structure prediction problem: Topics by Science.gov

Sample records for structure prediction problem

Learning to Predict Combinatorial Structures

NASA Astrophysics Data System (ADS)

Vembu, Shankar

2009-12-01

The major challenge in designing a discriminative learning algorithm for predicting structured data is to address the computational issues arising from the exponential size of the output space. Existing algorithms make different assumptions to ensure efficient, polynomial time estimation of model parameters. For several combinatorial structures, including cycles, partially ordered sets, permutations and other graph classes, these assumptions do not hold. In this thesis, we address the problem of designing learning algorithms for predicting combinatorial structures by introducing two new assumptions: (i) The first assumption is that a particular counting problem can be solved efficiently. The consequence is a generalisation of the classical ridge regression for structured prediction. (ii) The second assumption is that a particular sampling problem can be solved efficiently. The consequence is a new technique for designing and analysing probabilistic structured prediction models. These results can be applied to solve several complex learning problems including but not limited to multi-label classification, multi-category hierarchical classification, and label ranking.
Structural testing for static failure, flutter and other scary things

NASA Technical Reports Server (NTRS)

Ricketts, R. H.

1983-01-01

Ground test and flight test methods are described that may be used to highlight potential structural problems that occur on aircraft. Primary interest is focused on light-weight general aviation airplanes. The structural problems described include static strength failure, aileron reversal, static divergence, and flutter. An example of each of the problems is discussed to illustrate how the data acquired during the tests may be used to predict the occurrence of the structural problem. While some rules of thumb for the prediction of structural problems are given the report is not intended to be used explicitly as a structural analysis handbook.
Hill-Climbing search and diversification within an evolutionary approach to protein structure prediction.

PubMed

Chira, Camelia; Horvath, Dragos; Dumitrescu, D

2011-07-30

Proteins are complex structures made of amino acids having a fundamental role in the correct functioning of living cells. The structure of a protein is the result of the protein folding process. However, the general principles that govern the folding of natural proteins into a native structure are unknown. The problem of predicting a protein structure with minimum-energy starting from the unfolded amino acid sequence is a highly complex and important task in molecular and computational biology. Protein structure prediction has important applications in fields such as drug design and disease prediction. The protein structure prediction problem is NP-hard even in simplified lattice protein models. An evolutionary model based on hill-climbing genetic operators is proposed for protein structure prediction in the hydrophobic - polar (HP) model. Problem-specific search operators are implemented and applied using a steepest-ascent hill-climbing approach. Furthermore, the proposed model enforces an explicit diversification stage during the evolution in order to avoid local optimum. The main features of the resulting evolutionary algorithm - hill-climbing mechanism and diversification strategy - are evaluated in a set of numerical experiments for the protein structure prediction problem to assess their impact to the efficiency of the search process. Furthermore, the emerging consolidated model is compared to relevant algorithms from the literature for a set of difficult bidimensional instances from lattice protein models. The results obtained by the proposed algorithm are promising and competitive with those of related methods.
Predicting protein structures with a multiplayer online game.

PubMed

Cooper, Seth; Khatib, Firas; Treuille, Adrien; Barbero, Janos; Lee, Jeehyung; Beenen, Michael; Leaver-Fay, Andrew; Baker, David; Popović, Zoran; Players, Foldit

2010-08-05

People exert large amounts of problem-solving effort playing computer games. Simple image- and text-recognition tasks have been successfully 'crowd-sourced' through games, but it is not clear if more complex scientific problems can be solved with human-directed computing. Protein structure prediction is one such problem: locating the biologically relevant native conformation of a protein is a formidable computational challenge given the very large size of the search space. Here we describe Foldit, a multiplayer online game that engages non-scientists in solving hard prediction problems. Foldit players interact with protein structures using direct manipulation tools and user-friendly versions of algorithms from the Rosetta structure prediction methodology, while they compete and collaborate to optimize the computed energy. We show that top-ranked Foldit players excel at solving challenging structure refinement problems in which substantial backbone rearrangements are necessary to achieve the burial of hydrophobic residues. Players working collaboratively develop a rich assortment of new strategies and algorithms; unlike computational approaches, they explore not only the conformational space but also the space of possible search strategies. The integration of human visual problem-solving and strategy development capabilities with traditional computational algorithms through interactive multiplayer games is a powerful new approach to solving computationally-limited scientific problems.
Protein Structure Prediction with Evolutionary Algorithms

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

Hart, W.E.; Krasnogor, N.; Pelta, D.A.

1999-02-08

Evolutionary algorithms have been successfully applied to a variety of molecular structure prediction problems. In this paper we reconsider the design of genetic algorithms that have been applied to a simple protein structure prediction problem. Our analysis considers the impact of several algorithmic factors for this problem: the confirmational representation, the energy formulation and the way in which infeasible conformations are penalized, Further we empirically evaluated the impact of these factors on a small set of polymer sequences. Our analysis leads to specific recommendations for both GAs as well as other heuristic methods for solving PSP on the HP model.
Frnakenstein: multiple target inverse RNA folding.

PubMed

Lyngsø, Rune B; Anderson, James W J; Sizikova, Elena; Badugu, Amarendra; Hyland, Tomas; Hein, Jotun

2012-10-09

RNA secondary structure prediction, or folding, is a classic problem in bioinformatics: given a sequence of nucleotides, the aim is to predict the base pairs formed in its three dimensional conformation. The inverse problem of designing a sequence folding into a particular target structure has only more recently received notable interest. With a growing appreciation and understanding of the functional and structural properties of RNA motifs, and a growing interest in utilising biomolecules in nano-scale designs, the interest in the inverse RNA folding problem is bound to increase. However, whereas the RNA folding problem from an algorithmic viewpoint has an elegant and efficient solution, the inverse RNA folding problem appears to be hard. In this paper we present a genetic algorithm approach to solve the inverse folding problem. The main aims of the development was to address the hitherto mostly ignored extension of solving the inverse folding problem, the multi-target inverse folding problem, while simultaneously designing a method with superior performance when measured on the quality of designed sequences. The genetic algorithm has been implemented as a Python program called Frnakenstein. It was benchmarked against four existing methods and several data sets totalling 769 real and predicted single structure targets, and on 292 two structure targets. It performed as well as or better at finding sequences which folded in silico into the target structure than all existing methods, without the heavy bias towards CG base pairs that was observed for all other top performing methods. On the two structure targets it also performed well, generating a perfect design for about 80% of the targets. Our method illustrates that successful designs for the inverse RNA folding problem does not necessarily have to rely on heavy biases in base pair and unpaired base distributions. The design problem seems to become more difficult on larger structures when the target structures are real structures, while no deterioration was observed for predicted structures. Design for two structure targets is considerably more difficult, but far from impossible, demonstrating the feasibility of automated design of artificial riboswitches. The Python implementation is available at http://www.stats.ox.ac.uk/research/genome/software/frnakenstein.
Frnakenstein: multiple target inverse RNA folding

PubMed Central

2012-01-01

Background RNA secondary structure prediction, or folding, is a classic problem in bioinformatics: given a sequence of nucleotides, the aim is to predict the base pairs formed in its three dimensional conformation. The inverse problem of designing a sequence folding into a particular target structure has only more recently received notable interest. With a growing appreciation and understanding of the functional and structural properties of RNA motifs, and a growing interest in utilising biomolecules in nano-scale designs, the interest in the inverse RNA folding problem is bound to increase. However, whereas the RNA folding problem from an algorithmic viewpoint has an elegant and efficient solution, the inverse RNA folding problem appears to be hard. Results In this paper we present a genetic algorithm approach to solve the inverse folding problem. The main aims of the development was to address the hitherto mostly ignored extension of solving the inverse folding problem, the multi-target inverse folding problem, while simultaneously designing a method with superior performance when measured on the quality of designed sequences. The genetic algorithm has been implemented as a Python program called Frnakenstein. It was benchmarked against four existing methods and several data sets totalling 769 real and predicted single structure targets, and on 292 two structure targets. It performed as well as or better at finding sequences which folded in silico into the target structure than all existing methods, without the heavy bias towards CG base pairs that was observed for all other top performing methods. On the two structure targets it also performed well, generating a perfect design for about 80% of the targets. Conclusions Our method illustrates that successful designs for the inverse RNA folding problem does not necessarily have to rely on heavy biases in base pair and unpaired base distributions. The design problem seems to become more difficult on larger structures when the target structures are real structures, while no deterioration was observed for predicted structures. Design for two structure targets is considerably more difficult, but far from impossible, demonstrating the feasibility of automated design of artificial riboswitches. The Python implementation is available at http://www.stats.ox.ac.uk/research/genome/software/frnakenstein. PMID:23043260
Constraint Logic Programming approach to protein structure prediction.

