Functional Analysis of OMICs Data and Small Molecule Compounds in an Integrated "Knowledge-Based" Platform.

PubMed

Dubovenko, Alexey; Nikolsky, Yuri; Rakhmatulin, Eugene; Nikolskaya, Tatiana

2017-01-01

Analysis of NGS and other sequencing data, gene variants, gene expression, proteomics, and other high-throughput (OMICs) data is challenging because of its biological complexity and high level of technical and biological noise. One way to deal with both problems is to perform analysis with a high fidelity annotated knowledgebase of protein interactions, pathways, and functional ontologies. This knowledgebase has to be structured in a computer-readable format and must include software tools for managing experimental data, analysis, and reporting. Here, we present MetaCore™ and Key Pathway Advisor (KPA), an integrated platform for functional data analysis. On the content side, MetaCore and KPA encompass a comprehensive database of molecular interactions of different types, pathways, network models, and ten functional ontologies covering human, mouse, and rat genes. The analytical toolkit includes tools for gene/protein list enrichment analysis, statistical "interactome" tool for the identification of over- and under-connected proteins in the dataset, and a biological network analysis module made up of network generation algorithms and filters. The suite also features Advanced Search, an application for combinatorial search of the database content, as well as a Java-based tool called Pathway Map Creator for drawing and editing custom pathway maps. Applications of MetaCore and KPA include molecular mode of action of disease research, identification of potential biomarkers and drug targets, pathway hypothesis generation, analysis of biological effects for novel small molecule compounds and clinical applications (analysis of large cohorts of patients, and translational and personalized medicine).

A multilevel layout algorithm for visualizing physical and genetic interaction networks, with emphasis on their modular organization

PubMed Central

2012-01-01

Background Graph drawing is an integral part of many systems biology studies, enabling visual exploration and mining of large-scale biological networks. While a number of layout algorithms are available in popular network analysis platforms, such as Cytoscape, it remains poorly understood how well their solutions reflect the underlying biological processes that give rise to the network connectivity structure. Moreover, visualizations obtained using conventional layout algorithms, such as those based on the force-directed drawing approach, may become uninformative when applied to larger networks with dense or clustered connectivity structure. Methods We implemented a modified layout plug-in, named Multilevel Layout, which applies the conventional layout algorithms within a multilevel optimization framework to better capture the hierarchical modularity of many biological networks. Using a wide variety of real life biological networks, we carried out a systematic evaluation of the method in comparison with other layout algorithms in Cytoscape. Results The multilevel approach provided both biologically relevant and visually pleasant layout solutions in most network types, hence complementing the layout options available in Cytoscape. In particular, it could improve drawing of large-scale networks of yeast genetic interactions and human physical interactions. In more general terms, the biological evaluation framework developed here enables one to assess the layout solutions from any existing or future graph drawing algorithm as well as to optimize their performance for a given network type or structure. Conclusions By making use of the multilevel modular organization when visualizing biological networks, together with the biological evaluation of the layout solutions, one can generate convenient visualizations for many network biology applications. PMID:22448851

Automatically measuring the effect of strategy drawing features on pupils' handwriting and gender

NASA Astrophysics Data System (ADS)

Tabatabaey-Mashadi, Narges; Sudirman, Rubita; Guest, Richard M.; Khalid, Puspa Inayat

2013-12-01

Children's dynamic drawing strategies have been recently recognized as indicators of handwriting ability. However the influence of each feature in predicting handwriting is unknown due to lack of a measuring system. An automated measuring algorithm suitable for psychological assessment and non-subjective scoring is presented here. Using the weight vector and classification rate of a machine learning algorithm, an overall feature's effect is calculated which is comparable in different groupings. In this study thirteen previously detected drawing strategy features are measured for their influence on handwriting and gender. Features are extracted from drawing a triangle, Beery VMI and Bender Gestalt tangent patterns. Samples are related to 203 pupils (77 below average writers, and 101 female). The results show that the number of strokes in drawing the triangle pattern plays a major role in both groupings; however Left Tendency flag feature is affected by children's handwriting about 2.5 times greater than their gender. Experiments indicate that different forms of a feature sometimes show different influences.

Pathways to Resilience: How Drawings Reveal Pre-Service Teachers' Core Narratives Underpinning Their Future Teacher-Selves

ERIC Educational Resources Information Center

Dinham, Judith; Chalk, Beryl; Beltman, Susan; Glass, Christine; Nguyen, Bich

2017-01-01

For this study of teacher-identity, which is part of a larger research project on teacher resilience, preservice teachers were invited to draw "the kind of teacher you hope to become". In this, our study recognises drawing as a semiotic system of meaning-making and communication. The drawings were examined in terms of the…

Exploratory power of the harmony search algorithm: analysis and improvements for global numerical optimization.

PubMed

Das, Swagatam; Mukhopadhyay, Arpan; Roy, Anwit; Abraham, Ajith; Panigrahi, Bijaya K

2011-02-01

The theoretical analysis of evolutionary algorithms is believed to be very important for understanding their internal search mechanism and thus to develop more efficient algorithms. This paper presents a simple mathematical analysis of the explorative search behavior of a recently developed metaheuristic algorithm called harmony search (HS). HS is a derivative-free real parameter optimization algorithm, and it draws inspiration from the musical improvisation process of searching for a perfect state of harmony. This paper analyzes the evolution of the population-variance over successive generations in HS and thereby draws some important conclusions regarding the explorative power of HS. A simple but very useful modification to the classical HS has been proposed in light of the mathematical analysis undertaken here. A comparison with the most recently published variants of HS and four other state-of-the-art optimization algorithms over 15 unconstrained and five constrained benchmark functions reflects the efficiency of the modified HS in terms of final accuracy, convergence speed, and robustness.

Conical Perspective Image of an Architectural Object Close to Human Perception

NASA Astrophysics Data System (ADS)

Dzwierzynska, Jolanta

2017-10-01

The aim of the study is to develop a method of computer aided constructing conical perspective of an architectural object, which is close to human perception. The conical perspective considered in the paper is a central projection onto a projection surface being a conical rotary surface or a fragment of it. Whereas, the centre of projection is a stationary point or a point moving on a circular path. The graphical mapping results of the perspective representation is realized directly on an unrolled flat projection surface. The projective relation between a range of points on a line and the perspective image of the same range of points received on a cylindrical projection surface permitted to derive formulas for drawing perspective. Next, the analytical algorithms for drawing perspective image of a straight line passing through any two points were formulated. It enabled drawing a perspective wireframe image of a given 3D object. The use of the moving view point as well as the application of the changeable base elements of perspective as the variables in the algorithms enable drawing conical perspective from different viewing positions. Due to this fact, the perspective drawing method is universal. The algorithms are formulated and tested in Mathcad Professional software, but can be implemented in AutoCAD and majority of computer graphical packages, which makes drawing a perspective image more efficient and easier. The presented conical perspective representation, and the convenient method of its mapping directly on the flat unrolled surface can find application for numerous advertisement and art presentations.

Efficient RNA structure comparison algorithms.

PubMed

Arslan, Abdullah N; Anandan, Jithendar; Fry, Eric; Monschke, Keith; Ganneboina, Nitin; Bowerman, Jason

2017-12-01

Recently proposed relative addressing-based ([Formula: see text]) RNA secondary structure representation has important features by which an RNA structure database can be stored into a suffix array. A fast substructure search algorithm has been proposed based on binary search on this suffix array. Using this substructure search algorithm, we present a fast algorithm that finds the largest common substructure of given multiple RNA structures in [Formula: see text] format. The multiple RNA structure comparison problem is NP-hard in its general formulation. We introduced a new problem for comparing multiple RNA structures. This problem has more strict similarity definition and objective, and we propose an algorithm that solves this problem efficiently. We also develop another comparison algorithm that iteratively calls this algorithm to locate nonoverlapping large common substructures in compared RNAs. With the new resulting tools, we improved the RNASSAC website (linked from http://faculty.tamuc.edu/aarslan ). This website now also includes two drawing tools: one specialized for preparing RNA substructures that can be used as input by the search tool, and another one for automatically drawing the entire RNA structure from a given structure sequence.

Asynchronous Gossip for Averaging and Spectral Ranking

NASA Astrophysics Data System (ADS)

Borkar, Vivek S.; Makhijani, Rahul; Sundaresan, Rajesh

2014-08-01

We consider two variants of the classical gossip algorithm. The first variant is a version of asynchronous stochastic approximation. We highlight a fundamental difficulty associated with the classical asynchronous gossip scheme, viz., that it may not converge to a desired average, and suggest an alternative scheme based on reinforcement learning that has guaranteed convergence to the desired average. We then discuss a potential application to a wireless network setting with simultaneous link activation constraints. The second variant is a gossip algorithm for distributed computation of the Perron-Frobenius eigenvector of a nonnegative matrix. While the first variant draws upon a reinforcement learning algorithm for an average cost controlled Markov decision problem, the second variant draws upon a reinforcement learning algorithm for risk-sensitive control. We then discuss potential applications of the second variant to ranking schemes, reputation networks, and principal component analysis.

An algorithm for modularization of MAPK and calcium signaling pathways: comparative analysis among different species.

PubMed

Nayak, Losiana; De, Rajat K

2007-12-01

Signaling pathways are large complex biochemical networks. It is difficult to analyze the underlying mechanism of such networks as a whole. In the present article, we have proposed an algorithm for modularization of signal transduction pathways. Unlike studying a signaling pathway as a whole, this enables one to study the individual modules (less complex smaller units) easily and hence to study the entire pathway better. A comparative study of modules belonging to different species (for the same signaling pathway) has been made, which gives an overall idea about development of the signaling pathways over the taken set of species of calcium and MAPK signaling pathways. The superior performance, in terms of biological significance, of the proposed algorithm over an existing community finding algorithm of Newman [Newman MEJ. Modularity and community structure in networks. Proc Natl Acad Sci USA 2006;103(23):8577-82] has been demonstrated using the aforesaid pathways of H. sapiens.

A general algorithm for the construction of contour plots

NASA Technical Reports Server (NTRS)

Johnson, W.; Silva, F.

1981-01-01

An algorithm is described that performs the task of drawing equal level contours on a plane, which requires interpolation in two dimensions based on data prescribed at points distributed irregularly over the plane. The approach is described in detail. The computer program that implements the algorithm is documented and listed.

Disrupting the Dissertation: Linked Data, Enhanced Publication and Algorithmic Culture

ERIC Educational Resources Information Center

Tracy, Frances; Carmichael, Patrick

2017-01-01

This article explores how the three aspects of Striphas' notion of algorithmic culture (information, crowds and algorithms) might influence and potentially disrupt established educational practices. We draw on our experience of introducing semantic web and linked data technologies into higher education settings, focussing on extended student…

Object-oriented feature-tracking algorithms for SAR images of the marginal ice zone

NASA Technical Reports Server (NTRS)

Daida, Jason; Samadani, Ramin; Vesecky, John F.

1990-01-01

An unsupervised method that chooses and applies the most appropriate tracking algorithm from among different sea-ice tracking algorithms is reported. In contrast to current unsupervised methods, this method chooses and applies an algorithm by partially examining a sequential image pair to draw inferences about what was examined. Based on these inferences the reported method subsequently chooses which algorithm to apply to specific areas of the image pair where that algorithm should work best.

An Automated Pipeline for Engineering Many-Enzyme Pathways: Computational Sequence Design, Pathway Expression-Flux Mapping, and Scalable Pathway Optimization.

PubMed

Halper, Sean M; Cetnar, Daniel P; Salis, Howard M

2018-01-01

Engineering many-enzyme metabolic pathways suffers from the design curse of dimensionality. There are an astronomical number of synonymous DNA sequence choices, though relatively few will express an evolutionary robust, maximally productive pathway without metabolic bottlenecks. To solve this challenge, we have developed an integrated, automated computational-experimental pipeline that identifies a pathway's optimal DNA sequence without high-throughput screening or many cycles of design-build-test. The first step applies our Operon Calculator algorithm to design a host-specific evolutionary robust bacterial operon sequence with maximally tunable enzyme expression levels. The second step applies our RBS Library Calculator algorithm to systematically vary enzyme expression levels with the smallest-sized library. After characterizing a small number of constructed pathway variants, measurements are supplied to our Pathway Map Calculator algorithm, which then parameterizes a kinetic metabolic model that ultimately predicts the pathway's optimal enzyme expression levels and DNA sequences. Altogether, our algorithms provide the ability to efficiently map the pathway's sequence-expression-activity space and predict DNA sequences with desired metabolic fluxes. Here, we provide a step-by-step guide to applying the Pathway Optimization Pipeline on a desired multi-enzyme pathway in a bacterial host.

Accuracy of algorithms to predict accessory pathway location in children with Wolff-Parkinson-White syndrome.

PubMed

Wren, Christopher; Vogel, Melanie; Lord, Stephen; Abrams, Dominic; Bourke, John; Rees, Philip; Rosenthal, Eric

2012-02-01

The aim of this study was to examine the accuracy in predicting pathway location in children with Wolff-Parkinson-White syndrome for each of seven published algorithms. ECGs from 100 consecutive children with Wolff-Parkinson-White syndrome undergoing electrophysiological study were analysed by six investigators using seven published algorithms, six of which had been developed in adult patients. Accuracy and concordance of predictions were adjusted for the number of pathway locations. Accessory pathways were left-sided in 49, septal in 20 and right-sided in 31 children. Overall accuracy of prediction was 30-49% for the exact location and 61-68% including adjacent locations. Concordance between investigators varied between 41% and 86%. No algorithm was better at predicting septal pathways (accuracy 5-35%, improving to 40-78% including adjacent locations), but one was significantly worse. Predictive accuracy was 24-53% for the exact location of right-sided pathways (50-71% including adjacent locations) and 32-55% for the exact location of left-sided pathways (58-73% including adjacent locations). All algorithms were less accurate in our hands than in other authors' own assessment. None performed well in identifying midseptal or right anteroseptal accessory pathway locations.

Comparison of the accuracy of three algorithms in predicting accessory pathways among adult Wolff-Parkinson-White syndrome patients.

PubMed

Maden, Orhan; Balci, Kevser Gülcihan; Selcuk, Mehmet Timur; Balci, Mustafa Mücahit; Açar, Burak; Unal, Sefa; Kara, Meryem; Selcuk, Hatice

2015-12-01

The aim of this study was to investigate the accuracy of three algorithms in predicting accessory pathway locations in adult patients with Wolff-Parkinson-White syndrome in Turkish population. A total of 207 adult patients with Wolff-Parkinson-White syndrome were retrospectively analyzed. The most preexcited 12-lead electrocardiogram in sinus rhythm was used for analysis. Two investigators blinded to the patient data used three algorithms for prediction of accessory pathway location. Among all locations, 48.5% were left-sided, 44% were right-sided, and 7.5% were located in the midseptum or anteroseptum. When only exact locations were accepted as match, predictive accuracy for Chiang was 71.5%, 72.4% for d'Avila, and 71.5% for Arruda. The percentage of predictive accuracy of all algorithms did not differ between the algorithms (p = 1.000; p = 0.875; p = 0.885, respectively). The best algorithm for prediction of right-sided, left-sided, and anteroseptal and midseptal accessory pathways was Arruda (p < 0.001). Arruda was significantly better than d'Avila in predicting adjacent sites (p = 0.035) and the percent of the contralateral site prediction was higher with d'Avila than Arruda (p = 0.013). All algorithms were similar in predicting accessory pathway location and the predicted accuracy was lower than previously reported by their authors. However, according to the accessory pathway site, the algorithm designed by Arruda et al. showed better predictions than the other algorithms and using this algorithm may provide advantages before a planned ablation.

College Chemistry Students' Use of Memorized Algorithms in Chemical Reactions

ERIC Educational Resources Information Center

Nyachwaya, James M.; Warfa, Abdi-Rizak M; Roehrig, Gillian H.; Schneider, Jamie L.

2014-01-01

This study sought to uncover memorized algorithms and procedures that students relied on in responding to questions based on the particulate nature of matter (PNM). We describe various memorized algorithms or processes used by students. In the study, students were asked to balance three equations of chemical reaction and then draw particulate…

Discovering causal signaling pathways through gene-expression patterns

PubMed Central

Parikh, Jignesh R.; Klinger, Bertram; Xia, Yu; Marto, Jarrod A.; Blüthgen, Nils

2010-01-01

High-throughput gene-expression studies result in lists of differentially expressed genes. Most current meta-analyses of these gene lists include searching for significant membership of the translated proteins in various signaling pathways. However, such membership enrichment algorithms do not provide insight into which pathways caused the genes to be differentially expressed in the first place. Here, we present an intuitive approach for discovering upstream signaling pathways responsible for regulating these differentially expressed genes. We identify consistently regulated signature genes specific for signal transduction pathways from a panel of single-pathway perturbation experiments. An algorithm that detects overrepresentation of these signature genes in a gene group of interest is used to infer the signaling pathway responsible for regulation. We expose our novel resource and algorithm through a web server called SPEED: Signaling Pathway Enrichment using Experimental Data sets. SPEED can be freely accessed at http://speed.sys-bio.net/. PMID:20494976

Pathways of Youth Development in a Rural Trailer Park

ERIC Educational Resources Information Center

MacTavish, Katherine A.; Salamon, Sonya

2006-01-01

Limited empirical documentation exists for the developmental pathways available to "rural" youth growing up in low-resource community settings. Drawing on ethnographic data, this article examines the developmental pathways experienced by youth in a rural trailer park. Findings reveal how various factors, some inherent to working poor class status…

Sunspot drawings handwritten character recognition method based on deep learning

NASA Astrophysics Data System (ADS)

Zheng, Sheng; Zeng, Xiangyun; Lin, Ganghua; Zhao, Cui; Feng, Yongli; Tao, Jinping; Zhu, Daoyuan; Xiong, Li

2016-05-01

High accuracy scanned sunspot drawings handwritten characters recognition is an issue of critical importance to analyze sunspots movement and store them in the database. This paper presents a robust deep learning method for scanned sunspot drawings handwritten characters recognition. The convolution neural network (CNN) is one algorithm of deep learning which is truly successful in training of multi-layer network structure. CNN is used to train recognition model of handwritten character images which are extracted from the original sunspot drawings. We demonstrate the advantages of the proposed method on sunspot drawings provided by Chinese Academy Yunnan Observatory and obtain the daily full-disc sunspot numbers and sunspot areas from the sunspot drawings. The experimental results show that the proposed method achieves a high recognition accurate rate.

A generalization of algebraic surface drawing

NASA Technical Reports Server (NTRS)

Blinn, J. F.

1982-01-01

An implicit surface mathematical description of three-dimensional space is defined in terms of all points which satisfy some equation F(x, y, z) equals 0. This form is ideal for space-shaded picture drawing, where the coordinates are substituted for x and y and the equation is solved for z. A new algorithm is presented which is applicable to functional forms other than those of first- and second-order polynomial functions, such as the summation of several Gaussian density distributions. The algorithm was created in order to model electron density maps of molecular structures, but is shown to be capable of generating shapes of esthetic interest.

Probabilistic pathway construction.

PubMed

Yousofshahi, Mona; Lee, Kyongbum; Hassoun, Soha

2011-07-01

Expression of novel synthesis pathways in host organisms amenable to genetic manipulations has emerged as an attractive metabolic engineering strategy to overproduce natural products, biofuels, biopolymers and other commercially useful metabolites. We present a pathway construction algorithm for identifying viable synthesis pathways compatible with balanced cell growth. Rather than exhaustive exploration, we investigate probabilistic selection of reactions to construct the pathways. Three different selection schemes are investigated for the selection of reactions: high metabolite connectivity, low connectivity and uniformly random. For all case studies, which involved a diverse set of target metabolites, the uniformly random selection scheme resulted in the highest average maximum yield. When compared to an exhaustive search enumerating all possible reaction routes, our probabilistic algorithm returned nearly identical distributions of yields, while requiring far less computing time (minutes vs. years). The pathways identified by our algorithm have previously been confirmed in the literature as viable, high-yield synthesis routes. Prospectively, our algorithm could facilitate the design of novel, non-native synthesis routes by efficiently exploring the diversity of biochemical transformations in nature. Copyright © 2011 Elsevier Inc. All rights reserved.

Parallelization of Nullspace Algorithm for the computation of metabolic pathways

PubMed Central

Jevremović, Dimitrije; Trinh, Cong T.; Srienc, Friedrich; Sosa, Carlos P.; Boley, Daniel

2011-01-01

Elementary mode analysis is a useful metabolic pathway analysis tool in understanding and analyzing cellular metabolism, since elementary modes can represent metabolic pathways with unique and minimal sets of enzyme-catalyzed reactions of a metabolic network under steady state conditions. However, computation of the elementary modes of a genome- scale metabolic network with 100–1000 reactions is very expensive and sometimes not feasible with the commonly used serial Nullspace Algorithm. In this work, we develop a distributed memory parallelization of the Nullspace Algorithm to handle efficiently the computation of the elementary modes of a large metabolic network. We give an implementation in C++ language with the support of MPI library functions for the parallel communication. Our proposed algorithm is accompanied with an analysis of the complexity and identification of major bottlenecks during computation of all possible pathways of a large metabolic network. The algorithm includes methods to achieve load balancing among the compute-nodes and specific communication patterns to reduce the communication overhead and improve efficiency. PMID:22058581

Schematic representation of residue-based protein context-dependent data: an application to transmembrane proteins.

PubMed

Campagne, F; Weinstein, H

1999-01-01

An algorithmic method for drawing residue-based schematic diagrams of proteins on a 2D page is presented and illustrated. The method allows the creation of rendering engines dedicated to a given family of sequences, or fold. The initial implementation provides an engine that can produce a 2D diagram representing secondary structure for any transmembrane protein sequence. We present the details of the strategy for automating the drawing of these diagrams. The most important part of this strategy is the development of an algorithm for laying out residues of a loop that connects to arbitrary points of a 2D plane. As implemented, this algorithm is suitable for real-time modification of the loop layout. This work is of interest for the representation and analysis of data from (1) protein databases, (2) mutagenesis results, or (3) various kinds of protein context-dependent annotations or data.

What can numerical computation do for the history of science? (a study of an orbit drawn by Newton in a letter to Hooke)

NASA Astrophysics Data System (ADS)

Cardozo Dias, Penha Maria; Stuchi, T. J.

2013-11-01

In a letter to Robert Hooke, Isaac Newton drew the orbit of a mass moving under a constant attracting central force. The drawing of the orbit may indicate how and when Newton developed dynamic categories. Some historians claim that Newton used a method contrived by Hooke; others that he used some method of curvature. We prove that Hooke’s method is a second-order symplectic area-preserving algorithm, and the method of curvature is a first-order algorithm without special features; then we integrate the Hamiltonian equations. Integration by the method of curvature can also be done, exploring the geometric properties of curves. We compare three methods: Hooke’s method, the method of curvature and a first-order method. A fourth-order algorithm sets a standard of comparison. We analyze which of these methods best explains Newton’s drawing.

Developmental Changes in Early Comprehension and Production of Drawings: Evidence From Two Socioeconomic Backgrounds.

PubMed

Salsa, Analía M; Vivaldi, Romina

2017-01-01

Two studies examined young children's comprehension and production of representational drawings across and within 2 socioeconomic strata (SES). Participants were 130 middle-SES (MSES) and low-SES (LSES) Argentine children, from 30 to 60 months old, given a task with 2 phases, production and comprehension. The production phase assessed free drawing and drawings from simple 3-dimensional objects (model drawing); the comprehension phase assessed children's understanding of an adult's line drawings of the objects. MSES children solved the comprehension phase of the task within the studied age range; representational production emerged first in model drawing (42 months) and later in free drawing (48 months). The same developmental pathway was observed in LSES children but with a clear asynchrony in the age of onset of comprehension and production: Children understood the symbolic nature of drawings at 42 months old and the first representational drawings were found at 60 months old. These results provide empirical evidence that support the crucial influence of social experiences by organizing and constraining graphic development.

CAMPways: constrained alignment framework for the comparative analysis of a pair of metabolic pathways.

PubMed

Abaka, Gamze; Bıyıkoğlu, Türker; Erten, Cesim

2013-07-01

Given a pair of metabolic pathways, an alignment of the pathways corresponds to a mapping between similar substructures of the pair. Successful alignments may provide useful applications in phylogenetic tree reconstruction, drug design and overall may enhance our understanding of cellular metabolism. We consider the problem of providing one-to-many alignments of reactions in a pair of metabolic pathways. We first provide a constrained alignment framework applicable to the problem. We show that the constrained alignment problem even in a primitive setting is computationally intractable, which justifies efforts for designing efficient heuristics. We present our Constrained Alignment of Metabolic Pathways (CAMPways) algorithm designed for this purpose. Through extensive experiments involving a large pathway database, we demonstrate that when compared with a state-of-the-art alternative, the CAMPways algorithm provides better alignment results on metabolic networks as far as measures based on same-pathway inclusion and biochemical significance are concerned. The execution speed of our algorithm constitutes yet another important improvement over alternative algorithms. Open source codes, executable binary, useful scripts, all the experimental data and the results are freely available as part of the Supplementary Material at http://code.google.com/p/campways/. Supplementary data are available at Bioinformatics online.

Path lumping: An efficient algorithm to identify metastable path channels for conformational dynamics of multi-body systems

NASA Astrophysics Data System (ADS)

Meng, Luming; Sheong, Fu Kit; Zeng, Xiangze; Zhu, Lizhe; Huang, Xuhui

2017-07-01

Constructing Markov state models from large-scale molecular dynamics simulation trajectories is a promising approach to dissect the kinetic mechanisms of complex chemical and biological processes. Combined with transition path theory, Markov state models can be applied to identify all pathways connecting any conformational states of interest. However, the identified pathways can be too complex to comprehend, especially for multi-body processes where numerous parallel pathways with comparable flux probability often coexist. Here, we have developed a path lumping method to group these parallel pathways into metastable path channels for analysis. We define the similarity between two pathways as the intercrossing flux between them and then apply the spectral clustering algorithm to lump these pathways into groups. We demonstrate the power of our method by applying it to two systems: a 2D-potential consisting of four metastable energy channels and the hydrophobic collapse process of two hydrophobic molecules. In both cases, our algorithm successfully reveals the metastable path channels. We expect this path lumping algorithm to be a promising tool for revealing unprecedented insights into the kinetic mechanisms of complex multi-body processes.

Nassi-Schneiderman Diagram in HTML Based on AML

ERIC Educational Resources Information Center

Menyhárt, László

2013-01-01

In an earlier work I defined an extension of XML called Algorithm Markup Language (AML) for easy and understandable coding in an IDE which supports XML editing (e.g. NetBeans). The AML extension contains annotations and native language (English or Hungarian) tag names used when coding our algorithm. This paper presents a drawing tool with which…

Proposing a new iterative learning control algorithm based on a non-linear least square formulation - Minimising draw-in errors

NASA Astrophysics Data System (ADS)

Endelt, B.

2017-09-01

Forming operation are subject to external disturbances and changing operating conditions e.g. new material batch, increasing tool temperature due to plastic work, material properties and lubrication is sensitive to tool temperature. It is generally accepted that forming operations are not stable over time and it is not uncommon to adjust the process parameters during the first half hour production, indicating that process instability is gradually developing over time. Thus, in-process feedback control scheme might not-be necessary to stabilize the process and an alternative approach is to apply an iterative learning algorithm, which can learn from previously produced parts i.e. a self learning system which gradually reduces error based on historical process information. What is proposed in the paper is a simple algorithm which can be applied to a wide range of sheet-metal forming processes. The input to the algorithm is the final flange edge geometry and the basic idea is to reduce the least-square error between the current flange geometry and a reference geometry using a non-linear least square algorithm. The ILC scheme is applied to a square deep-drawing and the Numisheet’08 S-rail benchmark problem, the numerical tests shows that the proposed control scheme is able control and stabilise both processes.

Socioeconomic Status, Schooling, and the Developmental Trajectories of Adolescents

ERIC Educational Resources Information Center

Crosnoe, Robert; Huston, Aletha C.

2007-01-01

The socioeconomic stratification of American society profoundly influences how the life course unfolds by shaping various developmental pathways as well as the connections among these pathways. Drawing on a nationally representative sample of American adolescents, this study charted trajectories of personal control and parental consultation from…

The threshold algorithm: Description of the methodology and new developments

NASA Astrophysics Data System (ADS)

Neelamraju, Sridhar; Oligschleger, Christina; Schön, J. Christian

2017-10-01

Understanding the dynamics of complex systems requires the investigation of their energy landscape. In particular, the flow of probability on such landscapes is a central feature in visualizing the time evolution of complex systems. To obtain such flows, and the concomitant stable states of the systems and the generalized barriers among them, the threshold algorithm has been developed. Here, we describe the methodology of this approach starting from the fundamental concepts in complex energy landscapes and present recent new developments, the threshold-minimization algorithm and the molecular dynamics threshold algorithm. For applications of these new algorithms, we draw on landscape studies of three disaccharide molecules: lactose, maltose, and sucrose.

Bottom-up GGM algorithm for constructing multiple layered hierarchical gene regulatory networks

USDA-ARS?s Scientific Manuscript database

Multilayered hierarchical gene regulatory networks (ML-hGRNs) are very important for understanding genetics regulation of biological pathways. However, there are currently no computational algorithms available for directly building ML-hGRNs that regulate biological pathways. A bottom-up graphic Gaus...

Intra-Class Differences in the Post-16 Educational Trajectories of Young People from Lower Socioeconomic Groups

ERIC Educational Resources Information Center

Slack, Kim

2014-01-01

This paper draws on research with young people from similar working-class backgrounds in a deprived urban area in England. Although all the participants achieved a high enough attainment level to remain in full-time education beyond compulsory schooling, they each elected to follow different post-16 pathways. Drawing on in-depth interviews with…

Program for coordinated dental care under general anaesthesia for children with special needs.

PubMed

de Nova-García, M Joaquín; Martínez, M Rosa Mourelle; Sanjuán, Carmen Martín; López, Nuria E Gallardo; Cabaleiro, Esther Carracedo; García, Yolanda Alonso

2007-12-01

To draw up a program for coordination of dental care for children with special needs between the Course at the Universidad Complutense de Madrid (UCMC) (Specialisation in holistic dental care for children with special needs), and the Disabled Children's Oral Health Unit (DCOHU) within the Madrid Health Service (SERMAS). UCMC Protocol for children with special needs. Design of a clinical pathway based on consensus amongst the professionals involved. Algorithm for dental care for children with special needs. Matrix covering all activities and timing for full dental diagnosis in such patients (general health, oral health and behaviour) to facilitate proper referral of patients requiring general anaesthesia. Inclusion in the matrix of those responsible for each activity. Improved team work (University - primary health care) in patient evaluation, in provision of information to parents and guardians and in health care quality. From the teaching point of view, students learn to adopt a systematic approach in the decision-making process.

Recursive random forest algorithm for constructing multilayered hierarchical gene regulatory networks that govern biological pathways.

PubMed

Deng, Wenping; Zhang, Kui; Busov, Victor; Wei, Hairong

2017-01-01

Present knowledge indicates a multilayered hierarchical gene regulatory network (ML-hGRN) often operates above a biological pathway. Although the ML-hGRN is very important for understanding how a pathway is regulated, there is almost no computational algorithm for directly constructing ML-hGRNs. A backward elimination random forest (BWERF) algorithm was developed for constructing the ML-hGRN operating above a biological pathway. For each pathway gene, the BWERF used a random forest model to calculate the importance values of all transcription factors (TFs) to this pathway gene recursively with a portion (e.g. 1/10) of least important TFs being excluded in each round of modeling, during which, the importance values of all TFs to the pathway gene were updated and ranked until only one TF was remained in the list. The above procedure, termed BWERF. After that, the importance values of a TF to all pathway genes were aggregated and fitted to a Gaussian mixture model to determine the TF retention for the regulatory layer immediately above the pathway layer. The acquired TFs at the secondary layer were then set to be the new bottom layer to infer the next upper layer, and this process was repeated until a ML-hGRN with the expected layers was obtained. BWERF improved the accuracy for constructing ML-hGRNs because it used backward elimination to exclude the noise genes, and aggregated the individual importance values for determining the TFs retention. We validated the BWERF by using it for constructing ML-hGRNs operating above mouse pluripotency maintenance pathway and Arabidopsis lignocellulosic pathway. Compared to GENIE3, BWERF showed an improvement in recognizing authentic TFs regulating a pathway. Compared to the bottom-up Gaussian graphical model algorithm we developed for constructing ML-hGRNs, the BWERF can construct ML-hGRNs with significantly reduced edges that enable biologists to choose the implicit edges for experimental validation.

The Cognitive Visualization System with the Dynamic Projection of Multidimensional Data

NASA Astrophysics Data System (ADS)

Gorohov, V.; Vitkovskiy, V.

2008-08-01

The phenomenon of cognitive machine drawing consists in the generation on the screen the special graphic representations, which create in the brain of human operator entertainment means. These means seem man by aesthetically attractive and, thus, they stimulate its descriptive imagination, closely related to the intuitive mechanisms of thinking. The essence of cognitive effect lies in the fact that man receives the moving projection as pseudo-three-dimensional object characterizing multidimensional means in the multidimensional space. After the thorough qualitative study of the visual aspects of multidimensional means with the aid of the enumerated algorithms appears the possibility, using algorithms of standard machine drawing to paint the interesting user separate objects or the groups of objects. Then it is possible to again return to the dynamic behavior of the rotation of means for the purpose of checking the intuitive ideas of user about the clusters and the connections in multidimensional data. Is possible the development of the methods of cognitive machine drawing in combination with other information technologies, first of all with the packets of digital processing of images and multidimensional statistical analysis.

"It's Totally Destroyed Our Life": Exploring the Pathways and Mechanisms Between Precarious Employment and Health and Well-being Among Immigrant Men and Women in Toronto.

PubMed

Premji, Stephanie

2018-01-01

Precarious employment is rapidly growing, but qualitative data on pathways to and mechanisms for health and well-being is lacking. This article describes the cumulative and intersecting micro-level pathways and mechanisms between precarious employment and health among immigrant men and women in Toronto. It draws on semi-structured interviews conducted in 2014 with 15 women and 12 men from 11 countries of origin. The article describes how precarious employment, conceptualized by workers as encompassing powerlessness, economic insecurity, work for multiple employers, nonstandard and unpredictable schedules, hazardous working conditions, and lack of benefits and protections, negatively impacts workers' physical and mental health as well as that of their spouses or partners and children. It documents pathways to health and well-being, including stress, material and social deprivation, and exposure to hazards, as well as commuting difficulties and childcare challenges. Throughout, gender and migration are shown to influence experiences of work and health. The findings draw attention to dimensions of precarity and pathways to health that are not always highlighted in research and discourse on precarious employment and provide valuable insights into the vicious circle of precarious employment and health.

Recognizing simple polyhedron from a perspective drawing

NASA Astrophysics Data System (ADS)

Zhang, Guimei; Chu, Jun; Miao, Jun

2009-10-01

Existed methods can't be used for recognizing simple polyhedron. In this paper, three problems are researched. First, a method for recognizing triangle and quadrilateral is introduced based on geometry and angle constraint. Then Attribute Relation Graph (ARG) is employed to describe simple polyhedron and line drawing. Last, a new method is presented to recognize simple polyhedron from a line drawing. The method filters the candidate database before matching line drawing and model, thus the recognition efficiency is improved greatly. We introduced the geometrical characteristics and topological characteristics to describe each node of ARG, so the algorithm can not only recognize polyhedrons with different shape but also distinguish between polyhedrons with the same shape but with different sizes and proportions. Computer simulations demonstrate the effectiveness of the method preliminarily.

Beyond "Commentaries of Despair:" Reengineering Pathways to Design in the Schooling of Black Men

ERIC Educational Resources Information Center

Matthews, Lou Edward; Williams, Brian A.

2007-01-01

Ensuring that young Black men are afforded equal educational opportunities, access, and outcomes in education remains the most pressing challenge of modern schooling. Critical to overcoming this challenge is a focus on engineering pathways to design for school-based initiatives which draw more comprehensively from research and the prevailing…

Educational Supports for High Functioning Youth with ASD: The Postsecondary Pathway to College

ERIC Educational Resources Information Center

Zeedyk, Sasha M.; Tipton, Leigh Ann; Blacher, Jan

2016-01-01

This article provides direction for educational decision making specifically for individuals with autism spectrum disorder (ASD), who have the cognitive and adaptive capabilities required to pursue postsecondary college education. The purpose is to draw attention to the available postsecondary pathways, 2- and 4-year college options, by addressing…

Image processing for drawing recognition

NASA Astrophysics Data System (ADS)

Feyzkhanov, Rustem; Zhelavskaya, Irina

2014-03-01

The task of recognizing edges of rectangular structures is well known. Still, almost all of them work with static images and has no limit on work time. We propose application of conducting homography for the video stream which can be obtained from the webcam. We propose algorithm which can be successfully used for this kind of application. One of the main use cases of such application is recognition of drawings by person on the piece of paper before webcam.

Live texturing of augmented reality characters from colored drawings.

PubMed

Magnenat, Stéphane; Ngo, Dat Tien; Zünd, Fabio; Ryffel, Mattia; Noris, Gioacchino; Rothlin, Gerhard; Marra, Alessia; Nitti, Maurizio; Fua, Pascal; Gross, Markus; Sumner, Robert W

2015-11-01

Coloring books capture the imagination of children and provide them with one of their earliest opportunities for creative expression. However, given the proliferation and popularity of digital devices, real-world activities like coloring can seem unexciting, and children become less engaged in them. Augmented reality holds unique potential to impact this situation by providing a bridge between real-world activities and digital enhancements. In this paper, we present an augmented reality coloring book App in which children color characters in a printed coloring book and inspect their work using a mobile device. The drawing is detected and tracked, and the video stream is augmented with an animated 3-D version of the character that is textured according to the child's coloring. This is possible thanks to several novel technical contributions. We present a texturing process that applies the captured texture from a 2-D colored drawing to both the visible and occluded regions of a 3-D character in real time. We develop a deformable surface tracking method designed for colored drawings that uses a new outlier rejection algorithm for real-time tracking and surface deformation recovery. We present a content creation pipeline to efficiently create the 2-D and 3-D content. And, finally, we validate our work with two user studies that examine the quality of our texturing algorithm and the overall App experience.

Modified harmony search

NASA Astrophysics Data System (ADS)

Mohamed, Najihah; Lutfi Amri Ramli, Ahmad; Majid, Ahmad Abd; Piah, Abd Rahni Mt

2017-09-01

A metaheuristic algorithm, called Harmony Search is quite highly applied in optimizing parameters in many areas. HS is a derivative-free real parameter optimization algorithm, and draws an inspiration from the musical improvisation process of searching for a perfect state of harmony. Propose in this paper Modified Harmony Search for solving optimization problems, which employs a concept from genetic algorithm method and particle swarm optimization for generating new solution vectors that enhances the performance of HS algorithm. The performances of MHS and HS are investigated on ten benchmark optimization problems in order to make a comparison to reflect the efficiency of the MHS in terms of final accuracy, convergence speed and robustness.

Fuzzy C-Means Algorithm for Segmentation of Aerial Photography Data Obtained Using Unmanned Aerial Vehicle

NASA Astrophysics Data System (ADS)

Akinin, M. V.; Akinina, N. V.; Klochkov, A. Y.; Nikiforov, M. B.; Sokolova, A. V.

2015-05-01

The report reviewed the algorithm fuzzy c-means, performs image segmentation, give an estimate of the quality of his work on the criterion of Xie-Beni, contain the results of experimental studies of the algorithm in the context of solving the problem of drawing up detailed two-dimensional maps with the use of unmanned aerial vehicles. According to the results of the experiment concluded that the possibility of applying the algorithm in problems of decoding images obtained as a result of aerial photography. The considered algorithm can significantly break the original image into a plurality of segments (clusters) in a relatively short period of time, which is achieved by modification of the original k-means algorithm to work in a fuzzy task.

Investigating ego modules and pathways in osteosarcoma by integrating the EgoNet algorithm and pathway analysis.

PubMed

Chen, X Y; Chen, Y H; Zhang, L J; Wang, Y; Tong, Z C

2017-02-16

Osteosarcoma (OS) is the most common primary bone malignancy, but current therapies are far from effective for all patients. A better understanding of the pathological mechanism of OS may help to achieve new treatments for this tumor. Hence, the objective of this study was to investigate ego modules and pathways in OS utilizing EgoNet algorithm and pathway-related analysis, and reveal pathological mechanisms underlying OS. The EgoNet algorithm comprises four steps: constructing background protein-protein interaction (PPI) network (PPIN) based on gene expression data and PPI data; extracting differential expression network (DEN) from the background PPIN; identifying ego genes according to topological features of genes in reweighted DEN; and collecting ego modules using module search by ego gene expansion. Consequently, we obtained 5 ego modules (Modules 2, 3, 4, 5, and 6) in total. After applying the permutation test, all presented statistical significance between OS and normal controls. Finally, pathway enrichment analysis combined with Reactome pathway database was performed to investigate pathways, and Fisher's exact test was conducted to capture ego pathways for OS. The ego pathway for Module 2 was CLEC7A/inflammasome pathway, while for Module 3 a tetrasaccharide linker sequence was required for glycosaminoglycan (GAG) synthesis, and for Module 6 was the Rho GTPase cycle. Interestingly, genes in Modules 4 and 5 were enriched in the same pathway, the 2-LTR circle formation. In conclusion, the ego modules and pathways might be potential biomarkers for OS therapeutic index, and give great insight of the molecular mechanism underlying this tumor.

Investigating ego modules and pathways in osteosarcoma by integrating the EgoNet algorithm and pathway analysis

PubMed Central

Chen, X.Y.; Chen, Y.H.; Zhang, L.J.; Wang, Y.; Tong, Z.C.

2017-01-01

Osteosarcoma (OS) is the most common primary bone malignancy, but current therapies are far from effective for all patients. A better understanding of the pathological mechanism of OS may help to achieve new treatments for this tumor. Hence, the objective of this study was to investigate ego modules and pathways in OS utilizing EgoNet algorithm and pathway-related analysis, and reveal pathological mechanisms underlying OS. The EgoNet algorithm comprises four steps: constructing background protein-protein interaction (PPI) network (PPIN) based on gene expression data and PPI data; extracting differential expression network (DEN) from the background PPIN; identifying ego genes according to topological features of genes in reweighted DEN; and collecting ego modules using module search by ego gene expansion. Consequently, we obtained 5 ego modules (Modules 2, 3, 4, 5, and 6) in total. After applying the permutation test, all presented statistical significance between OS and normal controls. Finally, pathway enrichment analysis combined with Reactome pathway database was performed to investigate pathways, and Fisher's exact test was conducted to capture ego pathways for OS. The ego pathway for Module 2 was CLEC7A/inflammasome pathway, while for Module 3 a tetrasaccharide linker sequence was required for glycosaminoglycan (GAG) synthesis, and for Module 6 was the Rho GTPase cycle. Interestingly, genes in Modules 4 and 5 were enriched in the same pathway, the 2-LTR circle formation. In conclusion, the ego modules and pathways might be potential biomarkers for OS therapeutic index, and give great insight of the molecular mechanism underlying this tumor. PMID:28225867

Pathways to Improvement: Using Psychological Strategies to Help College Students Master Developmental Math

ERIC Educational Resources Information Center

Silva, Elena; White, Taylor

2013-01-01

Drawing on a research base developed over many years in education, Carnegie is testing a set of strategies to help students persist and succeed academically. This kind of persistence, what the researchers and faculty who developed the Pathways call "productive persistence," is a key driver of Quantway® and Statway®. Broadly defined,…

The Pre-University Pathways of Disadvantaged Students for Gaining Entry to University Study

ERIC Educational Resources Information Center

Norodien-Fataar, Najwa

2016-01-01

This article focuses on the pre-university access pathways of disadvantaged first-generation students studying at a South African university. Based on data collected via qualitative methods, it draws on findings from a study of purposively selected students at a university in the Western Cape Province. It explores the ways they access and gain…

Cloud Computing Security Model with Combination of Data Encryption Standard Algorithm (DES) and Least Significant Bit (LSB)

NASA Astrophysics Data System (ADS)

Basri, M.; Mawengkang, H.; Zamzami, E. M.

2018-03-01

Limitations of storage sources is one option to switch to cloud storage. Confidentiality and security of data stored on the cloud is very important. To keep up the confidentiality and security of such data can be done one of them by using cryptography techniques. Data Encryption Standard (DES) is one of the block cipher algorithms used as standard symmetric encryption algorithm. This DES will produce 8 blocks of ciphers combined into one ciphertext, but the ciphertext are weak against brute force attacks. Therefore, the last 8 block cipher will be converted into 8 random images using Least Significant Bit (LSB) algorithm which later draws the result of cipher of DES algorithm to be merged into one.

TGMI: an efficient algorithm for identifying pathway regulators through evaluation of triple-gene mutual interaction

PubMed Central

Gunasekara, Chathura; Zhang, Kui; Deng, Wenping; Brown, Laura

2018-01-01

Abstract Despite their important roles, the regulators for most metabolic pathways and biological processes remain elusive. Presently, the methods for identifying metabolic pathway and biological process regulators are intensively sought after. We developed a novel algorithm called triple-gene mutual interaction (TGMI) for identifying these regulators using high-throughput gene expression data. It first calculated the regulatory interactions among triple gene blocks (two pathway genes and one transcription factor (TF)), using conditional mutual information, and then identifies significantly interacted triple genes using a newly identified novel mutual interaction measure (MIM), which was substantiated to reflect strengths of regulatory interactions within each triple gene block. The TGMI calculated the MIM for each triple gene block and then examined its statistical significance using bootstrap. Finally, the frequencies of all TFs present in all significantly interacted triple gene blocks were calculated and ranked. We showed that the TFs with higher frequencies were usually genuine pathway regulators upon evaluating multiple pathways in plants, animals and yeast. Comparison of TGMI with several other algorithms demonstrated its higher accuracy. Therefore, TGMI will be a valuable tool that can help biologists to identify regulators of metabolic pathways and biological processes from the exploded high-throughput gene expression data in public repositories. PMID:29579312

Human performance on visually presented Traveling Salesman problems.

PubMed

Vickers, D; Butavicius, M; Lee, M; Medvedev, A

2001-01-01

Little research has been carried out on human performance in optimization problems, such as the Traveling Salesman problem (TSP). Studies by Polivanova (1974, Voprosy Psikhologii, 4, 41-51) and by MacGregor and Ormerod (1996, Perception & Psychophysics, 58, 527-539) suggest that: (1) the complexity of solutions to visually presented TSPs depends on the number of points on the convex hull; and (2) the perception of optimal structure is an innate tendency of the visual system, not subject to individual differences. Results are reported from two experiments. In the first, measures of the total length and completion speed of pathways, and a measure of path uncertainty were compared with optimal solutions produced by an elastic net algorithm and by several heuristic methods. Performance was also compared under instructions to draw the shortest or the most attractive pathway. In the second, various measures of performance were compared with scores on Raven's advanced progressive matrices (APM). The number of points on the convex hull did not determine the relative optimality of solutions, although both this factor and the total number of points influenced solution speed and path uncertainty. Subjects' solutions showed appreciable individual differences, which had a strong correlation with APM scores. The relation between perceptual organization and the process of solving visually presented TSPs is briefly discussed, as is the potential of optimization for providing a conceptual framework for the study of intelligence.

Whisper: Tracing the Spatiotemporal Process of Information Diffusion in Real Time.

PubMed

Cao, Nan; Lin, Yu-Ru; Sun, Xiaohua; Lazer, D; Liu, Shixia; Qu, Huamin

2012-12-01

When and where is an idea dispersed? Social media, like Twitter, has been increasingly used for exchanging information, opinions and emotions about events that are happening across the world. Here we propose a novel visualization design, "Whisper", for tracing the process of information diffusion in social media in real time. Our design highlights three major characteristics of diffusion processes in social media: the temporal trend, social-spatial extent, and community response of a topic of interest. Such social, spatiotemporal processes are conveyed based on a sunflower metaphor whose seeds are often dispersed far away. In Whisper, we summarize the collective responses of communities on a given topic based on how tweets were retweeted by groups of users, through representing the sentiments extracted from the tweets, and tracing the pathways of retweets on a spatial hierarchical layout. We use an efficient flux line-drawing algorithm to trace multiple pathways so the temporal and spatial patterns can be identified even for a bursty event. A focused diffusion series highlights key roles such as opinion leaders in the diffusion process. We demonstrate how our design facilitates the understanding of when and where a piece of information is dispersed and what are the social responses of the crowd, for large-scale events including political campaigns and natural disasters. Initial feedback from domain experts suggests promising use for today's information consumption and dispersion in the wild.

A Novel Method to Identify Differential Pathways in Hippocampus Alzheimer's Disease.

PubMed

Liu, Chun-Han; Liu, Lian

2017-05-08

BACKGROUND Alzheimer's disease (AD) is the most common type of dementia. The objective of this paper is to propose a novel method to identify differential pathways in hippocampus AD. MATERIAL AND METHODS We proposed a combined method by merging existed methods. Firstly, pathways were identified by four known methods (DAVID, the neaGUI package, the pathway-based co-expressed method, and the pathway network approach), and differential pathways were evaluated through setting weight thresholds. Subsequently, we combined all pathways by a rank-based algorithm and called the method the combined method. Finally, common differential pathways across two or more of five methods were selected. RESULTS Pathways obtained from different methods were also different. The combined method obtained 1639 pathways and 596 differential pathways, which included all pathways gained from the four existing methods; hence, the novel method solved the problem of inconsistent results. Besides, a total of 13 common pathways were identified, such as metabolism, immune system, and cell cycle. CONCLUSIONS We have proposed a novel method by combining four existing methods based on a rank product algorithm, and identified 13 significant differential pathways based on it. These differential pathways might provide insight into treatment and diagnosis of hippocampus AD.

Simultaneous Identification of Multiple Driver Pathways in Cancer

PubMed Central

Leiserson, Mark D. M.; Blokh, Dima

2013-01-01

Distinguishing the somatic mutations responsible for cancer (driver mutations) from random, passenger mutations is a key challenge in cancer genomics. Driver mutations generally target cellular signaling and regulatory pathways consisting of multiple genes. This heterogeneity complicates the identification of driver mutations by their recurrence across samples, as different combinations of mutations in driver pathways are observed in different samples. We introduce the Multi-Dendrix algorithm for the simultaneous identification of multiple driver pathways de novo in somatic mutation data from a cohort of cancer samples. The algorithm relies on two combinatorial properties of mutations in a driver pathway: high coverage and mutual exclusivity. We derive an integer linear program that finds set of mutations exhibiting these properties. We apply Multi-Dendrix to somatic mutations from glioblastoma, breast cancer, and lung cancer samples. Multi-Dendrix identifies sets of mutations in genes that overlap with known pathways – including Rb, p53, PI(3)K, and cell cycle pathways – and also novel sets of mutually exclusive mutations, including mutations in several transcription factors or other genes involved in transcriptional regulation. These sets are discovered directly from mutation data with no prior knowledge of pathways or gene interactions. We show that Multi-Dendrix outperforms other algorithms for identifying combinations of mutations and is also orders of magnitude faster on genome-scale data. Software available at: http://compbio.cs.brown.edu/software. PMID:23717195

PerSubs: A Graph-Based Algorithm for the Identification of Perturbed Subpathways Caused by Complex Diseases.

PubMed

Vrahatis, Aristidis G; Rapti, Angeliki; Sioutas, Spyros; Tsakalidis, Athanasios

2017-01-01

In the era of Systems Biology and growing flow of omics experimental data from high throughput techniques, experimentalists are in need of more precise pathway-based tools to unravel the inherent complexity of diseases and biological processes. Subpathway-based approaches are the emerging generation of pathway-based analysis elucidating the biological mechanisms under the perspective of local topologies onto a complex pathway network. Towards this orientation, we developed PerSub, a graph-based algorithm which detects subpathways perturbed by a complex disease. The perturbations are imprinted through differentially expressed and co-expressed subpathways as recorded by RNA-seq experiments. Our novel algorithm is applied on data obtained from a real experimental study and the identified subpathways provide biological evidence for the brain aging.

Springback optimization in automotive Shock Absorber Cup with Genetic Algorithm

NASA Astrophysics Data System (ADS)

Kakandikar, Ganesh; Nandedkar, Vilas

2018-02-01

Drawing or forming is a process normally used to achieve a required component form from a metal blank by applying a punch which radially draws the blank into the die by a mechanical or hydraulic action or combining both. When the component is drawn for more depth than the diameter, it is usually seen as deep drawing, which involves complicated states of material deformation. Due to the radial drawing of the material as it enters the die, radial drawing stress occurs in the flange with existence of the tangential compressive stress. This compression generates wrinkles in the flange. Wrinkling is unwanted phenomenon and can be controlled by application of a blank-holding force. Tensile stresses cause thinning in the wall region of the cup. Three main types of the errors occur in such a process are wrinkling, fracturing and springback. This paper reports a work focused on the springback and control. Due to complexity of the process, tool try-outs and experimentation may be costly, bulky and time consuming. Numerical simulation proves to be a good option for studying the process and developing a control strategy for reducing the springback. Finite-element based simulations have been used popularly for such purposes. In this study, the springback in deep drawing of an automotive Shock Absorber Cup is simulated with finite element method. Taguchi design of experiments and analysis of variance are used to analyze the influencing process parameters on the springback. Mathematical relations are developed to relate the process parameters and the resulting springback. The optimization problem is formulated for the springback, referring to the displacement magnitude in the selected sections. Genetic Algorithm is then applied for process optimization with an objective to minimize the springback. The results indicate that a better prediction of the springback and process optimization could be achieved with a combined use of these methods and tools.

The Role of Habitus and Reflexivity in Young People Managing Pathways out of Crime

ERIC Educational Resources Information Center

France, Alan; Bottrell, Dorothy; Haddon, Edward

2013-01-01

This article draws on material from the ESRC funded "Pathways into and out of crime" research programme (Grant number L330253001) to explore how a group of educationally disaffected young people negotiate and try to manage their desistance from offending. We argue that the ability to be reflexive represents a form of embodied cultural…

Conversion of KEGG metabolic pathways to SBGN maps including automatic layout

PubMed Central

2013-01-01

Background Biologists make frequent use of databases containing large and complex biological networks. One popular database is the Kyoto Encyclopedia of Genes and Genomes (KEGG) which uses its own graphical representation and manual layout for pathways. While some general drawing conventions exist for biological networks, arbitrary graphical representations are very common. Recently, a new standard has been established for displaying biological processes, the Systems Biology Graphical Notation (SBGN), which aims to unify the look of such maps. Ideally, online repositories such as KEGG would automatically provide networks in a variety of notations including SBGN. Unfortunately, this is non‐trivial, since converting between notations may add, remove or otherwise alter map elements so that the existing layout cannot be simply reused. Results Here we describe a methodology for automatic translation of KEGG metabolic pathways into the SBGN format. We infer important properties of the KEGG layout and treat these as layout constraints that are maintained during the conversion to SBGN maps. Conclusions This allows for the drawing and layout conventions of SBGN to be followed while creating maps that are still recognizably the original KEGG pathways. This article details the steps in this process and provides examples of the final result. PMID:23953132

A global reaction route mapping-based kinetic Monte Carlo algorithm

NASA Astrophysics Data System (ADS)

Mitchell, Izaac; Irle, Stephan; Page, Alister J.

2016-07-01

We propose a new on-the-fly kinetic Monte Carlo (KMC) method that is based on exhaustive potential energy surface searching carried out with the global reaction route mapping (GRRM) algorithm. Starting from any given equilibrium state, this GRRM-KMC algorithm performs a one-step GRRM search to identify all surrounding transition states. Intrinsic reaction coordinate pathways are then calculated to identify potential subsequent equilibrium states. Harmonic transition state theory is used to calculate rate constants for all potential pathways, before a standard KMC accept/reject selection is performed. The selected pathway is then used to propagate the system forward in time, which is calculated on the basis of 1st order kinetics. The GRRM-KMC algorithm is validated here in two challenging contexts: intramolecular proton transfer in malonaldehyde and surface carbon diffusion on an iron nanoparticle. We demonstrate that in both cases the GRRM-KMC method is capable of reproducing the 1st order kinetics observed during independent quantum chemical molecular dynamics simulations using the density-functional tight-binding potential.

A global reaction route mapping-based kinetic Monte Carlo algorithm.

PubMed

Mitchell, Izaac; Irle, Stephan; Page, Alister J

2016-07-14

We propose a new on-the-fly kinetic Monte Carlo (KMC) method that is based on exhaustive potential energy surface searching carried out with the global reaction route mapping (GRRM) algorithm. Starting from any given equilibrium state, this GRRM-KMC algorithm performs a one-step GRRM search to identify all surrounding transition states. Intrinsic reaction coordinate pathways are then calculated to identify potential subsequent equilibrium states. Harmonic transition state theory is used to calculate rate constants for all potential pathways, before a standard KMC accept/reject selection is performed. The selected pathway is then used to propagate the system forward in time, which is calculated on the basis of 1st order kinetics. The GRRM-KMC algorithm is validated here in two challenging contexts: intramolecular proton transfer in malonaldehyde and surface carbon diffusion on an iron nanoparticle. We demonstrate that in both cases the GRRM-KMC method is capable of reproducing the 1st order kinetics observed during independent quantum chemical molecular dynamics simulations using the density-functional tight-binding potential.

Identification of inelastic parameters based on deep drawing forming operations using a global-local hybrid Particle Swarm approach

NASA Astrophysics Data System (ADS)

Vaz, Miguel; Luersen, Marco A.; Muñoz-Rojas, Pablo A.; Trentin, Robson G.

2016-04-01

Application of optimization techniques to the identification of inelastic material parameters has substantially increased in recent years. The complex stress-strain paths and high nonlinearity, typical of this class of problems, require the development of robust and efficient techniques for inverse problems able to account for an irregular topography of the fitness surface. Within this framework, this work investigates the application of the gradient-based Sequential Quadratic Programming method, of the Nelder-Mead downhill simplex algorithm, of Particle Swarm Optimization (PSO), and of a global-local PSO-Nelder-Mead hybrid scheme to the identification of inelastic parameters based on a deep drawing operation. The hybrid technique has shown to be the best strategy by combining the good PSO performance to approach the global minimum basin of attraction with the efficiency demonstrated by the Nelder-Mead algorithm to obtain the minimum itself.

An improved hybrid of particle swarm optimization and the gravitational search algorithm to produce a kinetic parameter estimation of aspartate biochemical pathways.

PubMed

Ismail, Ahmad Muhaimin; Mohamad, Mohd Saberi; Abdul Majid, Hairudin; Abas, Khairul Hamimah; Deris, Safaai; Zaki, Nazar; Mohd Hashim, Siti Zaiton; Ibrahim, Zuwairie; Remli, Muhammad Akmal

2017-12-01

Mathematical modelling is fundamental to understand the dynamic behavior and regulation of the biochemical metabolisms and pathways that are found in biological systems. Pathways are used to describe complex processes that involve many parameters. It is important to have an accurate and complete set of parameters that describe the characteristics of a given model. However, measuring these parameters is typically difficult and even impossible in some cases. Furthermore, the experimental data are often incomplete and also suffer from experimental noise. These shortcomings make it challenging to identify the best-fit parameters that can represent the actual biological processes involved in biological systems. Computational approaches are required to estimate these parameters. The estimation is converted into multimodal optimization problems that require a global optimization algorithm that can avoid local solutions. These local solutions can lead to a bad fit when calibrating with a model. Although the model itself can potentially match a set of experimental data, a high-performance estimation algorithm is required to improve the quality of the solutions. This paper describes an improved hybrid of particle swarm optimization and the gravitational search algorithm (IPSOGSA) to improve the efficiency of a global optimum (the best set of kinetic parameter values) search. The findings suggest that the proposed algorithm is capable of narrowing down the search space by exploiting the feasible solution areas. Hence, the proposed algorithm is able to achieve a near-optimal set of parameters at a fast convergence speed. The proposed algorithm was tested and evaluated based on two aspartate pathways that were obtained from the BioModels Database. The results show that the proposed algorithm outperformed other standard optimization algorithms in terms of accuracy and near-optimal kinetic parameter estimation. Nevertheless, the proposed algorithm is only expected to work well in small scale systems. In addition, the results of this study can be used to estimate kinetic parameter values in the stage of model selection for different experimental conditions. Copyright © 2017 Elsevier B.V. All rights reserved.

Cartography of Pathway Signal Perturbations Identifies Distinct Molecular Pathomechanisms in Malignant and Chronic Lung Diseases

PubMed Central

Arakelyan, Arsen; Nersisyan, Lilit; Petrek, Martin; Löffler-Wirth, Henry; Binder, Hans

2016-01-01

Lung diseases are described by a wide variety of developmental mechanisms and clinical manifestations. Accurate classification and diagnosis of lung diseases are the bases for development of effective treatments. While extensive studies are conducted toward characterization of various lung diseases at molecular level, no systematic approach has been developed so far. Here we have applied a methodology for pathway-centered mining of high throughput gene expression data to describe a wide range of lung diseases in the light of shared and specific pathway activity profiles. We have applied an algorithm combining a Pathway Signal Flow (PSF) algorithm for estimation of pathway activity deregulation states in lung diseases and malignancies, and a Self Organizing Maps algorithm for classification and clustering of the pathway activity profiles. The analysis results allowed clearly distinguish between cancer and non-cancer lung diseases. Lung cancers were characterized by pathways implicated in cell proliferation, metabolism, while non-malignant lung diseases were characterized by deregulations in pathways involved in immune/inflammatory response and fibrotic tissue remodeling. In contrast to lung malignancies, chronic lung diseases had relatively heterogeneous pathway deregulation profiles. We identified three groups of interstitial lung diseases and showed that the development of characteristic pathological processes, such as fibrosis, can be initiated by deregulations in different signaling pathways. In conclusion, this paper describes the pathobiology of lung diseases from systems viewpoint using pathway centered high-dimensional data mining approach. Our results contribute largely to current understanding of pathological events in lung cancers and non-malignant lung diseases. Moreover, this paper provides new insight into molecular mechanisms of a number of interstitial lung diseases that have been studied to a lesser extent. PMID:27200087

Rationally reduced libraries for combinatorial pathway optimization minimizing experimental effort.

PubMed

Jeschek, Markus; Gerngross, Daniel; Panke, Sven

2016-03-31

Rational flux design in metabolic engineering approaches remains difficult since important pathway information is frequently not available. Therefore empirical methods are applied that randomly change absolute and relative pathway enzyme levels and subsequently screen for variants with improved performance. However, screening is often limited on the analytical side, generating a strong incentive to construct small but smart libraries. Here we introduce RedLibs (Reduced Libraries), an algorithm that allows for the rational design of smart combinatorial libraries for pathway optimization thereby minimizing the use of experimental resources. We demonstrate the utility of RedLibs for the design of ribosome-binding site libraries by in silico and in vivo screening with fluorescent proteins and perform a simple two-step optimization of the product selectivity in the branched multistep pathway for violacein biosynthesis, indicating a general applicability for the algorithm and the proposed heuristics. We expect that RedLibs will substantially simplify the refactoring of synthetic metabolic pathways.

An editor for pathway drawing and data visualization in the Biopathways Workbench.

PubMed

Byrnes, Robert W; Cotter, Dawn; Maer, Andreia; Li, Joshua; Nadeau, David; Subramaniam, Shankar

2009-10-02

Pathway models serve as the basis for much of systems biology. They are often built using programs designed for the purpose. Constructing new models generally requires simultaneous access to experimental data of diverse types, to databases of well-characterized biological compounds and molecular intermediates, and to reference model pathways. However, few if any software applications provide all such capabilities within a single user interface. The Pathway Editor is a program written in the Java programming language that allows de-novo pathway creation and downloading of LIPID MAPS (Lipid Metabolites and Pathways Strategy) and KEGG lipid metabolic pathways, and of measured time-dependent changes to lipid components of metabolism. Accessed through Java Web Start, the program downloads pathways from the LIPID MAPS Pathway database (Pathway) as well as from the LIPID MAPS web server http://www.lipidmaps.org. Data arises from metabolomic (lipidomic), microarray, and protein array experiments performed by the LIPID MAPS consortium of laboratories and is arranged by experiment. Facility is provided to create, connect, and annotate nodes and processes on a drawing panel with reference to database objects and time course data. Node and interaction layout as well as data display may be configured in pathway diagrams as desired. Users may extend diagrams, and may also read and write data and non-lipidomic KEGG pathways to and from files. Pathway diagrams in XML format, containing database identifiers referencing specific compounds and experiments, can be saved to a local file for subsequent use. The program is built upon a library of classes, referred to as the Biopathways Workbench, that convert between different file formats and database objects. An example of this feature is provided in the form of read/construct/write access to models in SBML (Systems Biology Markup Language) contained in the local file system. Inclusion of access to multiple experimental data types and of pathway diagrams within a single interface, automatic updating through connectivity to an online database, and a focus on annotation, including reference to standardized lipid nomenclature as well as common lipid names, supports the view that the Pathway Editor represents a significant, practicable contribution to current pathway modeling tools.

Bayesian parameter estimation for nonlinear modelling of biological pathways.

