraction. They behave like batch systems themselves by managing and executing payloads of jobs internally. The number of real jobs being executed is unknown, as the original batch system has no access to internal information about the scheduling process inside the pilots. Therefore, the comparability of jobs and pilots for predicting run-time behaviour or network performance cannot be ensured. Hence, identifying the actual payload is important. At the GridKa Tier 1 centre a specific tool is in use that allows the monitoring of network traffic information at batch job level. This contribution presents the current monitoring approach and discusses recent efforts and importance to identify pilots and their substructures inside the batch system. It will also show how to determine monitoring data of specific jobs from identified pilots. Finally, the approach is evaluated.

  19. Computational approaches to motor learning by imitation.

    PubMed Central

    Schaal, Stefan; Ijspeert, Auke; Billard, Aude

    2003-01-01

    Movement imitation requires a complex set of mechanisms that map an observed movement of a teacher onto one's own movement apparatus. Relevant problems include movement recognition, pose estimation, pose tracking, body correspondence, coordinate transformation from external to egocentric space, matching of observed against previously learned movement, resolution of redundant degrees-of-freedom that are unconstrained by the observation, suitable movement representations for imitation, modularization of motor control, etc. All of these topics by themselves are active research problems in computational and neurobiological sciences, such that their combination into a complete imitation system remains a daunting undertaking-indeed, one could argue that we need to understand the complete perception-action loop. As a strategy to untangle the complexity of imitation, this paper will examine imitation purely from a computational point of view, i.e. we will review statistical and mathematical approaches that have been suggested for tackling parts of the imitation problem, and discuss their merits, disadvantages and underlying principles. Given the focus on action recognition of other contributions in this special issue, this paper will primarily emphasize the motor side of imitation, assuming that a perceptual system has already identified important features of a demonstrated movement and created their corresponding spatial information. Based on the formalization of motor control in terms of control policies and their associated performance criteria, useful taxonomies of imitation learning can be generated that clarify different approaches and future research directions. PMID:12689379

  20. Multilevel functional genomics data integration as a tool for understanding physiology: a network biology perspective.

    PubMed

    Davidsen, Peter K; Turan, Nil; Egginton, Stuart; Falciani, Francesco

    2016-02-01

    The overall aim of physiological research is to understand how living systems function in an integrative manner. Consequently, the discipline of physiology has since its infancy attempted to link multiple levels of biological organization. Increasingly this has involved mathematical and computational approaches, typically to model a small number of components spanning several levels of biological organization. With the advent of "omics" technologies, which can characterize the molecular state of a cell or tissue (intended as the level of expression and/or activity of its molecular components), the number of molecular components we can quantify has increased exponentially. Paradoxically, the unprecedented amount of experimental data has made it more difficult to derive conceptual models underlying essential mechanisms regulating mammalian physiology. We present an overview of state-of-the-art methods currently used to identifying biological networks underlying genomewide responses. These are based on a data-driven approach that relies on advanced computational methods designed to "learn" biology from observational data. In this review, we illustrate an application of these computational methodologies using a case study integrating an in vivo model representing the transcriptional state of hypoxic skeletal muscle with a clinical study representing muscle wasting in chronic obstructive pulmonary disease patients. The broader application of these approaches to modeling multiple levels of biological data in the context of modern physiology is discussed. Copyright © 2016 the American Physiological Society.

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