PubMed

Dal Palù, Alessandro; Dovier, Agostino; Fogolari, Federico

2004-11-30

The protein structure prediction problem is one of the most challenging problems in biological sciences. Many approaches have been proposed using database information and/or simplified protein models. The protein structure prediction problem can be cast in the form of an optimization problem. Notwithstanding its importance, the problem has very seldom been tackled by Constraint Logic Programming, a declarative programming paradigm suitable for solving combinatorial optimization problems. Constraint Logic Programming techniques have been applied to the protein structure prediction problem on the face-centered cube lattice model. Molecular dynamics techniques, endowed with the notion of constraint, have been also exploited. Even using a very simplified model, Constraint Logic Programming on the face-centered cube lattice model allowed us to obtain acceptable results for a few small proteins. As a test implementation their (known) secondary structure and the presence of disulfide bridges are used as constraints. Simplified structures obtained in this way have been converted to all atom models with plausible structure. Results have been compared with a similar approach using a well-established technique as molecular dynamics. The results obtained on small proteins show that Constraint Logic Programming techniques can be employed for studying protein simplified models, which can be converted into realistic all atom models. The advantage of Constraint Logic Programming over other, much more explored, methodologies, resides in the rapid software prototyping, in the easy way of encoding heuristics, and in exploiting all the advances made in this research area, e.g. in constraint propagation and its use for pruning the huge search space.
An improved stochastic fractal search algorithm for 3D protein structure prediction.

PubMed

Zhou, Changjun; Sun, Chuan; Wang, Bin; Wang, Xiaojun

2018-05-03

Protein structure prediction (PSP) is a significant area for biological information research, disease treatment, and drug development and so on. In this paper, three-dimensional structures of proteins are predicted based on the known amino acid sequences, and the structure prediction problem is transformed into a typical NP problem by an AB off-lattice model. This work applies a novel improved Stochastic Fractal Search algorithm (ISFS) to solve the problem. The Stochastic Fractal Search algorithm (SFS) is an effective evolutionary algorithm that performs well in exploring the search space but falls into local minimums sometimes. In order to avoid the weakness, Lvy flight and internal feedback information are introduced in ISFS. In the experimental process, simulations are conducted by ISFS algorithm on Fibonacci sequences and real peptide sequences. Experimental results prove that the ISFS performs more efficiently and robust in terms of finding the global minimum and avoiding getting stuck in local minimums.
Soft Computing Methods for Disulfide Connectivity Prediction.

PubMed

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

2015-01-01

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

PubMed Central

Khoury, George A.; Liwo, Adam; Khatib, Firas; Zhou, Hongyi; Chopra, Gaurav; Bacardit, Jaume; Bortot, Leandro O.; Faccioli, Rodrigo A.; Deng, Xin; He, Yi; Krupa, Pawel; Li, Jilong; Mozolewska, Magdalena A.; Sieradzan, Adam K.; Smadbeck, James; Wirecki, Tomasz; Cooper, Seth; Flatten, Jeff; Xu, Kefan; Baker, David; Cheng, Jianlin; Delbem, Alexandre C. B.; Floudas, Christodoulos A.; Keasar, Chen; Levitt, Michael; Popović, Zoran; Scheraga, Harold A.; Skolnick, Jeffrey; Crivelli, Silvia N.; Players, Foldit

2014-01-01

The protein structure prediction problem continues to elude scientists. Despite the introduction of many methods, only modest gains were made over the last decade for certain classes of prediction targets. To address this challenge, a social-media based worldwide collaborative effort, named WeFold, was undertaken by thirteen labs. During the collaboration, the labs were simultaneously competing with each other. Here, we present the first attempt at “coopetition” in scientific research applied to the protein structure prediction and refinement problems. The coopetition was possible by allowing the participating labs to contribute different components of their protein structure prediction pipelines and create new hybrid pipelines that they tested during CASP10. This manuscript describes both successes and areas needing improvement as identified throughout the first WeFold experiment and discusses the efforts that are underway to advance this initiative. A footprint of all contributions and structures are publicly accessible at http://www.wefold.org. PMID:24677212
Rosetta Structure Prediction as a Tool for Solving Difficult Molecular Replacement Problems.

PubMed

DiMaio, Frank

2017-01-01

Molecular replacement (MR), a method for solving the crystallographic phase problem using phases derived from a model of the target structure, has proven extremely valuable, accounting for the vast majority of structures solved by X-ray crystallography. However, when the resolution of data is low, or the starting model is very dissimilar to the target protein, solving structures via molecular replacement may be very challenging. In recent years, protein structure prediction methodology has emerged as a powerful tool in model building and model refinement for difficult molecular replacement problems. This chapter describes some of the tools available in Rosetta for model building and model refinement specifically geared toward difficult molecular replacement cases.
SVM-Fold: a tool for discriminative multi-class protein fold and superfamily recognition

PubMed Central

Melvin, Iain; Ie, Eugene; Kuang, Rui; Weston, Jason; Stafford, William Noble; Leslie, Christina

2007-01-01

Background Predicting a protein's structural class from its amino acid sequence is a fundamental problem in computational biology. Much recent work has focused on developing new representations for protein sequences, called string kernels, for use with support vector machine (SVM) classifiers. However, while some of these approaches exhibit state-of-the-art performance at the binary protein classification problem, i.e. discriminating between a particular protein class and all other classes, few of these studies have addressed the real problem of multi-class superfamily or fold recognition. Moreover, there are only limited software tools and systems for SVM-based protein classification available to the bioinformatics community. Results We present a new multi-class SVM-based protein fold and superfamily recognition system and web server called SVM-Fold, which can be found at . Our system uses an efficient implementation of a state-of-the-art string kernel for sequence profiles, called the profile kernel, where the underlying feature representation is a histogram of inexact matching k-mer frequencies. We also employ a novel machine learning approach to solve the difficult multi-class problem of classifying a sequence of amino acids into one of many known protein structural classes. Binary one-vs-the-rest SVM classifiers that are trained to recognize individual structural classes yield prediction scores that are not comparable, so that standard "one-vs-all" classification fails to perform well. Moreover, SVMs for classes at different levels of the protein structural hierarchy may make useful predictions, but one-vs-all does not try to combine these multiple predictions. To deal with these problems, our method learns relative weights between one-vs-the-rest classifiers and encodes information about the protein structural hierarchy for multi-class prediction. In large-scale benchmark results based on the SCOP database, our code weighting approach significantly improves on the standard one-vs-all method for both the superfamily and fold prediction in the remote homology setting and on the fold recognition problem. Moreover, our code weight learning algorithm strongly outperforms nearest-neighbor methods based on PSI-BLAST in terms of prediction accuracy on every structure classification problem we consider. Conclusion By combining state-of-the-art SVM kernel methods with a novel multi-class algorithm, the SVM-Fold system delivers efficient and accurate protein fold and superfamily recognition. PMID:17570145
Parent-rated externalizing behavior in preschoolers: the predictive utility of structured interviews, teacher reports, and classroom observations.