PubMed

Ghasemi, Omid; Lindsey, Merry L; Yang, Tianyi; Nguyen, Nguyen; Huang, Yufei; Jin, Yu-Fang

2011-01-01

The availability of temporal measurements on biological experiments has significantly promoted research areas in systems biology. To gain insight into the interaction and regulation of biological systems, mathematical frameworks such as ordinary differential equations have been widely applied to model biological pathways and interpret the temporal data. Hill equations are the preferred formats to represent the reaction rate in differential equation frameworks, due to their simple structures and their capabilities for easy fitting to saturated experimental measurements. However, Hill equations are highly nonlinearly parameterized functions, and parameters in these functions cannot be measured easily. Additionally, because of its high nonlinearity, adaptive parameter estimation algorithms developed for linear parameterized differential equations cannot be applied. Therefore, parameter estimation in nonlinearly parameterized differential equation models for biological pathways is both challenging and rewarding. In this study, we propose a Bayesian parameter estimation algorithm to estimate parameters in nonlinear mathematical models for biological pathways using time series data. We used the Runge-Kutta method to transform differential equations to difference equations assuming a known structure of the differential equations. This transformation allowed us to generate predictions dependent on previous states and to apply a Bayesian approach, namely, the Markov chain Monte Carlo (MCMC) method. We applied this approach to the biological pathways involved in the left ventricle (LV) response to myocardial infarction (MI) and verified our algorithm by estimating two parameters in a Hill equation embedded in the nonlinear model. We further evaluated our estimation performance with different parameter settings and signal to noise ratios. Our results demonstrated the effectiveness of the algorithm for both linearly and nonlinearly parameterized dynamic systems. Our proposed Bayesian algorithm successfully estimated parameters in nonlinear mathematical models for biological pathways. This method can be further extended to high order systems and thus provides a useful tool to analyze biological dynamics and extract information using temporal data.

Fast wavelet based algorithms for linear evolution equations

NASA Technical Reports Server (NTRS)

Engquist, Bjorn; Osher, Stanley; Zhong, Sifen

1992-01-01

A class was devised of fast wavelet based algorithms for linear evolution equations whose coefficients are time independent. The method draws on the work of Beylkin, Coifman, and Rokhlin which they applied to general Calderon-Zygmund type integral operators. A modification of their idea is applied to linear hyperbolic and parabolic equations, with spatially varying coefficients. A significant speedup over standard methods is obtained when applied to hyperbolic equations in one space dimension and parabolic equations in multidimensions.

About the algorithm for construction of coordinated university timetables

NASA Astrophysics Data System (ADS)

Dobrynin, A. S.; Kulakov, S. M.; Taraborina, E. N.

2018-05-01

The factual description of the task and an algorithm for drawing up a coordinated timetable of academic work of the faculty and students at the level of department (local timetable) is presented, as well as the procedure for integrating private schedules, i.e. the formation of a university-wide timetable. Coordination of the latter has not only spatio-temporal in nature, but also takes into account the preferences (interests) of agents (users and performers of works).

Specific algorithm method of scoring the Clock Drawing Test applied in cognitively normal elderly

PubMed Central

Mendes-Santos, Liana Chaves; Mograbi, Daniel; Spenciere, Bárbara; Charchat-Fichman, Helenice

2015-01-01

The Clock Drawing Test (CDT) is an inexpensive, fast and easily administered measure of cognitive function, especially in the elderly. This instrument is a popular clinical tool widely used in screening for cognitive disorders and dementia. The CDT can be applied in different ways and scoring procedures also vary. Objective The aims of this study were to analyze the performance of elderly on the CDT and evaluate inter-rater reliability of the CDT scored by using a specific algorithm method adapted from Sunderland et al. (1989). Methods We analyzed the CDT of 100 cognitively normal elderly aged 60 years or older. The CDT ("free-drawn") and Mini-Mental State Examination (MMSE) were administered to all participants. Six independent examiners scored the CDT of 30 participants to evaluate inter-rater reliability. Results and Conclusion A score of 5 on the proposed algorithm ("Numbers in reverse order or concentrated"), equivalent to 5 points on the original Sunderland scale, was the most frequent (53.5%). The CDT specific algorithm method used had high inter-rater reliability (p<0.01), and mean score ranged from 5.06 to 5.96. The high frequency of an overall score of 5 points may suggest the need to create more nuanced evaluation criteria, which are sensitive to differences in levels of impairment in visuoconstructive and executive abilities during aging. PMID:29213954

Tracing Pathways to Higher Education for Refugees: The Role of Virtual Support Networks and Mobile Phones for Women in Refugee Camps

ERIC Educational Resources Information Center

Dahya, Negin; Dryden-Peterson, Sarah

2017-01-01

In this paper, we explore the role of online social networks in the cultivation of pathways to higher education for refugees, particularly for women. We compare supports garnered in local and offline settings to those accrued through online social networks and examine the differences between women and men. The paper draws on complementary original…

Dilated contour extraction and component labeling algorithm for object vector representation

NASA Astrophysics Data System (ADS)

Skourikhine, Alexei N.

2005-08-01

Object boundary extraction from binary images is important for many applications, e.g., image vectorization, automatic interpretation of images containing segmentation results, printed and handwritten documents and drawings, maps, and AutoCAD drawings. Efficient and reliable contour extraction is also important for pattern recognition due to its impact on shape-based object characterization and recognition. The presented contour tracing and component labeling algorithm produces dilated (sub-pixel) contours associated with corresponding regions. The algorithm has the following features: (1) it always produces non-intersecting, non-degenerate contours, including the case of one-pixel wide objects; (2) it associates the outer and inner (i.e., around hole) contours with the corresponding regions during the process of contour tracing in a single pass over the image; (3) it maintains desired connectivity of object regions as specified by 8-neighbor or 4-neighbor connectivity of adjacent pixels; (4) it avoids degenerate regions in both background and foreground; (5) it allows an easy augmentation that will provide information about the containment relations among regions; (6) it has a time complexity that is dominantly linear in the number of contour points. This early component labeling (contour-region association) enables subsequent efficient object-based processing of the image information.

Fast Drawing of Traffic Sign Using Mobile Mapping System

NASA Astrophysics Data System (ADS)

Yao, Q.; Tan, B.; Huang, Y.

2016-06-01

Traffic sign provides road users with the specified instruction and information to enhance traffic safety. Automatic detection of traffic sign is important for navigation, autonomous driving, transportation asset management, etc. With the advance of laser and imaging sensors, Mobile Mapping System (MMS) becomes widely used in transportation agencies to map the transportation infrastructure. Although many algorithms of traffic sign detection are developed in the literature, they are still a tradeoff between the detection speed and accuracy, especially for the large-scale mobile mapping of both the rural and urban roads. This paper is motivated to efficiently survey traffic signs while mapping the road network and the roadside landscape. Inspired by the manual delineation of traffic sign, a drawing strategy is proposed to quickly approximate the boundary of traffic sign. Both the shape and color prior of the traffic sign are simultaneously involved during the drawing process. The most common speed-limit sign circle and the statistic color model of traffic sign are studied in this paper. Anchor points of traffic sign edge are located with the local maxima of color and gradient difference. Starting with the anchor points, contour of traffic sign is drawn smartly along the most significant direction of color and intensity consistency. The drawing process is also constrained by the curvature feature of the traffic sign circle. The drawing of linear growth is discarded immediately if it fails to form an arc over some steps. The Kalman filter principle is adopted to predict the temporal context of traffic sign. Based on the estimated point,we can predict and double check the traffic sign in consecutive frames.The event probability of having a traffic sign over the consecutive observations is compared with the null hypothesis of no perceptible traffic sign. The temporally salient traffic sign is then detected statistically and automatically as the rare event of having a traffic sign.The proposed algorithm is tested with a diverse set of images that are taken inWuhan, China with theMMS ofWuhan University. Experimental results demonstrate that the proposed algorithm can detect traffic signs at the rate of over 80% in around 10 milliseconds. It is promising for the large-scale traffic sign survey and change detection using the mobile mapping system.

PROPER: global protein interaction network alignment through percolation matching.

PubMed

Kazemi, Ehsan; Hassani, Hamed; Grossglauser, Matthias; Pezeshgi Modarres, Hassan

2016-12-12

The alignment of protein-protein interaction (PPI) networks enables us to uncover the relationships between different species, which leads to a deeper understanding of biological systems. Network alignment can be used to transfer biological knowledge between species. Although different PPI-network alignment algorithms were introduced during the last decade, developing an accurate and scalable algorithm that can find alignments with high biological and structural similarities among PPI networks is still challenging. In this paper, we introduce a new global network alignment algorithm for PPI networks called PROPER. Compared to other global network alignment methods, our algorithm shows higher accuracy and speed over real PPI datasets and synthetic networks. We show that the PROPER algorithm can detect large portions of conserved biological pathways between species. Also, using a simple parsimonious evolutionary model, we explain why PROPER performs well based on several different comparison criteria. We highlight that PROPER has high potential in further applications such as detecting biological pathways, finding protein complexes and PPI prediction. The PROPER algorithm is available at http://proper.epfl.ch .

Service-based analysis of biological pathways

PubMed Central

Zheng, George; Bouguettaya, Athman

2009-01-01

Background Computer-based pathway discovery is concerned with two important objectives: pathway identification and analysis. Conventional mining and modeling approaches aimed at pathway discovery are often effective at achieving either objective, but not both. Such limitations can be effectively tackled leveraging a Web service-based modeling and mining approach. Results Inspired by molecular recognitions and drug discovery processes, we developed a Web service mining tool, named PathExplorer, to discover potentially interesting biological pathways linking service models of biological processes. The tool uses an innovative approach to identify useful pathways based on graph-based hints and service-based simulation verifying user's hypotheses. Conclusion Web service modeling of biological processes allows the easy access and invocation of these processes on the Web. Web service mining techniques described in this paper enable the discovery of biological pathways linking these process service models. Algorithms presented in this paper for automatically highlighting interesting subgraph within an identified pathway network enable the user to formulate hypothesis, which can be tested out using our simulation algorithm that are also described in this paper. PMID:19796403

A global reaction route mapping-based kinetic Monte Carlo algorithm

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

Mitchell, Izaac; Page, Alister J., E-mail: sirle@chem.nagoya-u.ac.jp, E-mail: alister.page@newcastle.edu.au; Irle, Stephan, E-mail: sirle@chem.nagoya-u.ac.jp, E-mail: alister.page@newcastle.edu.au

2016-07-14

We propose a new on-the-fly kinetic Monte Carlo (KMC) method that is based on exhaustive potential energy surface searching carried out with the global reaction route mapping (GRRM) algorithm. Starting from any given equilibrium state, this GRRM-KMC algorithm performs a one-step GRRM search to identify all surrounding transition states. Intrinsic reaction coordinate pathways are then calculated to identify potential subsequent equilibrium states. Harmonic transition state theory is used to calculate rate constants for all potential pathways, before a standard KMC accept/reject selection is performed. The selected pathway is then used to propagate the system forward in time, which is calculatedmore » on the basis of 1st order kinetics. The GRRM-KMC algorithm is validated here in two challenging contexts: intramolecular proton transfer in malonaldehyde and surface carbon diffusion on an iron nanoparticle. We demonstrate that in both cases the GRRM-KMC method is capable of reproducing the 1st order kinetics observed during independent quantum chemical molecular dynamics simulations using the density-functional tight-binding potential.« less

Kinase Pathway Dependence in Primary Human Leukemias Determined by Rapid Inhibitor Screening

PubMed Central

Tyner, Jeffrey W.; Yang, Wayne F.; Bankhead, Armand; Fan, Guang; Fletcher, Luke B.; Bryant, Jade; Glover, Jason M.; Chang, Bill H.; Spurgeon, Stephen E.; Fleming, William H.; Kovacsovics, Tibor; Gotlib, Jason R.; Oh, Stephen T.; Deininger, Michael W.; Zwaan, C. Michel; Den Boer, Monique L.; van den Heuvel-Eibrink, Marry M.; O’Hare, Thomas; Druker, Brian J.; Loriaux, Marc M.

2012-01-01

Kinases are dysregulated in most cancer but the frequency of specific kinase mutations is low, indicating a complex etiology in kinase dysregulation. Here we report a strategy to rapidly identify functionally important kinase targets, irrespective of the etiology of kinase pathway dysregulation, ultimately enabling a correlation of patient genetic profiles to clinically effective kinase inhibitors. Our methodology assessed the sensitivity of primary leukemia patient samples to a panel of 66 small-molecule kinase inhibitors over 3 days. Screening of 151 leukemia patient samples revealed a wide diversity of drug sensitivities, with 70% of the clinical specimens exhibiting hypersensitivity to one or more drugs. From this data set, we developed an algorithm to predict kinase pathway dependence based on analysis of inhibitor sensitivity patterns. Applying this algorithm correctly identified pathway dependence in proof-of-principle specimens with known oncogenes, including a rare FLT3 mutation outside regions covered by standard molecular diagnostic tests. Interrogation of all 151 patient specimens with this algorithm identified a diversity of gene targets and signaling pathways that could aid prioritization of deep sequencing data sets, permitting a cumulative analysis to understand kinase pathway dependence within leukemia subsets. In a proof-of-principle case, we showed that in vitro drug sensitivity could predict both a clinical response and the development of drug resistance. Taken together, our results suggested that drug target scores derived from a comprehensive kinase inhibitor panel could predict pathway dependence in cancer cells while simultaneously identifying potential therapeutic options. PMID:23087056

Compensatory changes accompanying chronic forced use of the nondominant hand by unilateral amputees.

PubMed

Philip, Benjamin A; Frey, Scott H

2014-03-05

Amputation of the dominant hand forces patients to use the nondominant hand exclusively, including for tasks (e.g., writing and drawing) that were formerly the sole domain of the dominant hand. The behavioral and neurological effects of this chronic forced use of the nondominant hand remain largely unknown. Yet, these effects may shed light on the potential to compensate for degradation or loss of dominant hand function, as well as the mechanisms that support motor learning under conditions of very long-term training. We used a novel precision drawing task and fMRI to investigate 8 adult human amputees with chronic (mean 33 years) unilateral dominant (right) hand absence, and right-handed matched controls (8 for fMRI, 19 for behavior). Amputees' precision drawing performances with their left hands reached levels of smoothness (associated with left hemisphere control), acceleration time (associated with right hemisphere control), and speed equivalent to controls' right hands, whereas accuracy maintained a level comparable with controls' left hands. This compensation is supported by an experience-dependent shift from heavy reliance on the dorsodorsal parietofrontal pathway (feedback control) to the ventrodorsal pathway and prefrontal regions involved in the cognitive control of goal-directed actions. Relative to controls, amputees also showed increased activity within the former cortical sensorimotor hand territory in the left (ipsilateral) hemisphere. These data demonstrate that, with chronic and exclusive forced use, the speed and quality of nondominant hand precision endpoint control in drawing can achieve levels nearly comparable with the dominant hand.

A Christoffel function weighted least squares algorithm for collocation approximations

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

Narayan, Akil; Jakeman, John D.; Zhou, Tao

Here, we propose, theoretically investigate, and numerically validate an algorithm for the Monte Carlo solution of least-squares polynomial approximation problems in a collocation framework. Our investigation is motivated by applications in the collocation approximation of parametric functions, which frequently entails construction of surrogates via orthogonal polynomials. A standard Monte Carlo approach would draw samples according to the density defining the orthogonal polynomial family. Our proposed algorithm instead samples with respect to the (weighted) pluripotential equilibrium measure of the domain, and subsequently solves a weighted least-squares problem, with weights given by evaluations of the Christoffel function. We present theoretical analysis tomore » motivate the algorithm, and numerical results that show our method is superior to standard Monte Carlo methods in many situations of interest.« less

A Christoffel function weighted least squares algorithm for collocation approximations

DOE PAGES

Narayan, Akil; Jakeman, John D.; Zhou, Tao

2016-11-28

Here, we propose, theoretically investigate, and numerically validate an algorithm for the Monte Carlo solution of least-squares polynomial approximation problems in a collocation framework. Our investigation is motivated by applications in the collocation approximation of parametric functions, which frequently entails construction of surrogates via orthogonal polynomials. A standard Monte Carlo approach would draw samples according to the density defining the orthogonal polynomial family. Our proposed algorithm instead samples with respect to the (weighted) pluripotential equilibrium measure of the domain, and subsequently solves a weighted least-squares problem, with weights given by evaluations of the Christoffel function. We present theoretical analysis tomore » motivate the algorithm, and numerical results that show our method is superior to standard Monte Carlo methods in many situations of interest.« less

Partitioning Pythagorean Triangles Using Pythagorean Angles

ERIC Educational Resources Information Center

Swenson, Carl E.; Yandl, Andre L.

2012-01-01

Inside any Pythagorean right triangle, it is possible to find a point M so that drawing segments from M to each vertex of the triangle yields angles whose sines and cosines are all rational. This article describes an algorithm that generates an infinite number of such points.

Nonschematic drawing recognition: a new approach based on attributed graph grammar with flexible embedding

NASA Astrophysics Data System (ADS)

Lee, Kyu J.; Kunii, T. L.; Noma, T.

1993-01-01

In this paper, we propose a syntactic pattern recognition method for non-schematic drawings, based on a new attributed graph grammar with flexible embedding. In our graph grammar, the embedding rule permits the nodes of a guest graph to be arbitrarily connected with the nodes of a host graph. The ambiguity caused by this flexible embedding is controlled with the evaluation of synthesized attributes and the check of context sensitivity. To integrate parsing with the synthesized attribute evaluation and the context sensitivity check, we also develop a bottom up parsing algorithm.

fMRI-activation during drawing a naturalistic or sketchy portrait.

PubMed

Schaer, K; Jahn, G; Lotze, M

2012-07-15

Neural processes for naturalistic drawing might be discerned into object recognition and analysis, attention processes guiding eye hand interaction, encoding of visual features in an allocentric reference frame, a transfer into the motor command and precise motor guidance with tight sensorimotor feedback. Cerebral representations in a real life paradigm during naturalistic drawing have sparsely been investigated. Using a functional Magnetic Resonance Imaging (fMRI) paradigm we measured 20 naive subjects during drawing a portrait from a frontal face presented as a photograph. Participants were asked to draw the portrait in either a naturalistic or a sketchy characteristic way. Tracing the contours of the face with a pencil or passive viewing of the face served as control conditions. Compared to passive viewing, naturalistic and sketchy drawing recruited predominantly the dorsal visual pathway, somatosensory and motor areas and bilateral BA 44. The right occipital lobe, middle temporal (MT) and the fusiform face area were increasingly active during drawing compared to passive viewing as well. Compared to tracing with a pencil, both drawing tasks increasingly involved the bilateral precuneus together with the cuneus and right inferior temporal lobe. Overall, our study identified cerebral areas characteristic for previously proposed aspects of drawing: face perception and analysis (fusiform gyrus and higher visual areas), encoding and retrieval of locations in an allocentric reference frame (precuneus), and continuous feedback processes during motor output (parietal sulcus, cerebellar hemisphere). Copyright © 2012 Elsevier B.V. All rights reserved.

From Gender-Not-an-Issue to Gender Is the Issue: The Educational and Migrational Pathways of Middle-Class Women Moving from Urban Bangladesh to Britain

ERIC Educational Resources Information Center

Mahbub, Rifat

2015-01-01

This paper explores the educational and migrational pathways which a number of middle-class women from Bangladesh took as they grew up in the 1980s and 1990s. It draws on qualitative research, conducted between July and November 2011, with highly educated Bangladeshi women who migrated to Britain in the early 2000s. French Sociologist Pierre…

VHBuild.com: A Web-Based System for Managing Knowledge in Projects.

ERIC Educational Resources Information Center

Li, Heng; Tang, Sandy; Man, K. F.; Love, Peter E. D.

2002-01-01

Describes an intelligent Web-based construction project management system called VHBuild.com which integrates project management, knowledge management, and artificial intelligence technologies. Highlights include an information flow model; time-cost optimization based on genetic algorithms; rule-based drawing interpretation; and a case-based…

Computer Drawing Method for Operating Characteristic Curve of PV Power Plant Array Unit

NASA Astrophysics Data System (ADS)

Tan, Jianbin

2018-02-01

According to the engineering design of large-scale grid-connected photovoltaic power stations and the research and development of many simulation and analysis systems, it is necessary to draw a good computer graphics of the operating characteristic curves of photovoltaic array elements and to propose a good segmentation non-linear interpolation algorithm. In the calculation method, Component performance parameters as the main design basis, the computer can get 5 PV module performances. At the same time, combined with the PV array series and parallel connection, the computer drawing of the performance curve of the PV array unit can be realized. At the same time, the specific data onto the module of PV development software can be calculated, and the good operation of PV array unit can be improved on practical application.

Analyzing milestoning networks for molecular kinetics: definitions, algorithms, and examples.

PubMed

Viswanath, Shruthi; Kreuzer, Steven M; Cardenas, Alfredo E; Elber, Ron

2013-11-07

Network representations are becoming increasingly popular for analyzing kinetic data from techniques like Milestoning, Markov State Models, and Transition Path Theory. Mapping continuous phase space trajectories into a relatively small number of discrete states helps in visualization of the data and in dissecting complex dynamics to concrete mechanisms. However, not only are molecular networks derived from molecular dynamics simulations growing in number, they are also getting increasingly complex, owing partly to the growth in computer power that allows us to generate longer and better converged trajectories. The increased complexity of the networks makes simple interpretation and qualitative insight of the molecular systems more difficult to achieve. In this paper, we focus on various network representations of kinetic data and algorithms to identify important edges and pathways in these networks. The kinetic data can be local and partial (such as the value of rate coefficients between states) or an exact solution to kinetic equations for the entire system (such as the stationary flux between vertices). In particular, we focus on the Milestoning method that provides fluxes as the main output. We proposed Global Maximum Weight Pathways as a useful tool for analyzing molecular mechanism in Milestoning networks. A closely related definition was made in the context of Transition Path Theory. We consider three algorithms to find Global Maximum Weight Pathways: Recursive Dijkstra's, Edge-Elimination, and Edge-List Bisection. The asymptotic efficiency of the algorithms is analyzed and numerical tests on finite networks show that Edge-List Bisection and Recursive Dijkstra's algorithms are most efficient for sparse and dense networks, respectively. Pathways are illustrated for two examples: helix unfolding and membrane permeation. Finally, we illustrate that networks based on local kinetic information can lead to incorrect interpretation of molecular mechanisms.

Development of an algorithm for assessing the risk to food safety posed by a new animal disease.

PubMed

Parker, E M; Jenson, I; Jordan, D; Ward, M P

2012-05-01

An algorithm was developed as a tool to rapidly assess the potential for a new or emerging disease of livestock to adversely affect humans via consumption or handling of meat product, so that the risks and uncertainties can be understood and appropriate risk management and communication implemented. An algorithm describing the sequence of events from occurrence of the disease in livestock, release of the causative agent from an infected animal, contamination of fresh meat and then possible adverse effects in humans following meat handling and consumption was created. A list of questions complements the algorithm to help the assessors address the issues of concern at each step of the decision pathway. The algorithm was refined and validated through consultation with a panel of experts and a review group of animal health and food safety policy advisors via five case studies of potential emerging diseases of cattle. Tasks for model validation included describing the path taken in the algorithm and stating an outcome. Twenty-nine per cent of the 62 experts commented on the model, and one-third of those responding also completed the tasks required for model validation. The feedback from the panel of experts and the review group was used to further develop the tool and remove redundancies and ambiguities. There was agreement in the pathways and assessments for diseases in which the causative agent was well understood (for example, bovine pneumonia due to Mycoplasma bovis). The stated pathways and assessments of other diseases (for example, bovine Johne's disease) were not as consistent. The framework helps to promote objectivity by requiring questions to be answered sequentially and providing the opportunity to record consensus or differences of opinion. Areas for discussion and future investigation are highlighted by the points of diversion on the pathway taken by different assessors. © 2011 Blackwell Verlag GmbH.

Scale of association: hierarchical linear models and the measurement of ecological systems

Treesearch

Sean M. McMahon; Jeffrey M. Diez

2007-01-01

A fundamental challenge to understanding patterns in ecological systems lies in employing methods that can analyse, test and draw inference from measured associations between variables across scales. Hierarchical linear models (HLM) use advanced estimation algorithms to measure regression relationships and variance-covariance parameters in hierarchically structured...

Data management and language enhancement for generalized set theory computer language for operation of large relational databases

NASA Technical Reports Server (NTRS)

Finley, Gail T.

1988-01-01

This report covers the study of the relational database implementation in the NASCAD computer program system. The existing system is used primarily for computer aided design. Attention is also directed to a hidden-surface algorithm for final drawing output.

Governing Software: Networks, Databases and Algorithmic Power in the Digital Governance of Public Education

ERIC Educational Resources Information Center

Williamson, Ben

2015-01-01

This article examines the emergence of "digital governance" in public education in England. Drawing on and combining concepts from software studies, policy and political studies, it identifies some specific approaches to digital governance facilitated by network-based communications and database-driven information processing software…

Hemodialysis Catheters: How to Keep Yours Working Well

MedlinePlus

... Catheters have two openings inside; one is a red (arterial) opening to draw blood from your vein and out of your body into the dialysis pathway and the other is a blue (venous) opening that allows cleaned blood to return ...

Discovering shared segments on the migration route of the bar-headed goose by time-based plane-sweeping trajectory clustering

USGS Publications Warehouse

Luo, Ze; Baoping, Yan; Takekawa, John Y.; Prosser, Diann J.

2012-01-01

We propose a new method to help ornithologists and ecologists discover shared segments on the migratory pathway of the bar-headed geese by time-based plane-sweeping trajectory clustering. We present a density-based time parameterized line segment clustering algorithm, which extends traditional comparable clustering algorithms from temporal and spatial dimensions. We present a time-based plane-sweeping trajectory clustering algorithm to reveal the dynamic evolution of spatial-temporal object clusters and discover common motion patterns of bar-headed geese in the process of migration. Experiments are performed on GPS-based satellite telemetry data from bar-headed geese and results demonstrate our algorithms can correctly discover shared segments of the bar-headed geese migratory pathway. We also present findings on the migratory behavior of bar-headed geese determined from this new analytical approach.

Algorithms on Flag Manifolds for Knowledge Discovery in N-way Arrays

DTIC Science & Technology

2015-11-20

that three of 18 subjects will become symptomatic after only 8 hours. Host pathway analysis of a human endotoxin gene expression data set revealed a 14...pathway analysis of a human endotoxin gene expression data set revealed a 14 pathway signature that identified symptomatic subjects within 2-3 hours post

Generalized binomial τ-leap method for biochemical kinetics incorporating both delay and intrinsic noise

NASA Astrophysics Data System (ADS)

Leier, André; Marquez-Lago, Tatiana T.; Burrage, Kevin

2008-05-01

The delay stochastic simulation algorithm (DSSA) by Barrio et al. [Plos Comput. Biol. 2, 117(E) (2006)] was developed to simulate delayed processes in cell biology in the presence of intrinsic noise, that is, when there are small-to-moderate numbers of certain key molecules present in a chemical reaction system. These delayed processes can faithfully represent complex interactions and mechanisms that imply a number of spatiotemporal processes often not explicitly modeled such as transcription and translation, basic in the modeling of cell signaling pathways. However, for systems with widely varying reaction rate constants or large numbers of molecules, the simulation time steps of both the stochastic simulation algorithm (SSA) and the DSSA can become very small causing considerable computational overheads. In order to overcome the limit of small step sizes, various τ-leap strategies have been suggested for improving computational performance of the SSA. In this paper, we present a binomial τ-DSSA method that extends the τ-leap idea to the delay setting and avoids drawing insufficient numbers of reactions, a common shortcoming of existing binomial τ-leap methods that becomes evident when dealing with complex chemical interactions. The resulting inaccuracies are most evident in the delayed case, even when considering reaction products as potential reactants within the same time step in which they are produced. Moreover, we extend the framework to account for multicellular systems with different degrees of intercellular communication. We apply these ideas to two important genetic regulatory models, namely, the hes1 gene, implicated as a molecular clock, and a Her1/Her 7 model for coupled oscillating cells.

B-cell Ligand Processing Pathways Detected by Large-scale Comparative Analysis

PubMed Central

Towfic, Fadi; Gupta, Shakti; Honavar, Vasant; Subramaniam, Shankar

2012-01-01

The initiation of B-cell ligand recognition is a critical step for the generation of an immune response against foreign bodies. We sought to identify the biochemical pathways involved in the B-cell ligand recognition cascade and sets of ligands that trigger similar immunological responses. We utilized several comparative approaches to analyze the gene coexpression networks generated from a set of microarray experiments spanning 33 different ligands. First, we compared the degree distributions of the generated networks. Second, we utilized a pairwise network alignment algorithm, BiNA, to align the networks based on the hubs in the networks. Third, we aligned the networks based on a set of KEGG pathways. We summarized our results by constructing a consensus hierarchy of pathways that are involved in B cell ligand recognition. The resulting pathways were further validated through literature for their common physiological responses. Collectively, the results based on our comparative analyses of degree distributions, alignment of hubs, and alignment based on KEGG pathways provide a basis for molecular characterization of the immune response states of B-cells and demonstrate the power of comparative approaches (e.g., gene coexpression network alignment algorithms) in elucidating biochemical pathways involved in complex signaling events in cells. PMID:22917187

Multi-membership gene regulation in pathway based microarray analysis

PubMed Central

2011-01-01

Background Gene expression analysis has been intensively researched for more than a decade. Recently, there has been elevated interest in the integration of microarray data analysis with other types of biological knowledge in a holistic analytical approach. We propose a methodology that can be facilitated for pathway based microarray data analysis, based on the observation that a substantial proportion of genes present in biochemical pathway databases are members of a number of distinct pathways. Our methodology aims towards establishing the state of individual pathways, by identifying those truly affected by the experimental conditions based on the behaviour of such genes. For that purpose it considers all the pathways in which a gene participates and the general census of gene expression per pathway. Results We utilise hill climbing, simulated annealing and a genetic algorithm to analyse the consistency of the produced results, through the application of fuzzy adjusted rand indexes and hamming distance. All algorithms produce highly consistent genes to pathways allocations, revealing the contribution of genes to pathway functionality, in agreement with current pathway state visualisation techniques, with the simulated annealing search proving slightly superior in terms of efficiency. Conclusions We show that the expression values of genes, which are members of a number of biochemical pathways or modules, are the net effect of the contribution of each gene to these biochemical processes. We show that by manipulating the pathway and module contribution of such genes to follow underlying trends we can interpret microarray results centred on the behaviour of these genes. PMID:21939531

Multi-membership gene regulation in pathway based microarray analysis.

PubMed

Pavlidis, Stelios P; Payne, Annette M; Swift, Stephen M

2011-09-22

Gene expression analysis has been intensively researched for more than a decade. Recently, there has been elevated interest in the integration of microarray data analysis with other types of biological knowledge in a holistic analytical approach. We propose a methodology that can be facilitated for pathway based microarray data analysis, based on the observation that a substantial proportion of genes present in biochemical pathway databases are members of a number of distinct pathways. Our methodology aims towards establishing the state of individual pathways, by identifying those truly affected by the experimental conditions based on the behaviour of such genes. For that purpose it considers all the pathways in which a gene participates and the general census of gene expression per pathway. We utilise hill climbing, simulated annealing and a genetic algorithm to analyse the consistency of the produced results, through the application of fuzzy adjusted rand indexes and hamming distance. All algorithms produce highly consistent genes to pathways allocations, revealing the contribution of genes to pathway functionality, in agreement with current pathway state visualisation techniques, with the simulated annealing search proving slightly superior in terms of efficiency. We show that the expression values of genes, which are members of a number of biochemical pathways or modules, are the net effect of the contribution of each gene to these biochemical processes. We show that by manipulating the pathway and module contribution of such genes to follow underlying trends we can interpret microarray results centred on the behaviour of these genes.

Pathway-based personalized analysis of cancer

PubMed Central

Drier, Yotam; Sheffer, Michal; Domany, Eytan

2013-01-01

We introduce Pathifier, an algorithm that infers pathway deregulation scores for each tumor sample on the basis of expression data. This score is determined, in a context-specific manner, for every particular dataset and type of cancer that is being investigated. The algorithm transforms gene-level information into pathway-level information, generating a compact and biologically relevant representation of each sample. We demonstrate the algorithm’s performance on three colorectal cancer datasets and two glioblastoma multiforme datasets and show that our multipathway-based representation is reproducible, preserves much of the original information, and allows inference of complex biologically significant information. We discovered several pathways that were significantly associated with survival of glioblastoma patients and two whose scores are predictive of survival in colorectal cancer: CXCR3-mediated signaling and oxidative phosphorylation. We also identified a subclass of proneural and neural glioblastoma with significantly better survival, and an EGF receptor-deregulated subclass of colon cancers. PMID:23547110

R-Based Software for the Integration of Pathway Data into Bioinformatic Algorithms

PubMed Central

Kramer, Frank; Bayerlová, Michaela; Beißbarth, Tim

2014-01-01

Putting new findings into the context of available literature knowledge is one approach to deal with the surge of high-throughput data results. Furthermore, prior knowledge can increase the performance and stability of bioinformatic algorithms, for example, methods for network reconstruction. In this review, we examine software packages for the statistical computing framework R, which enable the integration of pathway data for further bioinformatic analyses. Different approaches to integrate and visualize pathway data are identified and packages are stratified concerning their features according to a number of different aspects: data import strategies, the extent of available data, dependencies on external tools, integration with further analysis steps and visualization options are considered. A total of 12 packages integrating pathway data are reviewed in this manuscript. These are supplemented by five R-specific packages for visualization and six connector packages, which provide access to external tools. PMID:24833336

Time scale bridging in atomistic simulation of slow dynamics: viscous relaxation and defect activation

NASA Astrophysics Data System (ADS)

Kushima, A.; Eapen, J.; Li, Ju; Yip, S.; Zhu, T.

2011-08-01

Atomistic simulation methods are known for timescale limitations in resolving slow dynamical processes. Two well-known scenarios of slow dynamics are viscous relaxation in supercooled liquids and creep deformation in stressed solids. In both phenomena the challenge to theory and simulation is to sample the transition state pathways efficiently and follow the dynamical processes on long timescales. We present a perspective based on the biased molecular simulation methods such as metadynamics, autonomous basin climbing (ABC), strain-boost and adaptive boost simulations. Such algorithms can enable an atomic-level explanation of the temperature variation of the shear viscosity of glassy liquids, and the relaxation behavior in solids undergoing creep deformation. By discussing the dynamics of slow relaxation in two quite different areas of condensed matter science, we hope to draw attention to other complex problems where anthropological or geological-scale time behavior can be simulated at atomic resolution and understood in terms of micro-scale processes of molecular rearrangements and collective interactions. As examples of a class of phenomena that can be broadly classified as materials ageing, we point to stress corrosion cracking and cement setting as opportunities for atomistic modeling and simulations.

Research and Development for Technology Evolution Potential Forecasting System

NASA Astrophysics Data System (ADS)

Gao, Changqing; Cao, Shukun; Wang, Yuzeng; Ai, Changsheng; Ze, Xiangbo

Technology forecasting is a powerful weapon for many enterprises to gain an animate future. Evolutionary potential radar plot is a necessary step of some valuable methods to help the technology managers with right technical strategy. A software system for Technology Evolution Potential Forecasting (TEPF) with automatic radar plot drawing is introduced in this paper. The framework of the system and the date structure describing the concrete evolution pattern are illustrated in details. And the algorithm for radar plot drawing is researched. It is proved that the TEPF system is an effective tool during the technology strategy analyzing process with a referenced case study.

minepath.org: a free interactive pathway analysis web server.

PubMed

Koumakis, Lefteris; Roussos, Panos; Potamias, George

2017-07-03

( www.minepath.org ) is a web-based platform that elaborates on, and radically extends the identification of differentially expressed sub-paths in molecular pathways. Besides the network topology, the underlying MinePath algorithmic processes exploit exact gene-gene molecular relationships (e.g. activation, inhibition) and are able to identify differentially expressed pathway parts. Each pathway is decomposed into all its constituent sub-paths, which in turn are matched with corresponding gene expression profiles. The highly ranked, and phenotype inclined sub-paths are kept. Apart from the pathway analysis algorithm, the fundamental innovation of the MinePath web-server concerns its advanced visualization and interactive capabilities. To our knowledge, this is the first pathway analysis server that introduces and offers visualization of the underlying and active pathway regulatory mechanisms instead of genes. Other features include live interaction, immediate visualization of functional sub-paths per phenotype and dynamic linked annotations for the engaged genes and molecular relations. The user can download not only the results but also the corresponding web viewer framework of the performed analysis. This feature provides the flexibility to immediately publish results without publishing source/expression data, and get all the functionality of a web based pathway analysis viewer. © The Author(s) 2017. Published by Oxford University Press on behalf of Nucleic Acids Research.

The visual display of regulatory information and networks.

PubMed

Pirson, I; Fortemaison, N; Jacobs, C; Dremier, S; Dumont, J E; Maenhaut, C

2000-10-01

Cell regulation and signal transduction are becoming increasingly complex, with reports of new cross-signalling, feedback, and feedforward regulations between pathways and between the multiple isozymes discovered at each step of these pathways. However, this information, which requires pages of text for its description, can be summarized in very simple schemes, although there is no consensus on the drawing of such schemes. This article presents a simple set of rules that allows a lot of information to be inserted in easily understandable displays.

Inaccuracy of Wolff-Parkinson-white accessory pathway localization algorithms in children and patients with congenital heart defects.

PubMed

Bar-Cohen, Yaniv; Khairy, Paul; Morwood, James; Alexander, Mark E; Cecchin, Frank; Berul, Charles I

2006-07-01

ECG algorithms used to localize accessory pathways (AP) in patients with Wolff-Parkinson-White (WPW) syndrome have been validated in adults, but less is known of their use in children, especially in patients with congenital heart disease (CHD). We hypothesize that these algorithms have low diagnostic accuracy in children and even lower in those with CHD. Pre-excited ECGs in 43 patients with WPW and CHD (median age 5.4 years [0.9-32 years]) were evaluated and compared to 43 consecutive WPW control patients without CHD (median age 14.5 years [1.8-18 years]). Two blinded observers predicted AP location using 2 adult and 1 pediatric WPW algorithms, and a third blinded observer served as a tiebreaker. Predicted locations were compared with ablation-verified AP location to identify (a) exact match for AP location and (b) match for laterality (left-sided vs right-sided AP). In control children, adult algorithms were accurate in only 56% and 60%, while the pediatric algorithm was correct in 77%. In 19 patients with Ebstein's anomaly, diagnostic accuracy was similar to controls with at times an even better ability to predict laterality. In non-Ebstein's CHD, however, the algorithms were markedly worse (29% for the adult algorithms and 42% for the pediatric algorithms). A relatively large degree of interobserver variability was seen (kappa values from 0.30 to 0.58). Adult localization algorithms have poor diagnostic accuracy in young patients with and without CHD. Both adult and pediatric algorithms are particularly misleading in non-Ebstein's CHD patients and should be interpreted with caution.

Integrating disease management and wound care critical pathways in home care.

PubMed

Barr, J E

1999-10-01

This article discusses the need for an integration of the concepts of disease management and critical pathways as a foundation of a healthcare delivery system. The steps in the process for development, implementation, and evaluation of a wound care critical pathway are reviewed and variance classifications are defined. Co-pathways and algorithms are presented as methodologies for dealing with variances. A template of a wound care critical pathway that has been developed for use in the home care setting is included.

Possible pathways used to predict different stages of lung adenocarcinoma.

PubMed

Chen, Xiaodong; Duan, Qiongyu; Xuan, Ying; Sun, Yunan; Wu, Rong

2017-04-01

We aimed to find some specific pathways that can be used to predict the stage of lung adenocarcinoma.RNA-Seq expression profile data and clinical data of lung adenocarcinoma (stage I [37], stage II 161], stage III [75], and stage IV [45]) were obtained from the TCGA dataset. The differentially expressed genes were merged, correlation coefficient matrix between genes was constructed with correlation analysis, and unsupervised clustering was carried out with hierarchical clustering method. The specific coexpression network in every stage was constructed with cytoscape software. Kyoto Encyclopedia of Genes and Genomes pathway enrichment analysis was performed with KOBAS database and Fisher exact test. Euclidean distance algorithm was used to calculate total deviation score. The diagnostic model was constructed with SVM algorithm.Eighteen specific genes were obtained by getting intersection of 4 group differentially expressed genes. Ten significantly enriched pathways were obtained. In the distribution map of 10 pathways score in different groups, degrees that sample groups deviated from the normal level were as follows: stage I < stage II < stage III < stage IV. The pathway score of 4 stages exhibited linear change in some pathways, and the score of 1 or 2 stages were significantly different from the rest stages in some pathways. There was significant difference between dead and alive for these pathways except thyroid hormone signaling pathway.Those 10 pathways are associated with the development of lung adenocarcinoma and may be able to predict different stages of it. Furthermore, these pathways except thyroid hormone signaling pathway may be able to predict the prognosis.

A note on bound constraints handling for the IEEE CEC'05 benchmark function suite.

PubMed

Liao, Tianjun; Molina, Daniel; de Oca, Marco A Montes; Stützle, Thomas

2014-01-01

The benchmark functions and some of the algorithms proposed for the special session on real parameter optimization of the 2005 IEEE Congress on Evolutionary Computation (CEC'05) have played and still play an important role in the assessment of the state of the art in continuous optimization. In this article, we show that if bound constraints are not enforced for the final reported solutions, state-of-the-art algorithms produce infeasible best candidate solutions for the majority of functions of the IEEE CEC'05 benchmark function suite. This occurs even though the optima of the CEC'05 functions are within the specified bounds. This phenomenon has important implications on algorithm comparisons, and therefore on algorithm designs. This article's goal is to draw the attention of the community to the fact that some authors might have drawn wrong conclusions from experiments using the CEC'05 problems.

Sociocritical Matters: Migrant Students' College Access

ERIC Educational Resources Information Center

Núñez, Anne-Marie; Gildersleeve, Ryan Evely

2016-01-01

Migrant students face many educational, economic, social, and cultural challenges to college access. Anti-bilingual, anti-affirmative action, and anti-immigrant policies also constrain their postsecondary pathways. With these issues in mind, this article draws on quantitative and qualitative research to examine the influence of a residential…

Deriving pathway maps from automated text analysis using a grammar-based approach.

PubMed

Olsson, Björn; Gawronska, Barbara; Erlendsson, Björn

2006-04-01

We demonstrate how automated text analysis can be used to support the large-scale analysis of metabolic and regulatory pathways by deriving pathway maps from textual descriptions found in the scientific literature. The main assumption is that correct syntactic analysis combined with domain-specific heuristics provides a good basis for relation extraction. Our method uses an algorithm that searches through the syntactic trees produced by a parser based on a Referent Grammar formalism, identifies relations mentioned in the sentence, and classifies them with respect to their semantic class and epistemic status (facts, counterfactuals, hypotheses). The semantic categories used in the classification are based on the relation set used in KEGG (Kyoto Encyclopedia of Genes and Genomes), so that pathway maps using KEGG notation can be automatically generated. We present the current version of the relation extraction algorithm and an evaluation based on a corpus of abstracts obtained from PubMed. The results indicate that the method is able to combine a reasonable coverage with high accuracy. We found that 61% of all sentences were parsed, and 97% of the parse trees were judged to be correct. The extraction algorithm was tested on a sample of 300 parse trees and was found to produce correct extractions in 90.5% of the cases.

Cancer Transcriptome Dataset Analysis: Comparing Methods of Pathway and Gene Regulatory Network-Based Cluster Identification.

PubMed

Nam, Seungyoon

2017-04-01

Cancer transcriptome analysis is one of the leading areas of Big Data science, biomarker, and pharmaceutical discovery, not to forget personalized medicine. Yet, cancer transcriptomics and postgenomic medicine require innovation in bioinformatics as well as comparison of the performance of available algorithms. In this data analytics context, the value of network generation and algorithms has been widely underscored for addressing the salient questions in cancer pathogenesis. Analysis of cancer trancriptome often results in complicated networks where identification of network modularity remains critical, for example, in delineating the "druggable" molecular targets. Network clustering is useful, but depends on the network topology in and of itself. Notably, the performance of different network-generating tools for network cluster (NC) identification has been little investigated to date. Hence, using gastric cancer (GC) transcriptomic datasets, we compared two algorithms for generating pathway versus gene regulatory network-based NCs, showing that the pathway-based approach better agrees with a reference set of cancer-functional contexts. Finally, by applying pathway-based NC identification to GC transcriptome datasets, we describe cancer NCs that associate with candidate therapeutic targets and biomarkers in GC. These observations collectively inform future research on cancer transcriptomics, drug discovery, and rational development of new analysis tools for optimal harnessing of omics data.

Escher: A Web Application for Building, Sharing, and Embedding Data-Rich Visualizations of Biological Pathways

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

King, Zachary A.; Drager, Andreas; Ebrahim, Ali

Escher is a web application for visualizing data on biological pathways. Three key features make Escher a uniquely effective tool for pathway visualization. First, users can rapidly design new pathway maps. Escher provides pathway suggestions based on user data and genome-scale models, so users can draw pathways in a semi-automated way. Second, users can visualize data related to genes or proteins on the associated reactions and pathways, using rules that define which enzymes catalyze each reaction. Thus, users can identify trends in common genomic data types (e.g. RNA-Seq, proteomics, ChIP)—in conjunction with metabolite- and reaction-oriented data types (e.g. metabolomics, fluxomics).more » Third, Escher harnesses the strengths of web technologies (SVG, D3, developer tools) so that visualizations can be rapidly adapted, extended, shared, and embedded. This paper provides examples of each of these features and explains how the development approach used for Escher can be used to guide the development of future visualization tools.« less

Escher: A Web Application for Building, Sharing, and Embedding Data-Rich Visualizations of Biological Pathways

PubMed Central

King, Zachary A.; Dräger, Andreas; Ebrahim, Ali; Sonnenschein, Nikolaus; Lewis, Nathan E.; Palsson, Bernhard O.

2015-01-01

Escher is a web application for visualizing data on biological pathways. Three key features make Escher a uniquely effective tool for pathway visualization. First, users can rapidly design new pathway maps. Escher provides pathway suggestions based on user data and genome-scale models, so users can draw pathways in a semi-automated way. Second, users can visualize data related to genes or proteins on the associated reactions and pathways, using rules that define which enzymes catalyze each reaction. Thus, users can identify trends in common genomic data types (e.g. RNA-Seq, proteomics, ChIP)—in conjunction with metabolite- and reaction-oriented data types (e.g. metabolomics, fluxomics). Third, Escher harnesses the strengths of web technologies (SVG, D3, developer tools) so that visualizations can be rapidly adapted, extended, shared, and embedded. This paper provides examples of each of these features and explains how the development approach used for Escher can be used to guide the development of future visualization tools. PMID:26313928

Escher: A Web Application for Building, Sharing, and Embedding Data-Rich Visualizations of Biological Pathways

DOE PAGES

King, Zachary A.; Drager, Andreas; Ebrahim, Ali; ...

2015-08-27

Escher is a web application for visualizing data on biological pathways. Three key features make Escher a uniquely effective tool for pathway visualization. First, users can rapidly design new pathway maps. Escher provides pathway suggestions based on user data and genome-scale models, so users can draw pathways in a semi-automated way. Second, users can visualize data related to genes or proteins on the associated reactions and pathways, using rules that define which enzymes catalyze each reaction. Thus, users can identify trends in common genomic data types (e.g. RNA-Seq, proteomics, ChIP)—in conjunction with metabolite- and reaction-oriented data types (e.g. metabolomics, fluxomics).more » Third, Escher harnesses the strengths of web technologies (SVG, D3, developer tools) so that visualizations can be rapidly adapted, extended, shared, and embedded. This paper provides examples of each of these features and explains how the development approach used for Escher can be used to guide the development of future visualization tools.« less

Optimized feature-detection for on-board vision-based surveillance

NASA Astrophysics Data System (ADS)

Gond, Laetitia; Monnin, David; Schneider, Armin

2012-06-01

The detection and matching of robust features in images is an important step in many computer vision applications. In this paper, the importance of the keypoint detection algorithms and their inherent parameters in the particular context of an image-based change detection system for IED detection is studied. Through extensive application-oriented experiments, we draw an evaluation and comparison of the most popular feature detectors proposed by the computer vision community. We analyze how to automatically adjust these algorithms to changing imaging conditions and suggest improvements in order to achieve more exibility and robustness in their practical implementation.

Application of Approximate Pattern Matching in Two Dimensional Spaces to Grid Layout for Biochemical Network Maps

PubMed Central

Inoue, Kentaro; Shimozono, Shinichi; Yoshida, Hideaki; Kurata, Hiroyuki

2012-01-01

Background For visualizing large-scale biochemical network maps, it is important to calculate the coordinates of molecular nodes quickly and to enhance the understanding or traceability of them. The grid layout is effective in drawing compact, orderly, balanced network maps with node label spaces, but existing grid layout algorithms often require a high computational cost because they have to consider complicated positional constraints through the entire optimization process. Results We propose a hybrid grid layout algorithm that consists of a non-grid, fast layout (preprocessor) algorithm and an approximate pattern matching algorithm that distributes the resultant preprocessed nodes on square grid points. To demonstrate the feasibility of the hybrid layout algorithm, it is characterized in terms of the calculation time, numbers of edge-edge and node-edge crossings, relative edge lengths, and F-measures. The proposed algorithm achieves outstanding performances compared with other existing grid layouts. Conclusions Use of an approximate pattern matching algorithm quickly redistributes the laid-out nodes by fast, non-grid algorithms on the square grid points, while preserving the topological relationships among the nodes. The proposed algorithm is a novel use of the pattern matching, thereby providing a breakthrough for grid layout. This application program can be freely downloaded from http://www.cadlive.jp/hybridlayout/hybridlayout.html. PMID:22679486

Application of approximate pattern matching in two dimensional spaces to grid layout for biochemical network maps.

PubMed

Inoue, Kentaro; Shimozono, Shinichi; Yoshida, Hideaki; Kurata, Hiroyuki

2012-01-01

For visualizing large-scale biochemical network maps, it is important to calculate the coordinates of molecular nodes quickly and to enhance the understanding or traceability of them. The grid layout is effective in drawing compact, orderly, balanced network maps with node label spaces, but existing grid layout algorithms often require a high computational cost because they have to consider complicated positional constraints through the entire optimization process. We propose a hybrid grid layout algorithm that consists of a non-grid, fast layout (preprocessor) algorithm and an approximate pattern matching algorithm that distributes the resultant preprocessed nodes on square grid points. To demonstrate the feasibility of the hybrid layout algorithm, it is characterized in terms of the calculation time, numbers of edge-edge and node-edge crossings, relative edge lengths, and F-measures. The proposed algorithm achieves outstanding performances compared with other existing grid layouts. Use of an approximate pattern matching algorithm quickly redistributes the laid-out nodes by fast, non-grid algorithms on the square grid points, while preserving the topological relationships among the nodes. The proposed algorithm is a novel use of the pattern matching, thereby providing a breakthrough for grid layout. This application program can be freely downloaded from http://www.cadlive.jp/hybridlayout/hybridlayout.html.

Prediction of novel synthetic pathways for the production of desired chemicals.

PubMed

Cho, Ayoun; Yun, Hongseok; Park, Jin Hwan; Lee, Sang Yup; Park, Sunwon

2010-03-28

There have been several methods developed for the prediction of synthetic metabolic pathways leading to the production of desired chemicals. In these approaches, novel pathways were predicted based on chemical structure changes, enzymatic information, and/or reaction mechanisms, but the approaches generating a huge number of predicted results are difficult to be applied to real experiments. Also, some of these methods focus on specific pathways, and thus are limited to expansion to the whole metabolism. In the present study, we propose a system framework employing a retrosynthesis model with a prioritization scoring algorithm. This new strategy allows deducing the novel promising pathways for the synthesis of a desired chemical together with information on enzymes involved based on structural changes and reaction mechanisms present in the system database. The prioritization scoring algorithm employing Tanimoto coefficient and group contribution method allows examination of structurally qualified pathways to recognize which pathway is more appropriate. In addition, new concepts of binding site covalence, estimation of pathway distance and organism specificity were taken into account to identify the best synthetic pathway. Parameters of these factors can be evolutionarily optimized when a newly proven synthetic pathway is registered. As the proofs of concept, the novel synthetic pathways for the production of isobutanol, 3-hydroxypropionate, and butyryl-CoA were predicted. The prediction shows a high reliability, in which experimentally verified synthetic pathways were listed within the top 0.089% of the identified pathway candidates. It is expected that the system framework developed in this study would be useful for the in silico design of novel metabolic pathways to be employed for the efficient production of chemicals, fuels and materials.

The Roles of Photography for Developing Literacy across the Disciplines

ERIC Educational Resources Information Center

Cappello, Marva; Lafferty, Karen E.

2015-01-01

Teachers can capitalize on the overwhelmingly visual nature of contemporary society for learning and teaching through integrating photography in their classroom instruction. In offering an alternative pathway for acquiring and expressing knowledge, photography has the potential to strengthen instruction across disciplines by drawing on multiple…

The emerging relevance of the gut microbiome in cardiometabolic health

USDA-ARS?s Scientific Manuscript database

Host metabolic pathways and physiological responses are regulated by signals linking the host to the gut microbial community or microbiome. Here, we draw a spotlight on lipid and bile acid metabolism and inflammatory response as they pertain to cardiometabolic dysfunction. Gut microbial dysbiosis al...

How the Arts Develop the Young Brain

ERIC Educational Resources Information Center

Sousa, David A.

2006-01-01

The arts play an important role in human development, enhancing the growth of cognitive, emotional, and psychomotor pathways. Neuroscience research reveals the impressive impact of arts instruction, such as, music, drawing and physical activity, on students' cognitive, social and emotional development. Much of what young children do as…

Non Linear Programming (NLP) Formulation for Quantitative Modeling of Protein Signal Transduction Pathways

PubMed Central

Morris, Melody K.; Saez-Rodriguez, Julio; Lauffenburger, Douglas A.; Alexopoulos, Leonidas G.

2012-01-01

Modeling of signal transduction pathways plays a major role in understanding cells' function and predicting cellular response. Mathematical formalisms based on a logic formalism are relatively simple but can describe how signals propagate from one protein to the next and have led to the construction of models that simulate the cells response to environmental or other perturbations. Constrained fuzzy logic was recently introduced to train models to cell specific data to result in quantitative pathway models of the specific cellular behavior. There are two major issues in this pathway optimization: i) excessive CPU time requirements and ii) loosely constrained optimization problem due to lack of data with respect to large signaling pathways. Herein, we address both issues: the former by reformulating the pathway optimization as a regular nonlinear optimization problem; and the latter by enhanced algorithms to pre/post-process the signaling network to remove parts that cannot be identified given the experimental conditions. As a case study, we tackle the construction of cell type specific pathways in normal and transformed hepatocytes using medium and large-scale functional phosphoproteomic datasets. The proposed Non Linear Programming (NLP) formulation allows for fast optimization of signaling topologies by combining the versatile nature of logic modeling with state of the art optimization algorithms. PMID:23226239

Pathway-Based Kernel Boosting for the Analysis of Genome-Wide Association Studies

PubMed Central

Manitz, Juliane; Burger, Patricia; Amos, Christopher I.; Chang-Claude, Jenny; Wichmann, Heinz-Erich; Kneib, Thomas; Bickeböller, Heike

2017-01-01

The analysis of genome-wide association studies (GWAS) benefits from the investigation of biologically meaningful gene sets, such as gene-interaction networks (pathways). We propose an extension to a successful kernel-based pathway analysis approach by integrating kernel functions into a powerful algorithmic framework for variable selection, to enable investigation of multiple pathways simultaneously. We employ genetic similarity kernels from the logistic kernel machine test (LKMT) as base-learners in a boosting algorithm. A model to explain case-control status is created iteratively by selecting pathways that improve its prediction ability. We evaluated our method in simulation studies adopting 50 pathways for different sample sizes and genetic effect strengths. Additionally, we included an exemplary application of kernel boosting to a rheumatoid arthritis and a lung cancer dataset. Simulations indicate that kernel boosting outperforms the LKMT in certain genetic scenarios. Applications to GWAS data on rheumatoid arthritis and lung cancer resulted in sparse models which were based on pathways interpretable in a clinical sense. Kernel boosting is highly flexible in terms of considered variables and overcomes the problem of multiple testing. Additionally, it enables the prediction of clinical outcomes. Thus, kernel boosting constitutes a new, powerful tool in the analysis of GWAS data and towards the understanding of biological processes involved in disease susceptibility. PMID:28785300

Pathway-Based Kernel Boosting for the Analysis of Genome-Wide Association Studies.

PubMed

Friedrichs, Stefanie; Manitz, Juliane; Burger, Patricia; Amos, Christopher I; Risch, Angela; Chang-Claude, Jenny; Wichmann, Heinz-Erich; Kneib, Thomas; Bickeböller, Heike; Hofner, Benjamin

2017-01-01

The analysis of genome-wide association studies (GWAS) benefits from the investigation of biologically meaningful gene sets, such as gene-interaction networks (pathways). We propose an extension to a successful kernel-based pathway analysis approach by integrating kernel functions into a powerful algorithmic framework for variable selection, to enable investigation of multiple pathways simultaneously. We employ genetic similarity kernels from the logistic kernel machine test (LKMT) as base-learners in a boosting algorithm. A model to explain case-control status is created iteratively by selecting pathways that improve its prediction ability. We evaluated our method in simulation studies adopting 50 pathways for different sample sizes and genetic effect strengths. Additionally, we included an exemplary application of kernel boosting to a rheumatoid arthritis and a lung cancer dataset. Simulations indicate that kernel boosting outperforms the LKMT in certain genetic scenarios. Applications to GWAS data on rheumatoid arthritis and lung cancer resulted in sparse models which were based on pathways interpretable in a clinical sense. Kernel boosting is highly flexible in terms of considered variables and overcomes the problem of multiple testing. Additionally, it enables the prediction of clinical outcomes. Thus, kernel boosting constitutes a new, powerful tool in the analysis of GWAS data and towards the understanding of biological processes involved in disease susceptibility.

Non Linear Programming (NLP) formulation for quantitative modeling of protein signal transduction pathways.

PubMed

Mitsos, Alexander; Melas, Ioannis N; Morris, Melody K; Saez-Rodriguez, Julio; Lauffenburger, Douglas A; Alexopoulos, Leonidas G

2012-01-01

Modeling of signal transduction pathways plays a major role in understanding cells' function and predicting cellular response. Mathematical formalisms based on a logic formalism are relatively simple but can describe how signals propagate from one protein to the next and have led to the construction of models that simulate the cells response to environmental or other perturbations. Constrained fuzzy logic was recently introduced to train models to cell specific data to result in quantitative pathway models of the specific cellular behavior. There are two major issues in this pathway optimization: i) excessive CPU time requirements and ii) loosely constrained optimization problem due to lack of data with respect to large signaling pathways. Herein, we address both issues: the former by reformulating the pathway optimization as a regular nonlinear optimization problem; and the latter by enhanced algorithms to pre/post-process the signaling network to remove parts that cannot be identified given the experimental conditions. As a case study, we tackle the construction of cell type specific pathways in normal and transformed hepatocytes using medium and large-scale functional phosphoproteomic datasets. The proposed Non Linear Programming (NLP) formulation allows for fast optimization of signaling topologies by combining the versatile nature of logic modeling with state of the art optimization algorithms.

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.

The Person Behind the Picture: Influence of Social and Cultural Capital on Geoscience Career Pathways

NASA Astrophysics Data System (ADS)

Rappolee, E.; Libarkin, J. C.; McCallum, C.; Kurz, S.

2017-12-01

The amalgamation of fields in the geosciences share one desire: a better understanding of the natural world and the relationship humans have with that world. As issues such as climate change and clean water become globally recognized the geoscience job market grows. To insure these issues are resolved in ways that are fully representative of the entire human population, attention has been turned to increasing diversity of scientists in the geosciences. This study is based in the theory of social and cultural capital, types of non-financial wealth obtained by individuals and groups through connections and experiences. In particular, we investigated how individuals accessed specific resources and opportunities which eventually led to their entering the geosciences. Surveys were distributed to volunteers at a multinational geoscience conference held in fall of 2016. These surveys asked participants to "draw a picture of the people and experiences that have influenced your career up to this point." Nearly 150 completed drawings were coded through a thematic content analysis, wherein salient characteristics of drawings were documented and later grouped into common themes. We found that specific people (family, professors, peers) provided access to resources (education, museums, parks) as well as experiences (camping, traveling, research) that were instrumental in career building. Correlation analysis revealed two representative models of the drawings. These models aligned with the constructs of social and cultural capital. Cultural capital was more prevalent in majority white than nonwhite participants, suggesting different pathways into geoscience careers. We hope this research will inspire future work as well as highlight ways in which social and cultural capital can become accessible to future generations to produce a system with equal opportunities and increase diversity in the geosciences, resulting in better decision-making on global issues.

Algorithms for optimizing the treatment of depression: making the right decision at the right time.

PubMed

Adli, M; Rush, A J; Möller, H-J; Bauer, M

2003-11-01

Medication algorithms for the treatment of depression are designed to optimize both treatment implementation and the appropriateness of treatment strategies. Thus, they are essential tools for treating and avoiding refractory depression. Treatment algorithms are explicit treatment protocols that provide specific therapeutic pathways and decision-making tools at critical decision points throughout the treatment process. The present article provides an overview of major projects of algorithm research in the field of antidepressant therapy. The Berlin Algorithm Project and the Texas Medication Algorithm Project (TMAP) compare algorithm-guided treatments with treatment as usual. The Sequenced Treatment Alternatives to Relieve Depression Project (STAR*D) compares different treatment strategies in treatment-resistant patients.

Inferring genetic interactions via a nonlinear model and an optimization algorithm.

PubMed

Chen, Chung-Ming; Lee, Chih; Chuang, Cheng-Long; Wang, Chia-Chang; Shieh, Grace S

2010-02-26

Biochemical pathways are gradually becoming recognized as central to complex human diseases and recently genetic/transcriptional interactions have been shown to be able to predict partial pathways. With the abundant information made available by microarray gene expression data (MGED), nonlinear modeling of these interactions is now feasible. Two of the latest advances in nonlinear modeling used sigmoid models to depict transcriptional interaction of a transcription factor (TF) for a target gene, but do not model cooperative or competitive interactions of several TFs for a target. An S-shape model and an optimization algorithm (GASA) were developed to infer genetic interactions/transcriptional regulation of several genes simultaneously using MGED. GASA consists of a genetic algorithm (GA) and a simulated annealing (SA) algorithm, which is enhanced by a steepest gradient descent algorithm to avoid being trapped in local minimum. Using simulated data with various degrees of noise, we studied how GASA with two model selection criteria and two search spaces performed. Furthermore, GASA was shown to outperform network component analysis, the time series network inference algorithm (TSNI), GA with regular GA (GAGA) and GA with regular SA. Two applications are demonstrated. First, GASA is applied to infer a subnetwork of human T-cell apoptosis. Several of the predicted interactions are supported by the literature. Second, GASA was applied to infer the transcriptional factors of 34 cell cycle regulated targets in S. cerevisiae, and GASA performed better than one of the latest advances in nonlinear modeling, GAGA and TSNI. Moreover, GASA is able to predict multiple transcription factors for certain targets, and these results coincide with experiments confirmed data in YEASTRACT. GASA is shown to infer both genetic interactions and transcriptional regulatory interactions well. In particular, GASA seems able to characterize the nonlinear mechanism of transcriptional regulatory interactions (TIs) in yeast, and may be applied to infer TIs in other organisms. The predicted genetic interactions of a subnetwork of human T-cell apoptosis coincide with existing partial pathways, suggesting the potential of GASA on inferring biochemical pathways.

Combining Natural Language Processing and Statistical Text Mining: A Study of Specialized versus Common Languages

ERIC Educational Resources Information Center

Jarman, Jay

2011-01-01

This dissertation focuses on developing and evaluating hybrid approaches for analyzing free-form text in the medical domain. This research draws on natural language processing (NLP) techniques that are used to parse and extract concepts based on a controlled vocabulary. Once important concepts are extracted, additional machine learning algorithms,…

Reflections on High School Students' Graphing Skills and Their Conceptual Understanding of Drawing Chemistry Graphs

ERIC Educational Resources Information Center

Gültepe, Nejla

2016-01-01

Graphing subjects in chemistry has been used to provide alternatives to verbal and algorithmic descriptions of a subject by handing students another way of improving their manipulation of concepts. Teachers should therefore know the level of students' graphing skills. Studies have identified that students have difficulty making connections with…

Co-Authoring Gender-Queer Youth Identities: Discursive "Tellings" and "Retellings"

ERIC Educational Resources Information Center

Saltzburg, Susan; Davis, Tamara S.

2010-01-01

For youth who challenge the culturally fixed gender dichotomy through nonconventional gender expression, societal reaction can be harsh. Uncovering these youth voices as they pioneer new gender frontiers through pathways of language and social dialogue provides the focus for this manuscript. Drawing from discursive, narrative practices, we sat in…

Beyond Superheroes and Advocacy: The Pathway of Teacher Leadership Development

ERIC Educational Resources Information Center

Smylie, Mark A.; Eckert, Jonathan

2018-01-01

This article introduces new perspectives, principles and recommendations for the successful development of teacher leadership. It draws from literature on teacher leadership, work redesign, and on-the-job leadership development to explore key insights and questions for teacher leadership development, and it presents a conceptual-theoretical model…

Using Biological-Control Research in the Classroom to Promote Scientific Inquiry & Literacy

ERIC Educational Resources Information Center

Richardson, Matthew L.; Richardson, Scott L.; Hall, David G.