PubMed

Doctoroff, Greta L; Arnold, David H

2004-12-01

This study investigated multiple forms of home and school assessment as predictors of parent-rated behavior problems across a preschool year. Participants were a community sample of 79 preschool children, their parents, and their teachers. Parent ratings of behavior problems were obtained toward the beginning of the school year and approximately 6 months later. Behavior problems were also assessed early in the school year using parent structured interviews, teacher-rating scales, and classroom observations of problem and prosocial behavior. Consistent with hypotheses, each assessment method significantly predicted year-end parent ratings of behavior problems, even above initial ratings.
RNA folding: structure prediction, folding kinetics and ion electrostatics.

PubMed

Tan, Zhijie; Zhang, Wenbing; Shi, Yazhou; Wang, Fenghua

2015-01-01

Beyond the "traditional" functions such as gene storage, transport and protein synthesis, recent discoveries reveal that RNAs have important "new" biological functions including the RNA silence and gene regulation of riboswitch. Such functions of noncoding RNAs are strongly coupled to the RNA structures and proper structure change, which naturally leads to the RNA folding problem including structure prediction and folding kinetics. Due to the polyanionic nature of RNAs, RNA folding structure, stability and kinetics are strongly coupled to the ion condition of solution. The main focus of this chapter is to review the recent progress in the three major aspects in RNA folding problem: structure prediction, folding kinetics and ion electrostatics. This chapter will introduce both the recent experimental and theoretical progress, while emphasize the theoretical modelling on the three aspects in RNA folding.
Analysis of Free Modeling Predictions by RBO Aleph in CASP11

PubMed Central

Mabrouk, Mahmoud; Werner, Tim; Schneider, Michael; Putz, Ines; Brock, Oliver

2015-01-01

The CASP experiment is a biannual benchmark for assessing protein structure prediction methods. In CASP11, RBO Aleph ranked as one of the top-performing automated servers in the free modeling category. This category consists of targets for which structural templates are not easily retrievable. We analyze the performance of RBO Aleph and show that its success in CASP was a result of its ab initio structure prediction protocol. A detailed analysis of this protocol demonstrates that two components unique to our method greatly contributed to prediction quality: residue–residue contact prediction by EPC-map and contact–guided conformational space search by model-based search (MBS). Interestingly, our analysis also points to a possible fundamental problem in evaluating the performance of protein structure prediction methods: Improvements in components of the method do not necessarily lead to improvements of the entire method. This points to the fact that these components interact in ways that are poorly understood. This problem, if indeed true, represents a significant obstacle to community-wide progress. PMID:26492194
Workplace responsibility, stress, alcohol availability and norms as predictors of alcohol consumption-related problems among employed workers.

PubMed

Hodgins, David C; Williams, Robert; Munro, Gordon

2009-01-01

The objectives of this study were to determine the prevalence of alcohol use and problems among employed individuals in Alberta, Canada (N = 1,890), and to conduct a multivariate examination of predictors of alcohol consumption-related problems. General alcohol problems were identified by 10%, although very few workers described any specific work-related alcohol problems (1%). Structural equation modeling revealed that, as hypothesized, workplace alcohol availability predicted general alcohol problems. Job responsibility and workplace norms also predicted alcohol problems but only for men. Perceived work stress did not predict alcohol problems. Results support the development of interventions that focus on re-shaping alcohol use norms.
K-Partite RNA Secondary Structures

NASA Astrophysics Data System (ADS)

Jiang, Minghui; Tejada, Pedro J.; Lasisi, Ramoni O.; Cheng, Shanhong; Fechser, D. Scott

RNA secondary structure prediction is a fundamental problem in structural bioinformatics. The prediction problem is difficult because RNA secondary structures may contain pseudoknots formed by crossing base pairs. We introduce k-partite secondary structures as a simple classification of RNA secondary structures with pseudoknots. An RNA secondary structure is k-partite if it is the union of k pseudoknot-free sub-structures. Most known RNA secondary structures are either bipartite or tripartite. We show that there exists a constant number k such that any secondary structure can be modified into a k-partite secondary structure with approximately the same free energy. This offers a partial explanation of the prevalence of k-partite secondary structures with small k. We give a complete characterization of the computational complexities of recognizing k-partite secondary structures for all k ≥ 2, and show that this recognition problem is essentially the same as the k-colorability problem on circle graphs. We present two simple heuristics, iterated peeling and first-fit packing, for finding k-partite RNA secondary structures. For maximizing the number of base pair stackings, our iterated peeling heuristic achieves a constant approximation ratio of at most k for 2 ≤ k ≤ 5, and at most frac6{1-(1-6/k)^k} le frac6{1-e^{-6}} < 6.01491 for k ≥ 6. Experiment on sequences from PseudoBase shows that our first-fit packing heuristic outperforms the leading method HotKnots in predicting RNA secondary structures with pseudoknots. Source code, data set, and experimental results are available at http://www.cs.usu.edu/ mjiang/rna/kpartite/.
Improved hybrid optimization algorithm for 3D protein structure prediction.

PubMed

Zhou, Changjun; Hou, Caixia; Wei, Xiaopeng; Zhang, Qiang

2014-07-01

A new improved hybrid optimization algorithm - PGATS algorithm, which is based on toy off-lattice model, is presented for dealing with three-dimensional protein structure prediction problems. The algorithm combines the particle swarm optimization (PSO), genetic algorithm (GA), and tabu search (TS) algorithms. Otherwise, we also take some different improved strategies. The factor of stochastic disturbance is joined in the particle swarm optimization to improve the search ability; the operations of crossover and mutation that are in the genetic algorithm are changed to a kind of random liner method; at last tabu search algorithm is improved by appending a mutation operator. Through the combination of a variety of strategies and algorithms, the protein structure prediction (PSP) in a 3D off-lattice model is achieved. The PSP problem is an NP-hard problem, but the problem can be attributed to a global optimization problem of multi-extremum and multi-parameters. This is the theoretical principle of the hybrid optimization algorithm that is proposed in this paper. The algorithm combines local search and global search, which overcomes the shortcoming of a single algorithm, giving full play to the advantage of each algorithm. In the current universal standard sequences, Fibonacci sequences and real protein sequences are certified. Experiments show that the proposed new method outperforms single algorithms on the accuracy of calculating the protein sequence energy value, which is proved to be an effective way to predict the structure of proteins.
Beam-column joint shear prediction using hybridized deep learning neural network with genetic algorithm

NASA Astrophysics Data System (ADS)

Mundher Yaseen, Zaher; Abdulmohsin Afan, Haitham; Tran, Minh-Tung

2018-04-01

Scientifically evidenced that beam-column joints are a critical point in the reinforced concrete (RC) structure under the fluctuation loads effects. In this novel hybrid data-intelligence model developed to predict the joint shear behavior of exterior beam-column structure frame. The hybrid data-intelligence model is called genetic algorithm integrated with deep learning neural network model (GA-DLNN). The genetic algorithm is used as prior modelling phase for the input approximation whereas the DLNN predictive model is used for the prediction phase. To demonstrate this structural problem, experimental data is collected from the literature that defined the dimensional and specimens’ properties. The attained findings evidenced the efficitveness of the hybrid GA-DLNN in modelling beam-column joint shear problem. In addition, the accurate prediction achived with less input variables owing to the feasibility of the evolutionary phase.

Analysis of free modeling predictions by RBO aleph in CASP11.

PubMed

Mabrouk, Mahmoud; Werner, Tim; Schneider, Michael; Putz, Ines; Brock, Oliver

2016-09-01

The CASP experiment is a biannual benchmark for assessing protein structure prediction methods. In CASP11, RBO Aleph ranked as one of the top-performing automated servers in the free modeling category. This category consists of targets for which structural templates are not easily retrievable. We analyze the performance of RBO Aleph and show that its success in CASP was a result of its ab initio structure prediction protocol. A detailed analysis of this protocol demonstrates that two components unique to our method greatly contributed to prediction quality: residue-residue contact prediction by EPC-map and contact-guided conformational space search by model-based search (MBS). Interestingly, our analysis also points to a possible fundamental problem in evaluating the performance of protein structure prediction methods: Improvements in components of the method do not necessarily lead to improvements of the entire method. This points to the fact that these components interact in ways that are poorly understood. This problem, if indeed true, represents a significant obstacle to community-wide progress. Proteins 2016; 84(Suppl 1):87-104. © 2015 Wiley Periodicals, Inc. © 2015 Wiley Periodicals, Inc.
CELFE/NASTRAN Code for the Analysis of Structures Subjected to High Velocity Impact

NASA Technical Reports Server (NTRS)

Chamis, C. C.

1978-01-01

CELFE (Coupled Eulerian Lagrangian Finite Element)/NASTRAN Code three-dimensional finite element code has the capability for analyzing of structures subjected to high velocity impact. The local response is predicted by CELFE and, for large problems, the far-field impact response is predicted by NASTRAN. The coupling of the CELFE code with NASTRAN (CELFE/NASTRAN code) and the application of the code to selected three-dimensional high velocity impact problems are described.
From molecule to solid: The prediction of organic crystal structures

NASA Astrophysics Data System (ADS)

Dzyabchenko, A. V.

2008-10-01

A method for predicting the structure of a molecular crystal based on the systematic search for a global potential energy minimum is considered. The method takes into account unequal occurrences of the structural classes of organic crystals and symmetry of the multidimensional configuration space. The programs of global minimization PMC, comparison of crystal structures CRYCOM, and approximation to the distributions of the electrostatic potentials of molecules FitMEP are presented as tools for numerically solving the problem. Examples of predicted structures substantiated experimentally and the experience of author’s participation in international tests of crystal structure prediction organized by the Cambridge Crystallographic Data Center (Cambridge, UK) are considered.
The Proteome Folding Project: Proteome-scale prediction of structure and function

PubMed Central

Drew, Kevin; Winters, Patrick; Butterfoss, Glenn L.; Berstis, Viktors; Uplinger, Keith; Armstrong, Jonathan; Riffle, Michael; Schweighofer, Erik; Bovermann, Bill; Goodlett, David R.; Davis, Trisha N.; Shasha, Dennis; Malmström, Lars; Bonneau, Richard

2011-01-01

The incompleteness of proteome structure and function annotation is a critical problem for biologists and, in particular, severely limits interpretation of high-throughput and next-generation experiments. We have developed a proteome annotation pipeline based on structure prediction, where function and structure annotations are generated using an integration of sequence comparison, fold recognition, and grid-computing-enabled de novo structure prediction. We predict protein domain boundaries and three-dimensional (3D) structures for protein domains from 94 genomes (including human, Arabidopsis, rice, mouse, fly, yeast, Escherichia coli, and worm). De novo structure predictions were distributed on a grid of more than 1.5 million CPUs worldwide (World Community Grid). We generated significant numbers of new confident fold annotations (9% of domains that are otherwise unannotated in these genomes). We demonstrate that predicted structures can be combined with annotations from the Gene Ontology database to predict new and more specific molecular functions. PMID:21824995
AI AND SAR APPROACHES FOR PREDICTING CHEMICAL CARCINOGENICITY: SURVEY AND STATUS REPORT

EPA Science Inventory

A wide variety of artificial intelligence (AI) and structure-activity relationship (SAR approaches have been applied to tackling the general problem of predicting rodent chemical carcinogenicity. Given the diversity of chemical structures and mechanisms relative to this endpoin...
Intuitive reasoning about abstract and familiar physics problems

NASA Technical Reports Server (NTRS)

Kaiser, Mary Kister; Jonides, John; Alexander, Joanne

1986-01-01

Previous research has demonstrated that many people have misconceptions about basic properties of motion. Two experiments examined whether people are more likely to produce dynamically correct predictions about basic motion problems involving situations with which they are familiar, and whether solving such problems enhances performance on a subsequent abstract problem. In experiment 1, college students were asked to predict the trajectories of objects exiting a curved tube. Subjects were more accurate on the familiar version of the problem, and there was no evidence of transfer to the abstract problem. In experiment 2, two familiar problems were provided in an attempt to enhance subjects' tendency to extract the general structure of the problems. Once again, they gave more correct responses to the familiar problems but failed to generalize to the abstract problem. Formal physics training was associated with correct predictions for the abstract problem but was unrelated to performance on the familiar problems.
Child maltreatment, parent alcohol and drug-related problems, polydrug problems, and parenting practices: a test of gender differences and four theoretical perspectives.

PubMed

Locke, Thomas F; Newcomb, Michael

2004-03-01

The authors tested how adverse childhood experiences (child maltreatment and parent alcohol- and drug-related problems) and adult polydrug use (as a mediator) predict poor parenting in a community sample (237 mothers and 81 fathers). These relationships were framed within several theoretical perspectives, including observational learning, impaired functioning, self-medication, and parentification-pseudomaturity. Structural models revealed that child maltreatment predicted poor parenting practices among mothers. Parent alcohol- and drug-related problems had an indirect detrimental influence on mothers' parenting and practices through self-drug problems. Among fathers, emotional neglect experienced as a child predicted lack of parental warmth more parental neglect, and sexual abuse experienced as a child predicted a rejecting style of parenting.
The relation of locus-of-control orientation and task structure to problem-solving performance of sixth-grade student pairs

NASA Astrophysics Data System (ADS)

Main, June Dewey; Budd Rowe, Mary

This study investigated the relationship of locus-of-control orientations and task structure to the science problem-solving performance of 100 same-sex, sixth-grade student pairs. Pairs performed a four-variable problem-solving task, racing cylinders down a ramp in a series of trials to determine the 3 fastest of 18 different cylinders. The task was completed in one of two treatment conditions: the structured condition with moderate cuing and the unstructured condition with minimal cuing. Pairs completed an after-task assessment, predicting the results of proposed cylinder races, to measure the ability to understand and apply task concepts. Overall conclusions were: (1) There was no relationship between locus-of-control orientation and effectiveness of problem-solving strategy; (2) internality was significantly related to higher accuracy on task solutions and on after-task predictions; (3) there was no significant relationship between task structure and effectiveness of problem-solving strategy; (4) solutions to the task were more accurate in the unstructured task condition; (5) internality related to more accurate solutions in the unstructured task condition.
A protein-dependent side-chain rotamer library.

PubMed

Bhuyan, Md Shariful Islam; Gao, Xin

2011-12-14

Protein side-chain packing problem has remained one of the key open problems in bioinformatics. The three main components of protein side-chain prediction methods are a rotamer library, an energy function and a search algorithm. Rotamer libraries summarize the existing knowledge of the experimentally determined structures quantitatively. Depending on how much contextual information is encoded, there are backbone-independent rotamer libraries and backbone-dependent rotamer libraries. Backbone-independent libraries only encode sequential information, whereas backbone-dependent libraries encode both sequential and locally structural information. However, side-chain conformations are determined by spatially local information, rather than sequentially local information. Since in the side-chain prediction problem, the backbone structure is given, spatially local information should ideally be encoded into the rotamer libraries. In this paper, we propose a new type of backbone-dependent rotamer library, which encodes structural information of all the spatially neighboring residues. We call it protein-dependent rotamer libraries. Given any rotamer library and a protein backbone structure, we first model the protein structure as a Markov random field. Then the marginal distributions are estimated by the inference algorithms, without doing global optimization or search. The rotamers from the given library are then re-ranked and associated with the updated probabilities. Experimental results demonstrate that the proposed protein-dependent libraries significantly outperform the widely used backbone-dependent libraries in terms of the side-chain prediction accuracy and the rotamer ranking ability. Furthermore, without global optimization/search, the side-chain prediction power of the protein-dependent library is still comparable to the global-search-based side-chain prediction methods.
A Template-Based Protein Structure Reconstruction Method Using Deep Autoencoder Learning.