2012-01-01

Scientists researching biological control should engage in education because translating research programs into classroom activities is a pathway to increase scientific literacy among students. Classroom activities focused on biological control target all levels of biological organization and can be cross-disciplinary by drawing from subject areas…

Mathematical Description and Mechanistic Reasoning: A Pathway toward STEM Integration

ERIC Educational Resources Information Center

Weinberg, Paul J.

2017-01-01

Because reasoning about mechanism is critical to disciplined inquiry in science, technology, engineering, and mathematics (STEM) domains, this study focuses on ways to support the development of this form of reasoning. This study attends to how mechanistic reasoning is constituted through mathematical description. This study draws upon Smith's…

Second Chance Education: Barriers, Supports and Engagement Strategies

ERIC Educational Resources Information Center

Savelsberg, Harry; Pignata, Silvia; Weckert, Pauline

2017-01-01

Second chance education programs are now a well-established presence in institutions seeking to provide access and equity pathways for socio-economically disadvantaged groups. This paper focusses on the strategies used to support positive engagement in second chance equity programs, drawing upon evaluation research data from four TAFE sponsored…

Creative Work Careers: Pathways and Portfolios for the Creative Economy

ERIC Educational Resources Information Center

Ashton, Daniel

2015-01-01

This article examines the career opportunities, challenges and trajectories of creative work. As part of the Creative Trident approach to creative workforce measurements, the embedded mode draws attention to creative work as it is undertaken outside of the creative industries. This article further considers and conceptualises the complex careers…

Memory, Space and Time: Researching Children's Lives

ERIC Educational Resources Information Center

Moss, Dorothy

2010-01-01

This article discusses the research approach in "Pathways through Childhood", a small qualitative study drawing on memories of childhood. The research explores how wider social arrangements and social change influence children's everyday lives. The article discusses the way that the concepts of social memory, space and time have been…

Children of Misfortune: Early Adversity and Cumulative Inequality in Perceived Life Trajectories1

PubMed Central

Schafer, Markus H.; Ferraro, Kenneth F.; Mustillo, Sarah A.

2011-01-01

Adversity early in life may alter pathways of aging, but what interpretive processes can soften the blow of early insults? Drawing from cumulative inequality theory, the authors analyze trajectories of life evaluations and then consider whether early adversity offsets favorable expectations for the future. Results reveal that early adversity contributes to more negative views of the past but rising expectations for the future. Early adversity also has enduring effects on life evaluations, offsetting the influence of buoyant expectations. The findings draw attention to the limits of human agency under the constraints of early adversity—a process described as biographical structuration. PMID:21648247

System for line drawings interpretation

NASA Astrophysics Data System (ADS)

Boatto, L.; Consorti, Vincenzo; Del Buono, Monica; Eramo, Vincenzo; Esposito, Alessandra; Melcarne, F.; Meucci, Mario; Mosciatti, M.; Tucci, M.; Morelli, Arturo

1992-08-01

This paper describes an automatic system that extracts information from line drawings, in order to feed CAD or GIS systems. The line drawings that we analyze contain interconnected thin lines, dashed lines, text, and symbols. Characters and symbols may overlap with lines. Our approach is based on the properties of the run representation of a binary image that allow giving the image a graph structure. Using this graph structure, several algorithms have been designed to identify, directly in the raster image, straight segments, dashed lines, text, symbols, hatching lines, etc. Straight segments and dashed lines are converted into vectors, with high accuracy and good noise immunity. Characters and symbols are recognized by means of a recognizer, specifically developed for this application, designed to be insensitive to rotation and scaling. Subsequent processing steps include an `intelligent'' search through the graph in order to detect closed polygons, dashed lines, text strings, and other higher-level logical entities, followed by the identification of relationships (adjacency, inclusion, etc.) between them. Relationships are further translated into a formal description of the drawing. The output of the system can be used as input to a Geographic Information System package. The system is currently used by the Italian Land Register Authority to process cadastral maps.

P-Finder: Reconstruction of Signaling Networks from Protein-Protein Interactions and GO Annotations.

PubMed

Young-Rae Cho; Yanan Xin; Speegle, Greg

2015-01-01

Because most complex genetic diseases are caused by defects of cell signaling, illuminating a signaling cascade is essential for understanding their mechanisms. We present three novel computational algorithms to reconstruct signaling networks between a starting protein and an ending protein using genome-wide protein-protein interaction (PPI) networks and gene ontology (GO) annotation data. A signaling network is represented as a directed acyclic graph in a merged form of multiple linear pathways. An advanced semantic similarity metric is applied for weighting PPIs as the preprocessing of all three methods. The first algorithm repeatedly extends the list of nodes based on path frequency towards an ending protein. The second algorithm repeatedly appends edges based on the occurrence of network motifs which indicate the link patterns more frequently appearing in a PPI network than in a random graph. The last algorithm uses the information propagation technique which iteratively updates edge orientations based on the path strength and merges the selected directed edges. Our experimental results demonstrate that the proposed algorithms achieve higher accuracy than previous methods when they are tested on well-studied pathways of S. cerevisiae. Furthermore, we introduce an interactive web application tool, called P-Finder, to visualize reconstructed signaling networks.

Diametrical clustering for identifying anti-correlated gene clusters.

PubMed

Dhillon, Inderjit S; Marcotte, Edward M; Roshan, Usman

2003-09-01

Clustering genes based upon their expression patterns allows us to predict gene function. Most existing clustering algorithms cluster genes together when their expression patterns show high positive correlation. However, it has been observed that genes whose expression patterns are strongly anti-correlated can also be functionally similar. Biologically, this is not unintuitive-genes responding to the same stimuli, regardless of the nature of the response, are more likely to operate in the same pathways. We present a new diametrical clustering algorithm that explicitly identifies anti-correlated clusters of genes. Our algorithm proceeds by iteratively (i). re-partitioning the genes and (ii). computing the dominant singular vector of each gene cluster; each singular vector serving as the prototype of a 'diametric' cluster. We empirically show the effectiveness of the algorithm in identifying diametrical or anti-correlated clusters. Testing the algorithm on yeast cell cycle data, fibroblast gene expression data, and DNA microarray data from yeast mutants reveals that opposed cellular pathways can be discovered with this method. We present systems whose mRNA expression patterns, and likely their functions, oppose the yeast ribosome and proteosome, along with evidence for the inverse transcriptional regulation of a number of cellular systems.

Fast algorithm for the rendering of three-dimensional surfaces

NASA Astrophysics Data System (ADS)

Pritt, Mark D.

1994-02-01

It is often desirable to draw a detailed and realistic representation of surface data on a computer graphics display. One such representation is a 3D shaded surface. Conventional techniques for rendering shaded surfaces are slow, however, and require substantial computational power. Furthermore, many techniques suffer from aliasing effects, which appear as jagged lines and edges. This paper describes an algorithm for the fast rendering of shaded surfaces without aliasing effects. It is much faster than conventional ray tracing and polygon-based rendering techniques and is suitable for interactive use. On an IBM RISC System/6000TM workstation it renders a 1000 X 1000 surface in about 7 seconds.

Reconstruction software of the silicon tracker of DAMPE mission

NASA Astrophysics Data System (ADS)

Tykhonov, A.; Gallo, V.; Wu, X.; Zimmer, S.

2017-10-01

DAMPE is a satellite-borne experiment aimed to probe astroparticle physics in the GeV-TeV energy range. The Silicon tracker (STK) is one of the key components of DAMPE, which allows the reconstruction of trajectories (tracks) of detected particles. The non-negligible amount of material in the tracker poses a challenge to its reconstruction and alignment. In this paper we describe methods to address this challenge. We present the track reconstruction algorithm and give insight into the alignment algorithm. We also present our CAD-to-GDML converter, an in-house tool for implementing detector geometry in the software from the CAD drawings of the detector.

Line-Drawing Enhanced Interactive Mural Restoration for Dunhuang Mogao Grottoes

NASA Astrophysics Data System (ADS)

Fu, X. Y.; Han, Y.; Sun, Z. J.; Ma, X. J.; Xu, Y. Q.

2017-08-01

Dunhuang Mogao Grottoes in western China is one of the most famous World Cultural Heritage Sites, known for its glorious Chinese Buddhist art spanning a period of 1,000 years. However, it has been suffering from damage and degradation caused by man-made and natural factors. In this article, we present a novel line-drawing enhanced interactive system for digital restoration of damaged murals in Mogao Grottoes. Our system consists of four components, namely data pre-processing, damaged area selection, line-drawing segmentation, and mural restoration. Each component is a hybrid of efficient algorithms and user interactions. We introduce the infrastructure and process of using our system, from data capture and collection, database establishment, to interactive restoration. We conduct a user study with 15 participants who have varied experiences with and skills on repairing murals and editing images. Results and feedback suggest that our system can achieve satisfactory restoration results without overburdening the users. It can benefit both experts trained in restoration and amateurs interested in cultural heritage conservation.

Women's Work Pathways Across the Life Course.

PubMed

Damaske, Sarah; Frech, Adrianne

2016-04-01

Despite numerous changes in women's employment in the latter half of the twentieth century, women's employment continues to be uneven and stalled. Drawing from data on women's weekly work hours in the National Longitudinal Survey of Youth (NLSY79), we identify significant inequality in women's labor force experiences across adulthood. We find two pathways of stable full-time work for women, three pathways of part-time employment, and a pathway of unpaid labor. A majority of women follow one of the two full-time work pathways, while fewer than 10% follow a pathway of unpaid labor. Our findings provide evidence of the lasting influence of work-family conflict and early socioeconomic advantages and disadvantages on women's work pathways. Indeed, race, poverty, educational attainment, and early family characteristics significantly shaped women's work careers. Work-family opportunities and constraints also were related to women's work hours, as were a woman's gendered beliefs and expectations. We conclude that women's employment pathways are a product of both their resources and changing social environment as well as individual agency. Significantly, we point to social stratification, gender ideologies, and work-family constraints, all working in concert, as key explanations for how women are "tracked" onto work pathways from an early age.

A General, Adaptive, Roadmap-Based Algorithm for Protein Motion Computation.

PubMed

Molloy, Kevin; Shehu, Amarda

2016-03-01

Precious information on protein function can be extracted from a detailed characterization of protein equilibrium dynamics. This remains elusive in wet and dry laboratories, as function-modulating transitions of a protein between functionally-relevant, thermodynamically-stable and meta-stable structural states often span disparate time scales. In this paper we propose a novel, robotics-inspired algorithm that circumvents time-scale challenges by drawing analogies between protein motion and robot motion. The algorithm adapts the popular roadmap-based framework in robot motion computation to handle the more complex protein conformation space and its underlying rugged energy surface. Given known structures representing stable and meta-stable states of a protein, the algorithm yields a time- and energy-prioritized list of transition paths between the structures, with each path represented as a series of conformations. The algorithm balances computational resources between a global search aimed at obtaining a global view of the network of protein conformations and their connectivity and a detailed local search focused on realizing such connections with physically-realistic models. Promising results are presented on a variety of proteins that demonstrate the general utility of the algorithm and its capability to improve the state of the art without employing system-specific insight.

The Social Life of Learning Analytics: Cluster Analysis and the 'Performance' of Algorithmic Education

ERIC Educational Resources Information Center

Perrotta, Carlo; Williamson, Ben

2018-01-01

This paper argues that methods used for the classification and measurement of online education are not neutral and objective, but involved in the creation of the educational realities they claim to measure. In particular, the paper draws on material semiotics to examine cluster analysis as a 'performative device' that, to a significant extent,…

Visual improvement for bad handwriting based on Monte-Carlo method

NASA Astrophysics Data System (ADS)

Shi, Cao; Xiao, Jianguo; Xu, Canhui; Jia, Wenhua

2014-03-01

A visual improvement algorithm based on Monte Carlo simulation is proposed in this paper, in order to enhance visual effects for bad handwriting. The whole improvement process is to use well designed typeface so as to optimize bad handwriting image. In this process, a series of linear operators for image transformation are defined for transforming typeface image to approach handwriting image. And specific parameters of linear operators are estimated by Monte Carlo method. Visual improvement experiments illustrate that the proposed algorithm can effectively enhance visual effect for handwriting image as well as maintain the original handwriting features, such as tilt, stroke order and drawing direction etc. The proposed visual improvement algorithm, in this paper, has a huge potential to be applied in tablet computer and Mobile Internet, in order to improve user experience on handwriting.

CellNetVis: a web tool for visualization of biological networks using force-directed layout constrained by cellular components.

PubMed

Heberle, Henry; Carazzolle, Marcelo Falsarella; Telles, Guilherme P; Meirelles, Gabriela Vaz; Minghim, Rosane

2017-09-13

The advent of "omics" science has brought new perspectives in contemporary biology through the high-throughput analyses of molecular interactions, providing new clues in protein/gene function and in the organization of biological pathways. Biomolecular interaction networks, or graphs, are simple abstract representations where the components of a cell (e.g. proteins, metabolites etc.) are represented by nodes and their interactions are represented by edges. An appropriate visualization of data is crucial for understanding such networks, since pathways are related to functions that occur in specific regions of the cell. The force-directed layout is an important and widely used technique to draw networks according to their topologies. Placing the networks into cellular compartments helps to quickly identify where network elements are located and, more specifically, concentrated. Currently, only a few tools provide the capability of visually organizing networks by cellular compartments. Most of them cannot handle large and dense networks. Even for small networks with hundreds of nodes the available tools are not able to reposition the network while the user is interacting, limiting the visual exploration capability. Here we propose CellNetVis, a web tool to easily display biological networks in a cell diagram employing a constrained force-directed layout algorithm. The tool is freely available and open-source. It was originally designed for networks generated by the Integrated Interactome System and can be used with networks from others databases, like InnateDB. CellNetVis has demonstrated to be applicable for dynamic investigation of complex networks over a consistent representation of a cell on the Web, with capabilities not matched elsewhere.

Classification of early-stage non-small cell lung cancer by weighing gene expression profiles with connectivity information.

PubMed

Zhang, Ao; Tian, Suyan

2018-05-01

Pathway-based feature selection algorithms, which utilize biological information contained in pathways to guide which features/genes should be selected, have evolved quickly and become widespread in the field of bioinformatics. Based on how the pathway information is incorporated, we classify pathway-based feature selection algorithms into three major categories-penalty, stepwise forward, and weighting. Compared to the first two categories, the weighting methods have been underutilized even though they are usually the simplest ones. In this article, we constructed three different genes' connectivity information-based weights for each gene and then conducted feature selection upon the resulting weighted gene expression profiles. Using both simulations and a real-world application, we have demonstrated that when the data-driven connectivity information constructed from the data of specific disease under study is considered, the resulting weighted gene expression profiles slightly outperform the original expression profiles. In summary, a big challenge faced by the weighting method is how to estimate pathway knowledge-based weights more accurately and precisely. Only until the issue is conquered successfully will wide utilization of the weighting methods be impossible. © 2017 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Digital Games and Learning: Identifying Pathways of Influence

ERIC Educational Resources Information Center

Subrahmanyam, Kaveri; Renukarya, Bhavya

2015-01-01

Digital games and gamelike contexts have become an integral part of young people's lives, and scholars have speculated about their potential to engage and enhance children's learning. Given that digital games are complex systems, we propose that different aspects of game features and game play might influence learning in different ways. Drawing on…

Achievement, Motivation, and Educational Choices: A Longitudinal Study of Expectancy and Value Using a Multiplicative Perspective

ERIC Educational Resources Information Center

Guo, Jiesi; Parker, Philip D.; Marsh, Herbert W.; Morin, Alexandre J. S.

2015-01-01

Drawing on the expectancy-value model, the present study explored individual and gender differences in university entry and selection of educational pathway (e.g., science, technology, engineering, and mathematics [STEM] course selection). In particular, we examined the multiplicative effects of expectancy and task values on educational outcomes…

Learning Pathways in Environmental Science Education: The Case of Hazardous Household Items

ERIC Educational Resources Information Center

Malandrakis, George N.

2006-01-01

The present study draws on environmental science education to explore aspects of children's conceptual change regarding hazardous household items. Twelve children from a fifth-grade class attended a 300-h teaching module of environmentally oriented science activities aimed at assessing their awareness about the environmental and health hazards…

Developing a "Productive" Account of Young People's Transition Perspectives

ERIC Educational Resources Information Center

Vaughan, Karen; Roberts, Josie

2007-01-01

This article draws on the first two years of a longitudinal study of young people's pathway and career-related experiences and perspectives. It argues for a richer conceptualisation of young people's transition to study, training and employment than what simple school-to-labour market models allow. We present four clusters of young people's…

An Attachment Theory Approach to Narrating the Faith Journey of Children of Parental Divorce

ERIC Educational Resources Information Center

Kiesling, Chris

2011-01-01

This study explores the effects of parental divorce on a child's faith. Drawing from attachment theory, Granqvist and Kirkpatrick proposed two probable developmental pathways to religion. For those with secure attachment, whose cumulative experiences of sensitive, religious caregivers enhance the development of a God image as loving; belief…

Pathways to Achievement: Career and Educational Aspirations and Expectations of Latina/o Immigrant Parents and Early Adolescents

ERIC Educational Resources Information Center

Chavira, Gabriela; Cooper, Catherine R.; Vasquez-Salgado, Yolanda

2016-01-01

Drawing on sociocultural and related theories, 4 questions examined career and educational aspirations and expectations among 24 immigrant Latina/o early adolescents and their parents as predictors of students' grades. First, adolescents' career aspirations and expectations were correlated, and both parents and adolescents held educational…

Research-Based Insights on Initial Teacher Education in Spain

ERIC Educational Resources Information Center

Sancho-Gil, Juana M.; Sánchez-Valero, Joan-Anton; Domingo-Coscollola, Maria

2017-01-01

This paper builds on two research projects on initial and in-service education of teachers, their professional experience, and the pathways they travelled to become teachers. The policy documents analysed and the teachers' professional histories, the micro-ethnographies and the discussion groups developed, allowed us to draw a broad picture of the…

Pathways toward Peace: Negotiating National Unity and Ethnic Diversity through Education in Botswana

ERIC Educational Resources Information Center

Dryden-Peterson, Sarah; Mulimbi, Bethany

2017-01-01

This study examines how education can disrupt threats of conflict, specifically in the presence of ethnic diversity. We present a historical analysis of Botswana, using methods of process tracing drawing on documents, in-depth interviews, and Afrobarometer survey data. Postindependence Botswana engaged in redistribution of educational access…

"We're Locking the Door": Family Histories in a Sample of Homeless Youth

ERIC Educational Resources Information Center

Alvi, Shahid; Scott, Hannah; Stanyon, Wendy

2010-01-01

It is well known that the pathways to homelessness for young people are embedded in often ongoing negative childhood experiences. Many of these experiences are rooted in multiple and intersecting problems including, but not limited to: family conflict, abuse, addictions, and mental health issues. The authors draw upon qualitative interviews…

Being Judged, Being Assessed: Young People's Perspective of Assessment in Youth Justice and Education

ERIC Educational Resources Information Center

Ellis, Katie; France, Alan

2012-01-01

Research from the Economic and Social Research Council programme on Pathways Into and Out of Crime prioritised young people's "voices" in exploring experiences of crime and a range of intervention services. Drawing on data from interviews with 110 young people, this paper explores their perspectives of professional assessment. Embedded…

Negotiating Uncertain Economic Times: Youth Employment Strategies in England

ERIC Educational Resources Information Center

Hoskins, Bryony; Leonard, Pauline; Wilde, Rachel J.

2018-01-01

Higher education is commonly understood as the gateway to better, higher-paying jobs. This paper draws on longitudinal survey and interview data to explore how different groups of young people, those who left school at 18 and those graduating from higher education, negotiated pathways into employment or otherwise during the recent economic…

Rock climbing: A local-global algorithm to compute minimum energy and minimum free energy pathways.

PubMed

Templeton, Clark; Chen, Szu-Hua; Fathizadeh, Arman; Elber, Ron

2017-10-21

The calculation of minimum energy or minimum free energy paths is an important step in the quantitative and qualitative studies of chemical and physical processes. The computations of these coordinates present a significant challenge and have attracted considerable theoretical and computational interest. Here we present a new local-global approach to study reaction coordinates, based on a gradual optimization of an action. Like other global algorithms, it provides a path between known reactants and products, but it uses a local algorithm to extend the current path in small steps. The local-global approach does not require an initial guess to the path, a major challenge for global pathway finders. Finally, it provides an exact answer (the steepest descent path) at the end of the calculations. Numerical examples are provided for the Mueller potential and for a conformational transition in a solvated ring system.

Anomaly detection using temporal data mining in a smart home environment.

PubMed

Jakkula, V; Cook, D J

2008-01-01

To many people, home is a sanctuary. With the maturing of smart home technologies, many people with cognitive and physical disabilities can lead independent lives in their own homes for extended periods of time. In this paper, we investigate the design of machine learning algorithms that support this goal. We hypothesize that machine learning algorithms can be designed to automatically learn models of resident behavior in a smart home, and that the results can be used to perform automated health monitoring and to detect anomalies. Specifically, our algorithms draw upon the temporal nature of sensor data collected in a smart home to build a model of expected activities and to detect unexpected, and possibly health-critical, events in the home. We validate our algorithms using synthetic data and real activity data collected from volunteers in an automated smart environment. The results from our experiments support our hypothesis that a model can be learned from observed smart home data and used to report anomalies, as they occur, in a smart home.

Study on distributed generation algorithm of variable precision concept lattice based on ontology heterogeneous database

NASA Astrophysics Data System (ADS)

WANG, Qingrong; ZHU, Changfeng

2017-06-01

Integration of distributed heterogeneous data sources is the key issues under the big data applications. In this paper the strategy of variable precision is introduced to the concept lattice, and the one-to-one mapping mode of variable precision concept lattice and ontology concept lattice is constructed to produce the local ontology by constructing the variable precision concept lattice for each subsystem, and the distributed generation algorithm of variable precision concept lattice based on ontology heterogeneous database is proposed to draw support from the special relationship between concept lattice and ontology construction. Finally, based on the standard of main concept lattice of the existing heterogeneous database generated, a case study has been carried out in order to testify the feasibility and validity of this algorithm, and the differences between the main concept lattice and the standard concept lattice are compared. Analysis results show that this algorithm above-mentioned can automatically process the construction process of distributed concept lattice under the heterogeneous data sources.

Real-time particulate mass measurement based on laser scattering

NASA Astrophysics Data System (ADS)

Rentz, Julia H.; Mansur, David; Vaillancourt, Robert; Schundler, Elizabeth; Evans, Thomas

2005-11-01

OPTRA has developed a new approach to the determination of particulate size distribution from a measured, composite, laser angular scatter pattern. Drawing from the field of infrared spectroscopy, OPTRA has employed a multicomponent analysis technique which uniquely recognizes patterns associated with each particle size "bin" over a broad range of sizes. The technique is particularly appropriate for overlapping patterns where large signals are potentially obscuring weak ones. OPTRA has also investigated a method for accurately training the algorithms without the use of representative particles for any given application. This streamlined calibration applies a one-time measured "instrument function" to theoretical Mie patterns to create the training data for the algorithms. OPTRA has demonstrated this algorithmic technique on a compact, rugged, laser scatter sensor head we developed for gas turbine engine emissions measurements. The sensor contains a miniature violet solid state laser and an array of silicon photodiodes, both of which are commercial off the shelf. The algorithmic technique can also be used with any commercially available laser scatter system.

Effect of curcumin on aged Drosophila melanogaster: a pathway prediction analysis.

PubMed

Zhang, Zhi-guo; Niu, Xu-yan; Lu, Ai-ping; Xiao, Gary Guishan

2015-02-01

To re-analyze the data published in order to explore plausible biological pathways that can be used to explain the anti-aging effect of curcumin. Microarray data generated from other study aiming to investigate effect of curcumin on extending lifespan of Drosophila melanogaster were further used for pathway prediction analysis. The differentially expressed genes were identified by using GeneSpring GX with a criterion of 3.0-fold change. Two Cytoscape plugins including BisoGenet and molecular complex detection (MCODE) were used to establish the protein-protein interaction (PPI) network based upon differential genes in order to detect highly connected regions. The function annotation clustering tool of Database for Annotation, Visualization and Integrated Discovery (DAVID) was used for pathway analysis. A total of 87 genes expressed differentially in D. melanogaster melanogaster treated with curcumin were identified, among which 50 were up-regulated significantly and 37 were remarkably down-regulated in D. melanogaster melanogaster treated with curcumin. Based upon these differential genes, PPI network was constructed with 1,082 nodes and 2,412 edges. Five highly connected regions in PPI networks were detected by MCODE algorithm, suggesting anti-aging effect of curcumin may be underlined through five different pathways including Notch signaling pathway, basal transcription factors, cell cycle regulation, ribosome, Wnt signaling pathway, and p53 pathway. Genes and their associated pathways in D. melanogaster melanogaster treated with anti-aging agent curcumin were identified using PPI network and MCODE algorithm, suggesting that curcumin may be developed as an alternative therapeutic medicine for treating aging-associated diseases.

A robust data scaling algorithm to improve classification accuracies in biomedical data.

PubMed

Cao, Xi Hang; Stojkovic, Ivan; Obradovic, Zoran

2016-09-09

Machine learning models have been adapted in biomedical research and practice for knowledge discovery and decision support. While mainstream biomedical informatics research focuses on developing more accurate models, the importance of data preprocessing draws less attention. We propose the Generalized Logistic (GL) algorithm that scales data uniformly to an appropriate interval by learning a generalized logistic function to fit the empirical cumulative distribution function of the data. The GL algorithm is simple yet effective; it is intrinsically robust to outliers, so it is particularly suitable for diagnostic/classification models in clinical/medical applications where the number of samples is usually small; it scales the data in a nonlinear fashion, which leads to potential improvement in accuracy. To evaluate the effectiveness of the proposed algorithm, we conducted experiments on 16 binary classification tasks with different variable types and cover a wide range of applications. The resultant performance in terms of area under the receiver operation characteristic curve (AUROC) and percentage of correct classification showed that models learned using data scaled by the GL algorithm outperform the ones using data scaled by the Min-max and the Z-score algorithm, which are the most commonly used data scaling algorithms. The proposed GL algorithm is simple and effective. It is robust to outliers, so no additional denoising or outlier detection step is needed in data preprocessing. Empirical results also show models learned from data scaled by the GL algorithm have higher accuracy compared to the commonly used data scaling algorithms.

Use of Management Pathways or Algorithms in Children With Chronic Cough: CHEST Guideline and Expert Panel Report.

PubMed

Chang, Anne B; Oppenheimer, John J; Weinberger, Miles M; Rubin, Bruce K; Weir, Kelly; Grant, Cameron C; Irwin, Richard S

2017-04-01

Using management algorithms or pathways potentially improves clinical outcomes. We undertook systematic reviews to examine various aspects in the generic approach (use of cough algorithms and tests) to the management of chronic cough in children (aged ≤ 14 years) based on key questions (KQs) using the Population, Intervention, Comparison, Outcome format. We used the CHEST Expert Cough Panel's protocol for the systematic reviews and the American College of Chest Physicians (CHEST) methodological guidelines and Grading of Recommendations Assessment, Development and Evaluation framework. Data from the systematic reviews in conjunction with patients' values and preferences and the clinical context were used to form recommendations. Delphi methodology was used to obtain the final grading. Combining data from systematic reviews addressing five KQs, we found high-quality evidence that a systematic approach to the management of chronic cough improves clinical outcomes. Although there was evidence from several pathways, the highest evidence was from the use of the CHEST approach. However, there was no or little evidence to address some of the KQs posed. Compared with the 2006 Cough Guidelines, there is now high-quality evidence that in children aged ≤ 14 years with chronic cough (> 4 weeks' duration), the use of cough management protocols (or algorithms) improves clinical outcomes, and cough management or testing algorithms should differ depending on the associated characteristics of the cough and clinical history. A chest radiograph and, when age appropriate, spirometry (pre- and post-β 2 agonist) should be undertaken. Other tests should not be routinely performed and undertaken in accordance with the clinical setting and the child's clinical symptoms and signs (eg, tests for tuberculosis when the child has been exposed). Copyright © 2017 American College of Chest Physicians. All rights reserved.

iCOSSY: An Online Tool for Context-Specific Subnetwork Discovery from Gene Expression Data

PubMed Central

Saha, Ashis; Jeon, Minji; Tan, Aik Choon; Kang, Jaewoo

2015-01-01

Pathway analyses help reveal underlying molecular mechanisms of complex biological phenotypes. Biologists tend to perform multiple pathway analyses on the same dataset, as there is no single answer. It is often inefficient for them to implement and/or install all the algorithms by themselves. Online tools can help the community in this regard. Here we present an online gene expression analytical tool called iCOSSY which implements a novel pathway-based COntext-specific Subnetwork discoverY (COSSY) algorithm. iCOSSY also includes a few modifications of COSSY to increase its reliability and interpretability. Users can upload their gene expression datasets, and discover important subnetworks of closely interacting molecules to differentiate between two phenotypes (context). They can also interactively visualize the resulting subnetworks. iCOSSY is a web server that finds subnetworks that are differentially expressed in two phenotypes. Users can visualize the subnetworks to understand the biology of the difference. PMID:26147457

Quantum control via a genetic algorithm of the field ionization pathway of a Rydberg electron

NASA Astrophysics Data System (ADS)

Gregoric, Vincent C.; Kang, Xinyue; Liu, Zhimin Cheryl; Rowley, Zoe A.; Carroll, Thomas J.; Noel, Michael W.

2017-08-01

Quantum control of the pathway along which a Rydberg electron field ionizes is experimentally and computationally demonstrated. Selective field ionization is typically done with a slowly rising electric field pulse. The (1/n*)4 scaling of the classical ionization threshold leads to a rough mapping between arrival time of the electron signal and principal quantum number of the Rydberg electron. This is complicated by the many avoided level crossings that the electron must traverse on the way to ionization, which in general leads to broadening of the time-resolved field ionization signal. In order to control the ionization pathway, thus directing the signal to the desired arrival time, a perturbing electric field produced by an arbitrary wave-form generator is added to a slowly rising electric field. A genetic algorithm evolves the perturbing field in an effort to achieve the target time-resolved field ionization signal.

Parana Basin Structure from Multi-Objective Inversion of Surface Wave and Receiver Function by Competent Genetic Algorithm

NASA Astrophysics Data System (ADS)

An, M.; Assumpcao, M.

2003-12-01

The joint inversion of receiver function and surface wave is an effective way to diminish the influences of the strong tradeoff among parameters and the different sensitivity to the model parameters in their respective inversions, but the inversion problem becomes more complex. Multi-objective problems can be much more complicated than single-objective inversion in the model selection and optimization. If objectives are involved and conflicting, models can be ordered only partially. In this case, Pareto-optimal preference should be used to select solutions. On the other hand, the inversion to get only a few optimal solutions can not deal properly with the strong tradeoff between parameters, the uncertainties in the observation, the geophysical complexities and even the incompetency of the inversion technique. The effective way is to retrieve the geophysical information statistically from many acceptable solutions, which requires more competent global algorithms. Competent genetic algorithms recently proposed are far superior to the conventional genetic algorithm and can solve hard problems quickly, reliably and accurately. In this work we used one of competent genetic algorithms, Bayesian Optimization Algorithm as the main inverse procedure. This algorithm uses Bayesian networks to draw out inherited information and can use Pareto-optimal preference in the inversion. With this algorithm, the lithospheric structure of Paran"› basin is inverted to fit both the observations of inter-station surface wave dispersion and receiver function.

Caveat emptor: limitations of the automated reconstruction of metabolic pathways in Plasmodium.

PubMed

Ginsburg, Hagai

2009-01-01

The functional reconstruction of metabolic pathways from an annotated genome is a tedious and demanding enterprise. Automation of this endeavor using bioinformatics algorithms could cope with the ever-increasing number of sequenced genomes and accelerate the process. Here, the manual reconstruction of metabolic pathways in the functional genomic database of Plasmodium falciparum--Malaria Parasite Metabolic Pathways--is described and compared with pathways generated automatically as they appear in PlasmoCyc, metaSHARK and the Kyoto Encyclopedia for Genes and Genomes. A critical evaluation of this comparison discloses that the automatic reconstruction of pathways generates manifold paths that need an expert manual verification to accept some and reject most others based on manually curated gene annotation.

The GlycanBuilder: a fast, intuitive and flexible software tool for building and displaying glycan structures.

PubMed

Ceroni, Alessio; Dell, Anne; Haslam, Stuart M

2007-08-07

Carbohydrates play a critical role in human diseases and their potential utility as biomarkers for pathological conditions is a major driver for characterization of the glycome. However, the additional complexity of glycans compared to proteins and nucleic acids has slowed the advancement of glycomics in comparison to genomics and proteomics. The branched nature of carbohydrates, the great diversity of their constituents and the numerous alternative symbolic notations, make the input and display of glycans not as straightforward as for example the amino-acid sequence of a protein. Every glycoinformatic tool providing a user interface would benefit from a fast, intuitive, appealing mechanism for input and output of glycan structures in a computer readable format. A software tool for building and displaying glycan structures using a chosen symbolic notation is described here. The "GlycanBuilder" uses an automatic rendering algorithm to draw the saccharide symbols and to place them on the drawing board. The information about the symbolic notation is derived from a configurable graphical model as a set of rules governing the aspect and placement of residues and linkages. The algorithm is able to represent a structure using only few traversals of the tree and is inherently fast. The tool uses an XML format for import and export of encoded structures. The rendering algorithm described here is able to produce high-quality representations of glycan structures in a chosen symbolic notation. The automated rendering process enables the "GlycanBuilder" to be used both as a user-independent component for displaying glycans and as an easy-to-use drawing tool. The "GlycanBuilder" can be integrated in web pages as a Java applet for the visual editing of glycans. The same component is available as a web service to render an encoded structure into a graphical format. Finally, the "GlycanBuilder" can be integrated into other applications to create intuitive and appealing user interfaces: an example is the "GlycoWorkbench", a software tool for assisted annotation of glycan mass spectra. The "GlycanBuilder" represent a flexible, reliable and efficient solution to the problem of input and output of glycan structures in any glycomic tool or database.

European Union Students Studying in English Higher Education Institutions. DIUS Research Report 08-09

ERIC Educational Resources Information Center

Morris, Marian; Rutt, Simon

2008-01-01

This study aimed to identify the pathways, intentions and relevant perceptions of (non-UK) European Union (EU) students entering English higher education. It sought to identify why students wished to obtain an English HE qualification, their attitudes towards the uptake and repayment of tuition fee loans and their future career plans. Drawing on…

"Teach For"/"Teach First" Candidates: What Conclusions Do They Draw from Their Time in Teaching?

ERIC Educational Resources Information Center

Rice, Suzanne; Volkoff, Veronica; Dulfer, Nicky

2015-01-01

"Teach For" and "Teach First" programs now constitute a significant pathway into teaching in a number of countries. One criterion for selection into these programs is leadership capacity, and evidence indicates that many candidates do move into leadership roles in education, business, and policy in the years following their…

Market Transformation Pathways for Grid-Connected Rooftop Solar PV in Minnesota

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

Abbey, Ross; Ross, Brian

2013-06-03

This report presents the market and policy findings of the Minnesota Solar Challenge program. The report draws on information collected from state agencies, local government units, solar industry participants, rooftop photovoltaic (PV) adopters (sometimes called customer-generators), state and national experts, the Commerce distributed generation stakeholder process, and the numerous reports and data sets referenced herein.

Paradoxical Pathways: An Ethnographic Extension of Kohn's Findings on Class and Childrearing

ERIC Educational Resources Information Center

Weininger, Elliot B.; Lareau, Annette

2009-01-01

Stratification is a central issue in family research, yet relatively few studies highlight its impact on family processes. Drawing on in-depth interviews (N = 137) and observational data (N = 12), we extend Melvin Kohn's research on childrearing values by examining how parental commitments to self-direction and conformity are enacted in daily…

Black Students' Recollections of Pathways to Resilience: Lessons for School Psychologists

ERIC Educational Resources Information Center

Theron, Linda C.

2013-01-01

Drawing on narrative data from a multiple case study, I recount the life stories of two resilient Black South African university students to theorize about the processes that encouraged these students, familiar with penury and parental illiteracy, to resile. I aimed to uncover lessons for school psychologists about resilience, and their role in…

Limitations in the hydraulic pathway: Effects of xylem embolisms on sap velocity and flow

USDA-ARS?s Scientific Manuscript database

Sap flow in plants takes place in the xylem, a hydraulic system that is usually under negative pressure and in which gas and liquid phases are separated by nanoporous, fibrous pit membranes. It has long been known that this system is at risk of drawing gas nanobubbles through these membranes into th...

Structures and Technology Encouraging Discussion in Human Sexuality Courses: Strategies to Engage a Range of Students

ERIC Educational Resources Information Center

Angera, Jeffrey J.; Latty, Christopher R.

2015-01-01

Human sexuality courses are common across many college/university campuses. The methods of instruction typically encourage discussion to increase knowledge and critical thinking about self, relationships, and professional pathways. However, often the pedagogical practices do not include methods to draw out students with a range of personalities,…

Los Caminos: Latino/a Youth Forging Pathways in Pursuit of Higher Education

ERIC Educational Resources Information Center

Irizarry, Jason G.

2012-01-01

This article draws from data collected as part of a 3-year ethnographic study that followed two groups of Latino/a students through their final years of high school, the college application process, and for some, the inception of their postsecondary studies. Using Latino/a critical theory as an analytical framework, this article provides insights…

Syrian Mothers Producing Counterstories in Co-Constructed School Spaces: Rethinking the Role of Schools in Engaging Refugee Families

ERIC Educational Resources Information Center

Karsli-Calamak, Elif

2018-01-01

This ethnographically informed field study, drawing on a blend of feminist theories and cultural learning pathways framework, reveals that Syrian mothers show strong presence and readiness to take active roles when opportunities present themselves in alternative spaces in the public schools of Turkey. As mothers produce counterstories in relation…

How Socialization Happens on the Ground: Narrative Practices as Alternate Socializing Pathways in Taiwanese and European-American Families

ERIC Educational Resources Information Center

Miller, Peggy J.; Fung, Heidi; Lin, Shumin; Chen, Eva Chian-Hui; Boldt, Benjamin R.

2012-01-01

This monograph builds upon our cumulative efforts to investigate personal storytelling as a medium of socialization in two disparate cultural worlds. Drawing upon interdisciplinary fields of study that take a discourse-centered approach to socialization, we combined ethnography, longitudinal home observations, and microlevel analysis of everyday…

The Power of the Sketch(Book): Reflections from High School English Students

ERIC Educational Resources Information Center

Leigh, S. Rebecca

2012-01-01

This paper explores the potential of sketchbooks as a pathway for developing ideas for writing in high school English classrooms. This paper examines how shifting between drawing and writing impacts students' ability to develop ideas for writing and create meaningful texts. Specifically, this paper explores four types of visual sign making that…

"Most Learn Almost Nothing": Building Democratic Citizenship by Engaging Controversial History through Inquiry in Post-Communist Europe

ERIC Educational Resources Information Center

Misco, Thomas

2011-01-01

This article addresses the challenges and pathways of Holocaust education in post-communist countries through two case studies. I first examine historiographical, institutional and cultural obstacles to deep and meaningful treatments of the Holocaust within Latvian and Romanian schools. Drawing upon the unique experiences both countries had with…

Calibration of the computer model describing flows in the water supply system; example of the application of a genetic algorithm

NASA Astrophysics Data System (ADS)

Orłowska-Szostak, Maria; Orłowski, Ryszard

2017-11-01

The paper discusses some relevant aspects of the calibration of a computer model describing flows in the water supply system. The authors described an exemplary water supply system and used it as a practical illustration of calibration. A range of measures was discussed and applied, which improve the convergence and effective use of calculations in the calibration process and also the effect of such calibration which is the validity of the results obtained. Drawing up results of performed measurements, i.e. estimating pipe roughnesses, the authors performed using the genetic algorithm implementation of which is a software developed by Resan Labs company from Brazil.

New prior sampling methods for nested sampling - Development and testing

NASA Astrophysics Data System (ADS)

Stokes, Barrie; Tuyl, Frank; Hudson, Irene

2017-06-01

Nested Sampling is a powerful algorithm for fitting models to data in the Bayesian setting, introduced by Skilling [1]. The nested sampling algorithm proceeds by carrying out a series of compressive steps, involving successively nested iso-likelihood boundaries, starting with the full prior distribution of the problem parameters. The "central problem" of nested sampling is to draw at each step a sample from the prior distribution whose likelihood is greater than the current likelihood threshold, i.e., a sample falling inside the current likelihood-restricted region. For both flat and informative priors this ultimately requires uniform sampling restricted to the likelihood-restricted region. We present two new methods of carrying out this sampling step, and illustrate their use with the lighthouse problem [2], a bivariate likelihood used by Gregory [3] and a trivariate Gaussian mixture likelihood. All the algorithm development and testing reported here has been done with Mathematica® [4].

What can Numerical Computation do for the History of Science? (Study of an Orbit Drawn by Newton on a Letter to Hooke)

NASA Astrophysics Data System (ADS)

Stuchi, Teresa; Cardozo Dias, P.

2013-05-01

Abstract (2,250 Maximum Characters): On a letter to Robert Hooke, Isaac Newton drew the orbit of a mass moving under a constant attracting central force. How he drew the orbit may indicate how and when he developed dynamic categories. Some historians claim that Newton used a method contrived by Hooke; others that he used some method of curvature. We prove geometrically: Hooke’s method is a second order symplectic area preserving algorithm, and the method of curvature is a first order algorithm without special features; then we integrate the hamiltonian equations. Integration by the method of curvature can also be done exploring geometric properties of curves. We compare three methods: Hooke’s method, the method of curvature and a first order method. A fourth order algorithm sets a standard of comparison. We analyze which of these methods best explains Newton’s drawing.

Co-clustering phenome–genome for phenotype classification and disease gene discovery

PubMed Central

Hwang, TaeHyun; Atluri, Gowtham; Xie, MaoQiang; Dey, Sanjoy; Hong, Changjin; Kumar, Vipin; Kuang, Rui

2012-01-01

Understanding the categorization of human diseases is critical for reliably identifying disease causal genes. Recently, genome-wide studies of abnormal chromosomal locations related to diseases have mapped >2000 phenotype–gene relations, which provide valuable information for classifying diseases and identifying candidate genes as drug targets. In this article, a regularized non-negative matrix tri-factorization (R-NMTF) algorithm is introduced to co-cluster phenotypes and genes, and simultaneously detect associations between the detected phenotype clusters and gene clusters. The R-NMTF algorithm factorizes the phenotype–gene association matrix under the prior knowledge from phenotype similarity network and protein–protein interaction network, supervised by the label information from known disease classes and biological pathways. In the experiments on disease phenotype–gene associations in OMIM and KEGG disease pathways, R-NMTF significantly improved the classification of disease phenotypes and disease pathway genes compared with support vector machines and Label Propagation in cross-validation on the annotated phenotypes and genes. The newly predicted phenotypes in each disease class are highly consistent with human phenotype ontology annotations. The roles of the new member genes in the disease pathways are examined and validated in the protein–protein interaction subnetworks. Extensive literature review also confirmed many new members of the disease classes and pathways as well as the predicted associations between disease phenotype classes and pathways. PMID:22735708

Mapping the Conformation Space of Wildtype and Mutant H-Ras with a Memetic, Cellular, and Multiscale Evolutionary Algorithm

PubMed Central

Clausen, Rudy; Ma, Buyong; Nussinov, Ruth; Shehu, Amarda

2015-01-01

An important goal in molecular biology is to understand functional changes upon single-point mutations in proteins. Doing so through a detailed characterization of structure spaces and underlying energy landscapes is desirable but continues to challenge methods based on Molecular Dynamics. In this paper we propose a novel algorithm, SIfTER, which is based instead on stochastic optimization to circumvent the computational challenge of exploring the breadth of a protein’s structure space. SIfTER is a data-driven evolutionary algorithm, leveraging experimentally-available structures of wildtype and variant sequences of a protein to define a reduced search space from where to efficiently draw samples corresponding to novel structures not directly observed in the wet laboratory. The main advantage of SIfTER is its ability to rapidly generate conformational ensembles, thus allowing mapping and juxtaposing landscapes of variant sequences and relating observed differences to functional changes. We apply SIfTER to variant sequences of the H-Ras catalytic domain, due to the prominent role of the Ras protein in signaling pathways that control cell proliferation, its well-studied conformational switching, and abundance of documented mutations in several human tumors. Many Ras mutations are oncogenic, but detailed energy landscapes have not been reported until now. Analysis of SIfTER-computed energy landscapes for the wildtype and two oncogenic variants, G12V and Q61L, suggests that these mutations cause constitutive activation through two different mechanisms. G12V directly affects binding specificity while leaving the energy landscape largely unchanged, whereas Q61L has pronounced, starker effects on the landscape. An implementation of SIfTER is made available at http://www.cs.gmu.edu/~ashehu/?q=OurTools. We believe SIfTER is useful to the community to answer the question of how sequence mutations affect the function of a protein, when there is an abundance of experimental structures that can be exploited to reconstruct an energy landscape that would be computationally impractical to do via Molecular Dynamics. PMID:26325505

Women’s Work Pathways Across the Life Course1

PubMed Central

Damaske, Sarah; Frech, Adrianne

2016-01-01

Despite numerous changes in women’s employment in the latter half of the 20th century, women’s employment continues to be uneven and stalled. Drawing from data on women’s weekly work hours in the National Longitudinal Survey of Youth (NLSY79), we identify significant inequality in women’s labor force experiences across adulthood. We find two pathways of stable fulltime work for women, three pathways of part-time employment, and a pathway of unpaid labor. A majority of women follow one of the two fulltime work pathways, while fewer than 10 percent follow a pathway of unpaid labor. Our findings provide evidence of the lasting influence of work-family conflict and early socio-economic advantages and disadvantages on women’s work pathways. Indeed, race, poverty, educational attainment, and early family characteristics significantly shaped women’s work careers. Work-family opportunities and constraints also were related to women’s work hours, as were a woman’s gendered beliefs and expectations. We conclude that women’s employment pathways are a product of both their resources and changing social environment as well as individual agency. Significantly, we point to social stratification, gender ideologies, and work-family constraints, working in concert, as key explanations for how women are “tracked” onto work pathways from an early age. PMID:27001314

Use of Management Pathways or Algorithms in Children With Chronic Cough: Systematic Reviews.

PubMed

Chang, Anne B; Oppenheimer, John J; Weinberger, Miles; Weir, Kelly; Rubin, Bruce K; Irwin, Richard S

2016-01-01

Use of appropriate cough pathways or algorithms may reduce the morbidity of chronic cough, lead to earlier diagnosis of chronic underlying illness, and reduce unnecessary costs and medications. We undertook three systematic reviews to examine three related key questions (KQ): In children aged ?14 years with chronic cough (> 4 weeks' duration), KQ1, do cough management protocols (or algorithms) improve clinical outcomes? KQ2, should the cough management or testing algorithm differ depending on the duration and/or severity? KQ3, should the cough management or testing algorithm differ depending on the associated characteristics of the cough and clinical history? We used the CHEST expert cough panel's protocol. Two authors screened searches and selected and extracted data. Only systematic reviews, randomized controlled trials (RCTs), and cohort studies published in English were included. Data were presented in Preferred Reporting Items for Systematic Reviews and Meta-analyses flowcharts and summary tabulated. Nine studies were included in KQ1 (RCT = 1; cohort studies = 7) and eight in KQ3 (RCT = 2; cohort = 6), but none in KQ2. There is high-quality evidence that in children aged ?14 years with chronic cough (> 4 weeks' duration), the use of cough management protocols (or algorithms) improves clinical outcomes and cough management or the testing algorithm should differ depending on the associated characteristics of the cough and clinical history. It remains uncertain whether the management or testing algorithm should depend on the duration or severity of chronic cough. Pending new data, chronic cough in children should be defined as > 4 weeks' duration and children should be systematically evaluated with treatment targeted to the underlying cause irrespective of the cough severity. Copyright © 2016 American College of Chest Physicians. All rights reserved.

The alignment of enzymatic steps reveals similar metabolic pathways and probable recruitment events in Gammaproteobacteria.

PubMed

Poot-Hernandez, Augusto Cesar; Rodriguez-Vazquez, Katya; Perez-Rueda, Ernesto

2015-11-17

It is generally accepted that gene duplication followed by functional divergence is one of the main sources of metabolic diversity. In this regard, there is an increasing interest in the development of methods that allow the systematic identification of these evolutionary events in metabolism. Here, we used a method not based on biomolecular sequence analysis to compare and identify common and variable routes in the metabolism of 40 Gammaproteobacteria species. The metabolic maps deposited in the KEGG database were transformed into linear Enzymatic Step Sequences (ESS) by using the breadth-first search algorithm. These ESS represent subsequent enzymes linked to each other, where their catalytic activities are encoded in the Enzyme Commission numbers. The ESS were compared in an all-against-all (pairwise comparisons) approach by using a dynamic programming algorithm, leaving only a set of significant pairs. From these comparisons, we identified a set of functionally conserved enzymatic steps in different metabolic maps, in which cell wall components and fatty acid and lysine biosynthesis were included. In addition, we found that pathways associated with biosynthesis share a higher proportion of similar ESS than degradation pathways and secondary metabolism pathways. Also, maps associated with the metabolism of similar compounds contain a high proportion of similar ESS, such as those maps from nucleotide metabolism pathways, in particular the inosine monophosphate pathway. Furthermore, diverse ESS associated with the low part of the glycolysis pathway were identified as functionally similar to multiple metabolic pathways. In summary, our comparisons may help to identify similar reactions in different metabolic pathways and could reinforce the patchwork model in the evolution of metabolism in Gammaproteobacteria.

Does Motivation for Exercise Influence Post-Exercise Snacking Behavior?

PubMed

Dimmock, James A; Guelfi, Kym J; West, Jessica S; Masih, Tasmiah; Jackson, Ben

2015-06-15

It is well established that regular exercise plays an important role in achieving a number of health and wellbeing outcomes. However, certain post-exercise behaviors, including the consumption of unhealthy high-calorie foods, can counteract some of the benefits of physical activity. There are at least three overlapping pathways through which exercise may increase the likelihood of consuming pleasurable but unhealthy foods: through impulsive cognitive processes, reflective cognitive processes, and/or physiological responses. It is argued in this paper that motivation toward exercise can influence each of these pathways. Drawing from literature from various domains, we postulate that controlled exercise motivation, as opposed to autonomous exercise motivation, is more likely to influence each of these pathways in a manner that leaves individuals susceptible to the post-exercise consumption of pleasurable but unhealthy foods.

Automated circumferential construction of first-order aqueous humor outflow pathways using spectral-domain optical coherence tomography

NASA Astrophysics Data System (ADS)

Huang, Alex S.; Belghith, Akram; Dastiridou, Anna; Chopra, Vikas; Zangwill, Linda M.; Weinreb, Robert N.

2017-06-01

The purpose was to create a three-dimensional (3-D) model of circumferential aqueous humor outflow (AHO) in a living human eye with an automated detection algorithm for Schlemm's canal (SC) and first-order collector channels (CC) applied to spectral-domain optical coherence tomography (SD-OCT). Anterior segment SD-OCT scans from a subject were acquired circumferentially around the limbus. A Bayesian Ridge method was used to approximate the location of the SC on infrared confocal laser scanning ophthalmoscopic images with a cross multiplication tool developed to initiate SC/CC detection automated through a fuzzy hidden Markov Chain approach. Automatic segmentation of SC and initial CC's was manually confirmed by two masked graders. Outflow pathways detected by the segmentation algorithm were reconstructed into a 3-D representation of AHO. Overall, only <1% of images (5114 total B-scans) were ungradable. Automatic segmentation algorithm performed well with SC detection 98.3% of the time and <0.1% false positive detection compared to expert grader consensus. CC was detected 84.2% of the time with 1.4% false positive detection. 3-D representation of AHO pathways demonstrated variably thicker and thinner SC with some clear CC roots. Circumferential (360 deg), automated, and validated AHO detection of angle structures in the living human eye with reconstruction was possible.

Hierarchy of folding and unfolding events of protein G, CI2, and ACBP from explicit-solvent simulations

NASA Astrophysics Data System (ADS)

Camilloni, Carlo; Broglia, Ricardo A.; Tiana, Guido

2011-01-01

The study of the mechanism which is at the basis of the phenomenon of protein folding requires the knowledge of multiple folding trajectories under biological conditions. Using a biasing molecular-dynamics algorithm based on the physics of the ratchet-and-pawl system, we carry out all-atom, explicit solvent simulations of the sequence of folding events which proteins G, CI2, and ACBP undergo in evolving from the denatured to the folded state. Starting from highly disordered conformations, the algorithm allows the proteins to reach, at the price of a modest computational effort, nativelike conformations, within a root mean square deviation (RMSD) of approximately 1 Å. A scheme is developed to extract, from the myriad of events, information concerning the sequence of native contact formation and of their eventual correlation. Such an analysis indicates that all the studied proteins fold hierarchically, through pathways which, although not deterministic, are well-defined with respect to the order of contact formation. The algorithm also allows one to study unfolding, a process which looks, to a large extent, like the reverse of the major folding pathway. This is also true in situations in which many pathways contribute to the folding process, like in the case of protein G.

Design of control system for optical fiber drawing machine driven by double motor

NASA Astrophysics Data System (ADS)

Yu, Yue Chen; Bo, Yu Ming; Wang, Jun

2018-01-01

Micro channel Plate (MCP) is a kind of large-area array electron multiplier with high two-dimensional spatial resolution, used as high-performance night vision intensifier. The high precision control of the fiber is the key technology of the micro channel plate manufacturing process, and it was achieved by the control of optical fiber drawing machine driven by dual-motor in this paper. First of all, utilizing STM32 chip, the servo motor drive and control circuit was designed to realize the dual motor synchronization. Secondly, neural network PID control algorithm was designed for controlling the fiber diameter fabricated in high precision; Finally, the hexagonal fiber was manufactured by this system and it shows that multifilament diameter accuracy of the fiber is +/- 1.5μm.

Focus-based filtering + clustering technique for power-law networks with small world phenomenon

NASA Astrophysics Data System (ADS)

Boutin, François; Thièvre, Jérôme; Hascoët, Mountaz

2006-01-01

Realistic interaction networks usually present two main properties: a power-law degree distribution and a small world behavior. Few nodes are linked to many nodes and adjacent nodes are likely to share common neighbors. Moreover, graph structure usually presents a dense core that is difficult to explore with classical filtering and clustering techniques. In this paper, we propose a new filtering technique accounting for a user-focus. This technique extracts a tree-like graph with also power-law degree distribution and small world behavior. Resulting structure is easily drawn with classical force-directed drawing algorithms. It is also quickly clustered and displayed into a multi-level silhouette tree (MuSi-Tree) from any user-focus. We built a new graph filtering + clustering + drawing API and report a case study.

Algorithm of composing the schedule of construction and installation works

NASA Astrophysics Data System (ADS)

Nehaj, Rustam; Molotkov, Georgij; Rudchenko, Ivan; Grinev, Anatolij; Sekisov, Aleksandr

2017-10-01

An algorithm for scheduling works is developed, in which the priority of the work corresponds to the total weight of the subordinate works, the vertices of the graph, and it is proved that for graphs of the tree type the algorithm is optimal. An algorithm is synthesized to reduce the search for solutions when drawing up schedules of construction and installation works, allocating a subset with the optimal solution of the problem of the minimum power, which is determined by the structure of its initial data and numerical values. An algorithm for scheduling construction and installation work is developed, taking into account the schedule for the movement of brigades, which is characterized by the possibility to efficiently calculate the values of minimizing the time of work performance by the parameters of organizational and technological reliability through the use of the branch and boundary method. The program of the computational algorithm was compiled in the MatLAB-2008 program. For the initial data of the matrix, random numbers were taken, uniformly distributed in the range from 1 to 100. It takes 0.5; 2.5; 7.5; 27 minutes to solve the problem. Thus, the proposed method for estimating the lower boundary of the solution is sufficiently accurate and allows efficient solution of the minimax task of scheduling construction and installation works.

A stochastic multiple imputation algorithm for missing covariate data in tree-structured survival analysis.

PubMed

Wallace, Meredith L; Anderson, Stewart J; Mazumdar, Sati

2010-12-20

Missing covariate data present a challenge to tree-structured methodology due to the fact that a single tree model, as opposed to an estimated parameter value, may be desired for use in a clinical setting. To address this problem, we suggest a multiple imputation algorithm that adds draws of stochastic error to a tree-based single imputation method presented by Conversano and Siciliano (Technical Report, University of Naples, 2003). Unlike previously proposed techniques for accommodating missing covariate data in tree-structured analyses, our methodology allows the modeling of complex and nonlinear covariate structures while still resulting in a single tree model. We perform a simulation study to evaluate our stochastic multiple imputation algorithm when covariate data are missing at random and compare it to other currently used methods. Our algorithm is advantageous for identifying the true underlying covariate structure when complex data and larger percentages of missing covariate observations are present. It is competitive with other current methods with respect to prediction accuracy. To illustrate our algorithm, we create a tree-structured survival model for predicting time to treatment response in older, depressed adults. Copyright © 2010 John Wiley & Sons, Ltd.

Semi-automatic 3D lung nodule segmentation in CT using dynamic programming

NASA Astrophysics Data System (ADS)

Sargent, Dustin; Park, Sun Young

2017-02-01

We present a method for semi-automatic segmentation of lung nodules in chest CT that can be extended to general lesion segmentation in multiple modalities. Most semi-automatic algorithms for lesion segmentation or similar tasks use region-growing or edge-based contour finding methods such as level-set. However, lung nodules and other lesions are often connected to surrounding tissues, which makes these algorithms prone to growing the nodule boundary into the surrounding tissue. To solve this problem, we apply a 3D extension of the 2D edge linking method with dynamic programming to find a closed surface in a spherical representation of the nodule ROI. The algorithm requires a user to draw a maximal diameter across the nodule in the slice in which the nodule cross section is the largest. We report the lesion volume estimation accuracy of our algorithm on the FDA lung phantom dataset, and the RECIST diameter estimation accuracy on the lung nodule dataset from the SPIE 2016 lung nodule classification challenge. The phantom results in particular demonstrate that our algorithm has the potential to mitigate the disparity in measurements performed by different radiologists on the same lesions, which could improve the accuracy of disease progression tracking.

Cerebral activations related to writing and drawing with each hand.

PubMed

Potgieser, Adriaan R E; van der Hoorn, Anouk; de Jong, Bauke M

2015-01-01

Writing is a sequential motor action based on sensorimotor integration in visuospatial and linguistic functional domains. To test the hypothesis of lateralized circuitry concerning spatial and language components involved in such action, we employed an fMRI paradigm including writing and drawing with each hand. In this way, writing-related contributions of dorsal and ventral premotor regions in each hemisphere were assessed, together with effects in wider distributed circuitry. Given a right-hemisphere dominance for spatial action, right dorsal premotor cortex dominance was expected in left-hand writing while dominance of the left ventral premotor cortex was expected during right-hand writing. Sixteen healthy right-handed subjects were scanned during audition-guided writing of short sentences and simple figure drawing without visual feedback. Tapping with a pencil served as a basic control task for the two higher-order motor conditions. Activation differences were assessed with Statistical Parametric Mapping (SPM). Writing and drawing showed parietal-premotor and posterior inferior temporal activations in both hemispheres when compared to tapping. Drawing activations were rather symmetrical for each hand. Activations in left- and right-hand writing were left-hemisphere dominant, while right dorsal premotor activation only occurred in left-hand writing, supporting a spatial motor contribution of particularly the right hemisphere. Writing contrasted to drawing revealed left-sided activations in the dorsal and ventral premotor cortex, Broca's area, pre-Supplementary Motor Area and posterior middle and inferior temporal gyri, without parietal activation. The audition-driven postero-inferior temporal activations indicated retrieval of virtual visual form characteristics in writing and drawing, with additional activation concerning word form in the left hemisphere. Similar parietal processing in writing and drawing pointed at a common mechanism by which such visually formatted information is used for subsequent sensorimotor integration along a dorsal visuomotor pathway. In this, the left posterior middle temporal gyrus subserves phonological-orthographical conversion, dissociating dorsal parietal-premotor circuitry from perisylvian circuitry including Broca's area.

Cerebral Activations Related to Writing and Drawing with Each Hand

PubMed Central

Potgieser, Adriaan R. E.; van der Hoorn, Anouk; de Jong, Bauke M.

2015-01-01

Background Writing is a sequential motor action based on sensorimotor integration in visuospatial and linguistic functional domains. To test the hypothesis of lateralized circuitry concerning spatial and language components involved in such action, we employed an fMRI paradigm including writing and drawing with each hand. In this way, writing-related contributions of dorsal and ventral premotor regions in each hemisphere were assessed, together with effects in wider distributed circuitry. Given a right-hemisphere dominance for spatial action, right dorsal premotor cortex dominance was expected in left-hand writing while dominance of the left ventral premotor cortex was expected during right-hand writing. Methods Sixteen healthy right-handed subjects were scanned during audition-guided writing of short sentences and simple figure drawing without visual feedback. Tapping with a pencil served as a basic control task for the two higher-order motor conditions. Activation differences were assessed with Statistical Parametric Mapping (SPM). Results Writing and drawing showed parietal-premotor and posterior inferior temporal activations in both hemispheres when compared to tapping. Drawing activations were rather symmetrical for each hand. Activations in left- and right-hand writing were left-hemisphere dominant, while right dorsal premotor activation only occurred in left-hand writing, supporting a spatial motor contribution of particularly the right hemisphere. Writing contrasted to drawing revealed left-sided activations in the dorsal and ventral premotor cortex, Broca’s area, pre-Supplementary Motor Area and posterior middle and inferior temporal gyri, without parietal activation. Discussion The audition-driven postero-inferior temporal activations indicated retrieval of virtual visual form characteristics in writing and drawing, with additional activation concerning word form in the left hemisphere. Similar parietal processing in writing and drawing pointed at a common mechanism by which such visually formatted information is used for subsequent sensorimotor integration along a dorsal visuomotor pathway. In this, the left posterior middle temporal gyrus subserves phonological-orthographical conversion, dissociating dorsal parietal-premotor circuitry from perisylvian circuitry including Broca's area. PMID:25955655

Anthropomorphic robot for recognition and drawing generalized object images

NASA Astrophysics Data System (ADS)

Ginzburg, Vera M.

1998-10-01

The process of recognition, for instance, understanding the text, written by different fonts, consists in the depriving of the individual attributes of the letters in the particular font. It is shown that such process, in Nature and technique, can be provided by the narrowing the spatial frequency of the object's image by its defocusing. In defocusing images remain only areas, so-called Informative Fragments (IFs), which all together form the generalized (stylized) image of many identical objects. It is shown that the variety of shapes of IFs is restricted and can be presented by `Geometrical alphabet'. The `letters' for this alphabet can be created using two basic `genetic' figures: a stripe and round spot. It is known from physiology that the special cells of visual cortex response to these particular figures. The prototype of such `genetic' alphabet has been made using Boolean algebra (Venn's diagrams). The algorithm for drawing the letter's (`genlet's') shape in this alphabet and generalized images of objects (for example, `sleeping cat'), are given. A scheme of an anthropomorphic robot is shown together with results of model computer experiment of the robot's action--`drawing' the generalized image.

Comparison of Metabolic Pathways in Escherichia coli by Using Genetic Algorithms.

PubMed

Ortegon, Patricia; Poot-Hernández, Augusto C; Perez-Rueda, Ernesto; Rodriguez-Vazquez, Katya

2015-01-01

In order to understand how cellular metabolism has taken its modern form, the conservation and variations between metabolic pathways were evaluated by using a genetic algorithm (GA). The GA approach considered information on the complete metabolism of the bacterium Escherichia coli K-12, as deposited in the KEGG database, and the enzymes belonging to a particular pathway were transformed into enzymatic step sequences by using the breadth-first search algorithm. These sequences represent contiguous enzymes linked to each other, based on their catalytic activities as they are encoded in the Enzyme Commission numbers. In a posterior step, these sequences were compared using a GA in an all-against-all (pairwise comparisons) approach. Individual reactions were chosen based on their measure of fitness to act as parents of offspring, which constitute the new generation. The sequences compared were used to construct a similarity matrix (of fitness values) that was then considered to be clustered by using a k-medoids algorithm. A total of 34 clusters of conserved reactions were obtained, and their sequences were finally aligned with a multiple-sequence alignment GA optimized to align all the reaction sequences included in each group or cluster. From these comparisons, maps associated with the metabolism of similar compounds also contained similar enzymatic step sequences, reinforcing the Patchwork Model for the evolution of metabolism in E. coli K-12, an observation that can be expanded to other organisms, for which there is metabolism information. Finally, our mapping of these reactions is discussed, with illustrations from a particular case.