PubMed

Li, Haiou; Lyu, Qiang; Cheng, Jianlin

2016-12-01

Protein structure prediction is an important problem in computational biology, and is widely applied to various biomedical problems such as protein function study, protein design, and drug design. In this work, we developed a novel deep learning approach based on a deeply stacked denoising autoencoder for protein structure reconstruction. We applied our approach to a template-based protein structure prediction using only the 3D structural coordinates of homologous template proteins as input. The templates were identified for a target protein by a PSI-BLAST search. 3DRobot (a program that automatically generates diverse and well-packed protein structure decoys) was used to generate initial decoy models for the target from the templates. A stacked denoising autoencoder was trained on the decoys to obtain a deep learning model for the target protein. The trained deep model was then used to reconstruct the final structural model for the target sequence. With target proteins that have highly similar template proteins as benchmarks, the GDT-TS score of the predicted structures is greater than 0.7, suggesting that the deep autoencoder is a promising method for protein structure reconstruction.
Structured Parent-Child Observations Predict Development of Conduct Problems: the Importance of Parental Negative Attention in Child-Directed Play.

PubMed

Fleming, Andrew P; McMahon, Robert J; King, Kevin M

2017-04-01

Structured observations of parent-child interactions are commonly used in research and clinical settings, but require additional empirical support. The current study examined the capacity of child-directed play, parent-directed play, and parent-directed chore interaction analogs to uniquely predict the development of conduct problems across a 6-year follow-up period. Parent-child observations were collected from 338 families from high-risk neighborhoods during the summer following the child's first-grade year. Participating children were 49.2 % female, 54.4 % white, and 45.6 % black, and had an average age of 7.52 years at the first assessment. Conduct problems were assessed via parent report and teacher report at five assessment points between first grade and seventh grade. Latent growth curve modeling was used to analyze predictors of conduct problem trajectory across this 6-year follow-up period. When race, sex, socioeconomic status, and maternal depressive symptoms were controlled, parental negative attention during child-directed play predicted higher levels of parent-reported conduct problems concurrently and after a 6-year follow-up period. Parental negative attention during child-directed play also predicted higher teacher-reported conduct problems 6 years later. Findings support the use of child-directed play and parent-directed chore analogs in predicting longitudinal development of conduct problems. The presence of parental negative attention during child-directed play appears to be an especially important predictor of greater conduct problems over time and across multiple domains. Additionally, the potential importance of task-incongruent behavior is proposed for further study.
Multiobjective evolutionary algorithm with many tables for purely ab initio protein structure prediction.

PubMed

Brasil, Christiane Regina Soares; Delbem, Alexandre Claudio Botazzo; da Silva, Fernando Luís Barroso

2013-07-30

This article focuses on the development of an approach for ab initio protein structure prediction (PSP) without using any earlier knowledge from similar protein structures, as fragment-based statistics or inference of secondary structures. Such an approach is called purely ab initio prediction. The article shows that well-designed multiobjective evolutionary algorithms can predict relevant protein structures in a purely ab initio way. One challenge for purely ab initio PSP is the prediction of structures with β-sheets. To work with such proteins, this research has also developed procedures to efficiently estimate hydrogen bond and solvation contribution energies. Considering van der Waals, electrostatic, hydrogen bond, and solvation contribution energies, the PSP is a problem with four energetic terms to be minimized. Each interaction energy term can be considered an objective of an optimization method. Combinatorial problems with four objectives have been considered too complex for the available multiobjective optimization (MOO) methods. The proposed approach, called "Multiobjective evolutionary algorithms with many tables" (MEAMT), can efficiently deal with four objectives through the combination thereof, performing a more adequate sampling of the objective space. Therefore, this method can better map the promising regions in this space, predicting structures in a purely ab initio way. In other words, MEAMT is an efficient optimization method for MOO, which explores simultaneously the search space as well as the objective space. MEAMT can predict structures with one or two domains with RMSDs comparable to values obtained by recently developed ab initio methods (GAPFCG , I-PAES, and Quark) that use different levels of earlier knowledge. Copyright © 2013 Wiley Periodicals, Inc.
REVIEWS OF TOPICAL PROBLEMS: Prediction and discovery of new structures in spiral galaxies

NASA Astrophysics Data System (ADS)

Fridman, Aleksei M.

2007-02-01

A review is given of the last 20 years of published research into the nature, origin mechanisms, and observed features of spiral-vortex structures found in galaxies. The so-called rotating shallow water experiments are briefly discussed, carried out with a facility designed by the present author and built at the Russian Scientific Center 'Kurchatov Institute' to model the origin of galactic spiral structures. The discovery of new vortex-anticyclone structures in these experiments stimulated searching for them astronomically using the RAS Special Astrophysical Observatory's 6-meter BTA optical telescope, formerly the world's and now Europe's largest. Seven years after the pioneering experiments, Afanasyev and the present author discovered the predicted giant anticyclones in the galaxy Mrk 1040 by using BTA. Somewhat later, the theoretical prediction of giant cyclones in spiral galaxies was made, also to be verified by BTA afterwards. To use the observed line-of-sight velocity field for reconstructing the 3D velocity vector distribution in a galactic disk, a method for solving a problem from the class of ill-posed astrophysical problems was developed by the present author and colleagues. In addition to the vortex structure, other new features were discovered — in particular, slow bars (another theoretical prediction), for whose discovery an observational test capable of distinguishing them from their earlier-studied normal (fast) counterparts was designed.
Clusters of Behaviors and Beliefs Predicting Adolescent Depression: Implications for Prevention

PubMed Central

Paunesku, David; Ellis, Justin; Fogel, Joshua; Kuwabara, Sachiko A; Gollan, Jackie; Gladstone, Tracy; Reinecke, Mark; Van Voorhees, Benjamin W.

2009-01-01

OBJECTIVE Risk factors for various disorders are known to cluster. However, the factor structure for behaviors and beliefs predicting depressive disorder in adolescents is not known. Knowledge of this structure can facilitate prevention planning. METHODS We used the National Longitudinal Study of Adolescent Health (AddHealth) data set to conduct an exploratory factor analysis to identify clusters of behaviors/experiences predicting the onset of major depressive disorder (MDD) at 1-year follow-up (N=4,791). RESULTS Four factors were identified: family/interpersonal relations, self-emancipation, avoidant problem solving/low self-worth, and religious activity. Strong family/interpersonal relations were the most significantly protective against depression at one year follow-up. Avoidant problem solving/low self-worth was not predictive of MDD on its own, but significantly amplified the risks associated with delinquency. CONCLUSION Depression prevention interventions should consider giving family relationships a more central role in their efforts. Programs teaching problem solving skills may be most appropriate for reducing MDD risk in delinquent youth. PMID:20502621
A Confidant Support and Problem Solving Model of Divorced Fathers’ Parenting

PubMed Central

DeGarmo, David S.; Forgatch, Marion S.

2011-01-01

This study tested a hypothesized social interaction learning (SIL) model of confidant support and paternal parenting. The latent growth curve analysis employed 230 recently divorced fathers, of which 177 enrolled support confidants, to test confidant support as a predictor of problem solving outcomes and problem solving outcomes as predictors of change in fathers’ parenting. Fathers’ parenting was hypothesized to predict growth in child behavior. Observational measures of support behaviors and problem solving outcomes were obtained from structured discussions of personal and parenting issues faced by the fathers. Findings replicated and extended prior cross-sectional studies with divorced mothers and their confidants. Confidant support predicted better problem solving outcomes, problem solving predicted more effective parenting, and parenting in turn predicted growth in children’s reduced total problem behavior T scores over 18 months. Supporting a homophily perspective, fathers’ antisociality was associated with confidant antisociality but only fathers’ antisociality influenced the support process model. Intervention implications are discussed regarding SIL parent training and social support. PMID:21541814
A confidant support and problem solving model of divorced fathers' parenting.