Comparison of Metabolic Pathways in Escherichia coli by Using Genetic Algorithms

PubMed Central

Ortegon, Patricia; Poot-Hernández, Augusto C.; Perez-Rueda, Ernesto; Rodriguez-Vazquez, Katya

2015-01-01

In order to understand how cellular metabolism has taken its modern form, the conservation and variations between metabolic pathways were evaluated by using a genetic algorithm (GA). The GA approach considered information on the complete metabolism of the bacterium Escherichia coli K-12, as deposited in the KEGG database, and the enzymes belonging to a particular pathway were transformed into enzymatic step sequences by using the breadth-first search algorithm. These sequences represent contiguous enzymes linked to each other, based on their catalytic activities as they are encoded in the Enzyme Commission numbers. In a posterior step, these sequences were compared using a GA in an all-against-all (pairwise comparisons) approach. Individual reactions were chosen based on their measure of fitness to act as parents of offspring, which constitute the new generation. The sequences compared were used to construct a similarity matrix (of fitness values) that was then considered to be clustered by using a k-medoids algorithm. A total of 34 clusters of conserved reactions were obtained, and their sequences were finally aligned with a multiple-sequence alignment GA optimized to align all the reaction sequences included in each group or cluster. From these comparisons, maps associated with the metabolism of similar compounds also contained similar enzymatic step sequences, reinforcing the Patchwork Model for the evolution of metabolism in E. coli K-12, an observation that can be expanded to other organisms, for which there is metabolism information. Finally, our mapping of these reactions is discussed, with illustrations from a particular case. PMID:25973143

A hybrid MD-kMC algorithm for folding proteins in explicit solvent.

PubMed

Peter, Emanuel Karl; Shea, Joan-Emma

2014-04-14

We present a novel hybrid MD-kMC algorithm that is capable of efficiently folding proteins in explicit solvent. We apply this algorithm to the folding of a small protein, Trp-Cage. Different kMC move sets that capture different possible rate limiting steps are implemented. The first uses secondary structure formation as a relevant rate event (a combination of dihedral rotations and hydrogen-bonding formation and breakage). The second uses tertiary structure formation events through formation of contacts via translational moves. Both methods fold the protein, but via different mechanisms and with different folding kinetics. The first method leads to folding via a structured helical state, with kinetics fit by a single exponential. The second method leads to folding via a collapsed loop, with kinetics poorly fit by single or double exponentials. In both cases, folding times are faster than experimentally reported values, The secondary and tertiary move sets are integrated in a third MD-kMC implementation, which now leads to folding of the protein via both pathways, with single and double-exponential fits to the rates, and to folding rates in good agreement with experimental values. The competition between secondary and tertiary structure leads to a longer search for the helix-rich intermediate in the case of the first pathway, and to the emergence of a kinetically trapped long-lived molten-globule collapsed state in the case of the second pathway. The algorithm presented not only captures experimentally observed folding intermediates and kinetics, but yields insights into the relative roles of local and global interactions in determining folding mechanisms and rates.

Bayesian parameter estimation for the Wnt pathway: an infinite mixture models approach.

PubMed

Koutroumpas, Konstantinos; Ballarini, Paolo; Votsi, Irene; Cournède, Paul-Henry

2016-09-01

Likelihood-free methods, like Approximate Bayesian Computation (ABC), have been extensively used in model-based statistical inference with intractable likelihood functions. When combined with Sequential Monte Carlo (SMC) algorithms they constitute a powerful approach for parameter estimation and model selection of mathematical models of complex biological systems. A crucial step in the ABC-SMC algorithms, significantly affecting their performance, is the propagation of a set of parameter vectors through a sequence of intermediate distributions using Markov kernels. In this article, we employ Dirichlet process mixtures (DPMs) to design optimal transition kernels and we present an ABC-SMC algorithm with DPM kernels. We illustrate the use of the proposed methodology using real data for the canonical Wnt signaling pathway. A multi-compartment model of the pathway is developed and it is compared to an existing model. The results indicate that DPMs are more efficient in the exploration of the parameter space and can significantly improve ABC-SMC performance. In comparison to alternative sampling schemes that are commonly used, the proposed approach can bring potential benefits in the estimation of complex multimodal distributions. The method is used to estimate the parameters and the initial state of two models of the Wnt pathway and it is shown that the multi-compartment model fits better the experimental data. Python scripts for the Dirichlet Process Gaussian Mixture model and the Gibbs sampler are available at https://sites.google.com/site/kkoutroumpas/software konstantinos.koutroumpas@ecp.fr. © The Author 2016. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com.

Efficient searching and annotation of metabolic networks using chemical similarity

PubMed Central

Pertusi, Dante A.; Stine, Andrew E.; Broadbelt, Linda J.; Tyo, Keith E.J.

2015-01-01

Motivation: The urgent need for efficient and sustainable biological production of fuels and high-value chemicals has elicited a wave of in silico techniques for identifying promising novel pathways to these compounds in large putative metabolic networks. To date, these approaches have primarily used general graph search algorithms, which are prohibitively slow as putative metabolic networks may exceed 1 million compounds. To alleviate this limitation, we report two methods—SimIndex (SI) and SimZyme—which use chemical similarity of 2D chemical fingerprints to efficiently navigate large metabolic networks and propose enzymatic connections between the constituent nodes. We also report a Byers–Waterman type pathway search algorithm for further paring down pertinent networks. Results: Benchmarking tests run with SI show it can reduce the number of nodes visited in searching a putative network by 100-fold with a computational time improvement of up to 105-fold. Subsequent Byers–Waterman search application further reduces the number of nodes searched by up to 100-fold, while SimZyme demonstrates ∼90% accuracy in matching query substrates with enzymes. Using these modules, we have designed and annotated an alternative to the methylerythritol phosphate pathway to produce isopentenyl pyrophosphate with more favorable thermodynamics than the native pathway. These algorithms will have a significant impact on our ability to use large metabolic networks that lack annotation of promiscuous reactions. Availability and implementation: Python files will be available for download at http://tyolab.northwestern.edu/tools/. Contact: k-tyo@northwestern.edu Supplementary information: Supplementary data are available at Bioinformatics online. PMID:25417203

Efficient searching and annotation of metabolic networks using chemical similarity.

PubMed

Pertusi, Dante A; Stine, Andrew E; Broadbelt, Linda J; Tyo, Keith E J

2015-04-01

The urgent need for efficient and sustainable biological production of fuels and high-value chemicals has elicited a wave of in silico techniques for identifying promising novel pathways to these compounds in large putative metabolic networks. To date, these approaches have primarily used general graph search algorithms, which are prohibitively slow as putative metabolic networks may exceed 1 million compounds. To alleviate this limitation, we report two methods--SimIndex (SI) and SimZyme--which use chemical similarity of 2D chemical fingerprints to efficiently navigate large metabolic networks and propose enzymatic connections between the constituent nodes. We also report a Byers-Waterman type pathway search algorithm for further paring down pertinent networks. Benchmarking tests run with SI show it can reduce the number of nodes visited in searching a putative network by 100-fold with a computational time improvement of up to 10(5)-fold. Subsequent Byers-Waterman search application further reduces the number of nodes searched by up to 100-fold, while SimZyme demonstrates ∼ 90% accuracy in matching query substrates with enzymes. Using these modules, we have designed and annotated an alternative to the methylerythritol phosphate pathway to produce isopentenyl pyrophosphate with more favorable thermodynamics than the native pathway. These algorithms will have a significant impact on our ability to use large metabolic networks that lack annotation of promiscuous reactions. Python files will be available for download at http://tyolab.northwestern.edu/tools/. Supplementary data are available at Bioinformatics online. © The Author 2014. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com.

Specific Interference between a Cognitive Task and Sensory Organization for Stance Balance Control in Healthy Young Adults: Visuospatial Effects

ERIC Educational Resources Information Center

Chong, Raymond K. Y.; Mills, Bradley; Dailey, Leanna; Lane, Elizabeth; Smith, Sarah; Lee, Kyoung-Hyun

2010-01-01

We tested the hypothesis that a computational overload results when two activities, one motor and the other cognitive that draw on the same neural processing pathways, are performed concurrently. Healthy young adult subjects carried out two seemingly distinct tasks of maintaining standing balance control under conditions of low (eyes closed),…

Pathways to Labor Market Success: The Literacy Proficiency of U.S. Adults. Policy Information Report

ERIC Educational Resources Information Center

Sum, Andrew; Kirsch, Irwin; Yamamoto, Kentaro

2004-01-01

This is the fourth in a series of reports that draws upon the vast amount of background and assessment data and information that have been collected from the National Adult Literacy Survey (NALS) and the International Adult Literacy Survey (IALS). In this report, the authors find connections between the literacy skills of adults and their success…

Access to Elementary Education in India: Politics, Policies and Progress. CREATE Pathways to Access. Research Monograph No. 44

ERIC Educational Resources Information Center

Little, Angela W.

2010-01-01

This monograph examines progress in, and policies for, access to elementary education over the past 60 years, the role played by political factors in the process of policy formulation and implementation and the drivers and inhibitors of the implementation of reforms in elementary education in recent years in India. Drawing on interviews and…

Technology Teacher Education: Alternative Pathways Established in Response to Issues of Supply and Demand in NSW

ERIC Educational Resources Information Center

Cornius-Randall, Rachael

2004-01-01

Teacher shortages have encouraged initiatives to tailor training programs to meet the demand in both past, current and future contexts. Such programs have been streamlined to ensure a rapid response to shortages, in addition to also drawing participants from non-traditional groups as a source of potential educators. Within teacher education,…

Drawing with Light and Clay: Teaching and Learning in the Art Studio as Pathways to Engagement

ERIC Educational Resources Information Center

Albertson, Constant; Davidson, Miriam

2007-01-01

In this essay, Albertson and Davidson explore the attributes of photography and ceramic arts education to identify eight key elements integral to engagement in these art studios for under-served and disenchanted learners. They suggest that these key elements can provide numerous clues as to how teachers and school communities might reimagine both…

Pathway to a Baccalaureate in STEM Fields: Community Colleges a Viable Route and Does Early STEM Momentum Matter?

ERIC Educational Resources Information Center

Wang, Xueli

2015-01-01

This study examines the effect of beginning at a community college on baccalaureate success in science, technology, engineering, and mathematics (STEM) fields. In addition, drawing upon a new concept of "STEM momentum," this research explores whether and how STEM baccalaureate success is related to early STEM momentum, as well as whether…

Creating a Learning Environment: An Educational Leader's Guide to Managing School Culture

ERIC Educational Resources Information Center

Brucato, John M.

2005-01-01

Drawing on his personal experience as a modern day school administrator, John M. Brucato believes that there is a pathway to survival, success, and enjoyment in the role of a school leader that keeps the focus on meeting the needs of young people, while nurturing relationship with all stakeholders responsible for bringing this to fruition.…

Course-Taking Patterns of Community College Students Beginning in STEM: Using Data Mining Techniques to Reveal Viable STEM Transfer Pathways

ERIC Educational Resources Information Center

Wang, Xueli

2016-01-01

This research focuses on course-taking patterns of beginning community college students enrolled in one or more non-remedial science, technology, engineering, and mathematics (STEM) courses during their first year of college, and how these patterns are mapped against upward transfer in STEM fields of study. Drawing upon postsecondary transcript…

Distinct Pathways: Tracing the Origins and History of Private Higher Education in South Africa

ERIC Educational Resources Information Center

Kruss, Glenda

2005-01-01

In much of the literature on the privatisation of higher education, it appears as both a relatively recent phenomenon, and one that is homogenous in its causes, forms and effects. Drawing on the case of South Africa, this study challenges these assumptions, suggesting that without a sense of the long history of private provision and its interwoven…

Pathway Design, Engineering, and Optimization.

PubMed

Garcia-Ruiz, Eva; HamediRad, Mohammad; Zhao, Huimin

The microbial metabolic versatility found in nature has inspired scientists to create microorganisms capable of producing value-added compounds. Many endeavors have been made to transfer and/or combine pathways, existing or even engineered enzymes with new function to tractable microorganisms to generate new metabolic routes for drug, biofuel, and specialty chemical production. However, the success of these pathways can be impeded by different complications from an inherent failure of the pathway to cell perturbations. Pursuing ways to overcome these shortcomings, a wide variety of strategies have been developed. This chapter will review the computational algorithms and experimental tools used to design efficient metabolic routes, and construct and optimize biochemical pathways to produce chemicals of high interest.

History-based route selection for reactive ad hoc routing protocols

NASA Astrophysics Data System (ADS)

Medidi, Sirisha; Cappetto, Peter

2007-04-01

Ad hoc networks rely on cooperation in order to operate, but in a resource constrained environment not all nodes behave altruistically. Selfish nodes preserve their own resources and do not forward packets not in their own self interest. These nodes degrade the performance of the network, but judicious route selection can help maintain performance despite this behavior. Many route selection algorithms place importance on shortness of the route rather than its reliability. We introduce a light-weight route selection algorithm that uses past behavior to judge the quality of a route rather than solely on the length of the route. It draws information from the underlying routing layer at no extra cost and selects routes with a simple algorithm. This technique maintains this data in a small table, which does not place a high cost on memory. History-based route selection's minimalism suits the needs the portable wireless devices and is easy to implement. We implemented our algorithm and tested it in the ns2 environment. Our simulation results show that history-based route selection achieves higher packet delivery and improved stability than its length-based counterpart.

A star recognition method based on the Adaptive Ant Colony algorithm for star sensors.

PubMed

Quan, Wei; Fang, Jiancheng

2010-01-01

A new star recognition method based on the Adaptive Ant Colony (AAC) algorithm has been developed to increase the star recognition speed and success rate for star sensors. This method draws circles, with the center of each one being a bright star point and the radius being a special angular distance, and uses the parallel processing ability of the AAC algorithm to calculate the angular distance of any pair of star points in the circle. The angular distance of two star points in the circle is solved as the path of the AAC algorithm, and the path optimization feature of the AAC is employed to search for the optimal (shortest) path in the circle. This optimal path is used to recognize the stellar map and enhance the recognition success rate and speed. The experimental results show that when the position error is about 50″, the identification success rate of this method is 98% while the Delaunay identification method is only 94%. The identification time of this method is up to 50 ms.

Cytoprophet: a Cytoscape plug-in for protein and domain interaction networks inference.

PubMed

Morcos, Faruck; Lamanna, Charles; Sikora, Marcin; Izaguirre, Jesús

2008-10-01

Cytoprophet is a software tool that allows prediction and visualization of protein and domain interaction networks. It is implemented as a plug-in of Cytoscape, an open source software framework for analysis and visualization of molecular networks. Cytoprophet implements three algorithms that predict new potential physical interactions using the domain composition of proteins and experimental assays. The algorithms for protein and domain interaction inference include maximum likelihood estimation (MLE) using expectation maximization (EM); the set cover approach maximum specificity set cover (MSSC) and the sum-product algorithm (SPA). After accepting an input set of proteins with Uniprot ID/Accession numbers and a selected prediction algorithm, Cytoprophet draws a network of potential interactions with probability scores and GO distances as edge attributes. A network of domain interactions between the domains of the initial protein list can also be generated. Cytoprophet was designed to take advantage of the visual capabilities of Cytoscape and be simple to use. An example of inference in a signaling network of myxobacterium Myxococcus xanthus is presented and available at Cytoprophet's website. http://cytoprophet.cse.nd.edu.

Multiscale mutation clustering algorithm identifies pan-cancer mutational clusters associated with pathway-level changes in gene expression

PubMed Central

Poole, William; Leinonen, Kalle; Shmulevich, Ilya

2017-01-01

Cancer researchers have long recognized that somatic mutations are not uniformly distributed within genes. However, most approaches for identifying cancer mutations focus on either the entire-gene or single amino-acid level. We have bridged these two methodologies with a multiscale mutation clustering algorithm that identifies variable length mutation clusters in cancer genes. We ran our algorithm on 539 genes using the combined mutation data in 23 cancer types from The Cancer Genome Atlas (TCGA) and identified 1295 mutation clusters. The resulting mutation clusters cover a wide range of scales and often overlap with many kinds of protein features including structured domains, phosphorylation sites, and known single nucleotide variants. We statistically associated these multiscale clusters with gene expression and drug response data to illuminate the functional and clinical consequences of mutations in our clusters. Interestingly, we find multiple clusters within individual genes that have differential functional associations: these include PTEN, FUBP1, and CDH1. This methodology has potential implications in identifying protein regions for drug targets, understanding the biological underpinnings of cancer, and personalizing cancer treatments. Toward this end, we have made the mutation clusters and the clustering algorithm available to the public. Clusters and pathway associations can be interactively browsed at m2c.systemsbiology.net. The multiscale mutation clustering algorithm is available at https://github.com/IlyaLab/M2C. PMID:28170390

Multiscale mutation clustering algorithm identifies pan-cancer mutational clusters associated with pathway-level changes in gene expression.

PubMed

Poole, William; Leinonen, Kalle; Shmulevich, Ilya; Knijnenburg, Theo A; Bernard, Brady

2017-02-01

Cancer researchers have long recognized that somatic mutations are not uniformly distributed within genes. However, most approaches for identifying cancer mutations focus on either the entire-gene or single amino-acid level. We have bridged these two methodologies with a multiscale mutation clustering algorithm that identifies variable length mutation clusters in cancer genes. We ran our algorithm on 539 genes using the combined mutation data in 23 cancer types from The Cancer Genome Atlas (TCGA) and identified 1295 mutation clusters. The resulting mutation clusters cover a wide range of scales and often overlap with many kinds of protein features including structured domains, phosphorylation sites, and known single nucleotide variants. We statistically associated these multiscale clusters with gene expression and drug response data to illuminate the functional and clinical consequences of mutations in our clusters. Interestingly, we find multiple clusters within individual genes that have differential functional associations: these include PTEN, FUBP1, and CDH1. This methodology has potential implications in identifying protein regions for drug targets, understanding the biological underpinnings of cancer, and personalizing cancer treatments. Toward this end, we have made the mutation clusters and the clustering algorithm available to the public. Clusters and pathway associations can be interactively browsed at m2c.systemsbiology.net. The multiscale mutation clustering algorithm is available at https://github.com/IlyaLab/M2C.

Identification of mutated driver pathways in cancer using a multi-objective optimization model.

PubMed

Zheng, Chun-Hou; Yang, Wu; Chong, Yan-Wen; Xia, Jun-Feng

2016-05-01

New-generation high-throughput technologies, including next-generation sequencing technology, have been extensively applied to solve biological problems. As a result, large cancer genomics projects such as the Cancer Genome Atlas (TCGA) and the International Cancer Genome Consortium are producing large amount of rich and diverse data in multiple cancer types. The identification of mutated driver genes and driver pathways from these data is a significant challenge. Genome aberrations in cancer cells can be divided into two types: random 'passenger mutation' and functional 'driver mutation'. In this paper, we introduced a Multi-objective Optimization model based on a Genetic Algorithm (MOGA) to solve the maximum weight submatrix problem, which can be employed to identify driver genes and driver pathways promoting cancer proliferation. The maximum weight submatrix problem defined to find mutated driver pathways is based on two specific properties, i.e., high coverage and high exclusivity. The multi-objective optimization model can adjust the trade-off between high coverage and high exclusivity. We proposed an integrative model by combining gene expression data and mutation data to improve the performance of the MOGA algorithm in a biological context. Copyright © 2016 Elsevier Ltd. All rights reserved.

An algorithm for automated ROI definition in water or epoxy-filled NEMA NU-2 image quality phantoms.

PubMed

Pierce, Larry A; Byrd, Darrin W; Elston, Brian F; Karp, Joel S; Sunderland, John J; Kinahan, Paul E

2016-01-08

Drawing regions of interest (ROIs) in positron emission tomography/computed tomography (PET/CT) scans of the National Electrical Manufacturers Association (NEMA) NU-2 Image Quality (IQ) phantom is a time-consuming process that allows for interuser variability in the measurements. In order to reduce operator effort and allow batch processing of IQ phantom images, we propose a fast, robust, automated algorithm for performing IQ phantom sphere localization and analysis. The algorithm is easily altered to accommodate different configurations of the IQ phantom. The proposed algorithm uses information from both the PET and CT image volumes in order to overcome the challenges of detecting the smallest spheres in the PET volume. This algorithm has been released as an open-source plug-in to the Osirix medical image viewing software package. We test the algorithm under various noise conditions, positions within the scanner, air bubbles in the phantom spheres, and scanner misalignment conditions. The proposed algorithm shows run-times between 3 and 4 min and has proven to be robust under all tested conditions, with expected sphere localization deviations of less than 0.2 mm and variations of PET ROI mean and maximum values on the order of 0.5% and 2%, respectively, over multiple PET acquisitions. We conclude that the proposed algorithm is stable when challenged with a variety of physical and imaging anomalies, and that the algorithm can be a valuable tool for those who use the NEMA NU-2 IQ phantom for PET/CT scanner acceptance testing and QA/QC.

Application of Micro-segmentation Algorithms to the Healthcare Market:A Case Study

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

Sukumar, Sreenivas R; Aline, Frank

We draw inspiration from the recent success of loyalty programs and targeted personalized market campaigns of retail companies such as Kroger, Netflix, etc. to understand beneficiary behaviors in the healthcare system. Our posit is that we can emulate the financial success the companies have achieved by better understanding and predicting customer behaviors and translating such success to healthcare operations. Towards that goal, we survey current practices in market micro-segmentation research and analyze health insurance claims data using those algorithms. We present results and insights from micro-segmentation of the beneficiaries using different techniques and discuss how the interpretation can assist withmore » matching the cost-effective insurance payment models to the beneficiary micro-segments.« less

Multidirectional Scanning Model, MUSCLE, to Vectorize Raster Images with Straight Lines

PubMed Central

Karas, Ismail Rakip; Bayram, Bulent; Batuk, Fatmagul; Akay, Abdullah Emin; Baz, Ibrahim

2008-01-01

This paper presents a new model, MUSCLE (Multidirectional Scanning for Line Extraction), for automatic vectorization of raster images with straight lines. The algorithm of the model implements the line thinning and the simple neighborhood methods to perform vectorization. The model allows users to define specified criteria which are crucial for acquiring the vectorization process. In this model, various raster images can be vectorized such as township plans, maps, architectural drawings, and machine plans. The algorithm of the model was developed by implementing an appropriate computer programming and tested on a basic application. Results, verified by using two well known vectorization programs (WinTopo and Scan2CAD), indicated that the model can successfully vectorize the specified raster data quickly and accurately. PMID:27879843

Multidirectional Scanning Model, MUSCLE, to Vectorize Raster Images with Straight Lines.

PubMed

Karas, Ismail Rakip; Bayram, Bulent; Batuk, Fatmagul; Akay, Abdullah Emin; Baz, Ibrahim

2008-04-15

This paper presents a new model, MUSCLE (Multidirectional Scanning for Line Extraction), for automatic vectorization of raster images with straight lines. The algorithm of the model implements the line thinning and the simple neighborhood methods to perform vectorization. The model allows users to define specified criteria which are crucial for acquiring the vectorization process. In this model, various raster images can be vectorized such as township plans, maps, architectural drawings, and machine plans. The algorithm of the model was developed by implementing an appropriate computer programming and tested on a basic application. Results, verified by using two well known vectorization programs (WinTopo and Scan2CAD), indicated that the model can successfully vectorize the specified raster data quickly and accurately.

Does Motivation for Exercise Influence Post-Exercise Snacking Behavior?

PubMed Central

Dimmock, James A.; Guelfi, Kym J.; West, Jessica S.; Masih, Tasmiah; Jackson, Ben

2015-01-01

It is well established that regular exercise plays an important role in achieving a number of health and wellbeing outcomes. However, certain post-exercise behaviors, including the consumption of unhealthy high-calorie foods, can counteract some of the benefits of physical activity. There are at least three overlapping pathways through which exercise may increase the likelihood of consuming pleasurable but unhealthy foods: through impulsive cognitive processes, reflective cognitive processes, and/or physiological responses. It is argued in this paper that motivation toward exercise can influence each of these pathways. Drawing from literature from various domains, we postulate that controlled exercise motivation, as opposed to autonomous exercise motivation, is more likely to influence each of these pathways in a manner that leaves individuals susceptible to the post-exercise consumption of pleasurable but unhealthy foods. PMID:26083114

Pathway Activity Profiling (PAPi): from the metabolite profile to the metabolic pathway activity.

PubMed

Aggio, Raphael B M; Ruggiero, Katya; Villas-Bôas, Silas Granato

2010-12-01

Metabolomics is one of the most recent omics-technologies and uses robust analytical techniques to screen low molecular mass metabolites in biological samples. It has evolved very quickly during the last decade. However, metabolomics datasets are considered highly complex when used to relate metabolite levels to metabolic pathway activity. Despite recent developments in bioinformatics, which have improved the quality of metabolomics data, there is still no straightforward method capable of correlating metabolite level to the activity of different metabolic pathways operating within the cells. Thus, this kind of analysis still depends on extremely laborious and time-consuming processes. Here, we present a new algorithm Pathway Activity Profiling (PAPi) with which we are able to compare metabolic pathway activities from metabolite profiles. The applicability and potential of PAPi was demonstrated using a previously published data from the yeast Saccharomyces cerevisiae. PAPi was able to support the biological interpretations of the previously published observations and, in addition, generated new hypotheses in a straightforward manner. However, PAPi is time consuming to perform manually. Thus, we also present here a new R-software package (PAPi) which implements the PAPi algorithm and facilitates its usage to quickly compare metabolic pathways activities between different experimental conditions. Using the identified metabolites and their respective abundances as input, the PAPi package calculates pathways' Activity Scores, which represents the potential metabolic pathways activities and allows their comparison between conditions. PAPi also performs principal components analysis and analysis of variance or t-test to investigate differences in activity level between experimental conditions. In addition, PAPi generates comparative graphs highlighting up- and down-regulated pathway activity. These datasets are available in http://www.4shared.com/file/hTWyndYU/extra.html and http://www.4shared.com/file/VbQIIDeu/intra.html. PAPi package is available in: http://www.4shared.com/file/s0uIYWIg/PAPi_10.html s.villas-boas@auckland.ac.nz Supplementary data are available at Bioinformatics online.

JOURNAL CLUB: Plagiarism in Manuscripts Submitted to the AJR: Development of an Optimal Screening Algorithm and Management Pathways.

PubMed

Taylor, Donna B

2017-04-01

The objective of this study was to investigate the incidence of plagiarism in a sample of manuscripts submitted to the AJR using CrossCheck, develop an algorithm to identify significant plagiarism, and formulate management pathways. A sample of 110 of 1610 (6.8%) manuscripts submitted to AJR in 2014 in the categories of Original Research or Review were analyzed using CrossCheck and manual assessment. The overall similarity index (OSI), highest similarity score from a single source, whether duplication was from single or multiple origins, journal section, and presence or absence of referencing the source were recorded. The criteria outlined by the International Committee of Medical Journal Editors were the reference standard for identifying manuscripts containing plagiarism. Statistical analysis was used to develop a screening algorithm to maximize sensitivity and specificity for the detection of plagiarism. Criteria for defining the severity of plagiarism and management pathways based on the severity of the plagiarism were determined. Twelve manuscripts (10.9%) contained plagiarism. Nine had an OSI excluding quotations and references of less than 20%. In seven, the highest similarity score from a single source was less than 10%. The highest similarity score from a single source was the work of the same author or authors in nine. Common sections for duplication were the Materials and Methods, Discussion, and abstract. Referencing the original source was lacking in 11. Plagiarism was undetected at submission in five of these 12 articles; two had been accepted for publication. The most effective screening algorithm was to average the OSI including quotations and references and the highest similarity score from a single source and to submit manuscripts with an average value of more than 12% for further review. The current methods for detecting plagiarism are suboptimal. A new screening algorithm is proposed.

Scoping the proximal and distal dimensions of climate change on health and wellbeing.

PubMed

Morris, George Paterson; Reis, Stefan; Beck, Sheila Anne; Fleming, Lora Elderkin; Adger, William Neil; Benton, Timothy Guy; Depledge, Michael Harold

2017-12-05

The impacts of climate on health and wellbeing occur in time and space and through a range of indirect, complicated mechanisms. This diversity of pathways has major implications for national public health planning and influence on interventions that might help to mitigate and adapt to rapidly changing environmental conditions, nationally and internationally. This paper draws upon evidence from public health and adverse impact studies across climate science, hydrology, agriculture, public health, and the social sciences. It presents a conceptual model to support decision-making by recognizing both the proximal and distal pathways from climate-induced environmental change to national health and wellbeing. The proximal and distal pathways associated with food security, migration and mobility illustrate the diverse climate change influences in different geographic locations over different timescales. We argue that greater realization and articulation of proximal and distal pathways should radically alter how climate change is addressed as a national and international public health challenge.

Newer developments on self-modeling curve resolution implementing equality and unimodality constraints.

PubMed

Beyramysoltan, Samira; Abdollahi, Hamid; Rajkó, Róbert

2014-05-27

Analytical self-modeling curve resolution (SMCR) methods resolve data sets to a range of feasible solutions using only non-negative constraints. The Lawton-Sylvestre method was the first direct method to analyze a two-component system. It was generalized as a Borgen plot for determining the feasible regions in three-component systems. It seems that a geometrical view is required for considering curve resolution methods, because the complicated (only algebraic) conceptions caused a stop in the general study of Borgen's work for 20 years. Rajkó and István revised and elucidated the principles of existing theory in SMCR methods and subsequently introduced computational geometry tools for developing an algorithm to draw Borgen plots in three-component systems. These developments are theoretical inventions and the formulations are not always able to be given in close form or regularized formalism, especially for geometric descriptions, that is why several algorithms should have been developed and provided for even the theoretical deductions and determinations. In this study, analytical SMCR methods are revised and described using simple concepts. The details of a drawing algorithm for a developmental type of Borgen plot are given. Additionally, for the first time in the literature, equality and unimodality constraints are successfully implemented in the Lawton-Sylvestre method. To this end, a new state-of-the-art procedure is proposed to impose equality constraint in Borgen plots. Two- and three-component HPLC-DAD data set were simulated and analyzed by the new analytical curve resolution methods with and without additional constraints. Detailed descriptions and explanations are given based on the obtained abstract spaces. Copyright © 2014 Elsevier B.V. All rights reserved.

Development of an algorithm to provide awareness in choosing study designs for inclusion in systematic reviews of healthcare interventions: a method study

PubMed Central

Peinemann, Frank; Kleijnen, Jos

2015-01-01

Objectives To develop an algorithm that aims to provide guidance and awareness for choosing multiple study designs in systematic reviews of healthcare interventions. Design Method study: (1) To summarise the literature base on the topic. (2) To apply the integration of various study types in systematic reviews. (3) To devise decision points and outline a pragmatic decision tree. (4) To check the plausibility of the algorithm by backtracking its pathways in four systematic reviews. Results (1) The results of our systematic review of the published literature have already been published. (2) We recaptured the experience from our four previously conducted systematic reviews that required the integration of various study types. (3) We chose length of follow-up (long, short), frequency of events (rare, frequent) and types of outcome as decision points (death, disease, discomfort, disability, dissatisfaction) and aligned the study design labels according to the Cochrane Handbook. We also considered practical or ethical concerns, and the problem of unavailable high-quality evidence. While applying the algorithm, disease-specific circumstances and aims of interventions should be considered. (4) We confirmed the plausibility of the pathways of the algorithm. Conclusions We propose that the algorithm can assist to bring seminal features of a systematic review with multiple study designs to the attention of anyone who is planning to conduct a systematic review. It aims to increase awareness and we think that it may reduce the time burden on review authors and may contribute to the production of a higher quality review. PMID:26289450

Real-time interactive tractography analysis for multimodal brain visualization tool: MultiXplore

NASA Astrophysics Data System (ADS)

Bakhshmand, Saeed M.; de Ribaupierre, Sandrine; Eagleson, Roy

2017-03-01

Most debilitating neurological disorders can have anatomical origins. Yet unlike other body organs, the anatomy alone cannot easily provide an understanding of brain functionality. In fact, addressing the challenge of linking structural and functional connectivity remains in the frontiers of neuroscience. Aggregating multimodal neuroimaging datasets may be critical for developing theories that span brain functionality, global neuroanatomy and internal microstructures. Functional magnetic resonance imaging (fMRI) and diffusion tensor imaging (DTI) are main such techniques that are employed to investigate the brain under normal and pathological conditions. FMRI records blood oxygenation level of the grey matter (GM), whereas DTI is able to reveal the underlying structure of the white matter (WM). Brain global activity is assumed to be an integration of GM functional hubs and WM neural pathways that serve to connect them. In this study we developed and evaluated a two-phase algorithm. This algorithm is employed in a 3D interactive connectivity visualization framework and helps to accelerate clustering of virtual neural pathways. In this paper, we will detail an algorithm that makes use of an index-based membership array formed for a whole brain tractography file and corresponding parcellated brain atlas. Next, we demonstrate efficiency of the algorithm by measuring required times for extracting a variety of fiber clusters, which are chosen in such a way to resemble all sizes probable output data files that algorithm will generate. The proposed algorithm facilitates real-time visual inspection of neuroimaging data to further the discovery in structure-function relationship of the brain networks.

Current Treatment Algorithms for Patients with Metastatic Non-Small Cell, Non-Squamous Lung Cancer

PubMed Central

Melosky, Barbara

2017-01-01

The treatment paradigm for metastatic non-small cell, non-squamous lung cancer is continuously evolving due to new treatment options and our increasing knowledge of molecular signal pathways. As a result of treatments becoming more efficacious and more personalized, survival for selected groups of non-small cell lung cancer (NSCLC) patients is increasing. In this paper, three algorithms will be presented for treating patients with metastatic non-squamous, NSCLC. These include treatment algorithms for NSCLC patients whose tumors have EGFR mutations, ALK rearrangements, or wild-type/wild-type tumors. As the world of immunotherapy continues to evolve quickly, a future algorithm will also be presented. PMID:28373963

Binomial tau-leap spatial stochastic simulation algorithm for applications in chemical kinetics.

PubMed

Marquez-Lago, Tatiana T; Burrage, Kevin

2007-09-14

In cell biology, cell signaling pathway problems are often tackled with deterministic temporal models, well mixed stochastic simulators, and/or hybrid methods. But, in fact, three dimensional stochastic spatial modeling of reactions happening inside the cell is needed in order to fully understand these cell signaling pathways. This is because noise effects, low molecular concentrations, and spatial heterogeneity can all affect the cellular dynamics. However, there are ways in which important effects can be accounted without going to the extent of using highly resolved spatial simulators (such as single-particle software), hence reducing the overall computation time significantly. We present a new coarse grained modified version of the next subvolume method that allows the user to consider both diffusion and reaction events in relatively long simulation time spans as compared with the original method and other commonly used fully stochastic computational methods. Benchmarking of the simulation algorithm was performed through comparison with the next subvolume method and well mixed models (MATLAB), as well as stochastic particle reaction and transport simulations (CHEMCELL, Sandia National Laboratories). Additionally, we construct a model based on a set of chemical reactions in the epidermal growth factor receptor pathway. For this particular application and a bistable chemical system example, we analyze and outline the advantages of our presented binomial tau-leap spatial stochastic simulation algorithm, in terms of efficiency and accuracy, in scenarios of both molecular homogeneity and heterogeneity.

Automated circumferential construction of first-order aqueous humor outflow pathways using spectral-domain optical coherence tomography.

PubMed

Huang, Alex S; Belghith, Akram; Dastiridou, Anna; Chopra, Vikas; Zangwill, Linda M; Weinreb, Robert N

2017-06-01

The purpose was to create a three-dimensional (3-D) model of circumferential aqueous humor outflow (AHO) in a living human eye with an automated detection algorithm for Schlemm’s canal (SC) and first-order collector channels (CC) applied to spectral-domain optical coherence tomography (SD-OCT). Anterior segment SD-OCT scans from a subject were acquired circumferentially around the limbus. A Bayesian Ridge method was used to approximate the location of the SC on infrared confocal laser scanning ophthalmoscopic images with a cross multiplication tool developed to initiate SC/CC detection automated through a fuzzy hidden Markov Chain approach. Automatic segmentation of SC and initial CC’s was manually confirmed by two masked graders. Outflow pathways detected by the segmentation algorithm were reconstructed into a 3-D representation of AHO. Overall, only <1% of images (5114 total B-scans) were ungradable. Automatic segmentation algorithm performed well with SC detection 98.3% of the time and <0.1% false positive detection compared to expert grader consensus. CC was detected 84.2% of the time with 1.4% false positive detection. 3-D representation of AHO pathways demonstrated variably thicker and thinner SC with some clear CC roots. Circumferential (360 deg), automated, and validated AHO detection of angle structures in the living human eye with reconstruction was possible.

What does the electroencephalogram tell us about the mechanisms of action of ECT in major depressive disorders?

PubMed

Farzan, Faranak; Boutros, Nash N; Blumberger, Daniel M; Daskalakis, Zafiris J

2014-06-01

Electroconvulsive therapy (ECT) remains to be one of the most effective treatment options in treatment-resistant major depressive disorder (MDD). From the early days, researchers have embarked on extracting information from electroencephalography (EEG) recordings before, during, and after ECT to identify neurophysiological targets of ECT and discover EEG predictors of response to ECT in patients with MDD. In this article, we provide an overview of visually detected and quantitative EEG features that could help in furthering our understanding of the mechanisms of action of ECT in MDD. We further discuss the EEG findings in the context of postulated hypotheses of ECT therapeutic pathways. We introduce an alternative and unifying hypothesis suggesting that ECT may exert its therapeutic efficacy through resetting the aberrant functional connectivity and promoting the generation of new and healthy connections in brain regions implicated in MDD pathophysiology, a mechanism that may be in part mediated by the ECT-induced activation of inhibitory and neuroplasticity mechanisms. We further discuss the added value of EEG markers in the larger context of ECT research and as complementary to neuroimaging and genetic markers. We conclude by drawing attention to the need for longitudinal studies in large cohort of patients and the need for standardization and validation of EEG algorithms of functional connectivity across studies to facilitate the translation of EEG correlates of ECT response in routine clinical practice.

Writing Their Way into Talk: Emergent Bilinguals' Emergent Literacy Practices as Pathways to Peer Interaction and Oral Language Growth

ERIC Educational Resources Information Center

Bernstein, Katie A.

2017-01-01

This paper explores the idea that young children's emergent literacy practices can be tools for mediating peer interaction, and that, therefore, literacy, even in its earliest stages, can support oral language development, particularly for emergent bilinguals. The paper draws on data collected during a year-long ethnographic study of 11 Nepali-…

Gender Equity in Education: A Review of Trends and Factors. CREATE Pathways to Access. Research Monograph No. 18

ERIC Educational Resources Information Center

Bandyopadhyay, Madhumita; Subrahmanian, Ramya

2008-01-01

This review paper draws on recent data to map the access and participation rates of girls relative to boys. This paper offers a critical assessment of findings of different recent researches on school education in India identifying the areas that need further research. The paper reveals that while enrolment of girls has increased rapidly since the…

Women's Experiences of Principalship in Two South African High Schools in Multiply Deprived Rural Areas: A Life History Approach

ERIC Educational Resources Information Center

Faulkner, Caroline

2015-01-01

This article draws on data collected from a wider, longitudinal life history study conducted in South Africa between 2010 and 2014. The study focussed specifically on the personal and professional pathways to principalship of a sample of women leaders of co-educational high schools in South Africa, in both rural and peri-urban provinces: a role…

Network-Based Disease Module Discovery by a Novel Seed Connector Algorithm with Pathobiological Implications.

PubMed

Wang, Rui-Sheng; Loscalzo, Joseph

2018-05-20

Understanding the genetic basis of complex diseases is challenging. Prior work shows that disease-related proteins do not typically function in isolation. Rather, they often interact with each other to form a network module that underlies dysfunctional mechanistic pathways. Identifying such disease modules will provide insights into a systems-level understanding of molecular mechanisms of diseases. Owing to the incompleteness of our knowledge of disease proteins and limited information on the biological mediators of pathobiological processes, the key proteins (seed proteins) for many diseases appear scattered over the human protein-protein interactome and form a few small branches, rather than coherent network modules. In this paper, we develop a network-based algorithm, called the Seed Connector algorithm (SCA), to pinpoint disease modules by adding as few additional linking proteins (seed connectors) to the seed protein pool as possible. Such seed connectors are hidden disease module elements that are critical for interpreting the functional context of disease proteins. The SCA aims to connect seed disease proteins so that disease mechanisms and pathways can be decoded based on predicted coherent network modules. We validate the algorithm using a large corpus of 70 complex diseases and binding targets of over 200 drugs, and demonstrate the biological relevance of the seed connectors. Lastly, as a specific proof of concept, we apply the SCA to a set of seed proteins for coronary artery disease derived from a meta-analysis of large-scale genome-wide association studies and obtain a coronary artery disease module enriched with important disease-related signaling pathways and drug targets not previously recognized. Copyright © 2018 Elsevier Ltd. All rights reserved.

Searching social networks for subgraph patterns

NASA Astrophysics Data System (ADS)

Ogaard, Kirk; Kase, Sue; Roy, Heather; Nagi, Rakesh; Sambhoos, Kedar; Sudit, Moises

2013-06-01

Software tools for Social Network Analysis (SNA) are being developed which support various types of analysis of social networks extracted from social media websites (e.g., Twitter). Once extracted and stored in a database such social networks are amenable to analysis by SNA software. This data analysis often involves searching for occurrences of various subgraph patterns (i.e., graphical representations of entities and relationships). The authors have developed the Graph Matching Toolkit (GMT) which provides an intuitive Graphical User Interface (GUI) for a heuristic graph matching algorithm called the Truncated Search Tree (TruST) algorithm. GMT is a visual interface for graph matching algorithms processing large social networks. GMT enables an analyst to draw a subgraph pattern by using a mouse to select categories and labels for nodes and links from drop-down menus. GMT then executes the TruST algorithm to find the top five occurrences of the subgraph pattern within the social network stored in the database. GMT was tested using a simulated counter-insurgency dataset consisting of cellular phone communications within a populated area of operations in Iraq. The results indicated GMT (when executing the TruST graph matching algorithm) is a time-efficient approach to searching large social networks. GMT's visual interface to a graph matching algorithm enables intelligence analysts to quickly analyze and summarize the large amounts of data necessary to produce actionable intelligence.

An algorithm based on OmniView technology to reconstruct sagittal and coronal planes of the fetal brain from volume datasets acquired by three-dimensional ultrasound.

PubMed

Rizzo, G; Capponi, A; Pietrolucci, M E; Capece, A; Aiello, E; Mammarella, S; Arduini, D

2011-08-01

To describe a novel algorithm, based on the new display technology 'OmniView', developed to visualize diagnostic sagittal and coronal planes of the fetal brain from volumes obtained by three-dimensional (3D) ultrasonography. We developed an algorithm to image standard neurosonographic planes by drawing dissecting lines through the axial transventricular view of 3D volume datasets acquired transabdominally. The algorithm was tested on 106 normal fetuses at 18-24 weeks of gestation and the visualization rates of brain diagnostic planes were evaluated by two independent reviewers. The algorithm was also applied to nine cases with proven brain defects. The two reviewers, using the algorithm on normal fetuses, found satisfactory images with visualization rates ranging between 71.7% and 96.2% for sagittal planes and between 76.4% and 90.6% for coronal planes. The agreement rate between the two reviewers, as expressed by Cohen's kappa coefficient, was > 0.93 for sagittal planes and > 0.89 for coronal planes. All nine abnormal volumes were identified by a single observer from among a series including normal brains, and eight of these nine cases were diagnosed correctly. This novel algorithm can be used to visualize standard sagittal and coronal planes in the fetal brain. This approach may simplify the examination of the fetal brain and reduce dependency of success on operator skill. Copyright © 2011 ISUOG. Published by John Wiley & Sons, Ltd.

Automated Discovery of Elementary Chemical Reaction Steps Using Freezing String and Berny Optimization Methods.

PubMed

Suleimanov, Yury V; Green, William H

2015-09-08

We present a simple protocol which allows fully automated discovery of elementary chemical reaction steps using in cooperation double- and single-ended transition-state optimization algorithms--the freezing string and Berny optimization methods, respectively. To demonstrate the utility of the proposed approach, the reactivity of several single-molecule systems of combustion and atmospheric chemistry importance is investigated. The proposed algorithm allowed us to detect without any human intervention not only "known" reaction pathways, manually detected in the previous studies, but also new, previously "unknown", reaction pathways which involve significant atom rearrangements. We believe that applying such a systematic approach to elementary reaction path finding will greatly accelerate the discovery of new chemistry and will lead to more accurate computer simulations of various chemical processes.

Rational approximations to rational models: alternative algorithms for category learning.

PubMed

Sanborn, Adam N; Griffiths, Thomas L; Navarro, Daniel J

2010-10-01

Rational models of cognition typically consider the abstract computational problems posed by the environment, assuming that people are capable of optimally solving those problems. This differs from more traditional formal models of cognition, which focus on the psychological processes responsible for behavior. A basic challenge for rational models is thus explaining how optimal solutions can be approximated by psychological processes. We outline a general strategy for answering this question, namely to explore the psychological plausibility of approximation algorithms developed in computer science and statistics. In particular, we argue that Monte Carlo methods provide a source of rational process models that connect optimal solutions to psychological processes. We support this argument through a detailed example, applying this approach to Anderson's (1990, 1991) rational model of categorization (RMC), which involves a particularly challenging computational problem. Drawing on a connection between the RMC and ideas from nonparametric Bayesian statistics, we propose 2 alternative algorithms for approximate inference in this model. The algorithms we consider include Gibbs sampling, a procedure appropriate when all stimuli are presented simultaneously, and particle filters, which sequentially approximate the posterior distribution with a small number of samples that are updated as new data become available. Applying these algorithms to several existing datasets shows that a particle filter with a single particle provides a good description of human inferences.

LETTER TO THE EDITOR: Free-response operator characteristic models for visual search

NASA Astrophysics Data System (ADS)

Hutchinson, T. P.

2007-05-01

Computed tomography of diffraction enhanced imaging (DEI-CT) is a novel x-ray phase-contrast computed tomography which is applied to inspect weakly absorbing low-Z samples. Refraction-angle images which are extracted from a series of raw DEI images measured in different positions of the rocking curve of the analyser can be regarded as projections of DEI-CT. Based on them, the distribution of refractive index decrement in the sample can be reconstructed according to the principles of CT. How to combine extraction methods and reconstruction algorithms to obtain the most accurate reconstructed results is investigated in detail in this paper. Two kinds of comparison, the comparison of different extraction methods and the comparison between 'two-step' algorithms and the Hilbert filtered backprojection (HFBP) algorithm, draw the conclusion that the HFBP algorithm based on the maximum refraction-angle (MRA) method may be the best combination at present. Though all current extraction methods including the MRA method are approximate methods and cannot calculate very large refraction-angle values, the HFBP algorithm based on the MRA method is able to provide quite acceptable estimations of the distribution of refractive index decrement of the sample. The conclusion is proved by the experimental results at the Beijing Synchrotron Radiation Facility.

a Quadtree Organization Construction and Scheduling Method for Urban 3d Model Based on Weight

NASA Astrophysics Data System (ADS)

Yao, C.; Peng, G.; Song, Y.; Duan, M.

2017-09-01

The increasement of Urban 3D model precision and data quantity puts forward higher requirements for real-time rendering of digital city model. Improving the organization, management and scheduling of 3D model data in 3D digital city can improve the rendering effect and efficiency. This paper takes the complexity of urban models into account, proposes a Quadtree construction and scheduling rendering method for Urban 3D model based on weight. Divide Urban 3D model into different rendering weights according to certain rules, perform Quadtree construction and schedule rendering according to different rendering weights. Also proposed an algorithm for extracting bounding box extraction based on model drawing primitives to generate LOD model automatically. Using the algorithm proposed in this paper, developed a 3D urban planning&management software, the practice has showed the algorithm is efficient and feasible, the render frame rate of big scene and small scene are both stable at around 25 frames.

Non-Parametric Blur Map Regression for Depth of Field Extension.

PubMed

D'Andres, Laurent; Salvador, Jordi; Kochale, Axel; Susstrunk, Sabine

2016-04-01

Real camera systems have a limited depth of field (DOF) which may cause an image to be degraded due to visible misfocus or too shallow DOF. In this paper, we present a blind deblurring pipeline able to restore such images by slightly extending their DOF and recovering sharpness in regions slightly out of focus. To address this severely ill-posed problem, our algorithm relies first on the estimation of the spatially varying defocus blur. Drawing on local frequency image features, a machine learning approach based on the recently introduced regression tree fields is used to train a model able to regress a coherent defocus blur map of the image, labeling each pixel by the scale of a defocus point spread function. A non-blind spatially varying deblurring algorithm is then used to properly extend the DOF of the image. The good performance of our algorithm is assessed both quantitatively, using realistic ground truth data obtained with a novel approach based on a plenoptic camera, and qualitatively with real images.

KANTS: a stigmergic ant algorithm for cluster analysis and swarm art.

PubMed

Fernandes, Carlos M; Mora, Antonio M; Merelo, Juan J; Rosa, Agostinho C

2014-06-01

KANTS is a swarm intelligence clustering algorithm inspired by the behavior of social insects. It uses stigmergy as a strategy for clustering large datasets and, as a result, displays a typical behavior of complex systems: self-organization and global patterns emerging from the local interaction of simple units. This paper introduces a simplified version of KANTS and describes recent experiments with the algorithm in the context of a contemporary artistic and scientific trend called swarm art, a type of generative art in which swarm intelligence systems are used to create artwork or ornamental objects. KANTS is used here for generating color drawings from the input data that represent real-world phenomena, such as electroencephalogram sleep data. However, the main proposal of this paper is an art project based on well-known abstract paintings, from which the chromatic values are extracted and used as input. Colors and shapes are therefore reorganized by KANTS, which generates its own interpretation of the original artworks. The project won the 2012 Evolutionary Art, Design, and Creativity Competition.

MESSI: metabolic engineering target selection and best strain identification tool.

PubMed

Kang, Kang; Li, Jun; Lim, Boon Leong; Panagiotou, Gianni

2015-01-01

Metabolic engineering and synthetic biology are synergistically related fields for manipulating target pathways and designing microorganisms that can act as chemical factories. Saccharomyces cerevisiae's ideal bioprocessing traits make yeast a very attractive chemical factory for production of fuels, pharmaceuticals, nutraceuticals as well as a wide range of chemicals. However, future attempts of engineering S. cerevisiae's metabolism using synthetic biology need to move towards more integrative models that incorporate the high connectivity of metabolic pathways and regulatory processes and the interactions in genetic elements across those pathways and processes. To contribute in this direction, we have developed Metabolic Engineering target Selection and best Strain Identification tool (MESSI), a web server for predicting efficient chassis and regulatory components for yeast bio-based production. The server provides an integrative platform for users to analyse ready-to-use public high-throughput metabolomic data, which are transformed to metabolic pathway activities for identifying the most efficient S. cerevisiae strain for the production of a compound of interest. As input MESSI accepts metabolite KEGG IDs or pathway names. MESSI outputs a ranked list of S. cerevisiae strains based on aggregation algorithms. Furthermore, through a genome-wide association study of the metabolic pathway activities with the strains' natural variation, MESSI prioritizes genes and small variants as potential regulatory points and promising metabolic engineering targets. Users can choose various parameters in the whole process such as (i) weight and expectation of each metabolic pathway activity in the final ranking of the strains, (ii) Weighted AddScore Fuse or Weighted Borda Fuse aggregation algorithm, (iii) type of variants to be included, (iv) variant sets in different biological levels.Database URL: http://sbb.hku.hk/MESSI/. © The Author(s) 2015. Published by Oxford University Press.

Modulation of Metabolic Detoxification Pathways Using Foods and Food-Derived Components: A Scientific Review with Clinical Application.

PubMed

Hodges, Romilly E; Minich, Deanna M

2015-01-01

Research into human biotransformation and elimination systems continues to evolve. Various clinical and in vivo studies have been undertaken to evaluate the effects of foods and food-derived components on the activity of detoxification pathways, including phase I cytochrome P450 enzymes, phase II conjugation enzymes, Nrf2 signaling, and metallothionein. This review summarizes the research in this area to date, highlighting the potential for foods and nutrients to support and/or modulate detoxification functions. Clinical applications to alter detoxification pathway activity and improve patient outcomes are considered, drawing on the growing understanding of the relationship between detoxification functions and different disease states, genetic polymorphisms, and drug-nutrient interactions. Some caution is recommended, however, due to the limitations of current research as well as indications that many nutrients exert biphasic, dose-dependent effects and that genetic polymorphisms may alter outcomes. A whole-foods approach may, therefore, be prudent.

Modulation of Metabolic Detoxification Pathways Using Foods and Food-Derived Components: A Scientific Review with Clinical Application

PubMed Central

Hodges, Romilly E.; Minich, Deanna M.

2015-01-01

Research into human biotransformation and elimination systems continues to evolve. Various clinical and in vivo studies have been undertaken to evaluate the effects of foods and food-derived components on the activity of detoxification pathways, including phase I cytochrome P450 enzymes, phase II conjugation enzymes, Nrf2 signaling, and metallothionein. This review summarizes the research in this area to date, highlighting the potential for foods and nutrients to support and/or modulate detoxification functions. Clinical applications to alter detoxification pathway activity and improve patient outcomes are considered, drawing on the growing understanding of the relationship between detoxification functions and different disease states, genetic polymorphisms, and drug-nutrient interactions. Some caution is recommended, however, due to the limitations of current research as well as indications that many nutrients exert biphasic, dose-dependent effects and that genetic polymorphisms may alter outcomes. A whole-foods approach may, therefore, be prudent. PMID:26167297

Geomagnetism-Aided Indoor Wi-Fi Radio-Map Construction via Smartphone Crowdsourcing.

PubMed

Li, Wen; Wei, Dongyan; Lai, Qifeng; Li, Xianghong; Yuan, Hong

2018-05-08

Wi-Fi radio-map construction is an important phase in indoor fingerprint localization systems. Traditional methods for Wi-Fi radio-map construction have the problems of being time-consuming and labor-intensive. In this paper, an indoor Wi-Fi radio-map construction method is proposed which utilizes crowdsourcing data contributed by smartphone users. We draw indoor pathway map and construct Wi-Fi radio-map without requiring manual site survey, exact floor layout and extra infrastructure support. The key novelty is that it recognizes road segments from crowdsourcing traces by a cluster based on magnetism sequence similarity and constructs an indoor pathway map with Wi-Fi signal strengths annotated on. Through experiments in real world indoor areas, the method is proved to have good performance on magnetism similarity calculation, road segment clustering and pathway map construction. The Wi-Fi radio maps constructed by crowdsourcing data are validated to provide competitive indoor localization accuracy.

Geomagnetism-Aided Indoor Wi-Fi Radio-Map Construction via Smartphone Crowdsourcing

PubMed Central

Li, Wen; Wei, Dongyan; Lai, Qifeng; Li, Xianghong; Yuan, Hong

2018-01-01

Wi-Fi radio-map construction is an important phase in indoor fingerprint localization systems. Traditional methods for Wi-Fi radio-map construction have the problems of being time-consuming and labor-intensive. In this paper, an indoor Wi-Fi radio-map construction method is proposed which utilizes crowdsourcing data contributed by smartphone users. We draw indoor pathway map and construct Wi-Fi radio-map without requiring manual site survey, exact floor layout and extra infrastructure support. The key novelty is that it recognizes road segments from crowdsourcing traces by a cluster based on magnetism sequence similarity and constructs an indoor pathway map with Wi-Fi signal strengths annotated on. Through experiments in real world indoor areas, the method is proved to have good performance on magnetism similarity calculation, road segment clustering and pathway map construction. The Wi-Fi radio maps constructed by crowdsourcing data are validated to provide competitive indoor localization accuracy. PMID:29738454

It’s not only for the money: an analysis of adolescent versus adult entry into street prostitution.

PubMed

Cobbina, Jennifer E; Oselin, Sharon S

2011-01-01

Numerous studies examine the causal factors of entrance into prostitution and find economic marginalization, substance addiction, and interpersonal networks are common reasons women enter the trade. However, we know less about the role that age of onset plays in shaping female pathways into prostitution. Here, we build from insights into previous research by analyzing not only entry pathways but also how age categories are linked to time spent in the trade and whether the length of time in prostitution exacts a greater “toll” on women. Drawing from the feminist and age of onset literatures, we analyze 40 in-depth interviews with female street prostitutes from five U.S. cities. Our results underscore the importance of age as an organizing feature of women’s pathways into prostitution and the potential associated consequences of working in this trade.

An Adaptive Grid Algorithm for Nonequilibrium Hypersonic Flows

DTIC Science & Technology

1990-04-01

FORCE SYSTEMS COMMAND WRIGHT-PATTERSON AIR FORCE BASE, OHIO 45433-6553 NOTICE When Government drawings, specifications, or other data are used for any...any rights or permission to manufacture, use , or sell any patented invention that may in any way be related thereto. This technical report has been...WRDC/FIMN, WPAFB OH 45433-6553 to help us maintain a current mailing list. Copies of this report should not be returned unless it is required by security

Multimodal neuroelectric interface development

NASA Technical Reports Server (NTRS)

Trejo, Leonard J.; Wheeler, Kevin R.; Jorgensen, Charles C.; Rosipal, Roman; Clanton, Sam T.; Matthews, Bryan; Hibbs, Andrew D.; Matthews, Robert; Krupka, Michael

2003-01-01

We are developing electromyographic and electroencephalographic methods, which draw control signals for human-computer interfaces from the human nervous system. We have made progress in four areas: 1) real-time pattern recognition algorithms for decoding sequences of forearm muscle activity associated with control gestures; 2) signal-processing strategies for computer interfaces using electroencephalogram (EEG) signals; 3) a flexible computation framework for neuroelectric interface research; and d) noncontact sensors, which measure electromyogram or EEG signals without resistive contact to the body.

Simulation Learning: PC-Screen Based (PCSB) versus High Fidelity Simulation (HFS)

DTIC Science & Technology

2012-08-01

methods for the use of simulation for teaching clinical skills to military and civilian clinicians . High fidelity simulation is an expensive method of...without the knowledge and approval of the IRB. Changes include, but not limited to, modifications in study design, recruitment process and number of...Person C-Collar simulation algorithm Pathway A Scenario A - Spinal stabilization: Sub processes Legend: Pathway Points Complex task to be performed by

Impact of the Parameter Identification of Plastic Potentials on the Finite Element Simulation of Sheet Metal Forming

NASA Astrophysics Data System (ADS)

Rabahallah, M.; Bouvier, S.; Balan, T.; Bacroix, B.; Teodosiu, C.

2007-04-01

In this work, an implicit, backward Euler time integration scheme is developed for an anisotropic, elastic-plastic model based on strain-rate potentials. The constitutive algorithm includes a sub-stepping procedure to deal with the strong nonlinearity of the plastic potentials when applied to FCC materials. The algorithm is implemented in the static implicit version of the Abaqus finite element code. Several recent plastic potentials have been implemented in this framework. The most accurate potentials require the identification of about twenty material parameters. Both mechanical tests and micromechanical simulations have been used for their identification, for a number of BCC and FCC materials. The impact of the identification procedure on the prediction of ears in cup drawing is investigated.

Online graphic symbol recognition using neural network and ARG matching

NASA Astrophysics Data System (ADS)

Yang, Bing; Li, Changhua; Xie, Weixing

2001-09-01

This paper proposes a novel method for on-line recognition of line-based graphic symbol. The input strokes are usually warped into a cursive form due to the sundry drawing style, and classifying them is very difficult. To deal with this, an ART-2 neural network is used to classify the input strokes. It has the advantages of high recognition rate, less recognition time and forming classes in a self-organized manner. The symbol recognition is achieved by an Attribute Relational Graph (ARG) matching algorithm. The ARG is very efficient for representing complex objects, but computation cost is very high. To over come this, we suggest a fast graph matching algorithm using symbol structure information. The experimental results show that the proposed method is effective for recognition of symbols with hierarchical structure.

SS-mPMG and SS-GA: tools for finding pathways and dynamic simulation of metabolic networks.

PubMed

Katsuragi, Tetsuo; Ono, Naoaki; Yasumoto, Keiichi; Altaf-Ul-Amin, Md; Hirai, Masami Y; Sriyudthsak, Kansuporn; Sawada, Yuji; Yamashita, Yui; Chiba, Yukako; Onouchi, Hitoshi; Fujiwara, Toru; Naito, Satoshi; Shiraishi, Fumihide; Kanaya, Shigehiko

2013-05-01

Metabolomics analysis tools can provide quantitative information on the concentration of metabolites in an organism. In this paper, we propose the minimum pathway model generator tool for simulating the dynamics of metabolite concentrations (SS-mPMG) and a tool for parameter estimation by genetic algorithm (SS-GA). SS-mPMG can extract a subsystem of the metabolic network from the genome-scale pathway maps to reduce the complexity of the simulation model and automatically construct a dynamic simulator to evaluate the experimentally observed behavior of metabolites. Using this tool, we show that stochastic simulation can reproduce experimentally observed dynamics of amino acid biosynthesis in Arabidopsis thaliana. In this simulation, SS-mPMG extracts the metabolic network subsystem from published databases. The parameters needed for the simulation are determined using a genetic algorithm to fit the simulation results to the experimental data. We expect that SS-mPMG and SS-GA will help researchers to create relevant metabolic networks and carry out simulations of metabolic reactions derived from metabolomics data.

Lymph node segmentation by dynamic programming and active contours.

PubMed

Tan, Yongqiang; Lu, Lin; Bonde, Apurva; Wang, Deling; Qi, Jing; Schwartz, Lawrence H; Zhao, Binsheng

2018-03-03

Enlarged lymph nodes are indicators of cancer staging, and the change in their size is a reflection of treatment response. Automatic lymph node segmentation is challenging, as the boundary can be unclear and the surrounding structures complex. This work communicates a new three-dimensional algorithm for the segmentation of enlarged lymph nodes. The algorithm requires a user to draw a region of interest (ROI) enclosing the lymph node. Rays are cast from the center of the ROI, and the intersections of the rays and the boundary of the lymph node form a triangle mesh. The intersection points are determined by dynamic programming. The triangle mesh initializes an active contour which evolves to low-energy boundary. Three radiologists independently delineated the contours of 54 lesions from 48 patients. Dice coefficient was used to evaluate the algorithm's performance. The mean Dice coefficient between computer and the majority vote results was 83.2%. The mean Dice coefficients between the three radiologists' manual segmentations were 84.6%, 86.2%, and 88.3%. The performance of this segmentation algorithm suggests its potential clinical value for quantifying enlarged lymph nodes. © 2018 American Association of Physicists in Medicine.

The Gaze or the Groove? Emerging Themes from the "New Meanings and Pathways: Community Dance and Dance Education Symposium" in Beijing

ERIC Educational Resources Information Center

Rowe, Nicholas; Buck, Ralph; Martin, Rosemary

2015-01-01

Established in 1954 and drawing heavily on dance conservatory models from Russia, the Beijing Dance Academy (BDA) has become a predominant influence on dance education in East Asia. As it addresses the artistic, cultural and educational needs of China into the twenty-first century, the BDA is now negotiating a new terrain and seeking new…

Automated analysis for microcalcifications in high resolution digital mammograms

DOEpatents

Mascio, Laura N.

1996-01-01

A method for automatically locating microcalcifications indicating breast cancer. The invention assists mammographers in finding very subtle microcalcifications and in recognizing the pattern formed by all the microcalcifications. It also draws attention to microcalcifications that might be overlooked because a more prominent feature draws attention away from an important object. A new filter has been designed to weed out false positives in one of the steps of the method. Previously, iterative selection threshold was used to separate microcalcifications from the spurious signals resulting from texture or other background. A Selective Erosion or Enhancement (SEE) Filter has been invented to improve this step. Since the algorithm detects areas containing potential calcifications on the mammogram, it can be used to determine which areas need be stored at the highest resolution available, while, in addition, the full mammogram can be reduced to an appropriate resolution for the remaining cancer signs.

Automated analysis for microcalcifications in high resolution digital mammograms

DOEpatents

Mascio, L.N.

1996-12-17

A method is disclosed for automatically locating microcalcifications indicating breast cancer. The invention assists mammographers in finding very subtle microcalcifications and in recognizing the pattern formed by all the microcalcifications. It also draws attention to microcalcifications that might be overlooked because a more prominent feature draws attention away from an important object. A new filter has been designed to weed out false positives in one of the steps of the method. Previously, iterative selection threshold was used to separate microcalcifications from the spurious signals resulting from texture or other background. A Selective Erosion or Enhancement (SEE) Filter has been invented to improve this step. Since the algorithm detects areas containing potential calcifications on the mammogram, it can be used to determine which areas need be stored at the highest resolution available, while, in addition, the full mammogram can be reduced to an appropriate resolution for the remaining cancer signs. 8 figs.

Computational Selection of Transcriptomics Experiments Improves Guilt-by-Association Analyses

PubMed Central

Bhat, Prajwal; Yang, Haixuan; Bögre, László; Devoto, Alessandra; Paccanaro, Alberto

2012-01-01

The Guilt-by-Association (GBA) principle, according to which genes with similar expression profiles are functionally associated, is widely applied for functional analyses using large heterogeneous collections of transcriptomics data. However, the use of such large collections could hamper GBA functional analysis for genes whose expression is condition specific. In these cases a smaller set of condition related experiments should instead be used, but identifying such functionally relevant experiments from large collections based on literature knowledge alone is an impractical task. We begin this paper by analyzing, both from a mathematical and a biological point of view, why only condition specific experiments should be used in GBA functional analysis. We are able to show that this phenomenon is independent of the functional categorization scheme and of the organisms being analyzed. We then present a semi-supervised algorithm that can select functionally relevant experiments from large collections of transcriptomics experiments. Our algorithm is able to select experiments relevant to a given GO term, MIPS FunCat term or even KEGG pathways. We extensively test our algorithm on large dataset collections for yeast and Arabidopsis. We demonstrate that: using the selected experiments there is a statistically significant improvement in correlation between genes in the functional category of interest; the selected experiments improve GBA-based gene function prediction; the effectiveness of the selected experiments increases with annotation specificity; our algorithm can be successfully applied to GBA-based pathway reconstruction. Importantly, the set of experiments selected by the algorithm reflects the existing literature knowledge about the experiments. [A MATLAB implementation of the algorithm and all the data used in this paper can be downloaded from the paper website: http://www.paccanarolab.org/papers/CorrGene/]. PMID:22879875

Modeling evolution of crosstalk in noisy signal transduction networks

NASA Astrophysics Data System (ADS)

Tareen, Ammar; Wingreen, Ned S.; Mukhopadhyay, Ranjan

2018-02-01

Signal transduction networks can form highly interconnected systems within cells due to crosstalk between constituent pathways. To better understand the evolutionary design principles underlying such networks, we study the evolution of crosstalk for two parallel signaling pathways that arise via gene duplication. We use a sequence-based evolutionary algorithm and evolve the network based on two physically motivated fitness functions related to information transmission. We find that one fitness function leads to a high degree of crosstalk while the other leads to pathway specificity. Our results offer insights on the relationship between network architecture and information transmission for noisy biomolecular networks.

A signaling visualization toolkit to support rational design of combination therapies and biomarker discovery: SiViT.

PubMed

Bown, James L; Shovman, Mark; Robertson, Paul; Boiko, Andrei; Goltsov, Alexey; Mullen, Peter; Harrison, David J

2017-05-02

Targeted cancer therapy aims to disrupt aberrant cellular signalling pathways. Biomarkers are surrogates of pathway state, but there is limited success in translating candidate biomarkers to clinical practice due to the intrinsic complexity of pathway networks. Systems biology approaches afford better understanding of complex, dynamical interactions in signalling pathways targeted by anticancer drugs. However, adoption of dynamical modelling by clinicians and biologists is impeded by model inaccessibility. Drawing on computer games technology, we present a novel visualization toolkit, SiViT, that converts systems biology models of cancer cell signalling into interactive simulations that can be used without specialist computational expertise. SiViT allows clinicians and biologists to directly introduce for example loss of function mutations and specific inhibitors. SiViT animates the effects of these introductions on pathway dynamics, suggesting further experiments and assessing candidate biomarker effectiveness. In a systems biology model of Her2 signalling we experimentally validated predictions using SiViT, revealing the dynamics of biomarkers of drug resistance and highlighting the role of pathway crosstalk. No model is ever complete: the iteration of real data and simulation facilitates continued evolution of more accurate, useful models. SiViT will make accessible libraries of models to support preclinical research, combinatorial strategy design and biomarker discovery.

Chemical probes and engineered constructs reveal a detailed unfolding mechanism for a solvent-free multi-domain protein

PubMed Central

Eschweiler, Joseph D.; Martini, Rachel M.; Ruotolo, Brandon T.

2017-01-01

Despite the growing application of gas-phase measurements in structural biology and drug discovery, the factors that govern protein stabilities and structures in a solvent-free environment are still poorly understood. Here, we examine the solvent-free unfolding pathway for a group of homologous serum albumins. Utilizing a combination of chemical probes and non-covalent reconstructions, we draw new specific conclusions regarding the unfolding of albumins in the gas-phase, as well as more-general inferences regarding the sensitivity of collision induced unfolding to changes in protein primary and tertiary structure. Our findings suggest that the general unfolding pathway of low charge state albumin ions is largely unaffected by changes in primary structure; however, the stabilities of intermediates along these pathways vary widely as sequences diverge. Additionally, we find that human albumin follows a domain associated unfolding pathway, and are able to assign each unfolded form observed in our gas-phase dataset to the disruption of specific domains within the protein. The totality of our data informs the first detailed mechanism for multi-domain protein unfolding in the gas phase, and highlights key similarities and differences from the known the solution-phase pathway. PMID:27959526

A Web Tool for Generating High Quality Machine-readable Biological Pathways.

PubMed

Ramirez-Gaona, Miguel; Marcu, Ana; Pon, Allison; Grant, Jason; Wu, Anthony; Wishart, David S

2017-02-08

PathWhiz is a web server built to facilitate the creation of colorful, interactive, visually pleasing pathway diagrams that are rich in biological information. The pathways generated by this online application are machine-readable and fully compatible with essentially all web-browsers and computer operating systems. It uses a specially developed, web-enabled pathway drawing interface that permits the selection and placement of different combinations of pre-drawn biological or biochemical entities to depict reactions, interactions, transport processes and binding events. This palette of entities consists of chemical compounds, proteins, nucleic acids, cellular membranes, subcellular structures, tissues, and organs. All of the visual elements in it can be interactively adjusted and customized. Furthermore, because this tool is a web server, all pathways and pathway elements are publicly accessible. This kind of pathway "crowd sourcing" means that PathWhiz already contains a large and rapidly growing collection of previously drawn pathways and pathway elements. Here we describe a protocol for the quick and easy creation of new pathways and the alteration of existing pathways. To further facilitate pathway editing and creation, the tool contains replication and propagation functions. The replication function allows existing pathways to be used as templates to create or edit new pathways. The propagation function allows one to take an existing pathway and automatically propagate it across different species. Pathways created with this tool can be "re-styled" into different formats (KEGG-like or text-book like), colored with different backgrounds, exported to BioPAX, SBGN-ML, SBML, or PWML data exchange formats, and downloaded as PNG or SVG images. The pathways can easily be incorporated into online databases, integrated into presentations, posters or publications, or used exclusively for online visualization and exploration. This protocol has been successfully applied to generate over 2,000 pathway diagrams, which are now found in many online databases including HMDB, DrugBank, SMPDB, and ECMDB.

Interactive visualization of Earth and Space Science computations

NASA Technical Reports Server (NTRS)

Hibbard, William L.; Paul, Brian E.; Santek, David A.; Dyer, Charles R.; Battaiola, Andre L.; Voidrot-Martinez, Marie-Francoise

1994-01-01

Computers have become essential tools for scientists simulating and observing nature. Simulations are formulated as mathematical models but are implemented as computer algorithms to simulate complex events. Observations are also analyzed and understood in terms of mathematical models, but the number of these observations usually dictates that we automate analyses with computer algorithms. In spite of their essential role, computers are also barriers to scientific understanding. Unlike hand calculations, automated computations are invisible and, because of the enormous numbers of individual operations in automated computations, the relation between an algorithm's input and output is often not intuitive. This problem is illustrated by the behavior of meteorologists responsible for forecasting weather. Even in this age of computers, many meteorologists manually plot weather observations on maps, then draw isolines of temperature, pressure, and other fields by hand (special pads of maps are printed for just this purpose). Similarly, radiologists use computers to collect medical data but are notoriously reluctant to apply image-processing algorithms to that data. To these scientists with life-and-death responsibilities, computer algorithms are black boxes that increase rather than reduce risk. The barrier between scientists and their computations can be bridged by techniques that make the internal workings of algorithms visible and that allow scientists to experiment with their computations. Here we describe two interactive systems developed at the University of Wisconsin-Madison Space Science and Engineering Center (SSEC) that provide these capabilities to Earth and space scientists.

Large-Scale Simulation Network Design Study

DTIC Science & Technology

1983-10-01

video displays: three for the tank commander, three for the driver, one for the loader, and one for the gunner. The solid angles subtended by these...Newman Inc Range Sortr This process sorts the expanded display lists into range order for drawing according to the "painter’s algorithm’" The range sorter ...session could then be continued as soon as the network recovered. and the elapsed session time would not be wasted . The SimNet design is much more tolerant

Concealed object segmentation and three-dimensional localization with passive millimeter-wave imaging

NASA Astrophysics Data System (ADS)

Yeom, Seokwon

2013-05-01

Millimeter waves imaging draws increasing attention in security applications for weapon detection under clothing. In this paper, concealed object segmentation and three-dimensional localization schemes are reviewed. A concealed object is segmented by the k-means algorithm. A feature-based stereo-matching method estimates the longitudinal distance of the concealed object. The distance is estimated by the discrepancy between the corresponding centers of the segmented objects. Experimental results are provided with the analysis of the depth resolution.

Fast Identification of Biological Pathways Associated with a Quantitative Trait Using Group Lasso with Overlaps

PubMed Central

Silver, Matt; Montana, Giovanni

2012-01-01

Where causal SNPs (single nucleotide polymorphisms) tend to accumulate within biological pathways, the incorporation of prior pathways information into a statistical model is expected to increase the power to detect true associations in a genetic association study. Most existing pathways-based methods rely on marginal SNP statistics and do not fully exploit the dependence patterns among SNPs within pathways. We use a sparse regression model, with SNPs grouped into pathways, to identify causal pathways associated with a quantitative trait. Notable features of our “pathways group lasso with adaptive weights” (P-GLAW) algorithm include the incorporation of all pathways in a single regression model, an adaptive pathway weighting procedure that accounts for factors biasing pathway selection, and the use of a bootstrap sampling procedure for the ranking of important pathways. P-GLAW takes account of the presence of overlapping pathways and uses a novel combination of techniques to optimise model estimation, making it fast to run, even on whole genome datasets. In a comparison study with an alternative pathways method based on univariate SNP statistics, our method demonstrates high sensitivity and specificity for the detection of important pathways, showing the greatest relative gains in performance where marginal SNP effect sizes are small. PMID:22499682

Aging: Molecular Pathways and Implications on the Cardiovascular System.

PubMed

de Almeida, Arthur José Pontes Oliveira; Ribeiro, Thaís Porto; de Medeiros, Isac Almeida

2017-01-01

The world's population over 60 years is growing rapidly, reaching 22% of the global population in the next decades. Despite the increase in global longevity, individual healthspan needs to follow this growth. Several diseases have their prevalence increased by age, such as cardiovascular diseases, the leading cause of morbidity and mortality worldwide. Understanding the aging biology mechanisms is fundamental to the pursuit of cardiovascular health. In this way, aging is characterized by a gradual decline in physiological functions, involving the increased number in senescent cells into the body. Several pathways lead to senescence, including oxidative stress and persistent inflammation, as well as energy failure such as mitochondrial dysfunction and deregulated autophagy, being ROS, AMPK, SIRTs, mTOR, IGF-1, and p53 key regulators of the metabolic control, connecting aging to the pathways which drive towards diseases. In addition, senescence can be induced by cellular replication, which resulted from telomere shortening. Taken together, it is possible to draw a common pathway unifying aging to cardiovascular diseases, and the central point of this process, senescence, can be the target for new therapies, which may result in the healthspan matching the lifespan.

Novel method of extracting motion from natural movies.

PubMed

Suzuki, Wataru; Ichinohe, Noritaka; Tani, Toshiki; Hayami, Taku; Miyakawa, Naohisa; Watanabe, Satoshi; Takeichi, Hiroshige

2017-11-01

The visual system in primates can be segregated into motion and shape pathways. Interaction occurs at multiple stages along these pathways. Processing of shape-from-motion and biological motion is considered to be a higher-order integration process involving motion and shape information. However, relatively limited types of stimuli have been used in previous studies on these integration processes. We propose a new algorithm to extract object motion information from natural movies and to move random dots in accordance with the information. The object motion information is extracted by estimating the dynamics of local normal vectors of the image intensity projected onto the x-y plane of the movie. An electrophysiological experiment on two adult common marmoset monkeys (Callithrix jacchus) showed that the natural and random dot movies generated with this new algorithm yielded comparable neural responses in the middle temporal visual area. In principle, this algorithm provided random dot motion stimuli containing shape information for arbitrary natural movies. This new method is expected to expand the neurophysiological and psychophysical experimental protocols to elucidate the integration processing of motion and shape information in biological systems. The novel algorithm proposed here was effective in extracting object motion information from natural movies and provided new motion stimuli to investigate higher-order motion information processing. Copyright © 2017 The Author(s). Published by Elsevier B.V. All rights reserved.

A traveling salesman approach for predicting protein functions.

PubMed

Johnson, Olin; Liu, Jing

2006-10-12

Protein-protein interaction information can be used to predict unknown protein functions and to help study biological pathways. Here we present a new approach utilizing the classic Traveling Salesman Problem to study the protein-protein interactions and to predict protein functions in budding yeast Saccharomyces cerevisiae. We apply the global optimization tool from combinatorial optimization algorithms to cluster the yeast proteins based on the global protein interaction information. We then use this clustering information to help us predict protein functions. We use our algorithm together with the direct neighbor algorithm 1 on characterized proteins and compare the prediction accuracy of the two methods. We show our algorithm can produce better predictions than the direct neighbor algorithm, which only considers the immediate neighbors of the query protein. Our method is a promising one to be used as a general tool to predict functions of uncharacterized proteins and a successful sample of using computer science knowledge and algorithms to study biological problems.

A traveling salesman approach for predicting protein functions

PubMed Central

Johnson, Olin; Liu, Jing

2006-01-01

Background Protein-protein interaction information can be used to predict unknown protein functions and to help study biological pathways. Results Here we present a new approach utilizing the classic Traveling Salesman Problem to study the protein-protein interactions and to predict protein functions in budding yeast Saccharomyces cerevisiae. We apply the global optimization tool from combinatorial optimization algorithms to cluster the yeast proteins based on the global protein interaction information. We then use this clustering information to help us predict protein functions. We use our algorithm together with the direct neighbor algorithm [1] on characterized proteins and compare the prediction accuracy of the two methods. We show our algorithm can produce better predictions than the direct neighbor algorithm, which only considers the immediate neighbors of the query protein. Conclusion Our method is a promising one to be used as a general tool to predict functions of uncharacterized proteins and a successful sample of using computer science knowledge and algorithms to study biological problems. PMID:17147783

How might flukes and tapeworms maintain genome integrity without a canonical piRNA pathway?

PubMed Central

Skinner, Danielle E.; Rinaldi, Gabriel; Koziol, Uriel; Brehm, Klaus; Brindley, Paul J.

2014-01-01

Surveillance by RNA interference is central to controlling the mobilization of transposable elements (TEs). In stem cells, Piwi argonaute (Ago) proteins and associated proteins repress mobilization of TEs to maintain genome integrity. This defense mechanism targeting TEs is termed the Piwi-interacting RNA (Piwi-piRNA) pathway. In this Opinion, we draw attention to the situation that the genomes of cestodes and trematodes have lost the piwi and vasa genes that are hallmark characters of the germline multipotency program. This absence of Piwi-like Agos and Vasa helicases prompts the question: how does the germline of these flatworms withstand mobilization of TEs? Here we present an interpretation of mechanisms likely to defend the germline integrity of parasitic flatworms. PMID:24485046

Sharing, liking, commenting, and distressed? The pathway between Facebook interaction and psychological distress.

PubMed

Chen, Wenhong; Lee, Kye-Hyoung

2013-10-01

Studies on the mental health implications of social media have generated mixed results. Drawing on a survey of college students (N=513), this research uses structural equation modeling to assess the relationship between Facebook interaction and psychological distress and two underlying mechanisms: communication overload and self-esteem. It is the first study, to our knowledge, that examines how communication overload mediates the mental health implications of social media. Frequent Facebook interaction is associated with greater distress directly and indirectly via a two-step pathway that increases communication overload and reduces self-esteem. The research sheds light on new directions for understanding psychological well-being in an increasingly mediated social world as users share, like, and comment more and more.

Network reconstruction based on proteomic data and prior knowledge of protein connectivity using graph theory.

PubMed

Stavrakas, Vassilis; Melas, Ioannis N; Sakellaropoulos, Theodore; Alexopoulos, Leonidas G

2015-01-01

Modeling of signal transduction pathways is instrumental for understanding cells' function. People have been tackling modeling of signaling pathways in order to accurately represent the signaling events inside cells' biochemical microenvironment in a way meaningful for scientists in a biological field. In this article, we propose a method to interrogate such pathways in order to produce cell-specific signaling models. We integrate available prior knowledge of protein connectivity, in a form of a Prior Knowledge Network (PKN) with phosphoproteomic data to construct predictive models of the protein connectivity of the interrogated cell type. Several computational methodologies focusing on pathways' logic modeling using optimization formulations or machine learning algorithms have been published on this front over the past few years. Here, we introduce a light and fast approach that uses a breadth-first traversal of the graph to identify the shortest pathways and score proteins in the PKN, fitting the dependencies extracted from the experimental design. The pathways are then combined through a heuristic formulation to produce a final topology handling inconsistencies between the PKN and the experimental scenarios. Our results show that the algorithm we developed is efficient and accurate for the construction of medium and large scale signaling networks. We demonstrate the applicability of the proposed approach by interrogating a manually curated interaction graph model of EGF/TNFA stimulation against made up experimental data. To avoid the possibility of erroneous predictions, we performed a cross-validation analysis. Finally, we validate that the introduced approach generates predictive topologies, comparable to the ILP formulation. Overall, an efficient approach based on graph theory is presented herein to interrogate protein-protein interaction networks and to provide meaningful biological insights.

Molecular Ecology of Bacterial Populations in Environmental Hazardous Chemical Control

DTIC Science & Technology

1991-11-30

Reactor Figure 1. A schematic drawing of the bioreactor system for on-line studies of naphthalene degradation and light production by bioluminescent...the bioluminescent monitoring section. The reactor system consisted of a L. H. Fermentation Series 500 continuous flow bioreactor with a 1 L glass... studied the expression of the upper pathway operon of NAH7. Light induction in response to naphthalene in the strain HK44 was comparable in both

Detection of confinement and jumps in single-molecule membrane trajectories

NASA Astrophysics Data System (ADS)

Meilhac, N.; Le Guyader, L.; Salomé, L.; Destainville, N.

2006-01-01

We propose a variant of the algorithm by [R. Simson, E. D. Sheets, and K. Jacobson, Biophys. 69, 989 (1995)]. Their algorithm was developed to detect transient confinement zones in experimental single-particle tracking trajectories of diffusing membrane proteins or lipids. We show that our algorithm is able to detect confinement in a wider class of confining potential shapes than that of Simson Furthermore, it enables to detect not only temporary confinement but also jumps between confinement zones. Jumps are predicted by membrane skeleton fence and picket models. In the case of experimental trajectories of μ -opioid receptors, which belong to the family of G-protein-coupled receptors involved in a signal transduction pathway, this algorithm confirms that confinement cannot be explained solely by rigid fences.

Artificial Bee Colony Optimization of Capping Potentials for Hybrid Quantum Mechanical/Molecular Mechanical Calculations.

PubMed

Schiffmann, Christoph; Sebastiani, Daniel

2011-05-10

We present an algorithmic extension of a numerical optimization scheme for analytic capping potentials for use in mixed quantum-classical (quantum mechanical/molecular mechanical, QM/MM) ab initio calculations. Our goal is to minimize bond-cleavage-induced perturbations in the electronic structure, measured by means of a suitable penalty functional. The optimization algorithm-a variant of the artificial bee colony (ABC) algorithm, which relies on swarm intelligence-couples deterministic (downhill gradient) and stochastic elements to avoid local minimum trapping. The ABC algorithm outperforms the conventional downhill gradient approach, if the penalty hypersurface exhibits wiggles that prevent a straight minimization pathway. We characterize the optimized capping potentials by computing NMR chemical shifts. This approach will increase the accuracy of QM/MM calculations of complex biomolecules.

A Novel Energy-Aware Distributed Clustering Algorithm for Heterogeneous Wireless Sensor Networks in the Mobile Environment

PubMed Central

Gao, Ying; Wkram, Chris Hadri; Duan, Jiajie; Chou, Jarong

2015-01-01

In order to prolong the network lifetime, energy-efficient protocols adapted to the features of wireless sensor networks should be used. This paper explores in depth the nature of heterogeneous wireless sensor networks, and finally proposes an algorithm to address the problem of finding an effective pathway for heterogeneous clustering energy. The proposed algorithm implements cluster head selection according to the degree of energy attenuation during the network’s running and the degree of candidate nodes’ effective coverage on the whole network, so as to obtain an even energy consumption over the whole network for the situation with high degree of coverage. Simulation results show that the proposed clustering protocol has better adaptability to heterogeneous environments than existing clustering algorithms in prolonging the network lifetime. PMID:26690440

VitaPad: visualization tools for the analysis of pathway data.

PubMed

Holford, Matthew; Li, Naixin; Nadkarni, Prakash; Zhao, Hongyu

2005-04-15

Packages that support the creation of pathway diagrams are limited by their inability to be readily extended to new classes of pathway-related data. VitaPad is a cross-platform application that enables users to create and modify biological pathway diagrams and incorporate microarray data with them. It improves on existing software in the following areas: (i) It can create diagrams dynamically through graph layout algorithms. (ii) It is open-source and uses an open XML format to store data, allowing for easy extension or integration with other tools. (iii) It features a cutting-edge user interface with intuitive controls, high-resolution graphics and fully customizable appearance. http://bioinformatics.med.yale.edu matthew.holford@yale.edu; hongyu.zhao@yale.edu.

Calculating Higher-Order Moments of Phylogenetic Stochastic Mapping Summaries in Linear Time.

PubMed

Dhar, Amrit; Minin, Vladimir N

2017-05-01

Stochastic mapping is a simulation-based method for probabilistically mapping substitution histories onto phylogenies according to continuous-time Markov models of evolution. This technique can be used to infer properties of the evolutionary process on the phylogeny and, unlike parsimony-based mapping, conditions on the observed data to randomly draw substitution mappings that do not necessarily require the minimum number of events on a tree. Most stochastic mapping applications simulate substitution mappings only to estimate the mean and/or variance of two commonly used mapping summaries: the number of particular types of substitutions (labeled substitution counts) and the time spent in a particular group of states (labeled dwelling times) on the tree. Fast, simulation-free algorithms for calculating the mean of stochastic mapping summaries exist. Importantly, these algorithms scale linearly in the number of tips/leaves of the phylogenetic tree. However, to our knowledge, no such algorithm exists for calculating higher-order moments of stochastic mapping summaries. We present one such simulation-free dynamic programming algorithm that calculates prior and posterior mapping variances and scales linearly in the number of phylogeny tips. Our procedure suggests a general framework that can be used to efficiently compute higher-order moments of stochastic mapping summaries without simulations. We demonstrate the usefulness of our algorithm by extending previously developed statistical tests for rate variation across sites and for detecting evolutionarily conserved regions in genomic sequences.

Pediatric chest HRCT using the iDose4 Hybrid Iterative Reconstruction Algorithm: Which iDose level to choose?

NASA Astrophysics Data System (ADS)

Smarda, M.; Alexopoulou, E.; Mazioti, A.; Kordolaimi, S.; Ploussi, A.; Priftis, K.; Efstathopoulos, E.

2015-09-01

Purpose of the study is to determine the appropriate iterative reconstruction (IR) algorithm level that combines image quality and diagnostic confidence, for pediatric patients undergoing high-resolution computed tomography (HRCT). During the last 2 years, a total number of 20 children up to 10 years old with a clinical presentation of chronic bronchitis underwent HRCT in our department's 64-detector row CT scanner using the iDose IR algorithm, with almost similar image settings (80kVp, 40-50 mAs). CT images were reconstructed with all iDose levels (level 1 to 7) as well as with filtered-back projection (FBP) algorithm. Subjective image quality was evaluated by 2 experienced radiologists in terms of image noise, sharpness, contrast and diagnostic acceptability using a 5-point scale (1=excellent image, 5=non-acceptable image). Artifacts existance was also pointed out. All mean scores from both radiologists corresponded to satisfactory image quality (score ≤3), even with the FBP algorithm use. Almost excellent (score <2) overall image quality was achieved with iDose levels 5 to 7, but oversmoothing artifacts appearing with iDose levels 6 and 7 affected the diagnostic confidence. In conclusion, the use of iDose level 5 enables almost excellent image quality without considerable artifacts affecting the diagnosis. Further evaluation is needed in order to draw more precise conclusions.

Calculating Higher-Order Moments of Phylogenetic Stochastic Mapping Summaries in Linear Time

PubMed Central

Dhar, Amrit

2017-01-01

Abstract Stochastic mapping is a simulation-based method for probabilistically mapping substitution histories onto phylogenies according to continuous-time Markov models of evolution. This technique can be used to infer properties of the evolutionary process on the phylogeny and, unlike parsimony-based mapping, conditions on the observed data to randomly draw substitution mappings that do not necessarily require the minimum number of events on a tree. Most stochastic mapping applications simulate substitution mappings only to estimate the mean and/or variance of two commonly used mapping summaries: the number of particular types of substitutions (labeled substitution counts) and the time spent in a particular group of states (labeled dwelling times) on the tree. Fast, simulation-free algorithms for calculating the mean of stochastic mapping summaries exist. Importantly, these algorithms scale linearly in the number of tips/leaves of the phylogenetic tree. However, to our knowledge, no such algorithm exists for calculating higher-order moments of stochastic mapping summaries. We present one such simulation-free dynamic programming algorithm that calculates prior and posterior mapping variances and scales linearly in the number of phylogeny tips. Our procedure suggests a general framework that can be used to efficiently compute higher-order moments of stochastic mapping summaries without simulations. We demonstrate the usefulness of our algorithm by extending previously developed statistical tests for rate variation across sites and for detecting evolutionarily conserved regions in genomic sequences. PMID:28177780

Spatially Compact Neural Clusters in the Dorsal Striatum Encode Locomotion Relevant Information.

PubMed

Barbera, Giovanni; Liang, Bo; Zhang, Lifeng; Gerfen, Charles R; Culurciello, Eugenio; Chen, Rong; Li, Yun; Lin, Da-Ting

2016-10-05

An influential striatal model postulates that neural activities in the striatal direct and indirect pathways promote and inhibit movement, respectively. Normal behavior requires coordinated activity in the direct pathway to facilitate intended locomotion and indirect pathway to inhibit unwanted locomotion. In this striatal model, neuronal population activity is assumed to encode locomotion relevant information. Here, we propose a novel encoding mechanism for the dorsal striatum. We identified spatially compact neural clusters in both the direct and indirect pathways. Detailed characterization revealed similar cluster organization between the direct and indirect pathways, and cluster activities from both pathways were correlated with mouse locomotion velocities. Using machine-learning algorithms, cluster activities could be used to decode locomotion relevant behavioral states and locomotion velocity. We propose that neural clusters in the dorsal striatum encode locomotion relevant information and that coordinated activities of direct and indirect pathway neural clusters are required for normal striatal controlled behavior. VIDEO ABSTRACT. Published by Elsevier Inc.

KEGGtranslator: visualizing and converting the KEGG PATHWAY database to various formats.

PubMed

Wrzodek, Clemens; Dräger, Andreas; Zell, Andreas

2011-08-15

The KEGG PATHWAY database provides a widely used service for metabolic and nonmetabolic pathways. It contains manually drawn pathway maps with information about the genes, reactions and relations contained therein. To store these pathways, KEGG uses KGML, a proprietary XML-format. Parsers and translators are needed to process the pathway maps for usage in other applications and algorithms. We have developed KEGGtranslator, an easy-to-use stand-alone application that can visualize and convert KGML formatted XML-files into multiple output formats. Unlike other translators, KEGGtranslator supports a plethora of output formats, is able to augment the information in translated documents (e.g. MIRIAM annotations) beyond the scope of the KGML document, and amends missing components to fragmentary reactions within the pathway to allow simulations on those. KEGGtranslator is freely available as a Java(™) Web Start application and for download at http://www.cogsys.cs.uni-tuebingen.de/software/KEGGtranslator/. KGML files can be downloaded from within the application. clemens.wrzodek@uni-tuebingen.de Supplementary data are available at Bioinformatics online.

A novel dysregulated pathway-identification analysis based on global influence of within-pathway effects and crosstalk between pathways

PubMed Central

Han, Junwei; Li, Chunquan; Yang, Haixiu; Xu, Yanjun; Zhang, Chunlong; Ma, Jiquan; Shi, Xinrui; Liu, Wei; Shang, Desi; Yao, Qianlan; Zhang, Yunpeng; Su, Fei; Feng, Li; Li, Xia

2015-01-01

Identifying dysregulated pathways from high-throughput experimental data in order to infer underlying biological insights is an important task. Current pathway-identification methods focus on single pathways in isolation; however, consideration of crosstalk between pathways could improve our understanding of alterations in biological states. We propose a novel method of pathway analysis based on global influence (PAGI) to identify dysregulated pathways, by considering both within-pathway effects and crosstalk between pathways. We constructed a global gene–gene network based on the relationships among genes extracted from a pathway database. We then evaluated the extent of differential expression for each gene, and mapped them to the global network. The random walk with restart algorithm was used to calculate the extent of genes affected by global influence. Finally, we used cumulative distribution functions to determine the significance values of the dysregulated pathways. We applied the PAGI method to five cancer microarray datasets, and compared our results with gene set enrichment analysis and five other methods. Based on these analyses, we demonstrated that PAGI can effectively identify dysregulated pathways associated with cancer, with strong reproducibility and robustness. We implemented PAGI using the freely available R-based and Web-based tools (http://bioinfo.hrbmu.edu.cn/PAGI). PMID:25551156

Quantifying diffusion MRI tractography of the corticospinal tract in brain tumors with deterministic and probabilistic methods☆

PubMed Central

Bucci, Monica; Mandelli, Maria Luisa; Berman, Jeffrey I.; Amirbekian, Bagrat; Nguyen, Christopher; Berger, Mitchel S.; Henry, Roland G.

2013-01-01

Introduction Diffusion MRI tractography has been increasingly used to delineate white matter pathways in vivo for which the leading clinical application is presurgical mapping of eloquent regions. However, there is rare opportunity to quantify the accuracy or sensitivity of these approaches to delineate white matter fiber pathways in vivo due to the lack of a gold standard. Intraoperative electrical stimulation (IES) provides a gold standard for the location and existence of functional motor pathways that can be used to determine the accuracy and sensitivity of fiber tracking algorithms. In this study we used intraoperative stimulation from brain tumor patients as a gold standard to estimate the sensitivity and accuracy of diffusion tensor MRI (DTI) and q-ball models of diffusion with deterministic and probabilistic fiber tracking algorithms for delineation of motor pathways. Methods We used preoperative high angular resolution diffusion MRI (HARDI) data (55 directions, b = 2000 s/mm2) acquired in a clinically feasible time frame from 12 patients who underwent a craniotomy for resection of a cerebral glioma. The corticospinal fiber tracts were delineated with DTI and q-ball models using deterministic and probabilistic algorithms. We used cortical and white matter IES sites as a gold standard for the presence and location of functional motor pathways. Sensitivity was defined as the true positive rate of delineating fiber pathways based on cortical IES stimulation sites. For accuracy and precision of the course of the fiber tracts, we measured the distance between the subcortical stimulation sites and the tractography result. Positive predictive rate of the delineated tracts was assessed by comparison of subcortical IES motor function (upper extremity, lower extremity, face) with the connection of the tractography pathway in the motor cortex. Results We obtained 21 cortical and 8 subcortical IES sites from intraoperative mapping of motor pathways. Probabilistic q-ball had the best sensitivity (79%) as determined from cortical IES compared to deterministic q-ball (50%), probabilistic DTI (36%), and deterministic DTI (10%). The sensitivity using the q-ball algorithm (65%) was significantly higher than using DTI (23%) (p < 0.001) and the probabilistic algorithms (58%) were more sensitive than deterministic approaches (30%) (p = 0.003). Probabilistic q-ball fiber tracks had the smallest offset to the subcortical stimulation sites. The offsets between diffusion fiber tracks and subcortical IES sites were increased significantly for those cases where the diffusion fiber tracks were visibly thinner than expected. There was perfect concordance between the subcortical IES function (e.g. hand stimulation) and the cortical connection of the nearest diffusion fiber track (e.g. upper extremity cortex). Discussion This study highlights the tremendous utility of intraoperative stimulation sites to provide a gold standard from which to evaluate diffusion MRI fiber tracking methods and has provided an object standard for evaluation of different diffusion models and approaches to fiber tracking. The probabilistic q-ball fiber tractography was significantly better than DTI methods in terms of sensitivity and accuracy of the course through the white matter. The commonly used DTI fiber tracking approach was shown to have very poor sensitivity (as low as 10% for deterministic DTI fiber tracking) for delineation of the lateral aspects of the corticospinal tract in our study. Effects of the tumor/edema resulted in significantly larger offsets between the subcortical IES and the preoperative fiber tracks. The provided data show that probabilistic HARDI tractography is the most objective and reproducible analysis but given the small sample and number of stimulation points a generalization about our results should be given with caution. Indeed our results inform the capabilities of preoperative diffusion fiber tracking and indicate that such data should be used carefully when making pre-surgical and intra-operative management decisions. PMID:24273719

Network-centric decision architecture for financial or 1/f data models

NASA Astrophysics Data System (ADS)

Jaenisch, Holger M.; Handley, James W.; Massey, Stoney; Case, Carl T.; Songy, Claude G.

2002-12-01

This paper presents a decision architecture algorithm for training neural equation based networks to make autonomous multi-goal oriented, multi-class decisions. These architectures make decisions based on their individual goals and draw from the same network centric feature set. Traditionally, these architectures are comprised of neural networks that offer marginal performance due to lack of convergence of the training set. We present an approach for autonomously extracting sample points as I/O exemplars for generation of multi-branch, multi-node decision architectures populated by adaptively derived neural equations. To test the robustness of this architecture, open source data sets in the form of financial time series were used, requiring a three-class decision space analogous to the lethal, non-lethal, and clutter discrimination problem. This algorithm and the results of its application are presented here.

An uncertainty-based distributed fault detection mechanism for wireless sensor networks.

PubMed

Yang, Yang; Gao, Zhipeng; Zhou, Hang; Qiu, Xuesong

2014-04-25

Exchanging too many messages for fault detection will cause not only a degradation of the network quality of service, but also represents a huge burden on the limited energy of sensors. Therefore, we propose an uncertainty-based distributed fault detection through aided judgment of neighbors for wireless sensor networks. The algorithm considers the serious influence of sensing measurement loss and therefore uses Markov decision processes for filling in missing data. Most important of all, fault misjudgments caused by uncertainty conditions are the main drawbacks of traditional distributed fault detection mechanisms. We draw on the experience of evidence fusion rules based on information entropy theory and the degree of disagreement function to increase the accuracy of fault detection. Simulation results demonstrate our algorithm can effectively reduce communication energy overhead due to message exchanges and provide a higher detection accuracy ratio.

The use of Merging and Aggregation Operators for MRDB Data Feeding

NASA Astrophysics Data System (ADS)

Kozioł, Krystian; Lupa, Michał

2013-12-01

This paper presents the application of two generalization operators - merging and displacement - in the process of automatic data feeding in a multiresolution data base of topographic objects from large-scale data-bases (1 : 500-1 : 5000). An ordered collection of objects makes a layer of development that in the process of generalization is subjected to the processes of merging and displacement in order to maintain recognizability in the reduced scale of the map. The solution to the above problem is the algorithms described in the work; these algorithms use the standard recognition of drawings (Chrobak 2010), independent of the user. A digital cartographic generalization process is a set of consecutive operators where merging and aggregation play a key role. The proper operation has a significant impact on the qualitative assessment of data generalization

Right sided weakness with right subdural hematoma: motor deafferentation of left hemisphere resulted in paralysis of the right side.

PubMed

Derakhshan, Iraj

2009-08-01

A right handed man with trauma to the head was admitted with headache and seizures. A severe right sided weakness was noted after a blunt trauma to the right side of his head. MRI of brain revealed a right-sided subdural hematoma and a normal left hemisphere and downstream motor pathways. Bimanual simultaneous drawing and manual reaction times indicated that the patient was right hemispheric in laterality of his major hemisphere. The right sided weakness in this patient was due to temporary transcallosal disconnection (diaschisis) of the minor hemisphere (left, in this case) from the excitatory signals arising from those structures of his major hemisphere devoted to movements occurring on nondominant side, transmitted via the corpus callosum to his left hemisphere. This case draws attention to disparity between neural and behavioral handedness (laterality of major hemisphere versus that of the preferred hand). Drawing longer lines by the left hand in simultaneous bimanual tasks and a slower simple reaction time to central visual stimuli by the ostensible dominant hand permitted lateralization of the major hemisphere to the right. Thus, the neurally nondominant side (right) lagged behind the dominant (left) by an interval equal to interhemispheric transfer time.

A review of parameters and heuristics for guiding metabolic pathfinding.

PubMed

Kim, Sarah M; Peña, Matthew I; Moll, Mark; Bennett, George N; Kavraki, Lydia E

2017-09-15

Recent developments in metabolic engineering have led to the successful biosynthesis of valuable products, such as the precursor of the antimalarial compound, artemisinin, and opioid precursor, thebaine. Synthesizing these traditionally plant-derived compounds in genetically modified yeast cells introduces the possibility of significantly reducing the total time and resources required for their production, and in turn, allows these valuable compounds to become cheaper and more readily available. Most biosynthesis pathways used in metabolic engineering applications have been discovered manually, requiring a tedious search of existing literature and metabolic databases. However, the recent rapid development of available metabolic information has enabled the development of automated approaches for identifying novel pathways. Computer-assisted pathfinding has the potential to save biochemists time in the initial discovery steps of metabolic engineering. In this paper, we review the parameters and heuristics used to guide the search in recent pathfinding algorithms. These parameters and heuristics capture information on the metabolic network structure, compound structures, reaction features, and organism-specificity of pathways. No one metabolic pathfinding algorithm or search parameter stands out as the best to use broadly for solving the pathfinding problem, as each method and parameter has its own strengths and shortcomings. As assisted pathfinding approaches continue to become more sophisticated, the development of better methods for visualizing pathway results and integrating these results into existing metabolic engineering practices is also important for encouraging wider use of these pathfinding methods.

Network modeling of kinase inhibitor polypharmacology reveals pathways targeted in chemical screens

PubMed Central

Ursu, Oana; Gosline, Sara J. C.; Beeharry, Neil; Fink, Lauren; Bhattacharjee, Vikram; Huang, Shao-shan Carol; Zhou, Yan; Yen, Tim; Fraenkel, Ernest

2017-01-01

Small molecule screens are widely used to prioritize pharmaceutical development. However, determining the pathways targeted by these molecules is challenging, since the compounds are often promiscuous. We present a network strategy that takes into account the polypharmacology of small molecules in order to generate hypotheses for their broader mode of action. We report a screen for kinase inhibitors that increase the efficacy of gemcitabine, the first-line chemotherapy for pancreatic cancer. Eight kinase inhibitors emerge that are known to affect 201 kinases, of which only three kinases have been previously identified as modifiers of gemcitabine toxicity. In this work, we use the SAMNet algorithm to identify pathways linking these kinases and genetic modifiers of gemcitabine toxicity with transcriptional and epigenetic changes induced by gemcitabine that we measure using DNaseI-seq and RNA-seq. SAMNet uses a constrained optimization algorithm to connect genes from these complementary datasets through a small set of protein-protein and protein-DNA interactions. The resulting network recapitulates known pathways including DNA repair, cell proliferation and the epithelial-to-mesenchymal transition. We use the network to predict genes with important roles in the gemcitabine response, including six that have already been shown to modify gemcitabine efficacy in pancreatic cancer and ten novel candidates. Our work reveals the important role of polypharmacology in the activity of these chemosensitizing agents. PMID:29023490

Efficient search, mapping, and optimization of multi-protein genetic systems in diverse bacteria

PubMed Central

Farasat, Iman; Kushwaha, Manish; Collens, Jason; Easterbrook, Michael; Guido, Matthew; Salis, Howard M

2014-01-01

Developing predictive models of multi-protein genetic systems to understand and optimize their behavior remains a combinatorial challenge, particularly when measurement throughput is limited. We developed a computational approach to build predictive models and identify optimal sequences and expression levels, while circumventing combinatorial explosion. Maximally informative genetic system variants were first designed by the RBS Library Calculator, an algorithm to design sequences for efficiently searching a multi-protein expression space across a > 10,000-fold range with tailored search parameters and well-predicted translation rates. We validated the algorithm's predictions by characterizing 646 genetic system variants, encoded in plasmids and genomes, expressed in six gram-positive and gram-negative bacterial hosts. We then combined the search algorithm with system-level kinetic modeling, requiring the construction and characterization of 73 variants to build a sequence-expression-activity map (SEAMAP) for a biosynthesis pathway. Using model predictions, we designed and characterized 47 additional pathway variants to navigate its activity space, find optimal expression regions with desired activity response curves, and relieve rate-limiting steps in metabolism. Creating sequence-expression-activity maps accelerates the optimization of many protein systems and allows previous measurements to quantitatively inform future designs. PMID:24952589

Network-based machine learning and graph theory algorithms for precision oncology.

PubMed

Zhang, Wei; Chien, Jeremy; Yong, Jeongsik; Kuang, Rui

2017-01-01

Network-based analytics plays an increasingly important role in precision oncology. Growing evidence in recent studies suggests that cancer can be better understood through mutated or dysregulated pathways or networks rather than individual mutations and that the efficacy of repositioned drugs can be inferred from disease modules in molecular networks. This article reviews network-based machine learning and graph theory algorithms for integrative analysis of personal genomic data and biomedical knowledge bases to identify tumor-specific molecular mechanisms, candidate targets and repositioned drugs for personalized treatment. The review focuses on the algorithmic design and mathematical formulation of these methods to facilitate applications and implementations of network-based analysis in the practice of precision oncology. We review the methods applied in three scenarios to integrate genomic data and network models in different analysis pipelines, and we examine three categories of network-based approaches for repositioning drugs in drug-disease-gene networks. In addition, we perform a comprehensive subnetwork/pathway analysis of mutations in 31 cancer genome projects in the Cancer Genome Atlas and present a detailed case study on ovarian cancer. Finally, we discuss interesting observations, potential pitfalls and future directions in network-based precision oncology.

All-Optical Implementation of the Ant Colony Optimization Algorithm

PubMed Central

Hu, Wenchao; Wu, Kan; Shum, Perry Ping; Zheludev, Nikolay I.; Soci, Cesare

2016-01-01

We report all-optical implementation of the optimization algorithm for the famous “ant colony” problem. Ant colonies progressively optimize pathway to food discovered by one of the ants through identifying the discovered route with volatile chemicals (pheromones) secreted on the way back from the food deposit. Mathematically this is an important example of graph optimization problem with dynamically changing parameters. Using an optical network with nonlinear waveguides to represent the graph and a feedback loop, we experimentally show that photons traveling through the network behave like ants that dynamically modify the environment to find the shortest pathway to any chosen point in the graph. This proof-of-principle demonstration illustrates how transient nonlinearity in the optical system can be exploited to tackle complex optimization problems directly, on the hardware level, which may be used for self-routing of optical signals in transparent communication networks and energy flow in photonic systems. PMID:27222098

Network design and analysis for multi-enzyme biocatalysis.

PubMed

Blaß, Lisa Katharina; Weyler, Christian; Heinzle, Elmar

2017-08-10

As more and more biological reaction data become available, the full exploration of the enzymatic potential for the synthesis of valuable products opens up exciting new opportunities but is becoming increasingly complex. The manual design of multi-step biosynthesis routes involving enzymes from different organisms is very challenging. To harness the full enzymatic potential, we developed a computational tool for the directed design of biosynthetic production pathways for multi-step catalysis with in vitro enzyme cascades, cell hydrolysates and permeabilized cells. We present a method which encompasses the reconstruction of a genome-scale pan-organism metabolic network, path-finding and the ranking of the resulting pathway candidates for proposing suitable synthesis pathways. The network is based on reaction and reaction pair data from the Kyoto Encyclopedia of Genes and Genomes (KEGG) and the thermodynamics calculator eQuilibrator. The pan-organism network is especially useful for finding the most suitable pathway to a target metabolite from a thermodynamic or economic standpoint. However, our method can be used with any network reconstruction, e.g. for a specific organism. We implemented a path-finding algorithm based on a mixed-integer linear program (MILP) which takes into account both topology and stoichiometry of the underlying network. Unlike other methods we do not specify a single starting metabolite, but our algorithm searches for pathways starting from arbitrary start metabolites to a target product of interest. Using a set of biochemical ranking criteria including pathway length, thermodynamics and other biological characteristics such as number of heterologous enzymes or cofactor requirement, it is possible to obtain well-designed meaningful pathway alternatives. In addition, a thermodynamic profile, the overall reactant balance and potential side reactions as well as an SBML file for visualization are generated for each pathway alternative. We present an in silico tool for the design of multi-enzyme biosynthetic production pathways starting from a pan-organism network. The method is highly customizable and each module can be adapted to the focus of the project at hand. This method is directly applicable for (i) in vitro enzyme cascades, (ii) cell hydrolysates and (iii) permeabilized cells.

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NASA Astrophysics Data System (ADS)

Liu, Yuan; Zhu, Qing

2017-07-01

In order to achieve the simulation of elaborate stroke trajectories in Chinese calligraphy, this paper puts forward the innovative researching on writing momentum in the field of non-photorealistic rendering in the first time. Through the analysis of using pen in Chinese calligraphy, the writing momentum is divided into three parts: the center, the side and the back of writing brush by the judgment of the angle of brush holder. We design an algorithm for dynamic outputting writing rendering based on brush model. According to monitoring parameters such as the direction, position and normalized pressure of using pen, we calculate parameters like the footprint direction, the shape, size and nib bending after writing. The algorithm can also judge the dynamic writing trend of stroke trajectories, even automatic generate stroke trajectories by the algorithm forecasted. We achieve a more delicate rendering of Chinese calligraphy to enhance the user's operating results. And we finish the unique writing effect separated the Chinese calligraphy form other general writing results, which greatly enhances the Chinese calligraphy simulation. So that people who lack of writing skills can easily draw a beautiful charm font.

Identification of altered pathways in breast cancer based on individualized pathway aberrance score.

PubMed

Shi, Sheng-Hong; Zhang, Wei; Jiang, Jing; Sun, Long

2017-08-01

The objective of the present study was to identify altered pathways in breast cancer based on the individualized pathway aberrance score (iPAS) method combined with the normal reference (nRef). There were 4 steps to identify altered pathways using the iPAS method: Data preprocessing conducted by the robust multi-array average (RMA) algorithm; gene-level statistics based on average Z ; pathway-level statistics according to iPAS; and a significance test dependent on 1 sample Wilcoxon test. The altered pathways were validated by calculating the changed percentage of each pathway in tumor samples and comparing them with pathways from differentially expressed genes (DEGs). A total of 688 altered pathways with P<0.01 were identified, including kinesin (KIF)- and polo-like kinase (PLK)-mediated events. When the percentage of change reached 50%, 310 pathways were involved in the total 688 altered pathways, which may validate the present results. In addition, there were 324 DEGs and 155 common genes between DEGs and pathway genes. DEGs and common genes were enriched in the same 9 significant terms, which also were members of altered pathways. The iPAS method was suitable for identifying altered pathways in breast cancer. Altered pathways (such as KIF and PLK mediated events) were important for understanding breast cancer mechanisms and for the future application of customized therapeutic decisions.

Efficient methods and readily customizable libraries for managing complexity of large networks.

PubMed

Dogrusoz, Ugur; Karacelik, Alper; Safarli, Ilkin; Balci, Hasan; Dervishi, Leonard; Siper, Metin Can

2018-01-01

One common problem in visualizing real-life networks, including biological pathways, is the large size of these networks. Often times, users find themselves facing slow, non-scaling operations due to network size, if not a "hairball" network, hindering effective analysis. One extremely useful method for reducing complexity of large networks is the use of hierarchical clustering and nesting, and applying expand-collapse operations on demand during analysis. Another such method is hiding currently unnecessary details, to later gradually reveal on demand. Major challenges when applying complexity reduction operations on large networks include efficiency and maintaining the user's mental map of the drawing. We developed specialized incremental layout methods for preserving a user's mental map while managing complexity of large networks through expand-collapse and hide-show operations. We also developed open-source JavaScript libraries as plug-ins to the web based graph visualization library named Cytsocape.js to implement these methods as complexity management operations. Through efficient specialized algorithms provided by these extensions, one can collapse or hide desired parts of a network, yielding potentially much smaller networks, making them more suitable for interactive visual analysis. This work fills an important gap by making efficient implementations of some already known complexity management techniques freely available to tool developers through a couple of open source, customizable software libraries, and by introducing some heuristics which can be applied upon such complexity management techniques to ensure preserving mental map of users.

Conditional Spectral Analysis of Replicated Multiple Time Series with Application to Nocturnal Physiology.

PubMed

Krafty, Robert T; Rosen, Ori; Stoffer, David S; Buysse, Daniel J; Hall, Martica H

2017-01-01

This article considers the problem of analyzing associations between power spectra of multiple time series and cross-sectional outcomes when data are observed from multiple subjects. The motivating application comes from sleep medicine, where researchers are able to non-invasively record physiological time series signals during sleep. The frequency patterns of these signals, which can be quantified through the power spectrum, contain interpretable information about biological processes. An important problem in sleep research is drawing connections between power spectra of time series signals and clinical characteristics; these connections are key to understanding biological pathways through which sleep affects, and can be treated to improve, health. Such analyses are challenging as they must overcome the complicated structure of a power spectrum from multiple time series as a complex positive-definite matrix-valued function. This article proposes a new approach to such analyses based on a tensor-product spline model of Cholesky components of outcome-dependent power spectra. The approach exibly models power spectra as nonparametric functions of frequency and outcome while preserving geometric constraints. Formulated in a fully Bayesian framework, a Whittle likelihood based Markov chain Monte Carlo (MCMC) algorithm is developed for automated model fitting and for conducting inference on associations between outcomes and spectral measures. The method is used to analyze data from a study of sleep in older adults and uncovers new insights into how stress and arousal are connected to the amount of time one spends in bed.

GO Explorer: A gene-ontology tool to aid in the interpretation of shotgun proteomics data.

PubMed

Carvalho, Paulo C; Fischer, Juliana Sg; Chen, Emily I; Domont, Gilberto B; Carvalho, Maria Gc; Degrave, Wim M; Yates, John R; Barbosa, Valmir C

2009-02-24

Spectral counting is a shotgun proteomics approach comprising the identification and relative quantitation of thousands of proteins in complex mixtures. However, this strategy generates bewildering amounts of data whose biological interpretation is a challenge. Here we present a new algorithm, termed GO Explorer (GOEx), that leverages the gene ontology (GO) to aid in the interpretation of proteomic data. GOEx stands out because it combines data from protein fold changes with GO over-representation statistics to help draw conclusions. Moreover, it is tightly integrated within the PatternLab for Proteomics project and, thus, lies within a complete computational environment that provides parsers and pattern recognition tools designed for spectral counting. GOEx offers three independent methods to query data: an interactive directed acyclic graph, a specialist mode where key words can be searched, and an automatic search. Its usefulness is demonstrated by applying it to help interpret the effects of perillyl alcohol, a natural chemotherapeutic agent, on glioblastoma multiform cell lines (A172). We used a new multi-surfactant shotgun proteomic strategy and identified more than 2600 proteins; GOEx pinpointed key sets of differentially expressed proteins related to cell cycle, alcohol catabolism, the Ras pathway, apoptosis, and stress response, to name a few. GOEx facilitates organism-specific studies by leveraging GO and providing a rich graphical user interface. It is a simple to use tool, specialized for biologists who wish to analyze spectral counting data from shotgun proteomics. GOEx is available at http://pcarvalho.com/patternlab.

Chiropractic Integrated Care Pathway for Low Back Pain in Veterans: Results of a Delphi Consensus Process.

PubMed

Lisi, Anthony J; Salsbury, Stacie A; Hawk, Cheryl; Vining, Robert D; Wallace, Robert B; Branson, Richard; Long, Cynthia R; Burgo-Black, A Lucille; Goertz, Christine M

2018-02-01

The purpose of this study was to develop an integrated care pathway for doctors of chiropractic, primary care providers, and mental health professionals who manage veterans with low back pain, with or without mental health comorbidity, within Department of Veterans Affairs health care facilities. The research method used was a consensus process. A multidisciplinary investigative team reviewed clinical guidelines and Veterans Affairs pain and mental health initiatives to develop seed statements and care algorithms to guide chiropractic management and collaborative care of veterans with low back pain. A 5-member advisory committee approved initial recommendations. Veterans Affairs-based panelists (n = 58) evaluated the pathway via e-mail using a modified RAND/UCLA methodology. Consensus was defined as agreement by 80% of panelists. The modified Delphi process was conducted in July to December 2016. Most (93%) seed statements achieved consensus during the first round, with all statements reaching consensus after 2 rounds. The final care pathway addressed the topics of informed consent, clinical evaluation including history and examination, screening for red flags, documentation, diagnostic imaging, patient-reported outcomes, adverse event reporting, chiropractic treatment frequency and duration standards, tailored approaches to chiropractic care in veteran populations, and clinical presentation of common mental health conditions. Care algorithms outlined chiropractic case management and interprofessional collaboration and referrals between doctors of chiropractic and primary care and mental health providers. This study offers an integrative care pathway that includes chiropractic care for veterans with low back pain. Copyright © 2018. Published by Elsevier Inc.

Detecting uber-operons in prokaryotic genomes.

PubMed

Che, Dongsheng; Li, Guojun; Mao, Fenglou; Wu, Hongwei; Xu, Ying

2006-01-01

We present a study on computational identification of uber-operons in a prokaryotic genome, each of which represents a group of operons that are evolutionarily or functionally associated through operons in other (reference) genomes. Uber-operons represent a rich set of footprints of operon evolution, whose full utilization could lead to new and more powerful tools for elucidation of biological pathways and networks than what operons have provided, and a better understanding of prokaryotic genome structures and evolution. Our prediction algorithm predicts uber-operons through identifying groups of functionally or transcriptionally related operons, whose gene sets are conserved across the target and multiple reference genomes. Using this algorithm, we have predicted uber-operons for each of a group of 91 genomes, using the other 90 genomes as references. In particular, we predicted 158 uber-operons in Escherichia coli K12 covering 1830 genes, and found that many of the uber-operons correspond to parts of known regulons or biological pathways or are involved in highly related biological processes based on their Gene Ontology (GO) assignments. For some of the predicted uber-operons that are not parts of known regulons or pathways, our analyses indicate that their genes are highly likely to work together in the same biological processes, suggesting the possibility of new regulons and pathways. We believe that our uber-operon prediction provides a highly useful capability and a rich information source for elucidation of complex biological processes, such as pathways in microbes. All the prediction results are available at our Uber-Operon Database: http://csbl.bmb.uga.edu/uber, the first of its kind.

Detecting uber-operons in prokaryotic genomes

PubMed Central

Che, Dongsheng; Li, Guojun; Mao, Fenglou; Wu, Hongwei; Xu, Ying

2006-01-01

We present a study on computational identification of uber-operons in a prokaryotic genome, each of which represents a group of operons that are evolutionarily or functionally associated through operons in other (reference) genomes. Uber-operons represent a rich set of footprints of operon evolution, whose full utilization could lead to new and more powerful tools for elucidation of biological pathways and networks than what operons have provided, and a better understanding of prokaryotic genome structures and evolution. Our prediction algorithm predicts uber-operons through identifying groups of functionally or transcriptionally related operons, whose gene sets are conserved across the target and multiple reference genomes. Using this algorithm, we have predicted uber-operons for each of a group of 91 genomes, using the other 90 genomes as references. In particular, we predicted 158 uber-operons in Escherichia coli K12 covering 1830 genes, and found that many of the uber-operons correspond to parts of known regulons or biological pathways or are involved in highly related biological processes based on their Gene Ontology (GO) assignments. For some of the predicted uber-operons that are not parts of known regulons or pathways, our analyses indicate that their genes are highly likely to work together in the same biological processes, suggesting the possibility of new regulons and pathways. We believe that our uber-operon prediction provides a highly useful capability and a rich information source for elucidation of complex biological processes, such as pathways in microbes. All the prediction results are available at our Uber-Operon Database: , the first of its kind. PMID:16682449

GKS utilities for FORTRAN-77

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

Beach, R.C.

1992-01-01

This document describes a number of subroutines that can be useful in GKS graphic applications programmed in FORTRAN-77. The algorithms described here include subroutines to do the following: (1) Draw text characters in a more flexible manner than is possible with basic GKS. (2) Project two-dimensional and three-dimensional space onto two-dimensional space. (3) Draw smooth curves. (4) Draw two-dimensional projections of complex three-dimensional objects. FORTRAN-77 is described in American National Standard, Programming Language, FORTRAN. GKS is described in American National Standard for Information Systems: Computer Graphics -- Graphical Kernel System (GKS) Functional Description and the FORTRAN-77 interface is described inmore » American National Standard for Information Systems: Computer Graphics -- Graphical Kernel System (GKS) FORTRAN Binding. All of the subroutine names and additional enumeration types that will be described in this document begin with the letters ``GZ.`` Since GKS itself does not have any subroutine names or enumeration types that begin with these letters, no confusion between the usual GKS subroutines and the ones described here should occur. Many concepts will have to be defined in the following chapters. When a concept is first encountered, it will be given in italics. The information around the italicized word or phrase may be taken as its definition.« less

GKS utilities for FORTRAN-77

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

Beach, R.C.

1992-01-01

This document describes a number of subroutines that can be useful in GKS graphic applications programmed in FORTRAN-77. The algorithms described here include subroutines to do the following: (1) Draw text characters in a more flexible manner than is possible with basic GKS. (2) Project two-dimensional and three-dimensional space onto two-dimensional space. (3) Draw smooth curves. (4) Draw two-dimensional projections of complex three-dimensional objects. FORTRAN-77 is described in American National Standard, Programming Language, FORTRAN. GKS is described in American National Standard for Information Systems: Computer Graphics -- Graphical Kernel System (GKS) Functional Description and the FORTRAN-77 interface is described inmore » American National Standard for Information Systems: Computer Graphics -- Graphical Kernel System (GKS) FORTRAN Binding. All of the subroutine names and additional enumeration types that will be described in this document begin with the letters GZ.'' Since GKS itself does not have any subroutine names or enumeration types that begin with these letters, no confusion between the usual GKS subroutines and the ones described here should occur. Many concepts will have to be defined in the following chapters. When a concept is first encountered, it will be given in italics. The information around the italicized word or phrase may be taken as its definition.« less

Design of handwriting drawing board based on common copper clad laminate

NASA Astrophysics Data System (ADS)

Wang, Hongyuan; Gao, Wenzhi; Wang, Yuan

2015-02-01

Handwriting drawing board is not only a subject which can be used to write and draw, but also a method to measure and process weak signals. This design adopts 8051 single chip microprocessor as the main controller. It applies a constant-current source[1][2] to copper plate and collects the voltage value according to the resistance divider effect. Then it amplifies the signal with low-noise and high-precision amplifier[3] AD620 which is placed in the low impedance and anti-interference pen. It converts analog signal to digital signal by an 11-channel, 12-bit A/D converter TLC2543. Adoption of average filtering algorithm can effectively improve the measuring accuracy, reduce the error and make the collected voltage signal more stable. The accurate position can be detected by scanning the horizontal and vertical ordinates with the analog switch via the internal bridge of module L298 which can change the direction of X-Y axis signal scan. DM12864 is used as man-machine interface and this hominization design is convenient for man-machine communication. This collecting system has high accuracy, high stability and strong anti-interference capability. It's easy to control and has very large development space in the future.

Metabolomic Analysis and Visualization Engine for LC–MS Data

PubMed Central

Melamud, Eugene; Vastag, Livia; Rabinowitz, Joshua D.

2017-01-01

Metabolomic analysis by liquid chromatography–high-resolution mass spectrometry results in data sets with thousands of features arising from metabolites, fragments, isotopes, and adducts. Here we describe a software package, Metabolomic Analysis and Visualization ENgine (MAVEN), designed for efficient interactive analysis of LC–MS data, including in the presence of isotope labeling. The software contains tools for all aspects of the data analysis process, from feature extraction to pathway-based graphical data display. To facilitate data validation, a machine learning algorithm automatically assesses peak quality. Users interact with raw data primarily in the form of extracted ion chromatograms, which are displayed with overlaid circles indicating peak quality, and bar graphs of peak intensities for both unlabeled and isotope-labeled metabolite forms. Click-based navigation leads to additional information, such as raw data for specific isotopic forms or for metabolites changing significantly between conditions. Fast data processing algorithms result in nearly delay-free browsing. Drop-down menus provide tools for the overlay of data onto pathway maps. These tools enable animating series of pathway graphs, e.g., to show propagation of labeled forms through a metabolic network. MAVEN is released under an open source license at http://maven.princeton.edu. PMID:21049934

Metabolomic Modularity Analysis (MMA) to Quantify Human Liver Perfusion Dynamics.

PubMed

Sridharan, Gautham Vivek; Bruinsma, Bote Gosse; Bale, Shyam Sundhar; Swaminathan, Anandh; Saeidi, Nima; Yarmush, Martin L; Uygun, Korkut

2017-11-13

Large-scale -omics data are now ubiquitously utilized to capture and interpret global responses to perturbations in biological systems, such as the impact of disease states on cells, tissues, and whole organs. Metabolomics data, in particular, are difficult to interpret for providing physiological insight because predefined biochemical pathways used for analysis are inherently biased and fail to capture more complex network interactions that span multiple canonical pathways. In this study, we introduce a nov-el approach coined Metabolomic Modularity Analysis (MMA) as a graph-based algorithm to systematically identify metabolic modules of reactions enriched with metabolites flagged to be statistically significant. A defining feature of the algorithm is its ability to determine modularity that highlights interactions between reactions mediated by the production and consumption of cofactors and other hub metabolites. As a case study, we evaluated the metabolic dynamics of discarded human livers using time-course metabolomics data and MMA to identify modules that explain the observed physiological changes leading to liver recovery during subnormothermic machine perfusion (SNMP). MMA was performed on a large scale liver-specific human metabolic network that was weighted based on metabolomics data and identified cofactor-mediated modules that would not have been discovered by traditional metabolic pathway analyses.

Systematic identification and analysis of frequent gene fusion events in metabolic pathways

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

Henry, Christopher S.; Lerma-Ortiz, Claudia; Gerdes, Svetlana Y.

Here, gene fusions are the most powerful type of in silico-derived functional associations. However, many fusion compilations were made when <100 genomes were available, and algorithms for identifying fusions need updating to handle the current avalanche of sequenced genomes. The availability of a large fusion dataset would help probe functional associations and enable systematic analysis of where and why fusion events occur. As a result, here we present a systematic analysis of fusions in prokaryotes. We manually generated two training sets: (i) 121 fusions in the model organism Escherichia coli; (ii) 131 fusions found in B vitamin metabolism. These setsmore » were used to develop a fusion prediction algorithm that captured the training set fusions with only 7 % false negatives and 50 % false positives, a substantial improvement over existing approaches. This algorithm was then applied to identify 3.8 million potential fusions across 11,473 genomes. The results of the analysis are available in a searchable database. A functional analysis identified 3,000 reactions associated with frequent fusion events and revealed areas of metabolism where fusions are particularly prevalent. In conclusion, customary definitions of fusions were shown to be ambiguous, and a stricter one was proposed. Exploring the genes participating in fusion events showed that they most commonly encode transporters, regulators, and metabolic enzymes. The major rationales for fusions between metabolic genes appear to be overcoming pathway bottlenecks, avoiding toxicity, controlling competing pathways, and facilitating expression and assembly of protein complexes. Finally, our fusion dataset provides powerful clues to decipher the biological activities of domains of unknown function.« less

Systematic identification and analysis of frequent gene fusion events in metabolic pathways

DOE PAGES

Henry, Christopher S.; Lerma-Ortiz, Claudia; Gerdes, Svetlana Y.; ...