PubMed

Degarmo, David S; Forgatch, Marion S

2012-03-01

This study tested a hypothesized social interaction learning (SIL) model of confidant support and paternal parenting. The latent growth curve analysis employed 230 recently divorced fathers, of which 177 enrolled support confidants, to test confidant support as a predictor of problem solving outcomes and problem solving outcomes as predictors of change in fathers' parenting. Fathers' parenting was hypothesized to predict growth in child behavior. Observational measures of support behaviors and problem solving outcomes were obtained from structured discussions of personal and parenting issues faced by the fathers. Findings replicated and extended prior cross-sectional studies with divorced mothers and their confidants. Confidant support predicted better problem solving outcomes, problem solving predicted more effective parenting, and parenting in turn predicted growth in children's reduced total problem behavior T scores over 18 months. Supporting a homophily perspective, fathers' antisociality was associated with confidant antisociality but only fathers' antisociality influenced the support process model. Intervention implications are discussed regarding SIL parent training and social support.
Revealing how network structure affects accuracy of link prediction

NASA Astrophysics Data System (ADS)

Yang, Jin-Xuan; Zhang, Xiao-Dong

2017-08-01

Link prediction plays an important role in network reconstruction and network evolution. The network structure affects the accuracy of link prediction, which is an interesting problem. In this paper we use common neighbors and the Gini coefficient to reveal the relation between them, which can provide a good reference for the choice of a suitable link prediction algorithm according to the network structure. Moreover, the statistical analysis reveals correlation between the common neighbors index, Gini coefficient index and other indices to describe the network structure, such as Laplacian eigenvalues, clustering coefficient, degree heterogeneity, and assortativity of network. Furthermore, a new method to predict missing links is proposed. The experimental results show that the proposed algorithm yields better prediction accuracy and robustness to the network structure than existing currently used methods for a variety of real-world networks.
AGARD Manual on Aeroelasticity in Axial-Flow Turbomachines. Volume 2. Structural Dynamics and Aeroelasticity,

DTIC Science & Technology

1988-06-01

LEVELSKSI C. Q ac ca VANE OVERALL TOTAL-STATIC EXPANSION RATOS * Figure 12. Prediction of Response due to Second Stage Vane. 22-12 SAP /- MAXIMUM...assessment methods, written by Armstrong. The problem of life time prediction is reviewed by Labourdette, who also summarizes ONERA’s research in...applicable to single blades and bladed assemblies. The blade fatigue problem and its assessment methods, and life-time- prediction are considered. Aeroelastic
A Method for WD40 Repeat Detection and Secondary Structure Prediction

PubMed Central

Wang, Yang; Jiang, Fan; Zhuo, Zhu; Wu, Xian-Hui; Wu, Yun-Dong

2013-01-01

WD40-repeat proteins (WD40s), as one of the largest protein families in eukaryotes, play vital roles in assembling protein-protein/DNA/RNA complexes. WD40s fold into similar β-propeller structures despite diversified sequences. A program WDSP (WD40 repeat protein Structure Predictor) has been developed to accurately identify WD40 repeats and predict their secondary structures. The method is designed specifically for WD40 proteins by incorporating both local residue information and non-local family-specific structural features. It overcomes the problem of highly diversified protein sequences and variable loops. In addition, WDSP achieves a better prediction in identifying multiple WD40-domain proteins by taking the global combination of repeats into consideration. In secondary structure prediction, the average Q3 accuracy of WDSP in jack-knife test reaches 93.7%. A disease related protein LRRK2 was used as a representive example to demonstrate the structure prediction. PMID:23776530
A novel Multi-Agent Ada-Boost algorithm for predicting protein structural class with the information of protein secondary structure.

PubMed

Fan, Ming; Zheng, Bin; Li, Lihua

2015-10-01

Knowledge of the structural class of a given protein is important for understanding its folding patterns. Although a lot of efforts have been made, it still remains a challenging problem for prediction of protein structural class solely from protein sequences. The feature extraction and classification of proteins are the main problems in prediction. In this research, we extended our earlier work regarding these two aspects. In protein feature extraction, we proposed a scheme by calculating the word frequency and word position from sequences of amino acid, reduced amino acid, and secondary structure. For an accurate classification of the structural class of protein, we developed a novel Multi-Agent Ada-Boost (MA-Ada) method by integrating the features of Multi-Agent system into Ada-Boost algorithm. Extensive experiments were taken to test and compare the proposed method using four benchmark datasets in low homology. The results showed classification accuracies of 88.5%, 96.0%, 88.4%, and 85.5%, respectively, which are much better compared with the existing methods. The source code and dataset are available on request.

Reducing the worst case running times of a family of RNA and CFG problems, using Valiant's approach.

PubMed

Zakov, Shay; Tsur, Dekel; Ziv-Ukelson, Michal

2011-08-18

RNA secondary structure prediction is a mainstream bioinformatic domain, and is key to computational analysis of functional RNA. In more than 30 years, much research has been devoted to defining different variants of RNA structure prediction problems, and to developing techniques for improving prediction quality. Nevertheless, most of the algorithms in this field follow a similar dynamic programming approach as that presented by Nussinov and Jacobson in the late 70's, which typically yields cubic worst case running time algorithms. Recently, some algorithmic approaches were applied to improve the complexity of these algorithms, motivated by new discoveries in the RNA domain and by the need to efficiently analyze the increasing amount of accumulated genome-wide data. We study Valiant's classical algorithm for Context Free Grammar recognition in sub-cubic time, and extract features that are common to problems on which Valiant's approach can be applied. Based on this, we describe several problem templates, and formulate generic algorithms that use Valiant's technique and can be applied to all problems which abide by these templates, including many problems within the world of RNA Secondary Structures and Context Free Grammars. The algorithms presented in this paper improve the theoretical asymptotic worst case running time bounds for a large family of important problems. It is also possible that the suggested techniques could be applied to yield a practical speedup for these problems. For some of the problems (such as computing the RNA partition function and base-pair binding probabilities), the presented techniques are the only ones which are currently known for reducing the asymptotic running time bounds of the standard algorithms.
Reducing the worst case running times of a family of RNA and CFG problems, using Valiant's approach

PubMed Central

2011-01-01

Background RNA secondary structure prediction is a mainstream bioinformatic domain, and is key to computational analysis of functional RNA. In more than 30 years, much research has been devoted to defining different variants of RNA structure prediction problems, and to developing techniques for improving prediction quality. Nevertheless, most of the algorithms in this field follow a similar dynamic programming approach as that presented by Nussinov and Jacobson in the late 70's, which typically yields cubic worst case running time algorithms. Recently, some algorithmic approaches were applied to improve the complexity of these algorithms, motivated by new discoveries in the RNA domain and by the need to efficiently analyze the increasing amount of accumulated genome-wide data. Results We study Valiant's classical algorithm for Context Free Grammar recognition in sub-cubic time, and extract features that are common to problems on which Valiant's approach can be applied. Based on this, we describe several problem templates, and formulate generic algorithms that use Valiant's technique and can be applied to all problems which abide by these templates, including many problems within the world of RNA Secondary Structures and Context Free Grammars. Conclusions The algorithms presented in this paper improve the theoretical asymptotic worst case running time bounds for a large family of important problems. It is also possible that the suggested techniques could be applied to yield a practical speedup for these problems. For some of the problems (such as computing the RNA partition function and base-pair binding probabilities), the presented techniques are the only ones which are currently known for reducing the asymptotic running time bounds of the standard algorithms. PMID:21851589
Fast metabolite identification with Input Output Kernel Regression.