2016-06-24

Here, gene fusions are the most powerful type of in silico-derived functional associations. However, many fusion compilations were made when <100 genomes were available, and algorithms for identifying fusions need updating to handle the current avalanche of sequenced genomes. The availability of a large fusion dataset would help probe functional associations and enable systematic analysis of where and why fusion events occur. As a result, here we present a systematic analysis of fusions in prokaryotes. We manually generated two training sets: (i) 121 fusions in the model organism Escherichia coli; (ii) 131 fusions found in B vitamin metabolism. These setsmore » were used to develop a fusion prediction algorithm that captured the training set fusions with only 7 % false negatives and 50 % false positives, a substantial improvement over existing approaches. This algorithm was then applied to identify 3.8 million potential fusions across 11,473 genomes. The results of the analysis are available in a searchable database. A functional analysis identified 3,000 reactions associated with frequent fusion events and revealed areas of metabolism where fusions are particularly prevalent. In conclusion, customary definitions of fusions were shown to be ambiguous, and a stricter one was proposed. Exploring the genes participating in fusion events showed that they most commonly encode transporters, regulators, and metabolic enzymes. The major rationales for fusions between metabolic genes appear to be overcoming pathway bottlenecks, avoiding toxicity, controlling competing pathways, and facilitating expression and assembly of protein complexes. Finally, our fusion dataset provides powerful clues to decipher the biological activities of domains of unknown function.« less

Identification of Single- and Multiple-Class Specific Signature Genes from Gene Expression Profiles by Group Marker Index

PubMed Central

Tsai, Yu-Shuen; Aguan, Kripamoy; Pal, Nikhil R.; Chung, I-Fang

2011-01-01

Informative genes from microarray data can be used to construct prediction model and investigate biological mechanisms. Differentially expressed genes, the main targets of most gene selection methods, can be classified as single- and multiple-class specific signature genes. Here, we present a novel gene selection algorithm based on a Group Marker Index (GMI), which is intuitive, of low-computational complexity, and efficient in identification of both types of genes. Most gene selection methods identify only single-class specific signature genes and cannot identify multiple-class specific signature genes easily. Our algorithm can detect de novo certain conditions of multiple-class specificity of a gene and makes use of a novel non-parametric indicator to assess the discrimination ability between classes. Our method is effective even when the sample size is small as well as when the class sizes are significantly different. To compare the effectiveness and robustness we formulate an intuitive template-based method and use four well-known datasets. We demonstrate that our algorithm outperforms the template-based method in difficult cases with unbalanced distribution. Moreover, the multiple-class specific genes are good biomarkers and play important roles in biological pathways. Our literature survey supports that the proposed method identifies unique multiple-class specific marker genes (not reported earlier to be related to cancer) in the Central Nervous System data. It also discovers unique biomarkers indicating the intrinsic difference between subtypes of lung cancer. We also associate the pathway information with the multiple-class specific signature genes and cross-reference to published studies. We find that the identified genes participate in the pathways directly involved in cancer development in leukemia data. Our method gives a promising way to find genes that can involve in pathways of multiple diseases and hence opens up the possibility of using an existing drug on other diseases as well as designing a single drug for multiple diseases. PMID:21909426

Doubly Disadvantaged? Bullying Experiences among Disabled Children and Young People in England

PubMed Central

Chatzitheochari, Stella; Parsons, Samantha; Platt, Lucinda

2015-01-01

Bullying among school-aged children and adolescents is recognised as an important social problem, and the adverse consequences for victims are well established. However, despite growing interest in the socio-demographic profile of victims, there is limited evidence on the relationship between bullying victimisation and childhood disability. This article enhances our understanding of bullying experiences among disabled children in both early and later childhood, drawing on nationally representative longitudinal data from the Millennium Cohort Study and the Longitudinal Study of Young People in England. We model the association of disability measured in two different ways with the probability of being bullied at ages seven and 15, controlling for a wide range of known risk factors that vary with childhood disability. Results reveal an independent association of disability with bullying victimisation, suggesting a potential pathway to cumulative disability-related disadvantage, and drawing attention to the school as a site of reproduction of social inequalities. PMID:27546915

The artist emerges: visual art learning alters neural structure and function.

PubMed

Schlegel, Alexander; Alexander, Prescott; Fogelson, Sergey V; Li, Xueting; Lu, Zhengang; Kohler, Peter J; Riley, Enrico; Tse, Peter U; Meng, Ming

2015-01-15

How does the brain mediate visual artistic creativity? Here we studied behavioral and neural changes in drawing and painting students compared to students who did not study art. We investigated three aspects of cognition vital to many visual artists: creative cognition, perception, and perception-to-action. We found that the art students became more creative via the reorganization of prefrontal white matter but did not find any significant changes in perceptual ability or related neural activity in the art students relative to the control group. Moreover, the art students improved in their ability to sketch human figures from observation, and multivariate patterns of cortical and cerebellar activity evoked by this drawing task became increasingly separable between art and non-art students. Our findings suggest that the emergence of visual artistic skills is supported by plasticity in neural pathways that enable creative cognition and mediate perceptuomotor integration. Copyright © 2014 Elsevier Inc. All rights reserved.

Doubly Disadvantaged? Bullying Experiences among Disabled Children and Young People in England.

PubMed

Chatzitheochari, Stella; Parsons, Samantha; Platt, Lucinda

2016-08-01

Bullying among school-aged children and adolescents is recognised as an important social problem, and the adverse consequences for victims are well established. However, despite growing interest in the socio-demographic profile of victims, there is limited evidence on the relationship between bullying victimisation and childhood disability. This article enhances our understanding of bullying experiences among disabled children in both early and later childhood, drawing on nationally representative longitudinal data from the Millennium Cohort Study and the Longitudinal Study of Young People in England. We model the association of disability measured in two different ways with the probability of being bullied at ages seven and 15, controlling for a wide range of known risk factors that vary with childhood disability. Results reveal an independent association of disability with bullying victimisation, suggesting a potential pathway to cumulative disability-related disadvantage, and drawing attention to the school as a site of reproduction of social inequalities.

Methods and approaches in the topology-based analysis of biological pathways

PubMed Central

Mitrea, Cristina; Taghavi, Zeinab; Bokanizad, Behzad; Hanoudi, Samer; Tagett, Rebecca; Donato, Michele; Voichiţa, Călin; Drăghici, Sorin

2013-01-01

The goal of pathway analysis is to identify the pathways significantly impacted in a given phenotype. Many current methods are based on algorithms that consider pathways as simple gene lists, dramatically under-utilizing the knowledge that such pathways are meant to capture. During the past few years, a plethora of methods claiming to incorporate various aspects of the pathway topology have been proposed. These topology-based methods, sometimes referred to as “third generation,” have the potential to better model the phenomena described by pathways. Although there is now a large variety of approaches used for this purpose, no review is currently available to offer guidance for potential users and developers. This review covers 22 such topology-based pathway analysis methods published in the last decade. We compare these methods based on: type of pathways analyzed (e.g., signaling or metabolic), input (subset of genes, all genes, fold changes, gene p-values, etc.), mathematical models, pathway scoring approaches, output (one or more pathway scores, p-values, etc.) and implementation (web-based, standalone, etc.). We identify and discuss challenges, arising both in methodology and in pathway representation, including inconsistent terminology, different data formats, lack of meaningful benchmarks, and the lack of tissue and condition specificity. PMID:24133454

Role of NF-Kappa B Signaling in X-Box Binding Protein 1 (XBP1)-Mediated Antiestrogen Resistance in Breast Cancer

DTIC Science & Technology

2013-10-01

experiments, a statistically significant data is not yet available. Additional experiments are needed for us to be able to draw   conclu comb Figure...well-defined stress pathways, UPR and autophagy, are involved breast involution regulation. Using published gene expression array datasets from...performed involution time-course experiments using both low-dose drug interventions and an autophagy-related gene 7 (ATG7) deletion mouse model

Iterative Most-Likely Point Registration (IMLP): A Robust Algorithm for Computing Optimal Shape Alignment

PubMed Central

Billings, Seth D.; Boctor, Emad M.; Taylor, Russell H.

2015-01-01

We present a probabilistic registration algorithm that robustly solves the problem of rigid-body alignment between two shapes with high accuracy, by aptly modeling measurement noise in each shape, whether isotropic or anisotropic. For point-cloud shapes, the probabilistic framework additionally enables modeling locally-linear surface regions in the vicinity of each point to further improve registration accuracy. The proposed Iterative Most-Likely Point (IMLP) algorithm is formed as a variant of the popular Iterative Closest Point (ICP) algorithm, which iterates between point-correspondence and point-registration steps. IMLP’s probabilistic framework is used to incorporate a generalized noise model into both the correspondence and the registration phases of the algorithm, hence its name as a most-likely point method rather than a closest-point method. To efficiently compute the most-likely correspondences, we devise a novel search strategy based on a principal direction (PD)-tree search. We also propose a new approach to solve the generalized total-least-squares (GTLS) sub-problem of the registration phase, wherein the point correspondences are registered under a generalized noise model. Our GTLS approach has improved accuracy, efficiency, and stability compared to prior methods presented for this problem and offers a straightforward implementation using standard least squares. We evaluate the performance of IMLP relative to a large number of prior algorithms including ICP, a robust variant on ICP, Generalized ICP (GICP), and Coherent Point Drift (CPD), as well as drawing close comparison with the prior anisotropic registration methods of GTLS-ICP and A-ICP. The performance of IMLP is shown to be superior with respect to these algorithms over a wide range of noise conditions, outliers, and misalignments using both mesh and point-cloud representations of various shapes. PMID:25748700

Advances in Landslide Nowcasting: Evaluation of a Global and Regional Modeling Approach

NASA Technical Reports Server (NTRS)

Kirschbaum, Dalia Bach; Peters-Lidard, Christa; Adler, Robert; Hong, Yang; Kumar, Sujay; Lerner-Lam, Arthur

2011-01-01

The increasing availability of remotely sensed data offers a new opportunity to address landslide hazard assessment at larger spatial scales. A prototype global satellite-based landslide hazard algorithm has been developed to identify areas that may experience landslide activity. This system combines a calculation of static landslide susceptibility with satellite-derived rainfall estimates and uses a threshold approach to generate a set of nowcasts that classify potentially hazardous areas. A recent evaluation of this algorithm framework found that while this tool represents an important first step in larger-scale near real-time landslide hazard assessment efforts, it requires several modifications before it can be fully realized as an operational tool. This study draws upon a prior work s recommendations to develop a new approach for considering landslide susceptibility and hazard at the regional scale. This case study calculates a regional susceptibility map using remotely sensed and in situ information and a database of landslides triggered by Hurricane Mitch in 1998 over four countries in Central America. The susceptibility map is evaluated with a regional rainfall intensity duration triggering threshold and results are compared with the global algorithm framework for the same event. Evaluation of this regional system suggests that this empirically based approach provides one plausible way to approach some of the data and resolution issues identified in the global assessment. The presented methodology is straightforward to implement, improves upon the global approach, and allows for results to be transferable between regions. The results also highlight several remaining challenges, including the empirical nature of the algorithm framework and adequate information for algorithm validation. Conclusions suggest that integrating additional triggering factors such as soil moisture may help to improve algorithm performance accuracy. The regional algorithm scenario represents an important step forward in advancing regional and global-scale landslide hazard assessment.

T Lymphocyte Activation Threshold is Increased in Reduced Gravity

NASA Technical Reports Server (NTRS)

Adams, Charley L.; Gonzalez, M.; Sams, C. F.

2000-01-01

There have been substantial advances in molecular and cellular biology that have provided new insight into the biochemical and genetic basis of lymphocyte recognition, activation and expression of distinct functional phenotypes. It has now become evident that for both T and B cells, stimuli delivered through their receptors can result in either clonal expansion or apoptosis. In the case of T cells, clonal expansion of helper cells is accompanied by differentiation into two major functional subsets which regulate the immune response. The pathways between the membrane and the nucleus and their molecular components are an area of very active investigation. This meeting will draw together scientists working on diverse aspects of this problem, including receptor ligand interactions, intracellular pathways that transmit receptor mediated signals and the effect of such signal transduction pathways on gene regulation. The aim of this meeting is to integrate the information from these various experimental approaches into a new synthesis and molecular explanation of T cell activation, differentiation and death.

The optimal algorithm for Multi-source RS image fusion.

PubMed

Fu, Wei; Huang, Shui-Guang; Li, Zeng-Shun; Shen, Hao; Li, Jun-Shuai; Wang, Peng-Yuan

2016-01-01

In order to solve the issue which the fusion rules cannot be self-adaptively adjusted by using available fusion methods according to the subsequent processing requirements of Remote Sensing (RS) image, this paper puts forward GSDA (genetic-iterative self-organizing data analysis algorithm) by integrating the merit of genetic arithmetic together with the advantage of iterative self-organizing data analysis algorithm for multi-source RS image fusion. The proposed algorithm considers the wavelet transform of the translation invariance as the model operator, also regards the contrast pyramid conversion as the observed operator. The algorithm then designs the objective function by taking use of the weighted sum of evaluation indices, and optimizes the objective function by employing GSDA so as to get a higher resolution of RS image. As discussed above, the bullet points of the text are summarized as follows.•The contribution proposes the iterative self-organizing data analysis algorithm for multi-source RS image fusion.•This article presents GSDA algorithm for the self-adaptively adjustment of the fusion rules.•This text comes up with the model operator and the observed operator as the fusion scheme of RS image based on GSDA. The proposed algorithm opens up a novel algorithmic pathway for multi-source RS image fusion by means of GSDA.

A heuristic re-mapping algorithm reducing inter-level communication in SAMR applications.

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

Steensland, Johan; Ray, Jaideep

2003-07-01

This paper aims at decreasing execution time for large-scale structured adaptive mesh refinement (SAMR) applications by proposing a new heuristic re-mapping algorithm and experimentally showing its effectiveness in reducing inter-level communication. Tests were done for five different SAMR applications. The overall goal is to engineer a dynamically adaptive meta-partitioner capable of selecting and configuring the most appropriate partitioning strategy at run-time based on current system and application state. Such a metapartitioner can significantly reduce execution times for general SAMR applications. Computer simulations of physical phenomena are becoming increasingly popular as they constitute an important complement to real-life testing. In manymore » cases, such simulations are based on solving partial differential equations by numerical methods. Adaptive methods are crucial to efficiently utilize computer resources such as memory and CPU. But even with adaption, the simulations are computationally demanding and yield huge data sets. Thus parallelization and the efficient partitioning of data become issues of utmost importance. Adaption causes the workload to change dynamically, calling for dynamic (re-) partitioning to maintain efficient resource utilization. The proposed heuristic algorithm reduced inter-level communication substantially. Since the complexity of the proposed algorithm is low, this decrease comes at a relatively low cost. As a consequence, we draw the conclusion that the proposed re-mapping algorithm would be useful to lower overall execution times for many large SAMR applications. Due to its usefulness and its parameterization, the proposed algorithm would constitute a natural and important component of the meta-partitioner.« less

Intelligent ensemble T-S fuzzy neural networks with RCDPSO_DM optimization for effective handling of complex clinical pathway variances.

PubMed

Du, Gang; Jiang, Zhibin; Diao, Xiaodi; Yao, Yang

2013-07-01

Takagi-Sugeno (T-S) fuzzy neural networks (FNNs) can be used to handle complex, fuzzy, uncertain clinical pathway (CP) variances. However, there are many drawbacks, such as slow training rate, propensity to become trapped in a local minimum and poor ability to perform a global search. In order to improve overall performance of variance handling by T-S FNNs, a new CP variance handling method is proposed in this study. It is based on random cooperative decomposing particle swarm optimization with double mutation mechanism (RCDPSO_DM) for T-S FNNs. Moreover, the proposed integrated learning algorithm, combining the RCDPSO_DM algorithm with a Kalman filtering algorithm, is applied to optimize antecedent and consequent parameters of constructed T-S FNNs. Then, a multi-swarm cooperative immigrating particle swarm algorithm ensemble method is used for intelligent ensemble T-S FNNs with RCDPSO_DM optimization to further improve stability and accuracy of CP variance handling. Finally, two case studies on liver and kidney poisoning variances in osteosarcoma preoperative chemotherapy are used to validate the proposed method. The result demonstrates that intelligent ensemble T-S FNNs based on the RCDPSO_DM achieves superior performances, in terms of stability, efficiency, precision and generalizability, over PSO ensemble of all T-S FNNs with RCDPSO_DM optimization, single T-S FNNs with RCDPSO_DM optimization, standard T-S FNNs, standard Mamdani FNNs and T-S FNNs based on other algorithms (cooperative particle swarm optimization and particle swarm optimization) for CP variance handling. Therefore, it makes CP variance handling more effective. Copyright © 2013 Elsevier Ltd. All rights reserved.

How might flukes and tapeworms maintain genome integrity without a canonical piRNA pathway?

PubMed

Skinner, Danielle E; Rinaldi, Gabriel; Koziol, Uriel; Brehm, Klaus; Brindley, Paul J

2014-03-01

Surveillance by RNA interference is central to controlling the mobilization of transposable elements (TEs). In stem cells, Piwi argonaute (Ago) proteins and associated proteins repress mobilization of TEs to maintain genome integrity. This defense mechanism targeting TEs is termed the Piwi-interacting RNA (piRNA) pathway. In this opinion article, we draw attention to the situation that the genomes of cestodes and trematodes have lost the piwi and vasa genes that are hallmark characters of the germline multipotency program. This absence of Piwi-like Agos and Vasa helicases prompts the question: how does the germline of these flatworms withstand mobilization of TEs? Here, we present an interpretation of mechanisms likely to defend the germline integrity of parasitic flatworms. Copyright © 2014 Elsevier Ltd. All rights reserved.

Mental Models of Invisible Logical Networks

NASA Technical Reports Server (NTRS)

Sanderson, P.

1984-01-01

Subjects were required to discover the structure of a logical network whose links were invisible. Network structure had to be inferred from the behavior of the components after a failure. It was hypothesized that since such failure diagnosis tasks often draw on spatial processes, a good deal of spatial complexity in the network should affect network discovery. Results show that the ability to discover the linkages in the network is directly related to the spatial complexity of the pathway described by the linkages. This effect was generally independent of the amount of evidence available to subjects about the existence of the link. These results raise the question of whether inferences about spatially complex pathways were simply not made, or whether they were made but not retained because of a high load on memory resources.

Cyborg practices: call-handlers and computerised decision support systems in urgent and emergency care.

PubMed

Pope, Catherine; Halford, Susan; Turnbull, Joanne; Prichard, Jane

2014-06-01

This article draws on data collected during a 2-year project examining the deployment of a computerised decision support system. This computerised decision support system was designed to be used by non-clinical staff for dealing with calls to emergency (999) and urgent care (out-of-hours) services. One of the promises of computerised decisions support technologies is that they can 'hold' vast amounts of sophisticated clinical knowledge and combine it with decision algorithms to enable standardised decision-making by non-clinical (clerical) staff. This article draws on our ethnographic study of this computerised decision support system in use, and we use our analysis to question the 'automated' vision of decision-making in healthcare call-handling. We show that embodied and experiential (human) expertise remains central and highly salient in this work, and we propose that the deployment of the computerised decision support system creates something new, that this conjunction of computer and human creates a cyborg practice.

Comparative Analysis of Document level Text Classification Algorithms using R

NASA Astrophysics Data System (ADS)

Syamala, Maganti; Nalini, N. J., Dr; Maguluri, Lakshamanaphaneendra; Ragupathy, R., Dr.

2017-08-01

From the past few decades there has been tremendous volumes of data available in Internet either in structured or unstructured form. Also, there is an exponential growth of information on Internet, so there is an emergent need of text classifiers. Text mining is an interdisciplinary field which draws attention on information retrieval, data mining, machine learning, statistics and computational linguistics. And to handle this situation, a wide range of supervised learning algorithms has been introduced. Among all these K-Nearest Neighbor(KNN) is efficient and simplest classifier in text classification family. But KNN suffers from imbalanced class distribution and noisy term features. So, to cope up with this challenge we use document based centroid dimensionality reduction(CentroidDR) using R Programming. By combining these two text classification techniques, KNN and Centroid classifiers, we propose a scalable and effective flat classifier, called MCenKNN which works well substantially better than CenKNN.

An Uncertainty-Based Distributed Fault Detection Mechanism for Wireless Sensor Networks

PubMed Central

Yang, Yang; Gao, Zhipeng; Zhou, Hang; Qiu, Xuesong

2014-01-01

Exchanging too many messages for fault detection will cause not only a degradation of the network quality of service, but also represents a huge burden on the limited energy of sensors. Therefore, we propose an uncertainty-based distributed fault detection through aided judgment of neighbors for wireless sensor networks. The algorithm considers the serious influence of sensing measurement loss and therefore uses Markov decision processes for filling in missing data. Most important of all, fault misjudgments caused by uncertainty conditions are the main drawbacks of traditional distributed fault detection mechanisms. We draw on the experience of evidence fusion rules based on information entropy theory and the degree of disagreement function to increase the accuracy of fault detection. Simulation results demonstrate our algorithm can effectively reduce communication energy overhead due to message exchanges and provide a higher detection accuracy ratio. PMID:24776937

Acoustic Biometric System Based on Preprocessing Techniques and Linear Support Vector Machines

PubMed Central

del Val, Lara; Izquierdo-Fuente, Alberto; Villacorta, Juan J.; Raboso, Mariano

2015-01-01

Drawing on the results of an acoustic biometric system based on a MSE classifier, a new biometric system has been implemented. This new system preprocesses acoustic images, extracts several parameters and finally classifies them, based on Support Vector Machine (SVM). The preprocessing techniques used are spatial filtering, segmentation—based on a Gaussian Mixture Model (GMM) to separate the person from the background, masking—to reduce the dimensions of images—and binarization—to reduce the size of each image. An analysis of classification error and a study of the sensitivity of the error versus the computational burden of each implemented algorithm are presented. This allows the selection of the most relevant algorithms, according to the benefits required by the system. A significant improvement of the biometric system has been achieved by reducing the classification error, the computational burden and the storage requirements. PMID:26091392

Google DeepMind and healthcare in an age of algorithms.

PubMed

Powles, Julia; Hodson, Hal

2017-01-01

Data-driven tools and techniques, particularly machine learning methods that underpin artificial intelligence, offer promise in improving healthcare systems and services. One of the companies aspiring to pioneer these advances is DeepMind Technologies Limited, a wholly-owned subsidiary of the Google conglomerate, Alphabet Inc. In 2016, DeepMind announced its first major health project: a collaboration with the Royal Free London NHS Foundation Trust, to assist in the management of acute kidney injury. Initially received with great enthusiasm, the collaboration has suffered from a lack of clarity and openness, with issues of privacy and power emerging as potent challenges as the project has unfolded. Taking the DeepMind-Royal Free case study as its pivot, this article draws a number of lessons on the transfer of population-derived datasets to large private prospectors, identifying critical questions for policy-makers, industry and individuals as healthcare moves into an algorithmic age.

Acoustic Biometric System Based on Preprocessing Techniques and Linear Support Vector Machines.

PubMed

del Val, Lara; Izquierdo-Fuente, Alberto; Villacorta, Juan J; Raboso, Mariano

2015-06-17

Drawing on the results of an acoustic biometric system based on a MSE classifier, a new biometric system has been implemented. This new system preprocesses acoustic images, extracts several parameters and finally classifies them, based on Support Vector Machine (SVM). The preprocessing techniques used are spatial filtering, segmentation-based on a Gaussian Mixture Model (GMM) to separate the person from the background, masking-to reduce the dimensions of images-and binarization-to reduce the size of each image. An analysis of classification error and a study of the sensitivity of the error versus the computational burden of each implemented algorithm are presented. This allows the selection of the most relevant algorithms, according to the benefits required by the system. A significant improvement of the biometric system has been achieved by reducing the classification error, the computational burden and the storage requirements.

An improved Bayesian tensor regularization and sampling algorithm to track neuronal fiber pathways in the language circuit.

PubMed

Mishra, Arabinda; Anderson, Adam W; Wu, Xi; Gore, John C; Ding, Zhaohua

2010-08-01

The purpose of this work is to design a neuronal fiber tracking algorithm, which will be more suitable for reconstruction of fibers associated with functionally important regions in the human brain. The functional activations in the brain normally occur in the gray matter regions. Hence the fibers bordering these regions are weakly myelinated, resulting in poor performance of conventional tractography methods to trace the fiber links between them. A lower fractional anisotropy in this region makes it even difficult to track the fibers in the presence of noise. In this work, the authors focused on a stochastic approach to reconstruct these fiber pathways based on a Bayesian regularization framework. To estimate the true fiber direction (propagation vector), the a priori and conditional probability density functions are calculated in advance and are modeled as multivariate normal. The variance of the estimated tensor element vector is associated with the uncertainty due to noise and partial volume averaging (PVA). An adaptive and multiple sampling of the estimated tensor element vector, which is a function of the pre-estimated variance, overcomes the effect of noise and PVA in this work. The algorithm has been rigorously tested using a variety of synthetic data sets. The quantitative comparison of the results to standard algorithms motivated the authors to implement it for in vivo DTI data analysis. The algorithm has been implemented to delineate fibers in two major language pathways (Broca's to SMA and Broca's to Wernicke's) across 12 healthy subjects. Though the mean of standard deviation was marginally bigger than conventional (Euler's) approach [P. J. Basser et al., "In vivo fiber tractography using DT-MRI data," Magn. Reson. Med. 44(4), 625-632 (2000)], the number of extracted fibers in this approach was significantly higher. The authors also compared the performance of the proposed method to Lu's method [Y. Lu et al., "Improved fiber tractography with Bayesian tensor regularization," Neuroimage 31(3), 1061-1074 (2006)] and Friman's stochastic approach [O. Friman et al., "A Bayesian approach for stochastic white matter tractography," IEEE Trans. Med. Imaging 25(8), 965-978 (2006)]. Overall performance of the approach is found to be superior to above two methods, particularly when the signal-to-noise ratio was low. The authors observed that an adaptive sampling of the tensor element vectors, estimated as a function of the variance in a Bayesian framework, can effectively delineate neuronal fibers to analyze the structure-function relationship in human brain. The simulated and in vivo results are in good agreement with the theoretical aspects of the algorithm.

Economic evaluation of the one-hour rule-out and rule-in algorithm for acute myocardial infarction using the high-sensitivity cardiac troponin T assay in the emergency department.

PubMed

Ambavane, Apoorva; Lindahl, Bertil; Giannitsis, Evangelos; Roiz, Julie; Mendivil, Joan; Frankenstein, Lutz; Body, Richard; Christ, Michael; Bingisser, Roland; Alquezar, Aitor; Mueller, Christian

2017-01-01

The 1-hour (h) algorithm triages patients presenting with suspected acute myocardial infarction (AMI) to the emergency department (ED) towards "rule-out," "rule-in," or "observation," depending on baseline and 1-h levels of high-sensitivity cardiac troponin (hs-cTn). The economic consequences of applying the accelerated 1-h algorithm are unknown. We performed a post-hoc economic analysis in a large, diagnostic, multicenter study of hs-cTnT using central adjudication of the final diagnosis by two independent cardiologists. Length of stay (LoS), resource utilization (RU), and predicted diagnostic accuracy of the 1-h algorithm compared to standard of care (SoC) in the ED were estimated. The ED LoS, RU, and accuracy of the 1-h algorithm was compared to that achieved by the SoC at ED discharge. Expert opinion was sought to characterize clinical implementation of the 1-h algorithm, which required blood draws at ED presentation and 1h, after which "rule-in" patients were transferred for coronary angiography, "rule-out" patients underwent outpatient stress testing, and "observation" patients received SoC. Unit costs were for the United Kingdom, Switzerland, and Germany. The sensitivity and specificity for the 1-h algorithm were 87% and 96%, respectively, compared to 69% and 98% for SoC. The mean ED LoS for the 1-h algorithm was 4.3h-it was 6.5h for SoC, which is a reduction of 33%. The 1-h algorithm was associated with reductions in RU, driven largely by the shorter LoS in the ED for patients with a diagnosis other than AMI. The estimated total costs per patient were £2,480 for the 1-h algorithm compared to £4,561 for SoC, a reduction of up to 46%. The analysis shows that the use of 1-h algorithm is associated with reduction in overall AMI diagnostic costs, provided it is carefully implemented in clinical practice. These results need to be prospectively validated in the future.

Regulatory sequence analysis tools.

PubMed

van Helden, Jacques

2003-07-01

The web resource Regulatory Sequence Analysis Tools (RSAT) (http://rsat.ulb.ac.be/rsat) offers a collection of software tools dedicated to the prediction of regulatory sites in non-coding DNA sequences. These tools include sequence retrieval, pattern discovery, pattern matching, genome-scale pattern matching, feature-map drawing, random sequence generation and other utilities. Alternative formats are supported for the representation of regulatory motifs (strings or position-specific scoring matrices) and several algorithms are proposed for pattern discovery. RSAT currently holds >100 fully sequenced genomes and these data are regularly updated from GenBank.

Arachne—A web-based event viewer for MINERνA

NASA Astrophysics Data System (ADS)

Tagg, N.; Brangham, J.; Chvojka, J.; Clairemont, M.; Day, M.; Eberly, B.; Felix, J.; Fields, L.; Gago, A. M.; Gran, R.; Harris, D. A.; Kordosky, M.; Lee, H.; Maggi, G.; Maher, E.; Mann, W. A.; Marshall, C. M.; McFarland, K. S.; McGowan, A. M.; Mislivec, A.; Mousseau, J.; Osmanov, B.; Osta, J.; Paolone, V.; Perdue, G.; Ransome, R. D.; Ray, H.; Schellman, H.; Schmitz, D. W.; Simon, C.; Solano Salinas, C. J.; Tice, B. G.; Walding, J.; Walton, T.; Wolcott, J.; Zhang, D.; Ziemer, B. P.; MinerνA Collaboration

2012-06-01

Neutrino interaction events in the MINERνA detector are visually represented with a web-based tool called Arachne. Data are retrieved from a central server via AJAX, and client-side JavaScript draws images into the user's browser window using the draft HTML 5 standard. These technologies allow neutrino interactions to be viewed by anyone with a web browser, allowing for easy hand-scanning of particle interactions. Arachne has been used in MINERνA to evaluate neutrino data in a prototype detector, to tune reconstruction algorithms, and for public outreach and education.

Arachne - A web-based event viewer for MINERvA

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

Tagg, N.; /Otterbein Coll.; Brangham, J.

2011-11-01

Neutrino interaction events in the MINERvA detector are visually represented with a web-based tool called Arachne. Data are retrieved from a central server via AJAX, and client-side JavaScript draws images into the user's browser window using the draft HTML 5 standard. These technologies allow neutrino interactions to be viewed by anyone with a web browser, allowing for easy hand-scanning of particle interactions. Arachne has been used in MINERvA to evaluate neutrino data in a prototype detector, to tune reconstruction algorithms, and for public outreach and education.

Bayesian approach to inverse statistical mechanics.

PubMed

Habeck, Michael

2014-05-01

Inverse statistical mechanics aims to determine particle interactions from ensemble properties. This article looks at this inverse problem from a Bayesian perspective and discusses several statistical estimators to solve it. In addition, a sequential Monte Carlo algorithm is proposed that draws the interaction parameters from their posterior probability distribution. The posterior probability involves an intractable partition function that is estimated along with the interactions. The method is illustrated for inverse problems of varying complexity, including the estimation of a temperature, the inverse Ising problem, maximum entropy fitting, and the reconstruction of molecular interaction potentials.

Bayesian approach to inverse statistical mechanics

NASA Astrophysics Data System (ADS)

Habeck, Michael

2014-05-01

Inverse statistical mechanics aims to determine particle interactions from ensemble properties. This article looks at this inverse problem from a Bayesian perspective and discusses several statistical estimators to solve it. In addition, a sequential Monte Carlo algorithm is proposed that draws the interaction parameters from their posterior probability distribution. The posterior probability involves an intractable partition function that is estimated along with the interactions. The method is illustrated for inverse problems of varying complexity, including the estimation of a temperature, the inverse Ising problem, maximum entropy fitting, and the reconstruction of molecular interaction potentials.

Stability and Bifurcation Analysis in a Maglev System with Multiple Delays

NASA Astrophysics Data System (ADS)

Zhang, Lingling; Huang, Jianhua; Huang, Lihong; Zhang, Zhizhou

This paper considers the time-delayed feedback control for Maglev system with two discrete time delays. We determine constraints on the feedback time delays which ensure the stability of the Maglev system. An algorithm is developed for drawing a two-parametric bifurcation diagram with respect to two delays τ1 and τ2. Direction and stability of periodic solutions are also determined using the normal form method and center manifold theory by Hassard. The complex dynamical behavior of the Maglev system near the domain of stability is confirmed by exhaustive numerical simulation.

Pathway analysis of high-throughput biological data within a Bayesian network framework.

PubMed

Isci, Senol; Ozturk, Cengizhan; Jones, Jon; Otu, Hasan H

2011-06-15

Most current approaches to high-throughput biological data (HTBD) analysis either perform individual gene/protein analysis or, gene/protein set enrichment analysis for a list of biologically relevant molecules. Bayesian Networks (BNs) capture linear and non-linear interactions, handle stochastic events accounting for noise, and focus on local interactions, which can be related to causal inference. Here, we describe for the first time an algorithm that models biological pathways as BNs and identifies pathways that best explain given HTBD by scoring fitness of each network. Proposed method takes into account the connectivity and relatedness between nodes of the pathway through factoring pathway topology in its model. Our simulations using synthetic data demonstrated robustness of our approach. We tested proposed method, Bayesian Pathway Analysis (BPA), on human microarray data regarding renal cell carcinoma (RCC) and compared our results with gene set enrichment analysis. BPA was able to find broader and more specific pathways related to RCC. Accompanying BPA software (BPAS) package is freely available for academic use at http://bumil.boun.edu.tr/bpa.

Multi-objective AGV scheduling in an FMS using a hybrid of genetic algorithm and particle swarm optimization.

PubMed

Mousavi, Maryam; Yap, Hwa Jen; Musa, Siti Nurmaya; Tahriri, Farzad; Md Dawal, Siti Zawiah

2017-01-01

Flexible manufacturing system (FMS) enhances the firm's flexibility and responsiveness to the ever-changing customer demand by providing a fast product diversification capability. Performance of an FMS is highly dependent upon the accuracy of scheduling policy for the components of the system, such as automated guided vehicles (AGVs). An AGV as a mobile robot provides remarkable industrial capabilities for material and goods transportation within a manufacturing facility or a warehouse. Allocating AGVs to tasks, while considering the cost and time of operations, defines the AGV scheduling process. Multi-objective scheduling of AGVs, unlike single objective practices, is a complex and combinatorial process. In the main draw of the research, a mathematical model was developed and integrated with evolutionary algorithms (genetic algorithm (GA), particle swarm optimization (PSO), and hybrid GA-PSO) to optimize the task scheduling of AGVs with the objectives of minimizing makespan and number of AGVs while considering the AGVs' battery charge. Assessment of the numerical examples' scheduling before and after the optimization proved the applicability of all the three algorithms in decreasing the makespan and AGV numbers. The hybrid GA-PSO produced the optimum result and outperformed the other two algorithms, in which the mean of AGVs operation efficiency was found to be 69.4, 74, and 79.8 percent in PSO, GA, and hybrid GA-PSO, respectively. Evaluation and validation of the model was performed by simulation via Flexsim software.

Multi-objective AGV scheduling in an FMS using a hybrid of genetic algorithm and particle swarm optimization

PubMed Central

Yap, Hwa Jen; Musa, Siti Nurmaya; Tahriri, Farzad; Md Dawal, Siti Zawiah

2017-01-01

Flexible manufacturing system (FMS) enhances the firm’s flexibility and responsiveness to the ever-changing customer demand by providing a fast product diversification capability. Performance of an FMS is highly dependent upon the accuracy of scheduling policy for the components of the system, such as automated guided vehicles (AGVs). An AGV as a mobile robot provides remarkable industrial capabilities for material and goods transportation within a manufacturing facility or a warehouse. Allocating AGVs to tasks, while considering the cost and time of operations, defines the AGV scheduling process. Multi-objective scheduling of AGVs, unlike single objective practices, is a complex and combinatorial process. In the main draw of the research, a mathematical model was developed and integrated with evolutionary algorithms (genetic algorithm (GA), particle swarm optimization (PSO), and hybrid GA-PSO) to optimize the task scheduling of AGVs with the objectives of minimizing makespan and number of AGVs while considering the AGVs’ battery charge. Assessment of the numerical examples’ scheduling before and after the optimization proved the applicability of all the three algorithms in decreasing the makespan and AGV numbers. The hybrid GA-PSO produced the optimum result and outperformed the other two algorithms, in which the mean of AGVs operation efficiency was found to be 69.4, 74, and 79.8 percent in PSO, GA, and hybrid GA-PSO, respectively. Evaluation and validation of the model was performed by simulation via Flexsim software. PMID:28263994

Aging: Molecular Pathways and Implications on the Cardiovascular System

PubMed Central

Ribeiro, Thaís Porto

2017-01-01

The world's population over 60 years is growing rapidly, reaching 22% of the global population in the next decades. Despite the increase in global longevity, individual healthspan needs to follow this growth. Several diseases have their prevalence increased by age, such as cardiovascular diseases, the leading cause of morbidity and mortality worldwide. Understanding the aging biology mechanisms is fundamental to the pursuit of cardiovascular health. In this way, aging is characterized by a gradual decline in physiological functions, involving the increased number in senescent cells into the body. Several pathways lead to senescence, including oxidative stress and persistent inflammation, as well as energy failure such as mitochondrial dysfunction and deregulated autophagy, being ROS, AMPK, SIRTs, mTOR, IGF-1, and p53 key regulators of the metabolic control, connecting aging to the pathways which drive towards diseases. In addition, senescence can be induced by cellular replication, which resulted from telomere shortening. Taken together, it is possible to draw a common pathway unifying aging to cardiovascular diseases, and the central point of this process, senescence, can be the target for new therapies, which may result in the healthspan matching the lifespan. PMID:28874954

Understanding "globalization" as a determinant of health determinants: a critical perspective.

PubMed

Spiegel, Jerry M; Labonte, Ronald; Ostry, Aleck S

2004-01-01

The purpose of this article is to explain how globalization has evolved, in order to provide a context for assessing the health care restructuring that is occurring worldwide. The authors begin by defining globalization and introducing a framework for considering pathways that can affect social organization and health. They then draw attention to current trends, such as the GATS (General Agreement on Trade in Services), that promise to open health services provision to increased pressures of globalization.

Integration of oncologic margins in three-dimensional virtual planning for head and neck surgery, including a validation of the software pathway.

PubMed

Kraeima, Joep; Schepers, Rutger H; van Ooijen, Peter M A; Steenbakkers, Roel J H M; Roodenburg, Jan L N; Witjes, Max J H

2015-10-01

Three-dimensional (3D) virtual planning of reconstructive surgery, after resection, is a frequently used method for improving accuracy and predictability. However, when applied to malignant cases, the planning of the oncologic resection margins is difficult due to visualisation of tumours in the current 3D planning. Embedding tumour delineation on a magnetic resonance image, similar to the routinely performed radiotherapeutic contouring of tumours, is expected to provide better margin planning. A new software pathway was developed for embedding tumour delineation on magnetic resonance imaging (MRI) within the 3D virtual surgical planning. The software pathway was validated by the use of five bovine cadavers implanted with phantom tumour objects. MRI and computed tomography (CT) images were fused and the tumour was delineated using radiation oncology software. This data was converted to the 3D virtual planning software by means of a conversion algorithm. Tumour volumes and localization were determined in both software stages for comparison analysis. The approach was applied to three clinical cases. A conversion algorithm was developed to translate the tumour delineation data to the 3D virtual plan environment. The average difference in volume of the tumours was 1.7%. This study reports a validated software pathway, providing multi-modality image fusion for 3D virtual surgical planning. Copyright © 2015 European Association for Cranio-Maxillo-Facial Surgery. Published by Elsevier Ltd. All rights reserved.

Exhaustive identification of steady state cycles in large stoichiometric networks

PubMed Central

Wright, Jeremiah; Wagner, Andreas

2008-01-01

Background Identifying cyclic pathways in chemical reaction networks is important, because such cycles may indicate in silico violation of energy conservation, or the existence of feedback in vivo. Unfortunately, our ability to identify cycles in stoichiometric networks, such as signal transduction and genome-scale metabolic networks, has been hampered by the computational complexity of the methods currently used. Results We describe a new algorithm for the identification of cycles in stoichiometric networks, and we compare its performance to two others by exhaustively identifying the cycles contained in the genome-scale metabolic networks of H. pylori, M. barkeri, E. coli, and S. cerevisiae. Our algorithm can substantially decrease both the execution time and maximum memory usage in comparison to the two previous algorithms. Conclusion The algorithm we describe improves our ability to study large, real-world, biochemical reaction networks, although additional methodological improvements are desirable. PMID:18616835

Acceleration of the Particle Swarm Optimization for Peierls-Nabarro modeling of dislocations in conventional and high-entropy alloys

NASA Astrophysics Data System (ADS)

Pei, Zongrui; Eisenbach, Markus

2017-06-01

Dislocations are among the most important defects in determining the mechanical properties of both conventional alloys and high-entropy alloys. The Peierls-Nabarro model supplies an efficient pathway to their geometries and mobility. The difficulty in solving the integro-differential Peierls-Nabarro equation is how to effectively avoid the local minima in the energy landscape of a dislocation core. Among the other methods to optimize the dislocation core structures, we choose the algorithm of Particle Swarm Optimization, an algorithm that simulates the social behaviors of organisms. By employing more particles (bigger swarm) and more iterative steps (allowing them to explore for longer time), the local minima can be effectively avoided. But this would require more computational cost. The advantage of this algorithm is that it is readily parallelized in modern high computing architecture. We demonstrate the performance of our parallelized algorithm scales linearly with the number of employed cores.

The Construction of 3-d Neutral Density for Arbitrary Data Sets

NASA Astrophysics Data System (ADS)

Riha, S.; McDougall, T. J.; Barker, P. M.

2014-12-01

The Neutral Density variable allows inference of water pathways from thermodynamic properties in the global ocean, and is therefore an essential component of global ocean circulation analysis. The widely used algorithm for the computation of Neutral Density yields accurate results for data sets which are close to the observed climatological ocean. Long-term numerical climate simulations, however, often generate a significant drift from present-day climate, which renders the existing algorithm inaccurate. To remedy this problem, new algorithms which operate on arbitrary data have been developed, which may potentially be used to compute Neutral Density during runtime of a numerical model.We review existing approaches for the construction of Neutral Density in arbitrary data sets, detail their algorithmic structure, and present an analysis of the computational cost for implementations on a single-CPU computer. We discuss possible strategies for the implementation in state-of-the-art numerical models, with a focus on distributed computing environments.

A Prize-Collecting Steiner Tree Approach for Transduction Network Inference

NASA Astrophysics Data System (ADS)

Bailly-Bechet, Marc; Braunstein, Alfredo; Zecchina, Riccardo

Into the cell, information from the environment is mainly propagated via signaling pathways which form a transduction network. Here we propose a new algorithm to infer transduction networks from heterogeneous data, using both the protein interaction network and expression datasets. We formulate the inference problem as an optimization task, and develop a message-passing, probabilistic and distributed formalism to solve it. We apply our algorithm to the pheromone response in the baker’s yeast S. cerevisiae. We are able to find the backbone of the known structure of the MAPK cascade of pheromone response, validating our algorithm. More importantly, we make biological predictions about some proteins whose role could be at the interface between pheromone response and other cellular functions.

Querying Co-regulated Genes on Diverse Gene Expression Datasets Via Biclustering.

PubMed

Deveci, Mehmet; Küçüktunç, Onur; Eren, Kemal; Bozdağ, Doruk; Kaya, Kamer; Çatalyürek, Ümit V

2016-01-01

Rapid development and increasing popularity of gene expression microarrays have resulted in a number of studies on the discovery of co-regulated genes. One important way of discovering such co-regulations is the query-based search since gene co-expressions may indicate a shared role in a biological process. Although there exist promising query-driven search methods adapting clustering, they fail to capture many genes that function in the same biological pathway because microarray datasets are fraught with spurious samples or samples of diverse origin, or the pathways might be regulated under only a subset of samples. On the other hand, a class of clustering algorithms known as biclustering algorithms which simultaneously cluster both the items and their features are useful while analyzing gene expression data, or any data in which items are related in only a subset of their samples. This means that genes need not be related in all samples to be clustered together. Because many genes only interact under specific circumstances, biclustering may recover the relationships that traditional clustering algorithms can easily miss. In this chapter, we briefly summarize the literature using biclustering for querying co-regulated genes. Then we present a novel biclustering approach and evaluate its performance by a thorough experimental analysis.

Using the modified Delphi method to establish clinical consensus for the diagnosis and treatment of patients with rotator cuff pathology.

PubMed

Eubank, Breda H; Mohtadi, Nicholas G; Lafave, Mark R; Wiley, J Preston; Bois, Aaron J; Boorman, Richard S; Sheps, David M

2016-05-20

Patients presenting to the healthcare system with rotator cuff pathology do not always receive high quality care. High quality care occurs when a patient receives care that is accessible, appropriate, acceptable, effective, efficient, and safe. The aim of this study was twofold: 1) to develop a clinical pathway algorithm that sets forth a stepwise process for making decisions about the diagnosis and treatment of rotator cuff pathology presenting to primary, secondary, and tertiary healthcare settings; and 2) to establish clinical practice guidelines for the diagnosis and treatment of rotator cuff pathology to inform decision-making processes within the algorithm. A three-step modified Delphi method was used to establish consensus. Fourteen experts representing athletic therapy, physiotherapy, sport medicine, and orthopaedic surgery were invited to participate as the expert panel. In round 1, 123 best practice statements were distributed to the panel. Panel members were asked to mark "agree" or "disagree" beside each statement, and provide comments. The same voting method was again used for round 2. Round 3 consisted of a final face-to-face meeting. In round 1, statements were grouped and reduced to 44 statements that met consensus. In round 2, five statements reached consensus. In round 3, ten statements reached consensus. Consensus was reached for 59 statements representing five domains: screening, diagnosis, physical examination, investigations, and treatment. The final face-to-face meeting was also used to develop clinical pathway algorithms (i.e., clinical care pathways) for three types of rotator cuff pathology: acute, chronic, and acute-on-chronic. This consensus guideline will help to standardize care, provide guidance on the diagnosis and treatment of rotator cuff pathology, and assist in clinical decision-making for all healthcare professionals.

Neural Activity Associated with Visual Search for Line Drawings on AAC Displays: An Exploration of the Use of fMRI.

PubMed

Wilkinson, Krista M; Dennis, Nancy A; Webb, Christina E; Therrien, Mari; Stradtman, Megan; Farmer, Jacquelyn; Leach, Raevynn; Warrenfeltz, Megan; Zeuner, Courtney

2015-01-01

Visual aided augmentative and alternative communication (AAC) consists of books or technologies that contain visual symbols to supplement spoken language. A common observation concerning some forms of aided AAC is that message preparation can be frustratingly slow. We explored the uses of fMRI to examine the neural correlates of visual search for line drawings on AAC displays in 18 college students under two experimental conditions. Under one condition, the location of the icons remained stable and participants were able to learn the spatial layout of the display. Under the other condition, constant shuffling of the locations of the icons prevented participants from learning the layout, impeding rapid search. Brain activation was contrasted under these conditions. Rapid search in the stable display was associated with greater activation of cortical and subcortical regions associated with memory, motor learning, and dorsal visual pathways compared to the search in the unpredictable display. Rapid search for line drawings on stable AAC displays involves not just the conceptual knowledge of the symbol meaning but also the integration of motor, memory, and visual-spatial knowledge about the display layout. Further research must study individuals who use AAC, as well as the functional effect of interventions that promote knowledge about array layout.

A tunable algorithm for collective decision-making.

PubMed

Pratt, Stephen C; Sumpter, David J T

2006-10-24

Complex biological systems are increasingly understood in terms of the algorithms that guide the behavior of system components and the information pathways that link them. Much attention has been given to robust algorithms, or those that allow a system to maintain its functions in the face of internal or external perturbations. At the same time, environmental variation imposes a complementary need for algorithm versatility, or the ability to alter system function adaptively as external circumstances change. An important goal of systems biology is thus the identification of biological algorithms that can meet multiple challenges rather than being narrowly specified to particular problems. Here we show that emigrating colonies of the ant Temnothorax curvispinosus tune the parameters of a single decision algorithm to respond adaptively to two distinct problems: rapid abandonment of their old nest in a crisis and deliberative selection of the best available new home when their old nest is still intact. The algorithm uses a stepwise commitment scheme and a quorum rule to integrate information gathered by numerous individual ants visiting several candidate homes. By varying the rates at which they search for and accept these candidates, the ants yield a colony-level response that adaptively emphasizes either speed or accuracy. We propose such general but tunable algorithms as a design feature of complex systems, each algorithm providing elegant solutions to a wide range of problems.

Iterative algorithm-guided design of massive strain libraries, applied to itaconic acid production in yeast.

PubMed

Young, Eric M; Zhao, Zheng; Gielesen, Bianca E M; Wu, Liang; Benjamin Gordon, D; Roubos, Johannes A; Voigt, Christopher A

2018-05-09

Metabolic engineering requires multiple rounds of strain construction to evaluate alternative pathways and enzyme concentrations. Optimizing multigene pathways stepwise or by randomly selecting enzymes and expression levels is inefficient. Here, we apply methods from design of experiments (DOE) to guide the construction of strain libraries from which the maximum information can be extracted without sampling every possible combination. We use Saccharomyces cerevisiae as a host for a novel six-gene pathway to itaconic acid, selected by comparing alternative shunt pathways that bypass the mitochondrial TCA cycle. The pathway is distinctive for the use of acetylating acetaldehyde dehydrogenase to increase cytosolic acetyl-CoA pools, a bacterial enzyme to synthesize citrate in the cytosol, and an itaconic acid exporter. Precise control over the expression of each gene is enabled by a set of promoter-terminator pairs that span a 174-fold range. Two large combinatorial libraries (160 variants, 2.4Mb and 32 variants, 0.6Mb) are designed where the expression levels are selected by statistical methods (I-optimal response surface methodology, full factorial, or Plackett-Burman) with the intent of extracting different types of guiding information after the screen. This is applied to the design of a third library (24 variants, 0.5Mb) intended to alleviate a bottleneck in cis-aconitate decarboxylase (CAD) expression. The top strain produces 815mg/l itaconic acid, a 4-fold improvement over the initial strain achieved by iteratively balancing pathway expression. Including a methylated product in the total, the strain produces 1.3g/l combined itaconic acids. Further, a regression analysis of the libraries reveals the optimal expression level of CAD as well as pairwise interdependencies between genes that result in increased titer and purity of itaconic acid. This work demonstrates adapting algorithmic design strategies to guide automated yeast strain construction and learn information after each iteration. Copyright © 2018. Published by Elsevier Inc.

Service evaluation of the implementation of a digitally-enabled care pathway for the recognition and management of acute kidney injury.

PubMed

Connell, Alistair; Montgomery, Hugh; Morris, Stephen; Nightingale, Claire; Stanley, Sarah; Emerson, Mary; Jones, Gareth; Sadeghi-Alavijeh, Omid; Merrick, Charles; King, Dominic; Karthikesalingam, Alan; Hughes, Cian; Ledsam, Joseph; Back, Trevor; Rees, Geraint; Raine, Rosalind; Laing, Christopher

2017-01-01

Acute Kidney Injury (AKI), an abrupt deterioration in kidney function, is defined by changes in urine output or serum creatinine. AKI is common (affecting up to 20% of acute hospital admissions in the United Kingdom), associated with significant morbidity and mortality, and expensive (excess costs to the National Health Service in England alone may exceed £1 billion per year). NHS England has mandated the implementation of an automated algorithm to detect AKI based on changes in serum creatinine, and to alert clinicians. It is uncertain, however, whether 'alerting' alone improves care quality. We have thus developed a digitally-enabled care pathway as a clinical service to inpatients in the Royal Free Hospital (RFH), a large London hospital. This pathway incorporates a mobile software application - the "Streams-AKI" app, developed by DeepMind Health - that applies the NHS AKI algorithm to routinely collected serum creatinine data in hospital inpatients. Streams-AKI alerts clinicians to potential AKI cases, furnishing them with a trend view of kidney function alongside other relevant data, in real-time, on a mobile device. A clinical response team comprising nephrologists and critical care nurses responds to these AKI alerts by reviewing individual patients and administering interventions according to existing clinical practice guidelines. We propose a mixed methods service evaluation of the implementation of this care pathway. This evaluation will assess how the care pathway meets the health and care needs of service users (RFH inpatients), in terms of clinical outcome, processes of care, and NHS costs. It will also seek to assess acceptance of the pathway by members of the response team and wider hospital community. All analyses will be undertaken by the service evaluation team from UCL (Department of Applied Health Research) and St George's, University of London (Population Health Research Institute).

Metabolic engineering of the pentose phosphate pathway for enhanced limonene production in the cyanobacterium Synechocysti s sp. PCC 6803.

PubMed

Lin, Po-Cheng; Saha, Rajib; Zhang, Fuzhong; Pakrasi, Himadri B

2017-12-13

Isoprenoids are diverse natural compounds, which have various applications as pharmaceuticals, fragrances, and solvents. The low yield of isoprenoids in plants makes them difficult for cost-effective production, and chemical synthesis of complex isoprenoids is impractical. Microbial production of isoprenoids has been considered as a promising approach to increase the yield. In this study, we engineered the model cyanobacterium Synechocystis sp. PCC 6803 for sustainable production of a commercially valuable isoprenoid, limonene. Limonene synthases from the plants Mentha spicata and Citrus limon were expressed in cyanobacteria for limonene production. Production of limonene was two-fold higher with limonene synthase from M. spicata than that from C. limon. To enhance isoprenoid production, computational strain design was conducted by applying the OptForce strain design algorithm on Synechocystis 6803. Based on the metabolic interventions suggested by this algorithm, genes (ribose 5-phosphate isomerase and ribulose 5-phosphate 3-epimerase) in the pentose phosphate pathway were overexpressed, and a geranyl diphosphate synthase from the plant Abies grandis was expressed to optimize the limonene biosynthetic pathway. The optimized strain produced 6.7 mg/L of limonene, a 2.3-fold improvement in productivity. Thus, this study presents a feasible strategy to engineer cyanobacteria for photosynthetic production of isoprenoids.

Gene network biological validity based on gene-gene interaction relevance.

PubMed

Gómez-Vela, Francisco; Díaz-Díaz, Norberto

2014-01-01

In recent years, gene networks have become one of the most useful tools for modeling biological processes. Many inference gene network algorithms have been developed as techniques for extracting knowledge from gene expression data. Ensuring the reliability of the inferred gene relationships is a crucial task in any study in order to prove that the algorithms used are precise. Usually, this validation process can be carried out using prior biological knowledge. The metabolic pathways stored in KEGG are one of the most widely used knowledgeable sources for analyzing relationships between genes. This paper introduces a new methodology, GeneNetVal, to assess the biological validity of gene networks based on the relevance of the gene-gene interactions stored in KEGG metabolic pathways. Hence, a complete KEGG pathway conversion into a gene association network and a new matching distance based on gene-gene interaction relevance are proposed. The performance of GeneNetVal was established with three different experiments. Firstly, our proposal is tested in a comparative ROC analysis. Secondly, a randomness study is presented to show the behavior of GeneNetVal when the noise is increased in the input network. Finally, the ability of GeneNetVal to detect biological functionality of the network is shown.

Integrated learning in practical machine element design course: a case study of V-pulley design

NASA Astrophysics Data System (ADS)

Tantrabandit, Manop

2014-06-01

To achieve an effective integrated learning in Machine Element Design course, it is of importance to bridge the basic knowledge and skills of element designs. The multiple core learning leads the pathway which consists of two main parts. The first part involves teaching documents of which the contents are number of V-groove formulae, standard of V-grooved pulleys, and parallel key dimension's formulae. The second part relates to the subjects that the students have studied prior to participating in this integrated learning course, namely Material Selection, Manufacturing Process, Applied Engineering Drawing, CAD (Computer Aided Design) animation software. Moreover, an intensive cooperation between a lecturer and students is another key factor to fulfill the success of integrated learning. Last but not least, the students need to share their knowledge within the group and among the other groups aiming to gain knowledge of and skills in 1) the application of CAD-software to build up manufacture part drawings, 2) assembly drawing, 3) simulation to verify the strength of loaded pulley by method of Finite Element Analysis (FEA), 4) the software to create animation of mounting and dismounting of a pulley to a shaft, and 5) an instruction manual. The end product of this integrated learning, as a result of the above 1 to 5 knowledge and skills obtained, the participating students can create an assembly derived from manufacture part drawings and a video presentation with bilingual (English-Thai) audio description of Vpulley with datum diameter of 250 mm, 4 grooves, and type of groove: SPA.

Metabolic Flux Analysis in Isotope Labeling Experiments Using the Adjoint Approach.

PubMed

Mottelet, Stephane; Gaullier, Gil; Sadaka, Georges

2017-01-01

Comprehension of metabolic pathways is considerably enhanced by metabolic flux analysis (MFA-ILE) in isotope labeling experiments. The balance equations are given by hundreds of algebraic (stationary MFA) or ordinary differential equations (nonstationary MFA), and reducing the number of operations is therefore a crucial part of reducing the computation cost. The main bottleneck for deterministic algorithms is the computation of derivatives, particularly for nonstationary MFA. In this article, we explain how the overall identification process may be speeded up by using the adjoint approach to compute the gradient of the residual sum of squares. The proposed approach shows significant improvements in terms of complexity and computation time when it is compared with the usual (direct) approach. Numerical results are obtained for the central metabolic pathways of Escherichia coli and are validated against reference software in the stationary case. The methods and algorithms described in this paper are included in the sysmetab software package distributed under an Open Source license at http://forge.scilab.org/index.php/p/sysmetab/.

Plant and Insect Viruses in Managed and Natural Environments: Novel and Neglected Transmission Pathways.

PubMed

Jones, Roger A C

2018-01-01

The capacity to spread by diverse transmission pathways enhances a virus' ability to spread effectively and survive when circumstances change. This review aims to improve understanding of how plant and insect viruses spread through natural and managed environments by drawing attention to 12 novel or neglected virus transmission pathways whose contribution is underestimated. For plant viruses, the pathways reviewed are vertical and horizontal transmission via pollen, and horizontal transmission by parasitic plants, natural root grafts, wind-mediated contact, chewing insects, and contaminated water or soil. For insect viruses, they are transmission by plants serving as passive "vectors," arthropod vectors, and contamination of pollen and nectar. Based on current understanding of the spatiotemporal dynamics of virus spread, the likely roles of each pathway in creating new primary infection foci, enlarging previously existing infection foci, and promoting generalized virus spread are estimated. All pathways except transmission via parasitic plants, root grafts, and wind-mediated contact transmission are likely to produce new primary infection foci. All 12 pathways have the capability to enlarge existing infection foci, but only to a limited extent when spread occurs via virus-contaminated soil or vertical pollen transmission. All pathways except those via parasitic plant, root graft, contaminated soil, and vertical pollen transmission likely contribute to generalized virus spread, but to different extents. For worst-case scenarios, where mixed populations of host species occur under optimal virus spread conditions, the risk that host species jumps or virus emergence events will arise is estimated to be "high" for all four insect virus pathways considered, and, "very high" or "moderate" for plant viruses transmitted by parasitic plant and root graft pathways, respectively. To establish full understanding of virus spread and thereby optimize effective virus disease management, it is important to examine all transmission pathways potentially involved, regardless of whether the virus' ecology is already presumed to be well understood or otherwise. © 2018 Elsevier Inc. All rights reserved.

Lessons and challenges from adaptation pathways planning applications

NASA Astrophysics Data System (ADS)

Haasnoot, M.; Lawrence, J.; Kwakkel, J. H.; Walker, W.; Timmermans, J.; Bloemen, P.; Thissen, W.

2015-12-01

Planning for adaptation to dynamic risks (e.g., because of climate change) is a critical need. The concept of 'adaptive policies' is receiving increasing attention as a way of performing strategic planning that is able to address many of the inherent challenges of uncertainty and dynamic change. Several approaches for developing adaptive policies are available in the literature. One approach, for which several applications already exist, is Dynamic Adaptive Policy Pathways (DAPP). Pathway maps enable policy analysts, decision makers, and stakeholders to recognize potential 'locked-in' situations and to assess the flexibility, robustness, and efficacy of decision alternatives. Most of the applications of DAPP have been in deltas, coastal cities, or floodplains, often within the context of climate change adaptation. In this talk, we describe the DAPP approach and present a framework for designing signposts as adaptation signals, together with an illustrative application for the Rhine River in the Netherlands. We also draw lessons and challenges from pathways applications that differ in environment, culture, and institutional context. For example, the Dutch Delta Programme has used pathways to identify short-term decisions and long-term policy options. In Bangladesh, an application is in its early phase. Steps before generating pathways - such as long- term thinking in multiple possible futures and acknowledging uncertainties - are already a big challenge there. In New Zealand, the 'Sustainable Delta Game' has been used as the catalyst for pathways thinking by two local councils. This has led to its application in decision making for coastal and flood risk management and economic analysis of policy options.

PathVisio 3: an extendable pathway analysis toolbox.

PubMed

Kutmon, Martina; van Iersel, Martijn P; Bohler, Anwesha; Kelder, Thomas; Nunes, Nuno; Pico, Alexander R; Evelo, Chris T

2015-02-01

PathVisio is a commonly used pathway editor, visualization and analysis software. Biological pathways have been used by biologists for many years to describe the detailed steps in biological processes. Those powerful, visual representations help researchers to better understand, share and discuss knowledge. Since the first publication of PathVisio in 2008, the original paper was cited more than 170 times and PathVisio was used in many different biological studies. As an online editor PathVisio is also integrated in the community curated pathway database WikiPathways. Here we present the third version of PathVisio with the newest additions and improvements of the application. The core features of PathVisio are pathway drawing, advanced data visualization and pathway statistics. Additionally, PathVisio 3 introduces a new powerful extension systems that allows other developers to contribute additional functionality in form of plugins without changing the core application. PathVisio can be downloaded from http://www.pathvisio.org and in 2014 PathVisio 3 has been downloaded over 5,500 times. There are already more than 15 plugins available in the central plugin repository. PathVisio is a freely available, open-source tool published under the Apache 2.0 license (http://www.apache.org/licenses/LICENSE-2.0). It is implemented in Java and thus runs on all major operating systems. The code repository is available at http://svn.bigcat.unimaas.nl/pathvisio. The support mailing list for users is available on https://groups.google.com/forum/#!forum/wikipathways-discuss and for developers on https://groups.google.com/forum/#!forum/wikipathways-devel.

Comparison and assessment of semi-automatic image segmentation in computed tomography scans for image-guided kidney surgery.

PubMed

Glisson, Courtenay L; Altamar, Hernan O; Herrell, S Duke; Clark, Peter; Galloway, Robert L

2011-11-01

Image segmentation is integral to implementing intraoperative guidance for kidney tumor resection. Results seen in computed tomography (CT) data are affected by target organ physiology as well as by the segmentation algorithm used. This work studies variables involved in using level set methods found in the Insight Toolkit to segment kidneys from CT scans and applies the results to an image guidance setting. A composite algorithm drawing on the strengths of multiple level set approaches was built using the Insight Toolkit. This algorithm requires image contrast state and seed points to be identified as input, and functions independently thereafter, selecting and altering method and variable choice as needed. Semi-automatic results were compared to expert hand segmentation results directly and by the use of the resultant surfaces for registration of intraoperative data. Direct comparison using the Dice metric showed average agreement of 0.93 between semi-automatic and hand segmentation results. Use of the segmented surfaces in closest point registration of intraoperative laser range scan data yielded average closest point distances of approximately 1 mm. Application of both inverse registration transforms from the previous step to all hand segmented image space points revealed that the distance variability introduced by registering to the semi-automatically segmented surface versus the hand segmented surface was typically less than 3 mm both near the tumor target and at distal points, including subsurface points. Use of the algorithm shortened user interaction time and provided results which were comparable to the gold standard of hand segmentation. Further, the use of the algorithm's resultant surfaces in image registration provided comparable transformations to surfaces produced by hand segmentation. These data support the applicability and utility of such an algorithm as part of an image guidance workflow.

Selective mutism: an update and suggestions for future research.

PubMed

Scott, Samantha; Beidel, Deborah C

2011-08-01

Speculation continues regarding the accurate classification of selective mutism and potential etiologic factors. Current research has shed some light on several factors that may predispose some children to this disorder, but conclusions are difficult to draw due to reliance on subjective measures, few comparison groups, and/or limited theoretical grounding. This article provides an update on recent efforts to elucidate the etiologic pathways of selective mutism and on the current debate regarding its strong overlap with anxiety disorders, most notably social phobia. An additional attempt is made to examine findings based on a developmental perspective that accounts for multiple pathways, context, and the developmental stage of the child. Emotion regulation theory is offered as a potential factor in why some children may be more vulnerable to the etiologic factors described. Suggestions for future research are offered based on this integration of information.

Competitive resource allocation to metabolic pathways contributes to overflow metabolisms and emergent properties in cross-feeding microbial consortia.

PubMed

Carlson, Ross P; Beck, Ashley E; Phalak, Poonam; Fields, Matthew W; Gedeon, Tomas; Hanley, Luke; Harcombe, William R; Henson, Michael A; Heys, Jeffrey J

2018-04-17

Resource scarcity is a common stress in nature and has a major impact on microbial physiology. This review highlights microbial acclimations to resource scarcity, focusing on resource investment strategies for chemoheterotrophs from the molecular level to the pathway level. Competitive resource allocation strategies often lead to a phenotype known as overflow metabolism; the resulting overflow byproducts can stabilize cooperative interactions in microbial communities and can lead to cross-feeding consortia. These consortia can exhibit emergent properties such as enhanced resource usage and biomass productivity. The literature distilled here draws parallels between in silico and laboratory studies and ties them together with ecological theories to better understand microbial stress responses and mutualistic consortia functioning. © 2018 The Author(s). Published by Portland Press Limited on behalf of the Biochemical Society.

EDRN-WHI Pre-Clinical Colon Ca Specimens — EDRN Public Portal

Cancer.gov

Specifically, it is proposed to assess plasma proteins from postmenopausal women diagnosed with colon cancer within a span of 18 months after year-3 OS blood draw and from appropriate matched controls enrolled in the WHI OS study. The range of case-control differences sought in plasma include: 1 Detection and identification of proteins that may be derived from tumor cells through the classical secreted protein pathway and through non-classical pathways (eg protein cleavage and release) or through cell turnover. 2 Detection and identification of protein changes associated with the host response that occur during tumor development and that may be related to inflammation, angiogenesis, infiltration of tumor with host cells and other processes. 3 Identification of tumor derived proteins that induce a humoral immune response in the form of autoantibodies that are detectable at the preclinical stage.

An Evaluation of Active Learning Causal Discovery Methods for Reverse-Engineering Local Causal Pathways of Gene Regulation

PubMed Central

Ma, Sisi; Kemmeren, Patrick; Aliferis, Constantin F.; Statnikov, Alexander

2016-01-01

Reverse-engineering of causal pathways that implicate diseases and vital cellular functions is a fundamental problem in biomedicine. Discovery of the local causal pathway of a target variable (that consists of its direct causes and direct effects) is essential for effective intervention and can facilitate accurate diagnosis and prognosis. Recent research has provided several active learning methods that can leverage passively observed high-throughput data to draft causal pathways and then refine the inferred relations with a limited number of experiments. The current study provides a comprehensive evaluation of the performance of active learning methods for local causal pathway discovery in real biological data. Specifically, 54 active learning methods/variants from 3 families of algorithms were applied for local causal pathways reconstruction of gene regulation for 5 transcription factors in S. cerevisiae. Four aspects of the methods’ performance were assessed, including adjacency discovery quality, edge orientation accuracy, complete pathway discovery quality, and experimental cost. The results of this study show that some methods provide significant performance benefits over others and therefore should be routinely used for local causal pathway discovery tasks. This study also demonstrates the feasibility of local causal pathway reconstruction in real biological systems with significant quality and low experimental cost. PMID:26939894

Find_tfSBP: find thermodynamics-feasible and smallest balanced pathways with high yield from large-scale metabolic networks.

PubMed

Xu, Zixiang; Sun, Jibin; Wu, Qiaqing; Zhu, Dunming

2017-12-11

Biologically meaningful metabolic pathways are important references in the design of industrial bacterium. At present, constraint-based method is the only way to model and simulate a genome-scale metabolic network under steady-state criteria. Due to the inadequate assumption of the relationship in gene-enzyme-reaction as one-to-one unique association, computational difficulty or ignoring the yield from substrate to product, previous pathway finding approaches can't be effectively applied to find out the high yield pathways that are mass balanced in stoichiometry. In addition, the shortest pathways may not be the pathways with high yield. At the same time, a pathway, which exists in stoichiometry, may not be feasible in thermodynamics. By using mixed integer programming strategy, we put forward an algorithm to identify all the smallest balanced pathways which convert the source compound to the target compound in large-scale metabolic networks. The resulting pathways by our method can finely satisfy the stoichiometric constraints and non-decomposability condition. Especially, the functions of high yield and thermodynamics feasibility have been considered in our approach. This tool is tailored to direct the metabolic engineering practice to enlarge the metabolic potentials of industrial strains by integrating the extensive metabolic network information built from systems biology dataset.

Model-based optimization of near-field binary-pixelated beam shapers

DOE PAGES

Dorrer, C.; Hassett, J.

2017-01-23

The optimization of components that rely on spatially dithered distributions of transparent or opaque pixels and an imaging system with far-field filtering for transmission control is demonstrated. The binary-pixel distribution can be iteratively optimized to lower an error function that takes into account the design transmission and the characteristics of the required far-field filter. Simulations using a design transmission chosen in the context of high-energy lasers show that the beam-fluence modulation at an image plane can be reduced by a factor of 2, leading to performance similar to using a non-optimized spatial-dithering algorithm with pixels of size reduced by amore » factor of 2 without the additional fabrication complexity or cost. The optimization process preserves the pixel distribution statistical properties. Analysis shows that the optimized pixel distribution starting from a high-noise distribution defined by a random-draw algorithm should be more resilient to fabrication errors than the optimized pixel distributions starting from a low-noise, error-diffusion algorithm, while leading to similar beamshaping performance. Furthermore, this is confirmed by experimental results obtained with various pixel distributions and induced fabrication errors.« less

Intelligent Diagnostic Assistant for Complicated Skin Diseases through C5's Algorithm.

PubMed

Jeddi, Fatemeh Rangraz; Arabfard, Masoud; Kermany, Zahra Arab

2017-09-01

Intelligent Diagnostic Assistant can be used for complicated diagnosis of skin diseases, which are among the most common causes of disability. The aim of this study was to design and implement a computerized intelligent diagnostic assistant for complicated skin diseases through C5's Algorithm. An applied-developmental study was done in 2015. Knowledge base was developed based on interviews with dermatologists through questionnaires and checklists. Knowledge representation was obtained from the train data in the database using Excel Microsoft Office. Clementine Software and C5's Algorithms were applied to draw the decision tree. Analysis of test accuracy was performed based on rules extracted using inference chains. The rules extracted from the decision tree were entered into the CLIPS programming environment and the intelligent diagnostic assistant was designed then. The rules were defined using forward chaining inference technique and were entered into Clips programming environment as RULE. The accuracy and error rates obtained in the training phase from the decision tree were 99.56% and 0.44%, respectively. The accuracy of the decision tree was 98% and the error was 2% in the test phase. Intelligent diagnostic assistant can be used as a reliable system with high accuracy, sensitivity, specificity, and agreement.

Predicting Positive and Negative Relationships in Large Social Networks.

PubMed

Wang, Guan-Nan; Gao, Hui; Chen, Lian; Mensah, Dennis N A; Fu, Yan

2015-01-01

In a social network, users hold and express positive and negative attitudes (e.g. support/opposition) towards other users. Those attitudes exhibit some kind of binary relationships among the users, which play an important role in social network analysis. However, some of those binary relationships are likely to be latent as the scale of social network increases. The essence of predicting latent binary relationships have recently began to draw researchers' attention. In this paper, we propose a machine learning algorithm for predicting positive and negative relationships in social networks inspired by structural balance theory and social status theory. More specifically, we show that when two users in the network have fewer common neighbors, the prediction accuracy of the relationship between them deteriorates. Accordingly, in the training phase, we propose a segment-based training framework to divide the training data into two subsets according to the number of common neighbors between users, and build a prediction model for each subset based on support vector machine (SVM). Moreover, to deal with large-scale social network data, we employ a sampling strategy that selects small amount of training data while maintaining high accuracy of prediction. We compare our algorithm with traditional algorithms and adaptive boosting of them. Experimental results of typical data sets show that our algorithm can deal with large social networks and consistently outperforms other methods.

General Metropolis-Hastings jump diffusions for automatic target recognition in infrared scenes

NASA Astrophysics Data System (ADS)

Lanterman, Aaron D.; Miller, Michael I.; Snyder, Donald L.

1997-04-01

To locate and recognize ground-based targets in forward- looking IR (FLIR) images, 3D faceted models with associated pose parameters are formulated to accommodate the variability found in FLIR imagery. Taking a Bayesian approach, scenes are simulated from the emissive characteristics of the CAD models and compared with the collected data by a likelihood function based on sensor statistics. This likelihood is combined with a prior distribution defined over the set of possible scenes to form a posterior distribution. To accommodate scenes with variable numbers of targets, the posterior distribution is defined over parameter vectors of varying dimension. An inference algorithm based on Metropolis-Hastings jump- diffusion processes empirically samples from the posterior distribution, generating configurations of templates and transformations that match the collected sensor data with high probability. The jumps accommodate the addition and deletion of targets and the estimation of target identities; diffusions refine the hypotheses by drifting along the gradient of the posterior distribution with respect to the orientation and position parameters. Previous results on jumps strategies analogous to the Metropolis acceptance/rejection algorithm, with proposals drawn from the prior and accepted based on the likelihood, are extended to encompass general Metropolis-Hastings proposal densities. In particular, the algorithm proposes moves by drawing from the posterior distribution over computationally tractible subsets of the parameter space. The algorithm is illustrated by an implementation on a Silicon Graphics Onyx/Reality Engine.

Variance-reduced simulation of lattice discrete-time Markov chains with applications in reaction networks

NASA Astrophysics Data System (ADS)

Maginnis, P. A.; West, M.; Dullerud, G. E.

2016-10-01

We propose an algorithm to accelerate Monte Carlo simulation for a broad class of stochastic processes. Specifically, the class of countable-state, discrete-time Markov chains driven by additive Poisson noise, or lattice discrete-time Markov chains. In particular, this class includes simulation of reaction networks via the tau-leaping algorithm. To produce the speedup, we simulate pairs of fair-draw trajectories that are negatively correlated. Thus, when averaged, these paths produce an unbiased Monte Carlo estimator that has reduced variance and, therefore, reduced error. Numerical results for three example systems included in this work demonstrate two to four orders of magnitude reduction of mean-square error. The numerical examples were chosen to illustrate different application areas and levels of system complexity. The areas are: gene expression (affine state-dependent rates), aerosol particle coagulation with emission and human immunodeficiency virus infection (both with nonlinear state-dependent rates). Our algorithm views the system dynamics as a ;black-box;, i.e., we only require control of pseudorandom number generator inputs. As a result, typical codes can be retrofitted with our algorithm using only minor changes. We prove several analytical results. Among these, we characterize the relationship of covariances between paths in the general nonlinear state-dependent intensity rates case, and we prove variance reduction of mean estimators in the special case of affine intensity rates.

Unimolecular Reaction Pathways of a γ-Ketohydroperoxide from Combined Application of Automated Reaction Discovery Methods

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

Grambow, Colin A.; Jamal, Adeel; Li, Yi -Pei

Ketohydroperoxides are important in liquid-phase autoxidation and in gas-phase partial oxidation and pre-ignition chemistry, but because of their low concentration, instability, and various analytical chemistry limitations, it has been challenging to experimentally determine their reactivity, and only a few pathways are known. In the present work, 75 elementary-step unimolecular reactions of the simplest γ-ketohydroperoxide, 3-hydroperoxypropanal, were discovered by a combination of density functional theory with several automated transition-state search algorithms: the Berny algorithm coupled with the freezing string method, single- and double-ended growing string methods, the heuristic KinBot algorithm, and the single-component artificial force induced reaction method (SC-AFIR). The presentmore » joint approach significantly outperforms previous manual and automated transition-state searches – 68 of the reactions of γ-ketohydroperoxide discovered here were previously unknown and completely unexpected. All of the methods found the lowest-energy transition state, which corresponds to the first step of the Korcek mechanism, but each algorithm except for SC-AFIR detected several reactions not found by any of the other methods. We show that the low-barrier chemical reactions involve promising new chemistry that may be relevant in atmospheric and combustion systems. Our study highlights the complexity of chemical space exploration and the advantage of combined application of several approaches. Altogether, the present work demonstrates both the power and the weaknesses of existing fully automated approaches for reaction discovery which suggest possible directions for further method development and assessment in order to enable reliable discovery of all important reactions of any specified reactant(s).« less

Unimolecular Reaction Pathways of a γ-Ketohydroperoxide from Combined Application of Automated Reaction Discovery Methods

DOE PAGES

Grambow, Colin A.; Jamal, Adeel; Li, Yi -Pei; ...

2017-12-22

Ketohydroperoxides are important in liquid-phase autoxidation and in gas-phase partial oxidation and pre-ignition chemistry, but because of their low concentration, instability, and various analytical chemistry limitations, it has been challenging to experimentally determine their reactivity, and only a few pathways are known. In the present work, 75 elementary-step unimolecular reactions of the simplest γ-ketohydroperoxide, 3-hydroperoxypropanal, were discovered by a combination of density functional theory with several automated transition-state search algorithms: the Berny algorithm coupled with the freezing string method, single- and double-ended growing string methods, the heuristic KinBot algorithm, and the single-component artificial force induced reaction method (SC-AFIR). The presentmore » joint approach significantly outperforms previous manual and automated transition-state searches – 68 of the reactions of γ-ketohydroperoxide discovered here were previously unknown and completely unexpected. All of the methods found the lowest-energy transition state, which corresponds to the first step of the Korcek mechanism, but each algorithm except for SC-AFIR detected several reactions not found by any of the other methods. We show that the low-barrier chemical reactions involve promising new chemistry that may be relevant in atmospheric and combustion systems. Our study highlights the complexity of chemical space exploration and the advantage of combined application of several approaches. Altogether, the present work demonstrates both the power and the weaknesses of existing fully automated approaches for reaction discovery which suggest possible directions for further method development and assessment in order to enable reliable discovery of all important reactions of any specified reactant(s).« less

BFL: a node and edge betweenness based fast layout algorithm for large scale networks

PubMed Central

Hashimoto, Tatsunori B; Nagasaki, Masao; Kojima, Kaname; Miyano, Satoru

2009-01-01

Background Network visualization would serve as a useful first step for analysis. However, current graph layout algorithms for biological pathways are insensitive to biologically important information, e.g. subcellular localization, biological node and graph attributes, or/and not available for large scale networks, e.g. more than 10000 elements. Results To overcome these problems, we propose the use of a biologically important graph metric, betweenness, a measure of network flow. This metric is highly correlated with many biological phenomena such as lethality and clusters. We devise a new fast parallel algorithm calculating betweenness to minimize the preprocessing cost. Using this metric, we also invent a node and edge betweenness based fast layout algorithm (BFL). BFL places the high-betweenness nodes to optimal positions and allows the low-betweenness nodes to reach suboptimal positions. Furthermore, BFL reduces the runtime by combining a sequential insertion algorim with betweenness. For a graph with n nodes, this approach reduces the expected runtime of the algorithm to O(n2) when considering edge crossings, and to O(n log n) when considering only density and edge lengths. Conclusion Our BFL algorithm is compared against fast graph layout algorithms and approaches requiring intensive optimizations. For gene networks, we show that our algorithm is faster than all layout algorithms tested while providing readability on par with intensive optimization algorithms. We achieve a 1.4 second runtime for a graph with 4000 nodes and 12000 edges on a standard desktop computer. PMID:19146673

Elucidation of metabolic pathways from enzyme classification data.

PubMed

McDonald, Andrew G; Tipton, Keith F

2014-01-01

The IUBMB Enzyme List is widely used by other databases as a source for avoiding ambiguity in the recognition of enzymes as catalytic entities. However, it was not designed for metabolic pathway tracing, which has become increasingly important in systems biology. A Reactions Database has been created from the material in the Enzyme List to allow reactions to be searched by substrate/product, and pathways to be traced from any selected starting/seed substrate. An extensive synonym glossary allows searches by many of the alternative names, including accepted abbreviations, by which a chemical compound may be known. This database was necessary for the development of the application Reaction Explorer ( http://www.reaction-explorer.org ), which was written in Real Studio ( http://www.realsoftware.com/realstudio/ ) to search the Reactions Database and draw metabolic pathways from reactions selected by the user. Having input the name of the starting compound (the "seed"), the user is presented with a list of all reactions containing that compound and then selects the product of interest as the next point on the ensuing graph. The pathway diagram is then generated as the process iterates. A contextual menu is provided, which allows the user: (1) to remove a compound from the graph, along with all associated links; (2) to search the reactions database again for additional reactions involving the compound; (3) to search for the compound within the Enzyme List.

MIMO: an efficient tool for molecular interaction maps overlap

PubMed Central

2013-01-01

Background Molecular pathways represent an ensemble of interactions occurring among molecules within the cell and between cells. The identification of similarities between molecular pathways across organisms and functions has a critical role in understanding complex biological processes. For the inference of such novel information, the comparison of molecular pathways requires to account for imperfect matches (flexibility) and to efficiently handle complex network topologies. To date, these characteristics are only partially available in tools designed to compare molecular interaction maps. Results Our approach MIMO (Molecular Interaction Maps Overlap) addresses the first problem by allowing the introduction of gaps and mismatches between query and template pathways and permits -when necessary- supervised queries incorporating a priori biological information. It then addresses the second issue by relying directly on the rich graph topology described in the Systems Biology Markup Language (SBML) standard, and uses multidigraphs to efficiently handle multiple queries on biological graph databases. The algorithm has been here successfully used to highlight the contact point between various human pathways in the Reactome database. Conclusions MIMO offers a flexible and efficient graph-matching tool for comparing complex biological pathways. PMID:23672344

Systems biology by the rules: hybrid intelligent systems for pathway modeling and discovery.

PubMed

Bosl, William J

2007-02-15

Expert knowledge in journal articles is an important source of data for reconstructing biological pathways and creating new hypotheses. An important need for medical research is to integrate this data with high throughput sources to build useful models that span several scales. Researchers traditionally use mental models of pathways to integrate information and development new hypotheses. Unfortunately, the amount of information is often overwhelming and these are inadequate for predicting the dynamic response of complex pathways. Hierarchical computational models that allow exploration of semi-quantitative dynamics are useful systems biology tools for theoreticians, experimentalists and clinicians and may provide a means for cross-communication. A novel approach for biological pathway modeling based on hybrid intelligent systems or soft computing technologies is presented here. Intelligent hybrid systems, which refers to several related computing methods such as fuzzy logic, neural nets, genetic algorithms, and statistical analysis, has become ubiquitous in engineering applications for complex control system modeling and design. Biological pathways may be considered to be complex control systems, which medicine tries to manipulate to achieve desired results. Thus, hybrid intelligent systems may provide a useful tool for modeling biological system dynamics and computational exploration of new drug targets. A new modeling approach based on these methods is presented in the context of hedgehog regulation of the cell cycle in granule cells. Code and input files can be found at the Bionet website: www.chip.ord/~wbosl/Software/Bionet. This paper presents the algorithmic methods needed for modeling complicated biochemical dynamics using rule-based models to represent expert knowledge in the context of cell cycle regulation and tumor growth. A notable feature of this modeling approach is that it allows biologists to build complex models from their knowledge base without the need to translate that knowledge into mathematical form. Dynamics on several levels, from molecular pathways to tissue growth, are seamlessly integrated. A number of common network motifs are examined and used to build a model of hedgehog regulation of the cell cycle in cerebellar neurons, which is believed to play a key role in the etiology of medulloblastoma, a devastating childhood brain cancer.

Functional annotation of regulatory pathways.

PubMed

Pandey, Jayesh; Koyutürk, Mehmet; Kim, Yohan; Szpankowski, Wojciech; Subramaniam, Shankar; Grama, Ananth

2007-07-01

Standardized annotations of biomolecules in interaction networks (e.g. Gene Ontology) provide comprehensive understanding of the function of individual molecules. Extending such annotations to pathways is a critical component of functional characterization of cellular signaling at the systems level. We propose a framework for projecting gene regulatory networks onto the space of functional attributes using multigraph models, with the objective of deriving statistically significant pathway annotations. We first demonstrate that annotations of pairwise interactions do not generalize to indirect relationships between processes. Motivated by this result, we formalize the problem of identifying statistically overrepresented pathways of functional attributes. We establish the hardness of this problem by demonstrating the non-monotonicity of common statistical significance measures. We propose a statistical model that emphasizes the modularity of a pathway, evaluating its significance based on the coupling of its building blocks. We complement the statistical model by an efficient algorithm and software, Narada, for computing significant pathways in large regulatory networks. Comprehensive results from our methods applied to the Escherichia coli transcription network demonstrate that our approach is effective in identifying known, as well as novel biological pathway annotations. Narada is implemented in Java and is available at http://www.cs.purdue.edu/homes/jpandey/narada/.

Exploratory factor analysis of pathway copy number data with an application towards the integration with gene expression data.

PubMed

van Wieringen, Wessel N; van de Wiel, Mark A

2011-05-01

Realizing that genes often operate together, studies into the molecular biology of cancer shift focus from individual genes to pathways. In order to understand the regulatory mechanisms of a pathway, one must study its genes at all molecular levels. To facilitate such study at the genomic level, we developed exploratory factor analysis for the characterization of the variability of a pathway's copy number data. A latent variable model that describes the call probability data of a pathway is introduced and fitted with an EM algorithm. In two breast cancer data sets, it is shown that the first two latent variables of GO nodes, which inherit a clear interpretation from the call probabilities, are often related to the proportion of aberrations and a contrast of the probabilities of a loss and of a gain. Linking the latent variables to the node's gene expression data suggests that they capture the "global" effect of genomic aberrations on these transcript levels. In all, the proposed method provides an possibly insightful characterization of pathway copy number data, which may be fruitfully exploited to study the interaction between the pathway's DNA copy number aberrations and data from other molecular levels like gene expression.

Pathway cross-talk network analysis identifies critical pathways in neonatal sepsis.

PubMed

Meng, Yu-Xiu; Liu, Quan-Hong; Chen, Deng-Hong; Meng, Ying

2017-06-01

Despite advances in neonatal care, sepsis remains a major cause of morbidity and mortality in neonates worldwide. Pathway cross-talk analysis might contribute to the inference of the driving forces in bacterial sepsis and facilitate a better understanding of underlying pathogenesis of neonatal sepsis. This study aimed to explore the critical pathways associated with the progression of neonatal sepsis by the pathway cross-talk analysis. By integrating neonatal transcriptome data with known pathway data and protein-protein interaction data, we systematically uncovered the disease pathway cross-talks and constructed a disease pathway cross-talk network for neonatal sepsis. Then, attract method was employed to explore the dysregulated pathways associated with neonatal sepsis. To determine the critical pathways in neonatal sepsis, rank product (RP) algorithm, centrality analysis and impact factor (IF) were introduced sequentially, which synthetically considered the differential expression of genes and pathways, pathways cross-talks and pathway parameters in the network. The dysregulated pathways with the highest IF values as well as RP<0.01 were defined as critical pathways in neonatal sepsis. By integrating three kinds of data, only 6919 common genes were included to perform the pathway cross-talk analysis. By statistic analysis, a total of 1249 significant pathway cross-talks were selected to construct the pathway cross-talk network. Moreover, 47 dys-regulated pathways were identified via attract method, 20 pathways were identified under RP<0.01, and the top 10 pathways with the highest IF were also screened from the pathway cross-talk network. Among them, we selected 8 common pathways, i.e. critical pathways. In this study, we systematically tracked 8 critical pathways involved in neonatal sepsis by integrating attract method and pathway cross-talk network. These pathways might be responsible for the host response in infection, and of great value for advancing diagnosis and therapy of neonatal sepsis. Copyright © 2017 Elsevier Ltd. All rights reserved.

The Analysis of Alpha Beta Pruning and MTD(f) Algorithm to Determine the Best Algorithm to be Implemented at Connect Four Prototype

NASA Astrophysics Data System (ADS)

Tommy, Lukas; Hardjianto, Mardi; Agani, Nazori

2017-04-01

Connect Four is a two-player game which the players take turns dropping discs into a grid to connect 4 of one’s own discs next to each other vertically, horizontally, or diagonally. At Connect Four, Computer requires artificial intelligence (AI) in order to play properly like human. There are many AI algorithms that can be implemented to Connect Four, but the suitable algorithms are unknown. The suitable algorithm means optimal in choosing move and its execution time is not slow at search depth which is deep enough. In this research, analysis and comparison between standard alpha beta (AB) Pruning and MTD(f) will be carried out at the prototype of Connect Four in terms of optimality (win percentage) and speed (execution time and the number of leaf nodes). Experiments are carried out by running computer versus computer mode with 12 different conditions, i.e. varied search depth (5 through 10) and who moves first. The percentage achieved by MTD(f) based on experiments is win 45,83%, lose 37,5% and draw 16,67%. In the experiments with search depth 8, MTD(f) execution time is 35, 19% faster and evaluate 56,27% fewer leaf nodes than AB Pruning. The results of this research are MTD(f) is as optimal as AB Pruning at Connect Four prototype, but MTD(f) on average is faster and evaluates fewer leaf nodes than AB Pruning. The execution time of MTD(f) is not slow and much faster than AB Pruning at search depth which is deep enough.

Development of Personalized Urination Recognition Technology Using Smart Bands.

PubMed

Eun, Sung-Jong; Whangbo, Taeg-Keun; Park, Dong Kyun; Kim, Khae-Hawn

2017-04-01

This study collected and analyzed activity data sensed through smart bands worn by patients in order to resolve the clinical issues posed by using voiding charts. By developing a smart band-based algorithm for recognizing urination activity in patients, this study aimed to explore the feasibility of urination monitoring systems. This study aimed to develop an algorithm that recognizes urination based on a patient's posture and changes in posture. Motion data was obtained from a smart band on the arm. An algorithm that recognizes the 3 stages of urination (forward movement, urination, backward movement) was developed based on data collected from a 3-axis accelerometer and from tilt angle data. Real-time data were acquired from the smart band, and for data corresponding to a certain duration, the absolute value of the signals was calculated and then compared with the set threshold value to determine the occurrence of vibration signals. In feature extraction, the most essential information describing each pattern was identified after analyzing the characteristics of the data. The results of the feature extraction process were sorted using a classifier to detect urination. An experiment was carried out to assess the performance of the recognition technology proposed in this study. The final accuracy of the algorithm was calculated based on clinical guidelines for urologists. The experiment showed a high average accuracy of 90.4%, proving the robustness of the proposed algorithm. The proposed urination recognition technology draws on acceleration data and tilt angle data collected via a smart band; these data were then analyzed using a classifier after comparative analyses with standardized feature patterns.

Emissions Scenario Portal for Visualization of Low Carbon Pathways

NASA Astrophysics Data System (ADS)

Friedrich, J.; Hennig, R. J.; Mountford, H.; Altamirano, J. C.; Ge, M.; Fransen, T.

2016-12-01

This proposal for a presentation is centered around a new project which is developed collaboratively by the World Resources Institute (WRI), Google Inc., and Deep Decarbonization Pathways Project (DDPP). The project aims to develop an online, open portal, the Emissions Scenario Portal (ESP),to enable users to easily visualize a range of future greenhouse gas emission pathways linked to different scenarios of economic and energy developments, drawing from a variety of modeling tools. It is targeted to users who are not modelling experts, but instead policy analysts or advisors, investment analysts, and similar who draw on modelled scenarios to inform their work, and who can benefit from better access to, and transparency around, the wide range of emerging scenarios on ambitious climate action. The ESP will provide information from scenarios in a visually appealing and easy-to-understand manner that enable these users to recognize the opportunities to reduce GHG emissions, the implications of the different scenarios, and the underlying assumptions. To facilitate the application of the portal and tools in policy dialogues, a series of country-specific and potentially sector-specific workshops with key decision-makers and analysts, supported by relevant analysis, will be organized by the key partners and also in broader collaboration with others who might wish to convene relevant groups around the information. This project will provide opportunities for modelers to increase their outreach and visibility in the public space and to directly interact with key audiences of emissions scenarios, such as policy analysts and advisors. The information displayed on the portal will cover a wide range of indicators, sectors and important scenario characteristics such as macroeconomic information, emission factors and policy as well as technology assumptions in order to facilitate comparison. These indicators have been selected based on existing standards (such as the IIASA AR5 database, the Greenhouse Gas Protocol and accounting literature) and stakeholder consultations. Examples for use cases include: technical advisers for governments NGO/Civil Society advocates Investors and bankers Modelers and academics Business sustainability officers

A Review of Nonoccupational Pathways for Pesticide Exposure in Women Living in Agricultural Areas

PubMed Central

Friesen, Melissa C.; Hoppin, Jane A.; Hines, Cynthia J.; Thomas, Kent; Freeman, Laura E. Beane

2015-01-01

Background Women living in agricultural areas may experience high pesticide exposures compared with women in urban or suburban areas because of their proximity to farm activities. Objective Our objective was to review the evidence in the published literature for the contribution of nonoccupational pathways of pesticide exposure in women living in North American agricultural areas. Methods We evaluated the following nonoccupational exposure pathways: paraoccupational (i.e., take-home or bystander exposure), agricultural drift, residential pesticide use, and dietary ingestion. We also evaluated the role of hygiene factors (e.g., house cleaning, shoe removal). Results Among 35 publications identified (published 1995–2013), several reported significant or suggestive (p < 0.1) associations between paraoccupational (n = 19) and agricultural drift (n = 10) pathways and pesticide dust or biomarker levels, and 3 observed that residential use was associated with pesticide concentrations in dust. The 4 studies related to ingestion reported low detection rates of most pesticides in water; additional studies are needed to draw conclusions about the importance of this pathway. Hygiene factors were not consistently linked to exposure among the 18 relevant publications identified. Conclusions Evidence supported the importance of paraoccupational, drift, and residential use pathways. Disentangling exposure pathways was difficult because agricultural populations are concurrently exposed to pesticides via multiple pathways. Most evidence was based on measurements of pesticides in residential dust, which are applicable to any household member and are not specific to women. An improved understanding of nonoccupational pesticide exposure pathways in women living in agricultural areas is critical for studying health effects in women and for designing effective exposure-reduction strategies. Citation Deziel NC, Friesen MC, Hoppin JA, Hines CJ, Thomas K, Beane Freeman LE. 2015. A review of nonoccupational pathways for pesticide exposure in women living in agricultural areas. Environ Health Perspect 123:515–524; http://dx.doi.org/10.1289/ehp.1408273 PMID:25636067

FindPrimaryPairs: An efficient algorithm for predicting element-transferring reactant/product pairs in metabolic networks.

PubMed

Steffensen, Jon Lund; Dufault-Thompson, Keith; Zhang, Ying

2018-01-01

The metabolism of individual organisms and biological communities can be viewed as a network of metabolites connected to each other through chemical reactions. In metabolic networks, chemical reactions transform reactants into products, thereby transferring elements between these metabolites. Knowledge of how elements are transferred through reactant/product pairs allows for the identification of primary compound connections through a metabolic network. However, such information is not readily available and is often challenging to obtain for large reaction databases or genome-scale metabolic models. In this study, a new algorithm was developed for automatically predicting the element-transferring reactant/product pairs using the limited information available in the standard representation of metabolic networks. The algorithm demonstrated high efficiency in analyzing large datasets and provided accurate predictions when benchmarked with manually curated data. Applying the algorithm to the visualization of metabolic networks highlighted pathways of primary reactant/product connections and provided an organized view of element-transferring biochemical transformations. The algorithm was implemented as a new function in the open source software package PSAMM in the release v0.30 (https://zhanglab.github.io/psamm/).

Proposal and application of a regional methodology of comparative risk assessment for potentially contaminated sites.

PubMed

Marzocchini, Manrico; Tatàno, Fabio; Moretti, Michela Simona; Antinori, Caterina; Orilisi, Stefano

2018-06-05

A possible approach for determining soil and groundwater quality criteria for contaminated sites is the comparative risk assessment. Originating from but not limited to Italian interest in a decentralised (regional) implementation of comparative risk assessment, this paper first addresses the proposal of an original methodology called CORIAN REG-M , which was created with initial attention to the context of potentially contaminated sites in the Marche Region (Central Italy). To deepen the technical-scientific knowledge and applicability of the comparative risk assessment, the following characteristics of the CORIAN REG-M methodology appear to be relevant: the simplified but logical assumption of three categories of factors (source and transfer/transport of potential contamination, and impacted receptors) within each exposure pathway; the adaptation to quality and quantity of data that are available or derivable at the given scale of concern; the attention to a reliable but unsophisticated modelling; the achievement of a conceptual linkage to the absolute risk assessment approach; and the potential for easy updating and/or refining of the methodology. Further, the application of the CORIAN REG-M methodology to some case-study sites located in the Marche Region indicated the following: a positive correlation can be expected between air and direct contact pathway scores, as well as between individual pathway scores and the overall site scores based on a root-mean-square algorithm; the exposure pathway, which presents the highest variability of scores, tends to be dominant at sites with the highest computed overall site scores; and the adoption of a root-mean-square algorithm can be expected to emphasise the overall site scoring.

Judgement and the role of the metaphysics of values in medical ethics

PubMed Central

Thornton, T

2006-01-01

Despite its authors' intentions, the four principles approach to medical ethics can become crudely algorithmic in practice. The first section sets out the bare bones of the four principles approach drawing out those aspects of Beauchamp and Childress's Principles of biomedical ethics that encourage this misreading. The second section argues that if the emphasis on the guidance of moral judgement is augmented by a particularist account of what disciplines it, then the danger can be reduced. In the third section, I consider how much the resultant picture diverges from Beauchamp and Childress's actual position. PMID:16731739

Judgement and the role of the metaphysics of values in medical ethics.

PubMed

Thornton, T

2006-06-01

Despite its authors' intentions, the four principles approach to medical ethics can become crudely algorithmic in practice. The first section sets out the bare bones of the four principles approach drawing out those aspects of Beauchamp and Childress's Principles of biomedical ethics that encourage this misreading. The second section argues that if the emphasis on the guidance of moral judgement is augmented by a particularist account of what disciplines it, then the danger can be reduced. In the third section, I consider how much the resultant picture diverges from Beauchamp and Childress's actual position.

A novel algorithm for delineating wetland depressions and mapping surface hydrologic flow pathways using LiDAR data

EPA Science Inventory

In traditional watershed delineation and topographic modeling, surface depressions are generally treated as spurious features and simply removed from a digital elevation model (DEM) to enforce flow continuity of water across the topographic surface to the watershed outlets. In re...

MODELING FLUX PATHWAYS TO VEGETATION FOR VOLATILE AND SEMI-VOLATILE ORGANIC COMPOUNDS IN A MULTIMEDIA ENVIRONMENT

EPA Science Inventory

This study evaluates the treatment of gas-phase atmospheric deposition in a screening level model of the multimedia environmental distribution of toxics (MEND-TOX). Recent algorithmic additions to MEND-TOX for the estimation of gas-phase deposition velocity over vegetated surf...

Methylome evolution in plants.

PubMed

Vidalis, Amaryllis; Živković, Daniel; Wardenaar, René; Roquis, David; Tellier, Aurélien; Johannes, Frank

2016-12-20

Despite major progress in dissecting the molecular pathways that control DNA methylation patterns in plants, little is known about the mechanisms that shape plant methylomes over evolutionary time. Drawing on recent intra- and interspecific epigenomic studies, we show that methylome evolution over long timescales is largely a byproduct of genomic changes. By contrast, methylome evolution over short timescales appears to be driven mainly by spontaneous epimutational events. We argue that novel methods based on analyses of the methylation site frequency spectrum (mSFS) of natural populations can provide deeper insights into the evolutionary forces that act at each timescale.

10 Years of Asian Dust Storm Observations from SeaWiFS: Source, Pathway, and Interannual Variability

NASA Technical Reports Server (NTRS)

Hsu, N. Christina; Tsay, S.-C.; King, M.D.; Jeong, M.-J.

2008-01-01

In this paper, we will demonstrate the capability of a new satellite algorithm to retrieve aerosol optical thickness and single scattering albedo over bright-reflecting surfaces such as urban areas and deserts. Such retrievals have been difficult to perform using previously available algorithms that use wavelengths from the mid-visible to the near IR because they have trouble separating the aerosol signal from the contribution due to the bright surface reflectance. The new algorithm, called Deep Blue, utilizes blue-wavelength measurements from instruments such as SeaWiFS and MODIS to infer the properties of aerosols, since the surface reflectance over land in the blue part of the spectrum is much lower than for longer wavelength channels. We have validated the satellite retrieved aerosol optical thickness with data from AERONET sunphotometers over desert and semi-desert regions. The comparisons show reasonable agreements between these two. These new satellite products will allow scientists to determine quantitatively the aerosol properties near sources using high spatial resolution measurements from SeaWiFS and MODIS-like instruments. The multiyear satellite measurements (1998 - 2007) from SeaWiFS will be utilized to investigate the interannual variability of source, pathway, and dust loading associated with these dust outbreaks in East Asia. The monthly averaged aerosol optical thickness during the springtime from SeaWiFS will also be compared with the MODIS Deep Blue products.

Semiempirical prediction of protein folds

NASA Astrophysics Data System (ADS)

Fernández, Ariel; Colubri, Andrés; Appignanesi, Gustavo

2001-08-01

We introduce a semiempirical approach to predict ab initio expeditious pathways and native backbone geometries of proteins that fold under in vitro renaturation conditions. The algorithm is engineered to incorporate a discrete codification of local steric hindrances that constrain the movements of the peptide backbone throughout the folding process. Thus, the torsional state of the chain is assumed to be conditioned by the fact that hopping from one basin of attraction to another in the Ramachandran map (local potential energy surface) of each residue is energetically more costly than the search for a specific (Φ, Ψ) torsional state within a single basin. A combinatorial procedure is introduced to evaluate coarsely defined torsional states of the chain defined ``modulo basins'' and translate them into meaningful patterns of long range interactions. Thus, an algorithm for structure prediction is designed based on the fact that local contributions to the potential energy may be subsumed into time-evolving conformational constraints defining sets of restricted backbone geometries whereupon the patterns of nonbonded interactions are constructed. The predictive power of the algorithm is assessed by (a) computing ab initio folding pathways for mammalian ubiquitin that ultimately yield a stable structural pattern reproducing all of its native features, (b) determining the nucleating event that triggers the hydrophobic collapse of the chain, and (c) comparing coarse predictions of the stable folds of moderately large proteins (N~100) with structural information extracted from the protein data bank.

Improving clustering with metabolic pathway data.

PubMed

Milone, Diego H; Stegmayer, Georgina; López, Mariana; Kamenetzky, Laura; Carrari, Fernando

2014-04-10

It is a common practice in bioinformatics to validate each group returned by a clustering algorithm through manual analysis, according to a-priori biological knowledge. This procedure helps finding functionally related patterns to propose hypotheses for their behavior and the biological processes involved. Therefore, this knowledge is used only as a second step, after data are just clustered according to their expression patterns. Thus, it could be very useful to be able to improve the clustering of biological data by incorporating prior knowledge into the cluster formation itself, in order to enhance the biological value of the clusters. A novel training algorithm for clustering is presented, which evaluates the biological internal connections of the data points while the clusters are being formed. Within this training algorithm, the calculation of distances among data points and neurons centroids includes a new term based on information from well-known metabolic pathways. The standard self-organizing map (SOM) training versus the biologically-inspired SOM (bSOM) training were tested with two real data sets of transcripts and metabolites from Solanum lycopersicum and Arabidopsis thaliana species. Classical data mining validation measures were used to evaluate the clustering solutions obtained by both algorithms. Moreover, a new measure that takes into account the biological connectivity of the clusters was applied. The results of bSOM show important improvements in the convergence and performance for the proposed clustering method in comparison to standard SOM training, in particular, from the application point of view. Analyses of the clusters obtained with bSOM indicate that including biological information during training can certainly increase the biological value of the clusters found with the proposed method. It is worth to highlight that this fact has effectively improved the results, which can simplify their further analysis.The algorithm is available as a web-demo at http://fich.unl.edu.ar/sinc/web-demo/bsom-lite/. The source code and the data sets supporting the results of this article are available at http://sourceforge.net/projects/sourcesinc/files/bsom.

A quantum physical design flow using ILP and graph drawing

NASA Astrophysics Data System (ADS)

Yazdani, Maryam; Saheb Zamani, Morteza; Sedighi, Mehdi

2013-10-01

Implementing large-scale quantum circuits is one of the challenges of quantum computing. One of the central challenges of accurately modeling the architecture of these circuits is to schedule a quantum application and generate the layout while taking into account the cost of communications and classical resources as well as the maximum exploitable parallelism. In this paper, we present and evaluate a design flow for arbitrary quantum circuits in ion trap technology. Our design flow consists of two parts. First, a scheduler takes a description of a circuit and finds the best order for the execution of its quantum gates using integer linear programming regarding the classical resources (qubits) and instruction dependencies. Then a layout generator receives the schedule produced by the scheduler and generates a layout for this circuit using a graph-drawing algorithm. Our experimental results show that the proposed flow decreases the average latency of quantum circuits by about 11 % for a set of attempted benchmarks and by about 9 % for another set of benchmarks compared with the best in literature.

Acceleration of the Particle Swarm Optimization for Peierls–Nabarro modeling of dislocations in conventional and high-entropy alloys

DOE PAGES

Pei, Zongrui; Max-Planck-Inst. fur Eisenforschung, Duseldorf; Eisenbach, Markus

2017-02-06

Dislocations are among the most important defects in determining the mechanical properties of both conventional alloys and high-entropy alloys. The Peierls-Nabarro model supplies an efficient pathway to their geometries and mobility. The difficulty in solving the integro-differential Peierls-Nabarro equation is how to effectively avoid the local minima in the energy landscape of a dislocation core. Among the other methods to optimize the dislocation core structures, we choose the algorithm of Particle Swarm Optimization, an algorithm that simulates the social behaviors of organisms. By employing more particles (bigger swarm) and more iterative steps (allowing them to explore for longer time), themore » local minima can be effectively avoided. But this would require more computational cost. The advantage of this algorithm is that it is readily parallelized in modern high computing architecture. We demonstrate the performance of our parallelized algorithm scales linearly with the number of employed cores.« less

Clinical decision-making and secondary findings in systems medicine.

PubMed

Fischer, T; Brothers, K B; Erdmann, P; Langanke, M

2016-05-21

Systems medicine is the name for an assemblage of scientific strategies and practices that include bioinformatics approaches to human biology (especially systems biology); "big data" statistical analysis; and medical informatics tools. Whereas personalized and precision medicine involve similar analytical methods applied to genomic and medical record data, systems medicine draws on these as well as other sources of data. Given this distinction, the clinical translation of systems medicine poses a number of important ethical and epistemological challenges for researchers working to generate systems medicine knowledge and clinicians working to apply it. This article focuses on three key challenges: First, we will discuss the conflicts in decision-making that can arise when healthcare providers committed to principles of experimental medicine or evidence-based medicine encounter individualized recommendations derived from computer algorithms. We will explore in particular whether controlled experiments, such as comparative effectiveness trials, should mediate the translation of systems medicine, or if instead individualized findings generated through "big data" approaches can be applied directly in clinical decision-making. Second, we will examine the case of the Riyadh Intensive Care Program Mortality Prediction Algorithm, pejoratively referred to as the "death computer," to demonstrate the ethical challenges that can arise when big-data-driven scoring systems are applied in clinical contexts. We argue that the uncritical use of predictive clinical algorithms, including those envisioned for systems medicine, challenge basic understandings of the doctor-patient relationship. Third, we will build on the recent discourse on secondary findings in genomics and imaging to draw attention to the important implications of secondary findings derived from the joint analysis of data from diverse sources, including data recorded by patients in an attempt to realize their "quantified self." This paper examines possible ethical challenges that are likely to be raised as systems medicine to be translated into clinical medicine. These include the epistemological challenges for clinical decision-making, the use of scoring systems optimized by big data techniques and the risk that incidental and secondary findings will significantly increase. While some ethical implications remain still hypothetical we should use the opportunity to prospectively identify challenges to avoid making foreseeable mistakes when systems medicine inevitably arrives in routine care.

Transforming Patient Compliance Research in an Era of Biomedicalization.

PubMed

Spencer, Karen Lutfey

2018-06-01

The term patient noncompliance emerged in the 1970s as a tool for analyzing why people do not follow medical directives. Despite its early popularity, the term has languished in sociology while flourishing in biomedical arenas. It seems flaccid in a contemporary healthcare context as it overestimates physician authority and is tone-deaf to biomedicalization. I draw from sociological and anthropological traditions, as well as qualitative interviews with terminally ill patients ( N = 26) and their caregivers ( N = 16), to consider facets of a biomedicalized health experience and implications for an updated vision of compliance. First, pathways to care have proliferated under biomedicalization. With increased pathways comes increased need for understanding how treatment plans are socially constituted and assessed. Finally, increased complexity demands a more diverse vocabulary for understanding health related decisions. This paper is a call to sociologists to take the lead in transforming and updating this consequential concept.

Identification of active miRNA and transcription factor regulatory pathways in human obesity-related inflammation.

PubMed

Zhang, Xi-Mei; Guo, Lin; Chi, Mei-Hua; Sun, Hong-Mei; Chen, Xiao-Wen

2015-03-07

Obesity-induced chronic inflammation plays a fundamental role in the pathogenesis of metabolic syndrome (MS). Recently, a growing body of evidence supports that miRNAs are largely dysregulated in obesity and that specific miRNAs regulate obesity-associated inflammation. We applied an approach aiming to identify active miRNA-TF-gene regulatory pathways in obesity. Firstly, we detected differentially expressed genes (DEGs) and differentially expressed miRNAs (DEmiRs) from mRNA and miRNA expression profiles, respectively. Secondly, by mapping the DEGs and DEmiRs to the curated miRNA-TF-gene regulatory network as active seed nodes and connect them with their immediate neighbors, we obtained the potential active miRNA-TF-gene regulatory subnetwork in obesity. Thirdly, using a Breadth-First-Search (BFS) algorithm, we identified potential active miRNA-TF-gene regulatory pathways in obesity. Finally, through the hypergeometric test, we identified the active miRNA-TF-gene regulatory pathways that were significantly related to obesity. The potential active pathways with FDR < 0.0005 were considered to be the active miRNA-TF regulatory pathways in obesity. The union of the active pathways is visualized and identical nodes of the active pathways were merged. We identified 23 active miRNA-TF-gene regulatory pathways that were significantly related to obesity-related inflammation.

The effect of rugby match outcome on spectator aggression and intention to drink alcohol.

PubMed

Moore, Simon C; Shepherd, Jonathan P; Eden, Sam; Sivarajasingam, Vaseekaran

2007-01-01

Alcohol, aggression and assault injury are strongly associated with popular sporting events, but mediating factors are not clear. To explore aggression, happiness and plans to consume alcohol among spectators before and spectators after sports matches. Cross-sectional surveys of male rugby football fans at an international stadium generated four groups: a pre-match group of 111 men, and three post-match groups of supporters, 17 whose team had won, 23 whose team had lost and 46 whose team had drawn. Consenting participants were assessed using the assault sub-scale of the Buss-Durkee Hostility Inventory, on a self-rating of happiness (Likert scale), for planned alcohol consumption and demographic variables. Pre- and post-match group mean responses were compared. Analyses were performed on 197 male spectators (mean age 42 years). Spectators in 'win' (z = 2.63, p < 0.01) and 'draw' (z = 2.76, p < 0.01) groups rated themselves as more aggressive than those in the pre-game group, but those in the losing group did not (z = -0.03, p > 0.05). No differences, however, were observed between pre-match, 'win', 'draw' or 'lose' groups on the decision to drink after the match. Winning did not increase happiness (t = 0.25, p > 0.05), but losing (t = 2.09, p < 0.05) or drawing (t = 7.64, p < 0.001) decreased it. This study suggests that team success but not failure may increase aggression among supporters, and that aggression, not celebration, drives post-match alcohol consumption. Losing and drawing decreased happiness but winning did not increase it. Better understanding of pathways to violence in these circumstances will pave the way for more effective prevention and management strategies.

Molecular profiles to biology and pathways: a systems biology approach.

PubMed

Van Laere, Steven; Dirix, Luc; Vermeulen, Peter

2016-06-16

Interpreting molecular profiles in a biological context requires specialized analysis strategies. Initially, lists of relevant genes were screened to identify enriched concepts associated with pathways or specific molecular processes. However, the shortcoming of interpreting gene lists by using predefined sets of genes has resulted in the development of novel methods that heavily rely on network-based concepts. These algorithms have the advantage that they allow a more holistic view of the signaling properties of the condition under study as well as that they are suitable for integrating different data types like gene expression, gene mutation, and even histological parameters.

THE LONG REACH OF EDUCATION: EARLY RETIREMENT.

PubMed

Venti, Steven; Wise, David A

2015-12-01

The goal of this paper is to draw attention to the long lasting effect of education on economic outcomes. We use the relationship between education and two routes to early retirement - the receipt of Social Security Disability Insurance (DI) and the early claiming of Social Security retirement benefits - to illustrate the long-lasting influence of education. We find that for both men and women with less than a high school degree the median DI participation rate is 6.6 times the participation rate for those with a college degree or more. Similarly, men and women with less than a high school education are over 25 percentage points more likely to claim Social Security benefits early than those with a college degree or more. We focus on four critical "pathways" through which education may indirectly influence early retirement - health, employment, earnings, and the accumulation of assets. We find that for women health is the dominant pathway through which education influences DI participation. For men, the health, earnings, and wealth pathways are of roughly equal magnitude. For both men and women the principal channel through which education influences early Social Security claiming decisions is the earnings pathway. We also consider the direct effect of education that does not operate through these pathways. The direct effect of education is much greater for early claiming of Social Security benefits than for DI participation, accounting for 72 percent of the effect of education for men and 67 percent for women. For women the direct effect of education on DI participation is not statistically significant, suggesting that the total effect may be through the four pathways.

PhylArray: phylogenetic probe design algorithm for microarray.

PubMed

Militon, Cécile; Rimour, Sébastien; Missaoui, Mohieddine; Biderre, Corinne; Barra, Vincent; Hill, David; Moné, Anne; Gagne, Geneviève; Meier, Harald; Peyretaillade, Eric; Peyret, Pierre

2007-10-01

Microbial diversity is still largely unknown in most environments, such as soils. In order to get access to this microbial 'black-box', the development of powerful tools such as microarrays are necessary. However, the reliability of this approach relies on probe efficiency, in particular sensitivity, specificity and explorative power, in order to obtain an image of the microbial communities that is close to reality. We propose a new probe design algorithm that is able to select microarray probes targeting SSU rRNA at any phylogenetic level. This original approach, implemented in a program called 'PhylArray', designs a combination of degenerate and non-degenerate probes for each target taxon. Comparative experimental evaluations indicate that probes designed with PhylArray yield a higher sensitivity and specificity than those designed by conventional approaches. Applying the combined PhyArray/GoArrays strategy helps to optimize the hybridization performance of short probes. Finally, hybridizations with environmental targets have shown that the use of the PhylArray strategy can draw attention to even previously unknown bacteria.

PyBoolNet: a python package for the generation, analysis and visualization of boolean networks.

PubMed

Klarner, Hannes; Streck, Adam; Siebert, Heike

2017-03-01

The goal of this project is to provide a simple interface to working with Boolean networks. Emphasis is put on easy access to a large number of common tasks including the generation and manipulation of networks, attractor and basin computation, model checking and trap space computation, execution of established graph algorithms as well as graph drawing and layouts. P y B ool N et is a Python package for working with Boolean networks that supports simple access to model checking via N u SMV, standard graph algorithms via N etwork X and visualization via dot . In addition, state of the art attractor computation exploiting P otassco ASP is implemented. The package is function-based and uses only native Python and N etwork X data types. https://github.com/hklarner/PyBoolNet. hannes.klarner@fu-berlin.de. © The Author 2016. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com

Programming an interim report on the SETL project. Part I: generalities. Part II: the SETL language and examples of its use

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

Schwartz, J T

1975-06-01

A summary of work during the past several years on SETL, a new programming language drawing its dictions and basic concepts from the mathematical theory of sets, is presented. The work was started with the idea that a programming language modeled after an appropriate version of the formal language of mathematics might allow a programming style with some of the succinctness of mathematics, and that this might ultimately enable one to express and experiment with more complex algorithms than are now within reach. Part I discusses the general approach followed in the work. Part II focuses directly on the detailsmore » of the SETL language as it is now defined. It describes the facilities of SETL, includes short libraries of miscellaneous and of code optimization algorithms illustrating the use of SETL, and gives a detailed description of the manner in which the set-theoretic primitives provided by SETL are currently implemented. (RWR)« less

SAR-based sea traffic monitoring: a reliable approach for maritime surveillance

NASA Astrophysics Data System (ADS)

Renga, Alfredo; Graziano, Maria D.; D'Errico, M.; Moccia, A.; Cecchini, A.

2011-11-01

Maritime surveillance problems are drawing the attention of multiple institutional actors. National and international security agencies are interested in matters like maritime traffic security, maritime pollution control, monitoring migration flows and detection of illegal fishing activities. Satellite imaging is a good way to identify ships but, characterized by large swaths, it is likely that the imaged scenes contain a large number of ships, with the vast majority, hopefully, performing legal activities. Therefore, the imaging system needs a supporting system which identifies legal ships and limits the number of potential alarms to be further monitored by patrol boats or aircrafts. In this framework, spaceborne Synthetic Aperture Radar (SAR) sensors, terrestrial AIS and the ongoing satellite AIS systems can represent a great potential synergy for maritime security. Starting from this idea the paper develops different designs for an AIS constellation able to reduce the time lag between SAR image and AIS data acquisition. An analysis of SAR-based ship detection algorithms is also reported and candidate algorithms identified.

HolT Hunter: Software for Identifying and Characterizing Low-Strain DNA Holliday Triangles

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

Sherman W. B.

2012-06-05

Synthetic DNA nanostructures are most commonly held together via Holliday junctions. These junctions allow for a wide variety of different angles between the double helices they connect. Nevertheless, only constructs with a very limited selection of angles have been built, to date, because of the computational complexity of identifying structures that fit together with low strain at odd angles. I have developed an algorithm that finds over 95% of the possible solutions by breaking the problem down into two portions. First, there is a problem of how smooth rods can form triangles by lying across one another. This problem ismore » easily handled by numerical computation. Second, there is the question of how distorted DNA double helices would need to be to fit onto the rod structure. This strain is calculated directly. The algorithm has been implemented in a Mathematica 8 notebook called Holliday Triangle Hunter. A large database of solutions has been identified. Additional interface software is available to facilitate drawing and viewing models.« less

A genetic-algorithm-based remnant grey prediction model for energy demand forecasting.

PubMed

Hu, Yi-Chung

2017-01-01

Energy demand is an important economic index, and demand forecasting has played a significant role in drawing up energy development plans for cities or countries. As the use of large datasets and statistical assumptions is often impractical to forecast energy demand, the GM(1,1) model is commonly used because of its simplicity and ability to characterize an unknown system by using a limited number of data points to construct a time series model. This paper proposes a genetic-algorithm-based remnant GM(1,1) (GARGM(1,1)) with sign estimation to further improve the forecasting accuracy of the original GM(1,1) model. The distinctive feature of GARGM(1,1) is that it simultaneously optimizes the parameter specifications of the original and its residual models by using the GA. The results of experiments pertaining to a real case of energy demand in China showed that the proposed GARGM(1,1) outperforms other remnant GM(1,1) variants.

A genetic-algorithm-based remnant grey prediction model for energy demand forecasting

PubMed Central

2017-01-01

Energy demand is an important economic index, and demand forecasting has played a significant role in drawing up energy development plans for cities or countries. As the use of large datasets and statistical assumptions is often impractical to forecast energy demand, the GM(1,1) model is commonly used because of its simplicity and ability to characterize an unknown system by using a limited number of data points to construct a time series model. This paper proposes a genetic-algorithm-based remnant GM(1,1) (GARGM(1,1)) with sign estimation to further improve the forecasting accuracy of the original GM(1,1) model. The distinctive feature of GARGM(1,1) is that it simultaneously optimizes the parameter specifications of the original and its residual models by using the GA. The results of experiments pertaining to a real case of energy demand in China showed that the proposed GARGM(1,1) outperforms other remnant GM(1,1) variants. PMID:28981548

Adolescent physical activity and health: a systematic review.

PubMed

Hallal, Pedro C; Victora, Cesar G; Azevedo, Mario R; Wells, Jonathan C K

2006-01-01

Physical activity in adolescence may contribute to the development of healthy adult lifestyles, helping reduce chronic disease incidence. However, definition of the optimal amount of physical activity in adolescence requires addressing a number of scientific challenges. This article reviews the evidence on short- and long-term health effects of adolescent physical activity. Systematic reviews of the literature were undertaken using a reference period between 2000 and 2004, based primarily on the MEDLINE/PubMed database. Relevant studies were identified by examination of titles, abstracts and full papers, according to inclusion criteria defined a priori. A conceptual framework is proposed to outline how adolescent physical activity may contribute to adult health, including the following pathways: (i) pathway A--tracking of physical activity from adolescence to adulthood; (ii) pathway B--direct influence of adolescent physical activity on adult morbidity; (iii) pathway C--role of physical activity in treating adolescent morbidity; and (iv) pathway D - short-term benefits of physical activity in adolescence on health. The literature reviews showed consistent evidence supporting pathway 'A', although the magnitude of the association appears to be moderate. Thus, there is an indirect effect on all health benefits resulting from adult physical activity. Regarding pathway 'B', adolescent physical activity seems to provide long-term benefits on bone health, breast cancer and sedentary behaviours. In terms of pathway 'C', water physical activities in adolescence are effective in the treatment of asthma, and exercise is recommended in the treatment of cystic fibrosis. Self-esteem is also positively affected by adolescent physical activity. Regarding pathway 'D', adolescent physical activity provides short-term benefits; the strongest evidence refers to bone and mental health. Appreciation of different mechanisms through which adolescent physical activity may influence adult health is essential for drawing recommendations; however, the amount of exercise needed for achieving different benefits may vary. Physical activity promotion must start in early life; although the 'how much' remains unknown and needs further research, the lifelong benefits of adolescent physical activity on adult health are unequivocal.

Exploring metabolic pathway disruption in the subchronic phencyclidine model of schizophrenia with the Generalized Singular Value Decomposition

PubMed Central

2011-01-01

Background The quantification of experimentally-induced alterations in biological pathways remains a major challenge in systems biology. One example of this is the quantitative characterization of alterations in defined, established metabolic pathways from complex metabolomic data. At present, the disruption of a given metabolic pathway is inferred from metabolomic data by observing an alteration in the level of one or more individual metabolites present within that pathway. Not only is this approach open to subjectivity, as metabolites participate in multiple pathways, but it also ignores useful information available through the pairwise correlations between metabolites. This extra information may be incorporated using a higher-level approach that looks for alterations between a pair of correlation networks. In this way experimentally-induced alterations in metabolic pathways can be quantitatively defined by characterizing group differences in metabolite clustering. Taking this approach increases the objectivity of interpreting alterations in metabolic pathways from metabolomic data. Results We present and justify a new technique for comparing pairs of networks--in our case these networks are based on the same set of nodes and there are two distinct types of weighted edges. The algorithm is based on the Generalized Singular Value Decomposition (GSVD), which may be regarded as an extension of Principle Components Analysis to the case of two data sets. We show how the GSVD can be interpreted as a technique for reordering the two networks in order to reveal clusters that are exclusive to only one. Here we apply this algorithm to a new set of metabolomic data from the prefrontal cortex (PFC) of a translational model relevant to schizophrenia, rats treated subchronically with the N-methyl-D-Aspartic acid (NMDA) receptor antagonist phencyclidine (PCP). This provides us with a means to quantify which predefined metabolic pathways (Kyoto Encyclopedia of Genes and Genomes (KEGG) metabolite pathway database) were altered in the PFC of PCP-treated rats. Several significant changes were discovered, notably: 1) neuroactive ligands active at glutamate and GABA receptors are disrupted in the PFC of PCP-treated animals, 2) glutamate dysfunction in these animals was not limited to compromised glutamatergic neurotransmission but also involves the disruption of metabolic pathways linked to glutamate; and 3) a specific series of purine reactions Xanthine ← Hypoxyanthine ↔ Inosine ← IMP → adenylosuccinate is also disrupted in the PFC of PCP-treated animals. Conclusions Network reordering via the GSVD provides a means to discover statistically validated differences in clustering between a pair of networks. In practice this analytical approach, when applied to metabolomic data, allows us to quantify the alterations in metabolic pathways between two experimental groups. With this new computational technique we identified metabolic pathway alterations that are consistent with known results. Furthermore, we discovered disruption in a novel series of purine reactions that may contribute to the PFC dysfunction and cognitive deficits seen in schizophrenia. PMID:21575198

SutraPlot, a graphical post-processor for SUTRA, a model for ground-water flow with solute or energy transport

USGS Publications Warehouse

Souza, W.R.

1999-01-01

This report documents a graphical display post-processor (SutraPlot) for the U.S. Geological Survey Saturated-Unsaturated flow and solute or energy TRAnsport simulation model SUTRA, Version 2D3D.1. This version of SutraPlot is an upgrade to SutraPlot for the 2D-only SUTRA model (Souza, 1987). It has been modified to add 3D functionality, a graphical user interface (GUI), and enhanced graphic output options. Graphical options for 2D SUTRA (2-dimension) simulations include: drawing the 2D finite-element mesh, mesh boundary, and velocity vectors; plots of contours for pressure, saturation, concentration, and temperature within the model region; 2D finite-element based gridding and interpolation; and 2D gridded data export files. Graphical options for 3D SUTRA (3-dimension) simulations include: drawing the 3D finite-element mesh; plots of contours for pressure, saturation, concentration, and temperature in 2D sections of the 3D model region; 3D finite-element based gridding and interpolation; drawing selected regions of velocity vectors (projected on principal coordinate planes); and 3D gridded data export files. Installation instructions and a description of all graphic options are presented. A sample SUTRA problem is described and three step-by-step SutraPlot applications are provided. In addition, the methodology and numerical algorithms for the 2D and 3D finite-element based gridding and interpolation, developed for SutraPlot, are described. 1

Evaluating Recommendation Systems

NASA Astrophysics Data System (ADS)

Shani, Guy; Gunawardana, Asela

Recommender systems are now popular both commercially and in the research community, where many approaches have been suggested for providing recommendations. In many cases a system designer that wishes to employ a recommendation system must choose between a set of candidate approaches. A first step towards selecting an appropriate algorithm is to decide which properties of the application to focus upon when making this choice. Indeed, recommendation systems have a variety of properties that may affect user experience, such as accuracy, robustness, scalability, and so forth. In this paper we discuss how to compare recommenders based on a set of properties that are relevant for the application. We focus on comparative studies, where a few algorithms are compared using some evaluation metric, rather than absolute benchmarking of algorithms. We describe experimental settings appropriate for making choices between algorithms. We review three types of experiments, starting with an offline setting, where recommendation approaches are compared without user interaction, then reviewing user studies, where a small group of subjects experiment with the system and report on the experience, and finally describe large scale online experiments, where real user populations interact with the system. In each of these cases we describe types of questions that can be answered, and suggest protocols for experimentation. We also discuss how to draw trustworthy conclusions from the conducted experiments. We then review a large set of properties, and explain how to evaluate systems given relevant properties. We also survey a large set of evaluation metrics in the context of the properties that they evaluate.

Optimizing the 3D-reconstruction technique for serial block-face scanning electron microscopy.

PubMed

Wernitznig, Stefan; Sele, Mariella; Urschler, Martin; Zankel, Armin; Pölt, Peter; Rind, F Claire; Leitinger, Gerd

2016-05-01

Elucidating the anatomy of neuronal circuits and localizing the synaptic connections between neurons, can give us important insights in how the neuronal circuits work. We are using serial block-face scanning electron microscopy (SBEM) to investigate the anatomy of a collision detection circuit including the Lobula Giant Movement Detector (LGMD) neuron in the locust, Locusta migratoria. For this, thousands of serial electron micrographs are produced that allow us to trace the neuronal branching pattern. The reconstruction of neurons was previously done manually by drawing cell outlines of each cell in each image separately. This approach was very time consuming and troublesome. To make the process more efficient a new interactive software was developed. It uses the contrast between the neuron under investigation and its surrounding for semi-automatic segmentation. For segmentation the user sets starting regions manually and the algorithm automatically selects a volume within the neuron until the edges corresponding to the neuronal outline are reached. Internally the algorithm optimizes a 3D active contour segmentation model formulated as a cost function taking the SEM image edges into account. This reduced the reconstruction time, while staying close to the manual reference segmentation result. Our algorithm is easy to use for a fast segmentation process, unlike previous methods it does not require image training nor an extended computing capacity. Our semi-automatic segmentation algorithm led to a dramatic reduction in processing time for the 3D-reconstruction of identified neurons. Copyright © 2016 Elsevier B.V. All rights reserved.

Toward a Principled Sampling Theory for Quasi-Orders

PubMed Central

Ünlü, Ali; Schrepp, Martin

2016-01-01

Quasi-orders, that is, reflexive and transitive binary relations, have numerous applications. In educational theories, the dependencies of mastery among the problems of a test can be modeled by quasi-orders. Methods such as item tree or Boolean analysis that mine for quasi-orders in empirical data are sensitive to the underlying quasi-order structure. These data mining techniques have to be compared based on extensive simulation studies, with unbiased samples of randomly generated quasi-orders at their basis. In this paper, we develop techniques that can provide the required quasi-order samples. We introduce a discrete doubly inductive procedure for incrementally constructing the set of all quasi-orders on a finite item set. A randomization of this deterministic procedure allows us to generate representative samples of random quasi-orders. With an outer level inductive algorithm, we consider the uniform random extensions of the trace quasi-orders to higher dimension. This is combined with an inner level inductive algorithm to correct the extensions that violate the transitivity property. The inner level correction step entails sampling biases. We propose three algorithms for bias correction and investigate them in simulation. It is evident that, on even up to 50 items, the new algorithms create close to representative quasi-order samples within acceptable computing time. Hence, the principled approach is a significant improvement to existing methods that are used to draw quasi-orders uniformly at random but cannot cope with reasonably large item sets. PMID:27965601

Toward a Principled Sampling Theory for Quasi-Orders.