PubMed

Brouard, Céline; Shen, Huibin; Dührkop, Kai; d'Alché-Buc, Florence; Böcker, Sebastian; Rousu, Juho

2016-06-15

An important problematic of metabolomics is to identify metabolites using tandem mass spectrometry data. Machine learning methods have been proposed recently to solve this problem by predicting molecular fingerprint vectors and matching these fingerprints against existing molecular structure databases. In this work we propose to address the metabolite identification problem using a structured output prediction approach. This type of approach is not limited to vector output space and can handle structured output space such as the molecule space. We use the Input Output Kernel Regression method to learn the mapping between tandem mass spectra and molecular structures. The principle of this method is to encode the similarities in the input (spectra) space and the similarities in the output (molecule) space using two kernel functions. This method approximates the spectra-molecule mapping in two phases. The first phase corresponds to a regression problem from the input space to the feature space associated to the output kernel. The second phase is a preimage problem, consisting in mapping back the predicted output feature vectors to the molecule space. We show that our approach achieves state-of-the-art accuracy in metabolite identification. Moreover, our method has the advantage of decreasing the running times for the training step and the test step by several orders of magnitude over the preceding methods. celine.brouard@aalto.fi Supplementary data are available at Bioinformatics online. © The Author 2016. Published by Oxford University Press.
Fast metabolite identification with Input Output Kernel Regression

PubMed Central

Brouard, Céline; Shen, Huibin; Dührkop, Kai; d'Alché-Buc, Florence; Böcker, Sebastian; Rousu, Juho

2016-01-01

Motivation: An important problematic of metabolomics is to identify metabolites using tandem mass spectrometry data. Machine learning methods have been proposed recently to solve this problem by predicting molecular fingerprint vectors and matching these fingerprints against existing molecular structure databases. In this work we propose to address the metabolite identification problem using a structured output prediction approach. This type of approach is not limited to vector output space and can handle structured output space such as the molecule space. Results: We use the Input Output Kernel Regression method to learn the mapping between tandem mass spectra and molecular structures. The principle of this method is to encode the similarities in the input (spectra) space and the similarities in the output (molecule) space using two kernel functions. This method approximates the spectra-molecule mapping in two phases. The first phase corresponds to a regression problem from the input space to the feature space associated to the output kernel. The second phase is a preimage problem, consisting in mapping back the predicted output feature vectors to the molecule space. We show that our approach achieves state-of-the-art accuracy in metabolite identification. Moreover, our method has the advantage of decreasing the running times for the training step and the test step by several orders of magnitude over the preceding methods. Availability and implementation: Contact: celine.brouard@aalto.fi Supplementary information: Supplementary data are available at Bioinformatics online. PMID:27307628
Analysis of deep learning methods for blind protein contact prediction in CASP12.

PubMed

Wang, Sheng; Sun, Siqi; Xu, Jinbo

2018-03-01

Here we present the results of protein contact prediction achieved in CASP12 by our RaptorX-Contact server, which is an early implementation of our deep learning method for contact prediction. On a set of 38 free-modeling target domains with a median family size of around 58 effective sequences, our server obtained an average top L/5 long- and medium-range contact accuracy of 47% and 44%, respectively (L = length). A complete implementation has an average accuracy of 59% and 57%, respectively. Our deep learning method formulates contact prediction as a pixel-level image labeling problem and simultaneously predicts all residue pairs of a protein using a combination of two deep residual neural networks, taking as input the residue conservation information, predicted secondary structure and solvent accessibility, contact potential, and coevolution information. Our approach differs from existing methods mainly in (1) formulating contact prediction as a pixel-level image labeling problem instead of an image-level classification problem; (2) simultaneously predicting all contacts of an individual protein to make effective use of contact occurrence patterns; and (3) integrating both one-dimensional and two-dimensional deep convolutional neural networks to effectively learn complex sequence-structure relationship including high-order residue correlation. This paper discusses the RaptorX-Contact pipeline, both contact prediction and contact-based folding results, and finally the strength and weakness of our method. © 2017 Wiley Periodicals, Inc.
RNA design using simulated SHAPE data.

PubMed

Lotfi, Mohadeseh; Zare-Mirakabad, Fatemeh; Montaseri, Soheila

2018-05-03

It has long been established that in addition to being involved in protein translation, RNA plays essential roles in numerous other cellular processes, including gene regulation and DNA replication. Such roles are known to be dictated by higher-order structures of RNA molecules. It is therefore of prime importance to find an RNA sequence that can fold to acquire a particular function that is desirable for use in pharmaceuticals and basic research. The challenge of finding an RNA sequence for a given structure is known as the RNA design problem. Although there are several algorithms to solve this problem, they mainly consider hard constraints, such as minimum free energy, to evaluate the predicted sequences. Recently, SHAPE data has emerged as a new soft constraint for RNA secondary structure prediction. To take advantage of this new experimental constraint, we report here a new method for accurate design of RNA sequences based on their secondary structures using SHAPE data as pseudo-free energy. We then compare our algorithm with four others: INFO-RNA, ERD, MODENA and RNAifold 2.0. Our algorithm precisely predicts 26 out of 29 new sequences for the structures extracted from the Rfam dataset, while the other four algorithms predict no more than 22 out of 29. The proposed algorithm is comparable to the above algorithms on RNA-SSD datasets, where they can predict up to 33 appropriate sequences for RNA secondary structures out of 34.
Longitudinal Associations Between Marital Instability and Child Sleep Problems across Infancy and Toddlerhood in Adoptive Families

PubMed Central

Mannering, Anne M.; Harold, Gordon T.; Leve, Leslie D.; Shelton, Katherine H.; Shaw, Daniel S.; Conger, Rand D.; Neiderhiser, Jenae M.; Scaramella, Laura V.; Reiss, David

2009-01-01

This study examined the longitudinal association between marital instability and child sleep problems at ages 9 and 18 months in 357 families with a genetically unrelated infant adopted at birth. This design eliminates shared genes as an explanation for similarities between parent and child. Structural equation modeling indicated that T1 marital instability predicted T2 child sleep problems, but T1 child sleep problems did not predict T2 marital instability. This pattern of results was replicated when models were estimated separately for mothers and children and for fathers and children. Thus, even after controlling for stability in sleep problems and marital instability and eliminating shared genetic influences on associations using a longitudinal adoption design, marital instability prospectively predicts early childhood sleep patterns. PMID:21557740
Base pair probability estimates improve the prediction accuracy of RNA non-canonical base pairs

PubMed Central

2017-01-01

Prediction of RNA tertiary structure from sequence is an important problem, but generating accurate structure models for even short sequences remains difficult. Predictions of RNA tertiary structure tend to be least accurate in loop regions, where non-canonical pairs are important for determining the details of structure. Non-canonical pairs can be predicted using a knowledge-based model of structure that scores nucleotide cyclic motifs, or NCMs. In this work, a partition function algorithm is introduced that allows the estimation of base pairing probabilities for both canonical and non-canonical interactions. Pairs that are predicted to be probable are more likely to be found in the true structure than pairs of lower probability. Pair probability estimates can be further improved by predicting the structure conserved across multiple homologous sequences using the TurboFold algorithm. These pairing probabilities, used in concert with prior knowledge of the canonical secondary structure, allow accurate inference of non-canonical pairs, an important step towards accurate prediction of the full tertiary structure. Software to predict non-canonical base pairs and pairing probabilities is now provided as part of the RNAstructure software package. PMID:29107980
Validation of a Deterministic Vibroacoustic Response Prediction Model

NASA Technical Reports Server (NTRS)

Caimi, Raoul E.; Margasahayam, Ravi

1997-01-01

This report documents the recently completed effort involving validation of a deterministic theory for the random vibration problem of predicting the response of launch pad structures in the low-frequency range (0 to 50 hertz). Use of the Statistical Energy Analysis (SEA) methods is not suitable in this range. Measurements of launch-induced acoustic loads and subsequent structural response were made on a cantilever beam structure placed in close proximity (200 feet) to the launch pad. Innovative ways of characterizing random, nonstationary, non-Gaussian acoustics are used for the development of a structure's excitation model. Extremely good correlation was obtained between analytically computed responses and those measured on the cantilever beam. Additional tests are recommended to bound the problem to account for variations in launch trajectory and inclination.
A Conceptual Model for the Development of Externalizing Behavior Problems Among Kindergarten Children of Alcoholic Families: Role of Parenting and Children's Self-Regulation

PubMed Central

Eiden, Rina D.; Edwards, Ellen P.; Leonard, Kenneth E.