PubMed

Ünlü, Ali; Schrepp, Martin

2016-01-01

Quasi-orders, that is, reflexive and transitive binary relations, have numerous applications. In educational theories, the dependencies of mastery among the problems of a test can be modeled by quasi-orders. Methods such as item tree or Boolean analysis that mine for quasi-orders in empirical data are sensitive to the underlying quasi-order structure. These data mining techniques have to be compared based on extensive simulation studies, with unbiased samples of randomly generated quasi-orders at their basis. In this paper, we develop techniques that can provide the required quasi-order samples. We introduce a discrete doubly inductive procedure for incrementally constructing the set of all quasi-orders on a finite item set. A randomization of this deterministic procedure allows us to generate representative samples of random quasi-orders. With an outer level inductive algorithm, we consider the uniform random extensions of the trace quasi-orders to higher dimension. This is combined with an inner level inductive algorithm to correct the extensions that violate the transitivity property. The inner level correction step entails sampling biases. We propose three algorithms for bias correction and investigate them in simulation. It is evident that, on even up to 50 items, the new algorithms create close to representative quasi-order samples within acceptable computing time. Hence, the principled approach is a significant improvement to existing methods that are used to draw quasi-orders uniformly at random but cannot cope with reasonably large item sets.

Optimal Alignment of Structures for Finite and Periodic Systems.

PubMed

Griffiths, Matthew; Niblett, Samuel P; Wales, David J

2017-10-10

Finding the optimal alignment between two structures is important for identifying the minimum root-mean-square distance (RMSD) between them and as a starting point for calculating pathways. Most current algorithms for aligning structures are stochastic, scale exponentially with the size of structure, and the performance can be unreliable. We present two complementary methods for aligning structures corresponding to isolated clusters of atoms and to condensed matter described by a periodic cubic supercell. The first method (Go-PERMDIST), a branch and bound algorithm, locates the global minimum RMSD deterministically in polynomial time. The run time increases for larger RMSDs. The second method (FASTOVERLAP) is a heuristic algorithm that aligns structures by finding the global maximum kernel correlation between them using fast Fourier transforms (FFTs) and fast SO(3) transforms (SOFTs). For periodic systems, FASTOVERLAP scales with the square of the number of identical atoms in the system, reliably finds the best alignment between structures that are not too distant, and shows significantly better performance than existing algorithms. The expected run time for Go-PERMDIST is longer than FASTOVERLAP for periodic systems. For finite clusters, the FASTOVERLAP algorithm is competitive with existing algorithms. The expected run time for Go-PERMDIST to find the global RMSD between two structures deterministically is generally longer than for existing stochastic algorithms. However, with an earlier exit condition, Go-PERMDIST exhibits similar or better performance.

An algorithm for quantitatively estimating non-occupational pesticide exposure intensity for spouses in the Agricultural Health Study

EPA Science Inventory

Purpose: Women living or working on farms may be exposed to pesticides from direct occupational use of agricultural pesticides and from non-occupational pathways, such as take-home exposure from skin, clothes and shoes of farmworkers, drift from nearby fields, and pest treatments...

Detection algorithm for cracks on the surface of tomatoes using Multispectral Vis/NIR Reflectance Imagery

USDA-ARS?s Scientific Manuscript database

Tomatoes, an important agricultural product in fresh-cut markets, are sometimes a source of foodborne illness, mainly Salmonella spp. Growth cracks on tomatoes can be a pathway for bacteria, so its detection prior to consumption is important for public health. In this study, multispectral Visible/Ne...

TU-CD-BRB-10: 18F-FDG PET Image-Derived Tumor Features Highlight Altered Pathways Identified by Trancriptomic Analysis in Head and Neck Cancer

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

Tixier, F; INSERM UMR1101 LaTIM, Brest; Cheze-Le-Rest, C

2015-06-15

Purpose: Several quantitative features can be extracted from 18F-FDG PET images, such as standardized uptake values (SUVs), metabolic tumor volume (MTV), shape characterization (SC) or intra-tumor radiotracer heterogeneity quantification (HQ). Some of these features calculated from baseline 18F-FDG PET images have shown a prognostic and predictive clinical value. It has been hypothesized that these features highlight underlying tumor patho-physiological processes at smaller scales. The objective of this study was to investigate the ability of recovering alterations of signaling pathways from FDG PET image-derived features. Methods: 52 patients were prospectively recruited from two medical centers (Brest and Poitiers). All patients underwentmore » an FDG PET scan for staging and biopsies of both healthy and primary tumor tissues. Biopsies went through a transcriptomic analysis performed in four spates on 4×44k chips (Agilent™). Primary tumors were delineated in the PET images using the Fuzzy Locally Adaptive Bayesian algorithm and characterized using 10 features including SUVs, SC and HQ. A module network algorithm followed by functional annotation was exploited in order to link PET features with signaling pathways alterations. Results: Several PET-derived features were found to discriminate differentially expressed genes between tumor and healthy tissue (fold-change >2, p<0.01) into 30 co-regulated groups (p<0.05). Functional annotations applied to these groups of genes highlighted associations with well-known pathways involved in cancer processes, such as cell proliferation and apoptosis, as well as with more specific ones such as unsaturated fatty acids. Conclusion: Quantitative features extracted from baseline 18F-FDG PET images usually exploited only for diagnosis and staging, were identified in this work as being related to specific altered pathways and may show promise as tools for personalizing treatment decisions.« less

Reconstruction of Tissue-Specific Metabolic Networks Using CORDA

PubMed Central

Schultz, André; Qutub, Amina A.

2016-01-01

Human metabolism involves thousands of reactions and metabolites. To interpret this complexity, computational modeling becomes an essential experimental tool. One of the most popular techniques to study human metabolism as a whole is genome scale modeling. A key challenge to applying genome scale modeling is identifying critical metabolic reactions across diverse human tissues. Here we introduce a novel algorithm called Cost Optimization Reaction Dependency Assessment (CORDA) to build genome scale models in a tissue-specific manner. CORDA performs more efficiently computationally, shows better agreement to experimental data, and displays better model functionality and capacity when compared to previous algorithms. CORDA also returns reaction associations that can greatly assist in any manual curation to be performed following the automated reconstruction process. Using CORDA, we developed a library of 76 healthy and 20 cancer tissue-specific reconstructions. These reconstructions identified which metabolic pathways are shared across diverse human tissues. Moreover, we identified changes in reactions and pathways that are differentially included and present different capacity profiles in cancer compared to healthy tissues, including up-regulation of folate metabolism, the down-regulation of thiamine metabolism, and tight regulation of oxidative phosphorylation. PMID:26942765

Systems approach for the selection of micro-RNAs as therapeutic biomarkers of anti-EGFR monoclonal antibody treatment in colorectal cancer

NASA Astrophysics Data System (ADS)

Deyati, Avisek; Bagewadi, Shweta; Senger, Philipp; Hofmann-Apitius, Martin; Novac, Natalia

2015-01-01

miRNA plays an important role in tumourgenesis by regulating expression of oncogenes and tumour suppressors. Thus affects cell proliferation and differentiation, apoptosis, invasion and angiogenesis. miRNAs are potential biomarkers for diagnosis, prognosis and therapies of different forms of cancer. However, relationship between response of cancer patients towards targeted therapy and the resulting modifications of the miRNA transcriptome in the context of pathway regulation is poorly understood. With ever-increasing pathways and miRNA-mRNA interaction databases, freely available mRNA and miRNA expression data in multiple cancer therapy have produced an unprecedented opportunity to decipher the role of miRNAs in early prediction of therapeutic efficacy in diseases. Efficient translation of -omics data and accumulated knowledge to clinical decision-making are of paramount scientific and public health interest. Well-structured translational algorithms are needed to bridge the gap from databases to decisions. Herein, we present a novel SMARTmiR algorithm to prospectively predict the role of miRNA as therapeutic biomarker for an anti-EGFR monoclonal antibody i.e. cetuximab treatment in colorectal cancer.

Locating structures and evolution pathways of reconstructed rutile TiO2(011) using genetic algorithm aided density functional theory calculations.

PubMed

Ding, Pan; Gong, Xue-Qing

2016-05-01

Titanium dioxide (TiO2) is an important metal oxide that has been used in many different applications. TiO2 has also been widely employed as a model system to study basic processes and reactions in surface chemistry and heterogeneous catalysis. In this work, we investigated the (011) surface of rutile TiO2 by focusing on its reconstruction. Density functional theory calculations aided by a genetic algorithm based optimization scheme were performed to extensively sample the potential energy surfaces of reconstructed rutile TiO2 structures that obey (2 × 1) periodicity. A lot of stable surface configurations were located, including the global-minimum configuration that was proposed previously. The wide variety of surface structures determined through the calculations performed in this work provide insight into the relationship between the atomic configuration of a surface and its stability. More importantly, several analytical schemes were proposed and tested to gauge the differences and similarities among various surface structures, aiding the construction of the complete pathway for the reconstruction process.

Advances in Landslide Hazard Forecasting: Evaluation of Global and Regional Modeling Approach

NASA Technical Reports Server (NTRS)

Kirschbaum, Dalia B.; Adler, Robert; Hone, Yang; Kumar, Sujay; Peters-Lidard, Christa; Lerner-Lam, Arthur

2010-01-01

A prototype global satellite-based landslide hazard algorithm has been developed to identify areas that exhibit a high potential for landslide activity by combining a calculation of landslide susceptibility with satellite-derived rainfall estimates. A recent evaluation of this algorithm framework found that while this tool represents an important first step in larger-scale landslide forecasting efforts, it requires several modifications before it can be fully realized as an operational tool. The evaluation finds that the landslide forecasting may be more feasible at a regional scale. This study draws upon a prior work's recommendations to develop a new approach for considering landslide susceptibility and forecasting at the regional scale. This case study uses a database of landslides triggered by Hurricane Mitch in 1998 over four countries in Central America: Guatemala, Honduras, EI Salvador and Nicaragua. A regional susceptibility map is calculated from satellite and surface datasets using a statistical methodology. The susceptibility map is tested with a regional rainfall intensity-duration triggering relationship and results are compared to global algorithm framework for the Hurricane Mitch event. The statistical results suggest that this regional investigation provides one plausible way to approach some of the data and resolution issues identified in the global assessment, providing more realistic landslide forecasts for this case study. Evaluation of landslide hazards for this extreme event helps to identify several potential improvements of the algorithm framework, but also highlights several remaining challenges for the algorithm assessment, transferability and performance accuracy. Evaluation challenges include representation errors from comparing susceptibility maps of different spatial resolutions, biases in event-based landslide inventory data, and limited nonlandslide event data for more comprehensive evaluation. Additional factors that may improve algorithm performance accuracy include incorporating additional triggering factors such as tectonic activity, anthropogenic impacts and soil moisture into the algorithm calculation. Despite these limitations, the methodology presented in this regional evaluation is both straightforward to calculate and easy to interpret, making results transferable between regions and allowing findings to be placed within an inter-comparison framework. The regional algorithm scenario represents an important step in advancing regional and global-scale landslide hazard assessment and forecasting.

A clinical pathway for the postoperative management of hypocalcemia after pediatric thyroidectomy reduces blood draws.

PubMed

Patel, Neha A; Bly, Randall A; Adams, Seth; Carlin, Kristen; Parikh, Sanjay R; Dahl, John P; Manning, Scott

2018-02-01

Postoperative calcium management is challenging following pediatric thyroidectomy given potential limitations in self-reporting symptoms and compliance with phlebotomy. A protocol was created at our tertiary children's institution utilizing intraoperative parathyroid hormone (PTH) levels to guide electrolyte management during hospitalization. The objective of this study was to determine the effect of a new thyroidectomy postoperative management protocol on two primary outcomes: (1) the number of postoperative calcium blood draws and (2) the length of hospital stay. Institutional review board approved retrospective study (2010-2016). Consecutive pediatric total thyroidectomy and completion thyroidectomy ± neck dissection cases from 1/1/2010 through 8/5/2016 at a single tertiary children's institution were retrospectively reviewed before and after initiation of a new management protocol. All cases after 2/1/2014 comprised the experimental group (post-protocol implementation). The pre-protocol control group consisted of cases prior to 2/1/2014. Multivariable linear and Poisson regression models were used to compare the control and experimental groups for outcome measure of number of calcium lab draws and hospital length of stay. 53 patients were included (n = 23, control group; n = 30 experimental group). The median age was 15 years. 41 patients (77.4%) were female. Postoperative calcium draws decreased from a mean of 5.2 to 3.6 per day post-protocol implementation (Rate Ratio = 0.70, p < .001), adjusting for covariates. The mean number of total inpatient calcium draws before protocol initiation was 13.3 (±13.20) compared to 7.2 (±4.25) in the post-protocol implementation group. Length of stay was 2.1 days in the control group and 1.8 days post-protocol implementation (p = .29). Patients who underwent concurrent neck dissection had a longer mean length of stay of 2.32 days compared to 1.66 days in those patients who did not undergo a neck dissection (p = .02). Hypocalcemia was also associated with a longer mean length of stay of 2.41 days compared to 1.60 days in patients who did not develop hypocalcemia (p < .01). The number of calcium blood draws was significantly reduced after introduction of a standardized protocol based on intraoperative PTH levels. The hospital length of stay did not change. Adoption of a standardized postoperative protocol based on intraoperative PTH levels may reduce the number of blood draws in children undergoing thyroidectomy. Copyright © 2017 Elsevier B.V. All rights reserved.

Automated retinofugal visual pathway reconstruction with multi-shell HARDI and FOD-based analysis.

PubMed

Kammen, Alexandra; Law, Meng; Tjan, Bosco S; Toga, Arthur W; Shi, Yonggang

2016-01-15

Diffusion MRI tractography provides a non-invasive modality to examine the human retinofugal projection, which consists of the optic nerves, optic chiasm, optic tracts, the lateral geniculate nuclei (LGN) and the optic radiations. However, the pathway has several anatomic features that make it particularly challenging to study with tractography, including its location near blood vessels and bone-air interface at the base of the cerebrum, crossing fibers at the chiasm, somewhat-tortuous course around the temporal horn via Meyer's Loop, and multiple closely neighboring fiber bundles. To date, these unique complexities of the visual pathway have impeded the development of a robust and automated reconstruction method using tractography. To overcome these challenges, we develop a novel, fully automated system to reconstruct the retinofugal visual pathway from high-resolution diffusion imaging data. Using multi-shell, high angular resolution diffusion imaging (HARDI) data, we reconstruct precise fiber orientation distributions (FODs) with high order spherical harmonics (SPHARM) to resolve fiber crossings, which allows the tractography algorithm to successfully navigate the complicated anatomy surrounding the retinofugal pathway. We also develop automated algorithms for the identification of ROIs used for fiber bundle reconstruction. In particular, we develop a novel approach to extract the LGN region of interest (ROI) based on intrinsic shape analysis of a fiber bundle computed from a seed region at the optic chiasm to a target at the primary visual cortex. By combining automatically identified ROIs and FOD-based tractography, we obtain a fully automated system to compute the main components of the retinofugal pathway, including the optic tract and the optic radiation. We apply our method to the multi-shell HARDI data of 215 subjects from the Human Connectome Project (HCP). Through comparisons with post-mortem dissection measurements, we demonstrate the retinotopic organization of the optic radiation including a successful reconstruction of Meyer's loop. Then, using the reconstructed optic radiation bundle from the HCP cohort, we construct a probabilistic atlas and demonstrate its consistency with a post-mortem atlas. Finally, we generate a shape-based representation of the optic radiation for morphometry analysis. Copyright © 2015 Elsevier Inc. All rights reserved.

Prioritizing biological pathways by recognizing context in time-series gene expression data.

PubMed

Lee, Jusang; Jo, Kyuri; Lee, Sunwon; Kang, Jaewoo; Kim, Sun

2016-12-23

The primary goal of pathway analysis using transcriptome data is to find significantly perturbed pathways. However, pathway analysis is not always successful in identifying pathways that are truly relevant to the context under study. A major reason for this difficulty is that a single gene is involved in multiple pathways. In the KEGG pathway database, there are 146 genes, each of which is involved in more than 20 pathways. Thus activation of even a single gene will result in activation of many pathways. This complex relationship often makes the pathway analysis very difficult. While we need much more powerful pathway analysis methods, a readily available alternative way is to incorporate the literature information. In this study, we propose a novel approach for prioritizing pathways by combining results from both pathway analysis tools and literature information. The basic idea is as follows. Whenever there are enough articles that provide evidence on which pathways are relevant to the context, we can be assured that the pathways are indeed related to the context, which is termed as relevance in this paper. However, if there are few or no articles reported, then we should rely on the results from the pathway analysis tools, which is termed as significance in this paper. We realized this concept as an algorithm by introducing Context Score and Impact Score and then combining the two into a single score. Our method ranked truly relevant pathways significantly higher than existing pathway analysis tools in experiments with two data sets. Our novel framework was implemented as ContextTRAP by utilizing two existing tools, TRAP and BEST. ContextTRAP will be a useful tool for the pathway based analysis of gene expression data since the user can specify the context of the biological experiment in a set of keywords. The web version of ContextTRAP is available at http://biohealth.snu.ac.kr/software/contextTRAP .

Investigating power capping toward energy-efficient scientific applications: Investigating Power Capping toward Energy-Efficient Scientific Applications

DOE PAGES

Haidar, Azzam; Jagode, Heike; Vaccaro, Phil; ...

2018-03-22

The emergence of power efficiency as a primary constraint in processor and system design poses new challenges concerning power and energy awareness for numerical libraries and scientific applications. Power consumption also plays a major role in the design of data centers, which may house petascale or exascale-level computing systems. At these extreme scales, understanding and improving the energy efficiency of numerical libraries and their related applications becomes a crucial part of the successful implementation and operation of the computing system. In this paper, we study and investigate the practice of controlling a compute system's power usage, and we explore howmore » different power caps affect the performance of numerical algorithms with different computational intensities. Further, we determine the impact, in terms of performance and energy usage, that these caps have on a system running scientific applications. This analysis will enable us to characterize the types of algorithms that benefit most from these power management schemes. Our experiments are performed using a set of representative kernels and several popular scientific benchmarks. Lastly, we quantify a number of power and performance measurements and draw observations and conclusions that can be viewed as a roadmap to achieving energy efficiency in the design and execution of scientific algorithms.« less

Investigating power capping toward energy-efficient scientific applications: Investigating Power Capping toward Energy-Efficient Scientific Applications

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

Haidar, Azzam; Jagode, Heike; Vaccaro, Phil

The emergence of power efficiency as a primary constraint in processor and system design poses new challenges concerning power and energy awareness for numerical libraries and scientific applications. Power consumption also plays a major role in the design of data centers, which may house petascale or exascale-level computing systems. At these extreme scales, understanding and improving the energy efficiency of numerical libraries and their related applications becomes a crucial part of the successful implementation and operation of the computing system. In this paper, we study and investigate the practice of controlling a compute system's power usage, and we explore howmore » different power caps affect the performance of numerical algorithms with different computational intensities. Further, we determine the impact, in terms of performance and energy usage, that these caps have on a system running scientific applications. This analysis will enable us to characterize the types of algorithms that benefit most from these power management schemes. Our experiments are performed using a set of representative kernels and several popular scientific benchmarks. Lastly, we quantify a number of power and performance measurements and draw observations and conclusions that can be viewed as a roadmap to achieving energy efficiency in the design and execution of scientific algorithms.« less

Salient Point Detection in Protrusion Parts of 3D Object Robust to Isometric Variations

NASA Astrophysics Data System (ADS)

Mirloo, Mahsa; Ebrahimnezhad, Hosein

2018-03-01

In this paper, a novel method is proposed to detect 3D object salient points robust to isometric variations and stable against scaling and noise. Salient points can be used as the representative points from object protrusion parts in order to improve the object matching and retrieval algorithms. The proposed algorithm is started by determining the first salient point of the model based on the average geodesic distance of several random points. Then, according to the previous salient point, a new point is added to this set of points in each iteration. By adding every salient point, decision function is updated. Hence, a condition is created for selecting the next point in which the iterative point is not extracted from the same protrusion part so that drawing out of a representative point from every protrusion part is guaranteed. This method is stable against model variations with isometric transformations, scaling, and noise with different levels of strength due to using a feature robust to isometric variations and considering the relation between the salient points. In addition, the number of points used in averaging process is decreased in this method, which leads to lower computational complexity in comparison with the other salient point detection algorithms.

The BRAMS Zoo, a citizen science project

NASA Astrophysics Data System (ADS)

Calders, S.

2015-01-01

Currently, the BRAMS network comprises around 30 receiving stations, and each station collects 24 hours of data per day. With such a large number of raw data, automatic detection of meteor echoes is mandatory. Several algorithms have been developed, using different techniques. (They are discussed in the Proceedings of IMC 2014.) This task is complicated because of the presence of parasitic signals (mostly airplane echoes) on one hand and the fact that some meteor echoes (overdense) exhibit complex shapes that are hard to recognize on the other hand. Currently, none of the algorithms can perfectly mimic the human eye which stays the best detector. Therefore we plan to collaborate with Citizen Science in order to create a "BRAMS zoo". The idea is to ask their very large community of users to draw boxes around meteor echoes in spectrograms. The results will be used to assess the accuracy of the automatic detection algorithms on a large data set. We will focus on a few selected meteor showers which are always more fascinating for the large public than the sporadic background. Moreover, during meteor showers, many more complex overdense echoes are observed for which current automatic detection methods might fail. Finally, the dataset of manually detected meteors can also be useful e.g. for IMCCE to study the dynamic evolution of cometary dust.

A Compound Algorithm for Maximum Power Point Tracking Used in Laser Power Beaming

NASA Astrophysics Data System (ADS)

Chen, Cheng; Liu, Qiang; Gao, Shan; Teng, Yun; Cheng, Lin; Yu, Chengtao; Peng, Kai

2018-03-01

With the high voltage intelligent substation developing in a pretty high speed, more and more artificial intelligent techniques have been incorporated into the power devices to meet the automation needs. For the sake of the line maintenance staff’s safety, the high voltage isolating switch draws great attention among the most important power devices because of its capability of connecting and disconnecting the high voltage circuit. However, due to the very high level voltage of the high voltage isolating switch’s working environment, the power supply system of the surveillance devices could suffer from great electromagnetic interference. Laser power beaming exhibits its merits in such situation because it can provide steady power from a distance despite the day or the night. Then the energy conversion efficiency arises as a new concern. To make as much use of the laser power as possible, our work mainly focuses on extracting maximum power from the photovoltaic (PV) panel. In this paper, we proposed a neural network based algorithm which relates both the intrinsic and the extrinsic features of the PV panel to the proportion of the voltage at the maximum power point (MPP) to the open circuit voltage of the PV panel. Simulations and experiments were carried out to verify the validness of our algorithm.

Automatic Multi-sensor Data Quality Checking and Event Detection for Environmental Sensing

NASA Astrophysics Data System (ADS)

LIU, Q.; Zhang, Y.; Zhao, Y.; Gao, D.; Gallaher, D. W.; Lv, Q.; Shang, L.

2017-12-01

With the advances in sensing technologies, large-scale environmental sensing infrastructures are pervasively deployed to continuously collect data for various research and application fields, such as air quality study and weather condition monitoring. In such infrastructures, many sensor nodes are distributed in a specific area and each individual sensor node is capable of measuring several parameters (e.g., humidity, temperature, and pressure), providing massive data for natural event detection and analysis. However, due to the dynamics of the ambient environment, sensor data can be contaminated by errors or noise. Thus, data quality is still a primary concern for scientists before drawing any reliable scientific conclusions. To help researchers identify potential data quality issues and detect meaningful natural events, this work proposes a novel algorithm to automatically identify and rank anomalous time windows from multiple sensor data streams. More specifically, (1) the algorithm adaptively learns the characteristics of normal evolving time series and (2) models the spatial-temporal relationship among multiple sensor nodes to infer the anomaly likelihood of a time series window for a particular parameter in a sensor node. Case studies using different data sets are presented and the experimental results demonstrate that the proposed algorithm can effectively identify anomalous time windows, which may resulted from data quality issues and natural events.

Automated Detection of Epileptic Biomarkers in Resting-State Interictal MEG Data

PubMed Central

Soriano, Miguel C.; Niso, Guiomar; Clements, Jillian; Ortín, Silvia; Carrasco, Sira; Gudín, María; Mirasso, Claudio R.; Pereda, Ernesto

2017-01-01

Certain differences between brain networks of healthy and epilectic subjects have been reported even during the interictal activity, in which no epileptic seizures occur. Here, magnetoencephalography (MEG) data recorded in the resting state is used to discriminate between healthy subjects and patients with either idiopathic generalized epilepsy or frontal focal epilepsy. Signal features extracted from interictal periods without any epileptiform activity are used to train a machine learning algorithm to draw a diagnosis. This is potentially relevant to patients without frequent or easily detectable spikes. To analyze the data, we use an up-to-date machine learning algorithm and explore the benefits of including different features obtained from the MEG data as inputs to the algorithm. We find that the relative power spectral density of the MEG time-series is sufficient to distinguish between healthy and epileptic subjects with a high prediction accuracy. We also find that a combination of features such as the phase-locked value and the relative power spectral density allow to discriminate generalized and focal epilepsy, when these features are calculated over a filtered version of the signals in certain frequency bands. Machine learning algorithms are currently being applied to the analysis and classification of brain signals. It is, however, less evident to identify the proper features of these signals that are prone to be used in such machine learning algorithms. Here, we evaluate the influence of the input feature selection on a clinical scenario to distinguish between healthy and epileptic subjects. Our results indicate that such distinction is possible with a high accuracy (86%), allowing the discrimination between idiopathic generalized and frontal focal epilepsy types. PMID:28713260

Three-dimensional representations of salt-dome margins at four active strategic petroleum reserve sites.

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

Rautman, Christopher Arthur; Stein, Joshua S.

2003-01-01

Existing paper-based site characterization models of salt domes at the four active U.S. Strategic Petroleum Reserve sites have been converted to digital format and visualized using modern computer software. The four sites are the Bayou Choctaw dome in Iberville Parish, Louisiana; the Big Hill dome in Jefferson County, Texas; the Bryan Mound dome in Brazoria County, Texas; and the West Hackberry dome in Cameron Parish, Louisiana. A new modeling algorithm has been developed to overcome limitations of many standard geological modeling software packages in order to deal with structurally overhanging salt margins that are typical of many salt domes. Thismore » algorithm, and the implementing computer program, make use of the existing interpretive modeling conducted manually using professional geological judgement and presented in two dimensions in the original site characterization reports as structure contour maps on the top of salt. The algorithm makes use of concepts of finite-element meshes of general engineering usage. Although the specific implementation of the algorithm described in this report and the resulting output files are tailored to the modeling and visualization software used to construct the figures contained herein, the algorithm itself is generic and other implementations and output formats are possible. The graphical visualizations of the salt domes at the four Strategic Petroleum Reserve sites are believed to be major improvements over the previously available two-dimensional representations of the domes via conventional geologic drawings (cross sections and contour maps). Additionally, the numerical mesh files produced by this modeling activity are available for import into and display by other software routines. The mesh data are not explicitly tabulated in this report; however an electronic version in simple ASCII format is included on a PC-based compact disk.« less

Simulation Modeling to Compare High-Throughput, Low-Iteration Optimization Strategies for Metabolic Engineering

PubMed Central

Heinsch, Stephen C.; Das, Siba R.; Smanski, Michael J.

2018-01-01

Increasing the final titer of a multi-gene metabolic pathway can be viewed as a multivariate optimization problem. While numerous multivariate optimization algorithms exist, few are specifically designed to accommodate the constraints posed by genetic engineering workflows. We present a strategy for optimizing expression levels across an arbitrary number of genes that requires few design-build-test iterations. We compare the performance of several optimization algorithms on a series of simulated expression landscapes. We show that optimal experimental design parameters depend on the degree of landscape ruggedness. This work provides a theoretical framework for designing and executing numerical optimization on multi-gene systems. PMID:29535690

Cost unit accounting based on a clinical pathway: a practical tool for DRG implementation.

PubMed

Feyrer, R; Rösch, J; Weyand, M; Kunzmann, U

2005-10-01

Setting up a reliable cost unit accounting system in a hospital is a fundamental necessity for economic survival, given the current general conditions in the healthcare system. Definition of a suitable cost unit is a crucial factor for success. We present here the development and use of a clinical pathway as a cost unit as an alternative to the DRG. Elective coronary artery bypass grafting was selected as an example. Development of the clinical pathway was conducted according to a modular concept that mirrored all the treatment processes across various levels and modules. Using service records and analyses the process algorithms of the clinical pathway were developed and visualized with CorelTM iGrafix Process 2003. A detailed process cost record constituted the basis of the pathway costing, in which financial evaluation of the treatment processes was performed. The result of this study was a structured clinical pathway for coronary artery bypass grafting together with a cost calculation in the form of cost unit accounting. The use of a clinical pathway as a cost unit offers considerable advantages compared to the DRG or clinical case. The variance in the diagnoses and procedures within a pathway is minimal, so the consumption of resources is homogeneous. This leads to a considerable improvement in the value of cost unit accounting as a strategic control instrument in hospitals.

Exact and approximate stochastic simulation of intracellular calcium dynamics.

PubMed

Wieder, Nicolas; Fink, Rainer H A; Wegner, Frederic von

2011-01-01

In simulations of chemical systems, the main task is to find an exact or approximate solution of the chemical master equation (CME) that satisfies certain constraints with respect to computation time and accuracy. While Brownian motion simulations of single molecules are often too time consuming to represent the mesoscopic level, the classical Gillespie algorithm is a stochastically exact algorithm that provides satisfying results in the representation of calcium microdomains. Gillespie's algorithm can be approximated via the tau-leap method and the chemical Langevin equation (CLE). Both methods lead to a substantial acceleration in computation time and a relatively small decrease in accuracy. Elimination of the noise terms leads to the classical, deterministic reaction rate equations (RRE). For complex multiscale systems, hybrid simulations are increasingly proposed to combine the advantages of stochastic and deterministic algorithms. An often used exemplary cell type in this context are striated muscle cells (e.g., cardiac and skeletal muscle cells). The properties of these cells are well described and they express many common calcium-dependent signaling pathways. The purpose of the present paper is to provide an overview of the aforementioned simulation approaches and their mutual relationships in the spectrum ranging from stochastic to deterministic algorithms.

Network-based study reveals potential infection pathways of hepatitis-C leading to various diseases.

PubMed

Mukhopadhyay, Anirban; Maulik, Ujjwal

2014-01-01

Protein-protein interaction network-based study of viral pathogenesis has been gaining popularity among computational biologists in recent days. In the present study we attempt to investigate the possible pathways of hepatitis-C virus (HCV) infection by integrating the HCV-human interaction network, human protein interactome and human genetic disease association network. We have proposed quasi-biclique and quasi-clique mining algorithms to integrate these three networks to identify infection gateway host proteins and possible pathways of HCV pathogenesis leading to various diseases. Integrated study of three networks, namely HCV-human interaction network, human protein interaction network, and human proteins-disease association network reveals potential pathways of infection by the HCV that lead to various diseases including cancers. The gateway proteins have been found to be biologically coherent and have high degrees in human interactome compared to the other virus-targeted proteins. The analyses done in this study provide possible targets for more effective anti-hepatitis-C therapeutic involvement.

PumpKin: A tool to find principal pathways in plasma chemical models

NASA Astrophysics Data System (ADS)

Markosyan, A. H.; Luque, A.; Gordillo-Vázquez, F. J.; Ebert, U.

2014-10-01

PumpKin is a software package to find all principal pathways, i.e. the dominant reaction sequences, in chemical reaction systems. Although many tools are available to integrate numerically arbitrarily complex chemical reaction systems, few tools exist in order to analyze the results and interpret them in relatively simple terms. In particular, due to the large disparity in the lifetimes of the interacting components, it is often useful to group reactions into pathways that recycle the fastest species. This allows a researcher to focus on the slow chemical dynamics, eliminating the shortest timescales. Based on the algorithm described by Lehmann (2004), PumpKin automates the process of finding such pathways, allowing the user to analyze complex kinetics and to understand the consumption and production of a certain species of interest. We designed PumpKin with an emphasis on plasma chemical systems but it can also be applied to atmospheric modeling and to industrial applications such as plasma medicine and plasma-assisted combustion.

Double Dutch: A Tool for Designing Combinatorial Libraries of Biological Systems.

PubMed

Roehner, Nicholas; Young, Eric M; Voigt, Christopher A; Gordon, D Benjamin; Densmore, Douglas

2016-06-17

Recently, semirational approaches that rely on combinatorial assembly of characterized DNA components have been used to engineer biosynthetic pathways. In practice, however, it is not practical to assemble and test millions of pathway variants in order to elucidate how different DNA components affect the behavior of a pathway. To address this challenge, we apply a rigorous mathematical approach known as design of experiments (DOE) that can be used to construct empirical models of system behavior without testing all variants. To support this approach, we have developed a tool named Double Dutch, which uses a formal grammar and heuristic algorithms to automate the process of DOE library design. Compared to designing by hand, Double Dutch enables users to more efficiently and scalably design libraries of pathway variants that can be used in a DOE framework and uniquely provides a means to flexibly balance design considerations of statistical analysis, construction cost, and risk of homologous recombination, thereby demonstrating the utility of automating decision making when faced with complex design trade-offs.

Network-Based Study Reveals Potential Infection Pathways of Hepatitis-C Leading to Various Diseases

PubMed Central

Mukhopadhyay, Anirban; Maulik, Ujjwal

2014-01-01

Protein-protein interaction network-based study of viral pathogenesis has been gaining popularity among computational biologists in recent days. In the present study we attempt to investigate the possible pathways of hepatitis-C virus (HCV) infection by integrating the HCV-human interaction network, human protein interactome and human genetic disease association network. We have proposed quasi-biclique and quasi-clique mining algorithms to integrate these three networks to identify infection gateway host proteins and possible pathways of HCV pathogenesis leading to various diseases. Integrated study of three networks, namely HCV-human interaction network, human protein interaction network, and human proteins-disease association network reveals potential pathways of infection by the HCV that lead to various diseases including cancers. The gateway proteins have been found to be biologically coherent and have high degrees in human interactome compared to the other virus-targeted proteins. The analyses done in this study provide possible targets for more effective anti-hepatitis-C therapeutic involvement. PMID:24743187

Convergent genetic and expression data implicate immunity in Alzheimer's disease

PubMed Central

Jones, Lesley; Lambert, Jean-Charles; Wang, Li-San; Choi, Seung-Hoan; Harold, Denise; Vedernikov, Alexey; Escott-Price, Valentina; Stone, Timothy; Richards, Alexander; Bellenguez, Céline; Ibrahim-Verbaas, Carla A; Naj, Adam C; Sims, Rebecca; Gerrish, Amy; Jun, Gyungah; DeStefano, Anita L; Bis, Joshua C; Beecham, Gary W; Grenier-Boley, Benjamin; Russo, Giancarlo; Thornton-Wells, Tricia A; Jones, Nicola; Smith, Albert V; Chouraki, Vincent; Thomas, Charlene; Ikram, M Arfan; Zelenika, Diana; Vardarajan, Badri N; Kamatani, Yoichiro; Lin, Chiao-Feng; Schmidt, Helena; Kunkle, Brian; Dunstan, Melanie L; Ruiz, Agustin; Bihoreau, Marie-Thérèse; Reitz, Christiane; Pasquier, Florence; Hollingworth, Paul; Hanon, Olivier; Fitzpatrick, Annette L; Buxbaum, Joseph D; Campion, Dominique; Crane, Paul K; Becker, Tim; Gudnason, Vilmundur; Cruchaga, Carlos; Craig, David; Amin, Najaf; Berr, Claudine; Lopez, Oscar L; De Jager, Philip L; Deramecourt, Vincent; Johnston, Janet A; Evans, Denis; Lovestone, Simon; Letteneur, Luc; Kornhuber, Johanes; Tárraga, Lluís; Rubinsztein, David C; Eiriksdottir, Gudny; Sleegers, Kristel; Goate, Alison M; Fiévet, Nathalie; Huentelman, Matthew J; Gill, Michael; Emilsson, Valur; Brown, Kristelle; Kamboh, M Ilyas; Keller, Lina; Barberger-Gateau, Pascale; McGuinness, Bernadette; Larson, Eric B; Myers, Amanda J; Dufouil, Carole; Todd, Stephen; Wallon, David; Love, Seth; Kehoe, Pat; Rogaeva, Ekaterina; Gallacher, John; George-Hyslop, Peter St; Clarimon, Jordi; Lleὀ, Alberti; Bayer, Anthony; Tsuang, Debby W; Yu, Lei; Tsolaki, Magda; Bossù, Paola; Spalletta, Gianfranco; Proitsi, Petra; Collinge, John; Sorbi, Sandro; Garcia, Florentino Sanchez; Fox, Nick; Hardy, John; Naranjo, Maria Candida Deniz; Razquin, Cristina; Bosco, Paola; Clarke, Robert; Brayne, Carol; Galimberti, Daniela; Mancuso, Michelangelo; Moebus, Susanne; Mecocci, Patrizia; del Zompo, Maria; Maier, Wolfgang; Hampel, Harald; Pilotto, Alberto; Bullido, Maria; Panza, Francesco; Caffarra, Paolo; Nacmias, Benedetta; Gilbert, John R; Mayhaus, Manuel; Jessen, Frank; Dichgans, Martin; Lannfelt, Lars; Hakonarson, Hakon; Pichler, Sabrina; Carrasquillo, Minerva M; Ingelsson, Martin; Beekly, Duane; Alavarez, Victoria; Zou, Fanggeng; Valladares, Otto; Younkin, Steven G; Coto, Eliecer; Hamilton-Nelson, Kara L; Mateo, Ignacio; Owen, Michael J; Faber, Kelley M; Jonsson, Palmi V; Combarros, Onofre; O'Donovan, Michael C; Cantwell, Laura B; Soininen, Hilkka; Blacker, Deborah; Mead, Simon; Mosley, Thomas H; Bennett, David A; Harris, Tamara B; Fratiglioni, Laura; Holmes, Clive; de Bruijn, Renee FAG; Passmore, Peter; Montine, Thomas J; Bettens, Karolien; Rotter, Jerome I; Brice, Alexis; Morgan, Kevin; Foroud, Tatiana M; Kukull, Walter A; Hannequin, Didier; Powell, John F; Nalls, Michael A; Ritchie, Karen; Lunetta, Kathryn L; Kauwe, John SK; Boerwinkle, Eric; Riemenschneider, Matthias; Boada, Mercè; Hiltunen, Mikko; Martin, Eden R; Pastor, Pau; Schmidt, Reinhold; Rujescu, Dan; Dartigues, Jean-François; Mayeux, Richard; Tzourio, Christophe; Hofman, Albert; Nöthen, Markus M; Graff, Caroline; Psaty, Bruce M; Haines, Jonathan L; Lathrop, Mark; Pericak-Vance, Margaret A; Launer, Lenore J; Farrer, Lindsay A; van Duijn, Cornelia M; Van Broekhoven, Christine; Ramirez, Alfredo; Schellenberg, Gerard D; Seshadri, Sudha; Amouyel, Philippe; Holmans, Peter A

2015-01-01

Background Late–onset Alzheimer's disease (AD) is heritable with 20 genes showing genome wide association in the International Genomics of Alzheimer's Project (IGAP). To identify the biology underlying the disease we extended these genetic data in a pathway analysis. Methods The ALIGATOR and GSEA algorithms were used in the IGAP data to identify associated functional pathways and correlated gene expression networks in human brain. Results ALIGATOR identified an excess of curated biological pathways showing enrichment of association. Enriched areas of biology included the immune response (p = 3.27×10-12 after multiple testing correction for pathways), regulation of endocytosis (p = 1.31×10-11), cholesterol transport (p = 2.96 × 10-9) and proteasome-ubiquitin activity (p = 1.34×10-6). Correlated gene expression analysis identified four significant network modules, all related to the immune response (corrected p 0.002 – 0.05). Conclusions The immune response, regulation of endocytosis, cholesterol transport and protein ubiquitination represent prime targets for AD therapeutics. PMID:25533204

First dynamic model of dissolved organic carbon derived directly from high-frequency observations through contiguous storms.

PubMed

Jones, Timothy D; Chappell, Nick A; Tych, Wlodek

2014-11-18

The first dynamic model of dissolved organic carbon (DOC) export in streams derived directly from high frequency (subhourly) observations sampled at a regular interval through contiguous storms is presented. The optimal model, identified using the recently developed RIVC algorithm, captured the rapid dynamics of DOC load from 15 min monitored rainfall with high simulation efficiencies and constrained uncertainty with a second-order (two-pathway) structure. Most of the DOC export in the four headwater basins studied was associated with the faster hydrometric pathway (also modeled in parallel), and was soon exhausted in the slower pathway. A delay in the DOC mobilization became apparent as the ambient temperatures increased. These features of the component pathways were quantified in the dynamic response characteristics (DRCs) identified by RIVC. The model and associated DRCs are intended as a foundation for a better understanding of storm-related DOC dynamics and predictability, given the increasing availability of subhourly DOC concentration data.

Convergent genetic and expression data implicate immunity in Alzheimer's disease.

PubMed

2015-06-01

Late-onset Alzheimer's disease (AD) is heritable with 20 genes showing genome-wide association in the International Genomics of Alzheimer's Project (IGAP). To identify the biology underlying the disease, we extended these genetic data in a pathway analysis. The ALIGATOR and GSEA algorithms were used in the IGAP data to identify associated functional pathways and correlated gene expression networks in human brain. ALIGATOR identified an excess of curated biological pathways showing enrichment of association. Enriched areas of biology included the immune response (P = 3.27 × 10(-12) after multiple testing correction for pathways), regulation of endocytosis (P = 1.31 × 10(-11)), cholesterol transport (P = 2.96 × 10(-9)), and proteasome-ubiquitin activity (P = 1.34 × 10(-6)). Correlated gene expression analysis identified four significant network modules, all related to the immune response (corrected P = .002-.05). The immune response, regulation of endocytosis, cholesterol transport, and protein ubiquitination represent prime targets for AD therapeutics. Copyright © 2015. Published by Elsevier Inc.

Classification-based reasoning

NASA Technical Reports Server (NTRS)

Gomez, Fernando; Segami, Carlos

1991-01-01

A representation formalism for N-ary relations, quantification, and definition of concepts is described. Three types of conditions are associated with the concepts: (1) necessary and sufficient properties, (2) contingent properties, and (3) necessary properties. Also explained is how complex chains of inferences can be accomplished by representing existentially quantified sentences, and concepts denoted by restrictive relative clauses as classification hierarchies. The representation structures that make possible the inferences are explained first, followed by the reasoning algorithms that draw the inferences from the knowledge structures. All the ideas explained have been implemented and are part of the information retrieval component of a program called Snowy. An appendix contains a brief session with the program.

Hamilton-Jacobi-Bellman equations and approximate dynamic programming on time scales.

PubMed

Seiffertt, John; Sanyal, Suman; Wunsch, Donald C

2008-08-01

The time scales calculus is a key emerging area of mathematics due to its potential use in a wide variety of multidisciplinary applications. We extend this calculus to approximate dynamic programming (ADP). The core backward induction algorithm of dynamic programming is extended from its traditional discrete case to all isolated time scales. Hamilton-Jacobi-Bellman equations, the solution of which is the fundamental problem in the field of dynamic programming, are motivated and proven on time scales. By drawing together the calculus of time scales and the applied area of stochastic control via ADP, we have connected two major fields of research.

Path Similarity Analysis: A Method for Quantifying Macromolecular Pathways

PubMed Central

Seyler, Sean L.; Kumar, Avishek; Thorpe, M. F.; Beckstein, Oliver

2015-01-01

Diverse classes of proteins function through large-scale conformational changes and various sophisticated computational algorithms have been proposed to enhance sampling of these macromolecular transition paths. Because such paths are curves in a high-dimensional space, it has been difficult to quantitatively compare multiple paths, a necessary prerequisite to, for instance, assess the quality of different algorithms. We introduce a method named Path Similarity Analysis (PSA) that enables us to quantify the similarity between two arbitrary paths and extract the atomic-scale determinants responsible for their differences. PSA utilizes the full information available in 3N-dimensional configuration space trajectories by employing the Hausdorff or Fréchet metrics (adopted from computational geometry) to quantify the degree of similarity between piecewise-linear curves. It thus completely avoids relying on projections into low dimensional spaces, as used in traditional approaches. To elucidate the principles of PSA, we quantified the effect of path roughness induced by thermal fluctuations using a toy model system. Using, as an example, the closed-to-open transitions of the enzyme adenylate kinase (AdK) in its substrate-free form, we compared a range of protein transition path-generating algorithms. Molecular dynamics-based dynamic importance sampling (DIMS) MD and targeted MD (TMD) and the purely geometric FRODA (Framework Rigidity Optimized Dynamics Algorithm) were tested along with seven other methods publicly available on servers, including several based on the popular elastic network model (ENM). PSA with clustering revealed that paths produced by a given method are more similar to each other than to those from another method and, for instance, that the ENM-based methods produced relatively similar paths. PSA applied to ensembles of DIMS MD and FRODA trajectories of the conformational transition of diphtheria toxin, a particularly challenging example, showed that the geometry-based FRODA occasionally sampled the pathway space of force field-based DIMS MD. For the AdK transition, the new concept of a Hausdorff-pair map enabled us to extract the molecular structural determinants responsible for differences in pathways, namely a set of conserved salt bridges whose charge-charge interactions are fully modelled in DIMS MD but not in FRODA. PSA has the potential to enhance our understanding of transition path sampling methods, validate them, and to provide a new approach to analyzing conformational transitions. PMID:26488417

Emergency ultrasound-based algorithms for diagnosing blunt abdominal trauma.

PubMed

Stengel, Dirk; Bauwens, Kai; Rademacher, Grit; Ekkernkamp, Axel; Güthoff, Claas

2013-07-31

Ultrasonography is regarded as the tool of choice for early diagnostic investigations in patients with suspected blunt abdominal trauma. Although its sensitivity is too low for definite exclusion of abdominal organ injury, proponents of ultrasound argue that ultrasound-based clinical pathways enhance the speed of primary trauma assessment, reduce the number of computed tomography scans and cut costs. To assess the effects of trauma algorithms that include ultrasound examinations in patients with suspected blunt abdominal trauma. We searched the Cochrane Injuries Group's Specialised Register, CENTRAL (The Cochrane Library), MEDLINE (OvidSP), EMBASE (OvidSP), CINAHL (EBSCO), publishers' databases, controlled trials registers and the Internet. Bibliographies of identified articles and conference abstracts were searched for further elligible studies. Trial authors were contacted for further information and individual patient data. The searches were updated in February 2013. randomised controlled trials (RCTs) and quasi-randomised trials (qRCTs). patients with blunt torso, abdominal or multiple trauma undergoing diagnostic investigations for abdominal organ injury. diagnostic algorithms comprising emergency ultrasonography (US). diagnostic algorithms without ultrasound examinations (for example, primary computed tomography [CT] or diagnostic peritoneal lavage [DPL]). mortality, use of CT and DPL, cost-effectiveness, laparotomy and negative laparotomy rates, delayed diagnoses, and quality of life. Two authors independently selected trials for inclusion, assessed methodological quality and extracted data. Where possible, data were pooled and relative risks (RRs), risk differences (RDs) and weighted mean differences, each with 95% confidence intervals (CIs), were calculated by fixed- or random-effects modelling, as appropriate. We identified four studies meeting our inclusion criteria. Overall, trials were of moderate methodological quality. Few trial authors responded to our written inquiries seeking to resolve controversial issues and to obtain individual patient data. We pooled mortality data from three trials involving 1254 patients; relative risk in favour of the US arm was 1.00 (95% CI 0.50 to 2.00). US-based pathways significantly reduced the number of CT scans (random-effects RD -0.52, 95% CI -0.83 to -0.21), but the meaning of this result is unclear. Given the low sensitivity of ultrasound, the reduction in CT scans may either translate to a number needed to treat or number needed to harm of two. There is currently insufficient evidence from RCTs to justify promotion of ultrasound-based clinical pathways in diagnosing patients with suspected blunt abdominal trauma.

Satellite Monitoring of Long-Range Transport of Asian Dust Storms from Sources to Sinks

NASA Astrophysics Data System (ADS)

Hsu, N.; Tsay, S.; Jeong, M.; King, M.; Holben, B.

2007-05-01

Among the many components that contribute to air pollution, airborne mineral dust plays an important role due to its biogeochemical impact on the ecosystem and its radiative-forcing effect on the climate system. In East Asia, dust storms frequently accompany the cold and dry air masses that occur as part of spring-time cold front systems. China's capital, Beijing, and other large cities are on the primary pathway of these dust storm plumes, and their passage over such popu-lation centers causes flight delays, pushes grit through windows and doors, and forces people indoors. Furthermore, during the spring these anthropogenic and natural air pollutants, once generated over the source regions, can be transported out of the boundary layer into the free troposphere and can travel thousands of kilometers across the Pacific into the United States and beyond. In this paper, we will demonstrate the capability of a new satellite algorithm to retrieve aerosol optical thickness and single scattering albedo over bright-reflecting surfaces such as urban areas and deserts. Such retrievals have been dif-ficult to perform using previously available algorithms that use wavelengths from the mid-visible to the near IR because they have trouble separating the aerosol signal from the contribution due to the bright surface reflectance. The new algorithm, called Deep Blue, utilizes blue-wavelength measurements from instruments such as SeaWiFS and MODIS to infer the properties of aerosols, since the surface reflectance over land in the blue part of the spectrum is much lower than for longer wavelength channels. Deep Blue algorithm has recently been integrated into the MODIS processing stream and began to provide aerosol products over land as part of the opera-tional MYD04 products. In this talk, we will show the comparisons of the MODIS Deep Blue products with data from AERONET sunphotometers on a global ba-sis. The results indicate reasonable agreements between these two. These new satellite products will allow scientists to determine quantitatively the aerosol properties near sources and their evolution along transport pathway using high spatial resolution measurements from SeaWiFS and MODIS-like instruments. We will also utilize the multiyear satellite measurements from MODIS and SeaWiFS to investigate the interannual variability of source strength, pathway, and radia-tive forcing associated with these dust outbreaks in East Asia.

Text Mining in Cancer Gene and Pathway Prioritization

PubMed Central

Luo, Yuan; Riedlinger, Gregory; Szolovits, Peter

2014-01-01

Prioritization of cancer implicated genes has received growing attention as an effective way to reduce wet lab cost by computational analysis that ranks candidate genes according to the likelihood that experimental verifications will succeed. A multitude of gene prioritization tools have been developed, each integrating different data sources covering gene sequences, differential expressions, function annotations, gene regulations, protein domains, protein interactions, and pathways. This review places existing gene prioritization tools against the backdrop of an integrative Omic hierarchy view toward cancer and focuses on the analysis of their text mining components. We explain the relatively slow progress of text mining in gene prioritization, identify several challenges to current text mining methods, and highlight a few directions where more effective text mining algorithms may improve the overall prioritization task and where prioritizing the pathways may be more desirable than prioritizing only genes. PMID:25392685

Text mining in cancer gene and pathway prioritization.

PubMed

Luo, Yuan; Riedlinger, Gregory; Szolovits, Peter

2014-01-01

Prioritization of cancer implicated genes has received growing attention as an effective way to reduce wet lab cost by computational analysis that ranks candidate genes according to the likelihood that experimental verifications will succeed. A multitude of gene prioritization tools have been developed, each integrating different data sources covering gene sequences, differential expressions, function annotations, gene regulations, protein domains, protein interactions, and pathways. This review places existing gene prioritization tools against the backdrop of an integrative Omic hierarchy view toward cancer and focuses on the analysis of their text mining components. We explain the relatively slow progress of text mining in gene prioritization, identify several challenges to current text mining methods, and highlight a few directions where more effective text mining algorithms may improve the overall prioritization task and where prioritizing the pathways may be more desirable than prioritizing only genes.

Patients' Views of Treatment-Focused Genetic Testing (TFGT): Some Lessons for the Mainstreaming of BRCA1 and BRCA2 Testing.

PubMed

Wright, Sarah; Porteous, Mary; Stirling, Diane; Lawton, Julia; Young, Oliver; Gourley, Charlie; Hallowell, Nina

2018-05-11

This paper explores patients' views and experiences of undergoing treatment-focused BRCA1 and BRCA2 genetic testing (TFGT), either offered following triaging to clinical genetics (breast cancer) or as part of a mainstreamed care pathway in oncology (ovarian cancer). Drawing on 26 in-depth interviews with patients with breast or ovarian cancer who had undergone TFGT, this retrospective study examines patients' views of genetic testing at this point in their care pathway, focusing on issues, such as initial response to the offer of testing, motivations for undergoing testing, and views on care pathways. Patients were amenable to the incorporation of TFGT at an early stage in their cancer care irrespective of (any) prior anticipation of having a genetic test or family history. While patients were glad to have been offered TFGT as part of their care, some questioned the logic of the test's timing in relation to their cancer treatment. Crucially, patients appeared unable to disentangle the treatment role of TFGT from its preventative function for self and other family members, suggesting that some may undergo TFGT to obtain information for others rather than for self.

Between affiliation and autonomy: navigating pathways of women's empowerment and gender justice in rural Bangladesh.

PubMed

Kabeer, Naila

2011-01-01

Inasmuch as women's subordinate status is a product of the patriarchal structures of constraint that prevail in specific contexts, pathways of women's empowerment are likely to be "path dependent." They will be shaped by women's struggles to act on the constraints that prevail in their societies, as much by what they seek to defend as by what they seek to change. The universal value that many feminists claim for individual autonomy may not therefore have the same purchase in all contexts. This article examines processes of empowerment as they play out in the lives of women associated with social mobilization organizations in the specific context of rural Bangladesh. It draws on their narratives to explore the collective strategies through which these organizations sought to empower the women and how they in turn drew on their newly established "communities of practice" to navigate their own pathways to wider social change. It concludes that while the value attached to social affiliations by the women in the study is clearly a product of the societies in which they have grown up, it may be no more context-specific than the apparently universal value attached to individual autonomy by many feminists.

Point Analysis in Java applied to histological images of the perforant pathway: a user's account.

PubMed

Scorcioni, Ruggero; Wright, Susan N; Patrick Card, J; Ascoli, Giorgio A; Barrionuevo, Germán

2008-01-01

The freeware Java tool Point Analysis in Java (PAJ), created to perform 3D point analysis, was tested in an independent laboratory setting. The input data consisted of images of the hippocampal perforant pathway from serial immunocytochemical localizations of the rat brain in multiple views at different resolutions. The low magnification set (x2 objective) comprised the entire perforant pathway, while the high magnification set (x100 objective) allowed the identification of individual fibers. A preliminary stereological study revealed a striking linear relationship between the fiber count at high magnification and the optical density at low magnification. PAJ enabled fast analysis for down-sampled data sets and a friendly interface with automated plot drawings. Noted strengths included the multi-platform support as well as the free availability of the source code, conducive to a broad user base and maximum flexibility for ad hoc requirements. PAJ has great potential to extend its usability by (a) improving its graphical user interface, (b) increasing its input size limit, (c) improving response time for large data sets, and (d) potentially being integrated with other Java graphical tools such as ImageJ.

Poor health and loneliness in later life: the role of depressive symptoms, social resources, and rural environments.

PubMed

Burholt, Vanessa; Scharf, Thomas

2014-03-01

We draw on cognitive discrepancy theory to hypothesize and test a pathway from poor health to loneliness in later life. We hypothesize that poor health will have a negative influence on social participation and social resources, and these factors will mediate between health and loneliness. We hypothesize that rural environments will amplify any difficulties associated with social participation or accessing social resources and that depression will moderate how intensely people react to levels of social contact and support. We conceptualize a mediation model and a moderated-mediation model. Nationally representative data on older people living in the Republic of Ireland are used to validate the hypothesized pathways. In the mediation model, health has a significant indirect effect on loneliness through the mediating variables social resources and social participation. In the moderated-mediation model, rurality moderates the pathway between health and social resources but not social participation. Depressive symptoms moderate the effect of social resources on loneliness but not social participation. The results provide further credence to cognitive discrepancy theory, suggesting that depressive symptoms influence cognitive processes, interfering with judgments about the adequacy of social interaction. The theory is extended by demonstrating the impact of the environment on loneliness.

Efficiently sampling conformations and pathways using the concurrent adaptive sampling (CAS) algorithm

NASA Astrophysics Data System (ADS)

Ahn, Surl-Hee; Grate, Jay W.; Darve, Eric F.

2017-08-01

Molecular dynamics simulations are useful in obtaining thermodynamic and kinetic properties of bio-molecules, but they are limited by the time scale barrier. That is, we may not obtain properties' efficiently because we need to run microseconds or longer simulations using femtosecond time steps. To overcome this time scale barrier, we can use the weighted ensemble (WE) method, a powerful enhanced sampling method that efficiently samples thermodynamic and kinetic properties. However, the WE method requires an appropriate partitioning of phase space into discrete macrostates, which can be problematic when we have a high-dimensional collective space or when little is known a priori about the molecular system. Hence, we developed a new WE-based method, called the "Concurrent Adaptive Sampling (CAS) algorithm," to tackle these issues. The CAS algorithm is not constrained to use only one or two collective variables, unlike most reaction coordinate-dependent methods. Instead, it can use a large number of collective variables and adaptive macrostates to enhance the sampling in the high-dimensional space. This is especially useful for systems in which we do not know what the right reaction coordinates are, in which case we can use many collective variables to sample conformations and pathways. In addition, a clustering technique based on the committor function is used to accelerate sampling the slowest process in the molecular system. In this paper, we introduce the new method and show results from two-dimensional models and bio-molecules, specifically penta-alanine and a triazine trimer.

Graph-based semi-supervised learning with genomic data integration using condition-responsive genes applied to phenotype classification.

PubMed

Doostparast Torshizi, Abolfazl; Petzold, Linda R

2018-01-01

Data integration methods that combine data from different molecular levels such as genome, epigenome, transcriptome, etc., have received a great deal of interest in the past few years. It has been demonstrated that the synergistic effects of different biological data types can boost learning capabilities and lead to a better understanding of the underlying interactions among molecular levels. In this paper we present a graph-based semi-supervised classification algorithm that incorporates latent biological knowledge in the form of biological pathways with gene expression and DNA methylation data. The process of graph construction from biological pathways is based on detecting condition-responsive genes, where 3 sets of genes are finally extracted: all condition responsive genes, high-frequency condition-responsive genes, and P-value-filtered genes. The proposed approach is applied to ovarian cancer data downloaded from the Human Genome Atlas. Extensive numerical experiments demonstrate superior performance of the proposed approach compared to other state-of-the-art algorithms, including the latest graph-based classification techniques. Simulation results demonstrate that integrating various data types enhances classification performance and leads to a better understanding of interrelations between diverse omics data types. The proposed approach outperforms many of the state-of-the-art data integration algorithms. © The Author 2017. Published by Oxford University Press on behalf of the American Medical Informatics Association. All rights reserved. For Permissions, please email: journals.permissions@oup.com

Beyond Hosting Capacity: Using Shortest Path Methods to Minimize Upgrade Cost Pathways: Preprint

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

Gensollen, Nicolas; Horowitz, Kelsey A; Palmintier, Bryan S

We present in this paper a graph based forwardlooking algorithm applied to distribution planning in the context of distributed PV penetration. We study the target hosting capacity (THC) problem where the objective is to find the cheapest sequence of system upgrades to reach a predefined hosting capacity target value. We show in this paper that commonly used short-term cost minimization approaches lead most of the time to suboptimal solutions. By comparing our method against such myopic techniques on real distribution systems, we show that our algorithm is able to reduce the overall integration costs by looking at future decisions. Becausemore » hosting capacity is hard to compute, this problem requires efficient methods to search the space. We demonstrate here that heuristics using domain specific knowledge can be efficiently used to improve the algorithm performance such that real distribution systems can be studied.« less

An Improved Binary Differential Evolution Algorithm to Infer Tumor Phylogenetic Trees.

PubMed

Liang, Ying; Liao, Bo; Zhu, Wen

2017-01-01

Tumourigenesis is a mutation accumulation process, which is likely to start with a mutated founder cell. The evolutionary nature of tumor development makes phylogenetic models suitable for inferring tumor evolution through genetic variation data. Copy number variation (CNV) is the major genetic marker of the genome with more genes, disease loci, and functional elements involved. Fluorescence in situ hybridization (FISH) accurately measures multiple gene copy number of hundreds of single cells. We propose an improved binary differential evolution algorithm, BDEP, to infer tumor phylogenetic tree based on FISH platform. The topology analysis of tumor progression tree shows that the pathway of tumor subcell expansion varies greatly during different stages of tumor formation. And the classification experiment shows that tree-based features are better than data-based features in distinguishing tumor. The constructed phylogenetic trees have great performance in characterizing tumor development process, which outperforms other similar algorithms.

Immunodiagnosis of childhood malignancies.

PubMed

Parham, D M; Holt, H

1999-09-01

Immunodiagnosis utilizing immunohistochemical techniques is currently the most commonly utilized and readily available method of ancillary diagnosis in pediatric oncopathology. The methodology comprises relatively simple steps, based on straightforward biologic concepts, and the reagents used are generally well characterized and widely used. The principle of cancer immunodiagnosis is based on the determination of neoplastic lineage using detection of proteins typical of cell differentiation pathways. Methodology sensitivity varies and has become greater with each new generation of tests, but technical draw-backs should be considered to avoid excessive background or nonspecific results. Automated instrumentation offers a degree of accuracy and reproducibility not easily attainable by manual methods.

Status Concern and Relative Deprivation in China: Measures, Empirical Evidence and Economic and Policy Implications

PubMed Central

Xi, CHEN

2017-01-01

Status concern and feelings of relative deprivation affect individual behaviour and well-being. Traditional norms and the alarming inequality in China have made relative deprivation increasingly intense for the Chinese population. This article reviews empirical literature on China that attempts to test the relative deprivation hypothesis, and also reviews the origins and pathways of relative deprivation, compares its economic measures in the literature and summarises the scientific findings. Drawing from solid empirical evidence, the author discusses the important policy implications on redistribution, official regulations and grassroots sanctions, and relative poverty alleviation. PMID:29033479

Empowering self-defense training.

PubMed

Thompson, Martha E

2014-03-01

The purpose of self-defense training is to expand people's options, yet it is often framed as a solely physical, and limiting, response to violence. I draw on my own experience as a self-defense instructor and that of others in the self-defense movement to argue that an empowerment approach to self-defense training contributes to the anti-violence movement in multiple ways: providing a pathway to increase women's and girls' safety and their potential for becoming powerful and effective social change agents right now, providing an informed and embodied understanding of violence, and offering comprehensive options to recognize, prevent, and interrupt violence.

Complexity of Danger: The Diverse Nature of Damage-associated Molecular Patterns*

PubMed Central

Schaefer, Liliana

2014-01-01

In reply to internal or external danger stimuli, the body orchestrates an inflammatory response. The endogenous triggers of this process are the damage-associated molecular patterns (DAMPs). DAMPs represent a heterogeneous group of molecules that draw their origin either from inside the various compartments of the cell or from the extracellular space. Following interaction with pattern recognition receptors in cross-talk with various non-immune receptors, DAMPs determine the downstream signaling outcome of septic and aseptic inflammatory responses. In this review, the diverse nature, structural characteristics, and signaling pathways elicited by DAMPs will be critically evaluated. PMID:25391648

Status Concern and Relative Deprivation in China: Measures, Empirical Evidence and Economic and Policy Implications.

PubMed

Xi, Chen

2016-02-01

Status concern and feelings of relative deprivation affect individual behaviour and well-being. Traditional norms and the alarming inequality in China have made relative deprivation increasingly intense for the Chinese population. This article reviews empirical literature on China that attempts to test the relative deprivation hypothesis, and also reviews the origins and pathways of relative deprivation, compares its economic measures in the literature and summarises the scientific findings. Drawing from solid empirical evidence, the author discusses the important policy implications on redistribution, official regulations and grassroots sanctions, and relative poverty alleviation.

Reaction pathways involved in CH4 conversion on Pd/Al2O3 catalysts : TAP as a powerful tool for the elucidation of the effective role of the metal/support interface

NASA Astrophysics Data System (ADS)

Granger, Pascal

2016-02-01

Temporal Analysis of Products (TAP) reactor offers an alternative to draw direct structure/activity relationships checked on Natural Gas-fuelled Vehicle (NGV) catalysts Determination of accurate kinetic constants for methane adsorption from single pulse experiments and subsequent investigation of sequential surface reactions from alternative CH4/O2 pulse experiments provides a straightforward visualization of the involvement of the metal/support interface on freshly-prepared catalysts and the loss of this effect on aged single palladium based catalysts.

Ketene Formation in Interstellar Ices: A Laboratory Study

NASA Technical Reports Server (NTRS)

Hudson, Reggie L.; Loeffler, Mark Josiah

2013-01-01

The formation of ketene (H2CCO, ethenone) in polar and apolar ices was studied with in situ 0.8 MeV proton irradiation, far-UVphotolysis, and infrared spectroscopic analyses at 10-20 K. Using isotopically enriched reagents, unequivocal evidencewas obtained for ketene synthesis in H2O-rich and CO2-rich ices, and several reaction products were identified. Results from scavenging experiments suggested that ketene was formed by free-radical pathways, as opposed to acid-base processes or redox reactions. Finally, we use our results to draw conclusions about the formation and stability of ketene in the interstellar medium.