2009-01-01

The purpose of this study was to test a conceptual model predicting children's externalizing behavior problems in kindergarten in a sample of children with alcoholic (n = 130) and nonalcoholic (n = 97) parents. The model examined the role of parents' alcohol diagnoses, depression, and antisocial behavior at 12–18 months of child age in predicting parental warmth/sensitivity at 2 years of child age. Parental warmth/sensitivity at 2 years was hypothesized to predict children's self-regulation at 3 years (effortful control and internalization of rules), which in turn was expected to predict externalizing behavior problems in kindergarten. Structural equation modeling was largely supportive of this conceptual model. Fathers' alcohol diagnosis at 12–18 months was associated with lower maternal and paternal warmth/sensitivity at 2 years. Lower maternal warmth/sensitivity was longitudinally predictive of lower child self-regulation at 3 years, which in turn was longitudinally predictive of higher externalizing behavior problems in kindergarten, after controlling for prior behavior problems. There was a direct association between parents' depression and children's externalizing behavior problems. Results indicate that one pathway to higher externalizing behavior problems among children of alcoholics may be via parenting and self-regulation in the toddler to preschool years. PMID:17723044
Ab initio NMR Confirmed Evolutionary Structure Prediction for Organic Molecular Crystals

NASA Astrophysics Data System (ADS)

Pham, Cong-Huy; Kucukbenli, Emine; de Gironcoli, Stefano

2015-03-01

Ab initio crystal structure prediction of even small organic compounds is extremely challenging due to polymorphism, molecular flexibility and difficulties in addressing the dispersion interaction from first principles. We recently implemented vdW-aware density functionals and demonstrated their success in energy ordering of aminoacid crystals. In this work we combine this development with the evolutionary structure prediction method to study cholesterol polymorphs. Cholesterol crystals have paramount importance in various diseases, from cancer to atherosclerosis. The structure of some polymorphs (e.g. ChM, ChAl, ChAh) have already been resolved while some others, which display distinct NMR spectra and are involved in disease formation, are yet to be determined. Here we thoroughly assess the applicability of evolutionary structure prediction to address such real world problems. We validate the newly predicted structures with ab initio NMR chemical shift data using secondary referencing for an improved comparison with experiments.
Predictive and Experimental Approaches for Elucidating Protein–Protein Interactions and Quaternary Structures

PubMed Central

Nealon, John Oliver; Philomina, Limcy Seby

2017-01-01

The elucidation of protein–protein interactions is vital for determining the function and action of quaternary protein structures. Here, we discuss the difficulty and importance of establishing protein quaternary structure and review in vitro and in silico methods for doing so. Determining the interacting partner proteins of predicted protein structures is very time-consuming when using in vitro methods, this can be somewhat alleviated by use of predictive methods. However, developing reliably accurate predictive tools has proved to be difficult. We review the current state of the art in predictive protein interaction software and discuss the problem of scoring and therefore ranking predictions. Current community-based predictive exercises are discussed in relation to the growth of protein interaction prediction as an area within these exercises. We suggest a fusion of experimental and predictive methods that make use of sparse experimental data to determine higher resolution predicted protein interactions as being necessary to drive forward development. PMID:29206185
Distributed Prognostics based on Structural Model Decomposition

NASA Technical Reports Server (NTRS)

Daigle, Matthew J.; Bregon, Anibal; Roychoudhury, I.

2014-01-01

Within systems health management, prognostics focuses on predicting the remaining useful life of a system. In the model-based prognostics paradigm, physics-based models are constructed that describe the operation of a system and how it fails. Such approaches consist of an estimation phase, in which the health state of the system is first identified, and a prediction phase, in which the health state is projected forward in time to determine the end of life. Centralized solutions to these problems are often computationally expensive, do not scale well as the size of the system grows, and introduce a single point of failure. In this paper, we propose a novel distributed model-based prognostics scheme that formally describes how to decompose both the estimation and prediction problems into independent local subproblems whose solutions may be easily composed into a global solution. The decomposition of the prognostics problem is achieved through structural decomposition of the underlying models. The decomposition algorithm creates from the global system model a set of local submodels suitable for prognostics. Independent local estimation and prediction problems are formed based on these local submodels, resulting in a scalable distributed prognostics approach that allows the local subproblems to be solved in parallel, thus offering increases in computational efficiency. Using a centrifugal pump as a case study, we perform a number of simulation-based experiments to demonstrate the distributed approach, compare the performance with a centralized approach, and establish its scalability. Index Terms-model-based prognostics, distributed prognostics, structural model decomposition ABBREVIATIONS
Predicting Development of Mathematical Word Problem Solving Across the Intermediate Grades

PubMed Central

Tolar, Tammy D.; Fuchs, Lynn; Cirino, Paul T.; Fuchs, Douglas; Hamlett, Carol L.; Fletcher, Jack M.

2012-01-01

This study addressed predictors of the development of word problem solving (WPS) across the intermediate grades. At beginning of 3rd grade, 4 cohorts of students (N = 261) were measured on computation, language, nonverbal reasoning skills, and attentive behavior and were assessed 4 times from beginning of 3rd through end of 5th grade on 2 measures of WPS at low and high levels of complexity. Language skills were related to initial performance at both levels of complexity and did not predict growth at either level. Computational skills had an effect on initial performance in low- but not high-complexity problems and did not predict growth at either level of complexity. Attentive behavior did not predict initial performance but did predict growth in low-complexity, whereas it predicted initial performance but not growth for high-complexity problems. Nonverbal reasoning predicted initial performance and growth for low-complexity WPS, but only growth for high-complexity WPS. This evidence suggests that although mathematical structure is fixed, different cognitive resources may act as limiting factors in WPS development when the WPS context is varied. PMID:23325985
MicroRNAfold: pre-microRNA secondary structure prediction based on modified NCM model with thermodynamics-based scoring strategy.

PubMed

Han, Dianwei; Zhang, Jun; Tang, Guiliang

2012-01-01

An accurate prediction of the pre-microRNA secondary structure is important in miRNA informatics. Based on a recently proposed model, nucleotide cyclic motifs (NCM), to predict RNA secondary structure, we propose and implement a Modified NCM (MNCM) model with a physics-based scoring strategy to tackle the problem of pre-microRNA folding. Our microRNAfold is implemented using a global optimal algorithm based on the bottom-up local optimal solutions. Our experimental results show that microRNAfold outperforms the current leading prediction tools in terms of True Negative rate, False Negative rate, Specificity, and Matthews coefficient ratio.
Dynamics of representational change: entropy, action, and cognition.

PubMed

Stephen, Damian G; Dixon, James A; Isenhower, Robert W

2009-12-01

Explaining how the cognitive system can create new structures has been a major challenge for cognitive science. Self-organization from the theory of nonlinear dynamics offers an account of this remarkable phenomenon. Two studies provide an initial test of the hypothesis that the emergence of new cognitive structure follows the same universal principles as emergence in other domains (e.g., fluids, lasers). In both studies, participants initially solved gear-system problems by manually tracing the force across a system of gears. Subsequently, they discovered that the gears form an alternating sequence, thereby demonstrating a new cognitive structure. In both studies, dynamical analyses of action during problem solving predicted the spontaneous emergence of the new cognitive structure. Study 1 showed that a peak in entropy, followed by negentropy, key indicators of self-organization, predicted discovery of alternation. Study 2 replicated these effects, and showed that increasing environmental entropy accelerated discovery, a classic prediction from dynamics. Additional analyses based on the relationship between phase transitions and power-law behavior provide converging evidence. The studies provide an initial demonstration of the emergence of cognitive structure through self-organization.
Longitudinal associations between marital instability and child sleep problems across infancy and toddlerhood in adoptive families.

PubMed

Mannering, Anne M; Harold, Gordon T; Leve, Leslie D; Shelton, Katherine H; Shaw, Daniel S; Conger, Rand D; Neiderhiser, Jenae M; Scaramella, Laura V; Reiss, David

2011-01-01

This study examined the longitudinal association between marital instability and child sleep problems at ages 9 and 18 months in 357 families with a genetically unrelated infant adopted at birth. This design eliminates shared genes as an explanation for similarities between parent and child. Structural equation modeling indicated that T1 marital instability predicted T2 child sleep problems, but T1 child sleep problems did not predict T2 marital instability. This result was replicated when models were estimated separately for mothers and fathers. Thus, even after controlling for stability in sleep problems and marital instability and eliminating shared genetic influences on associations using a longitudinal adoption design, marital instability prospectively predicts early childhood sleep patterns. © 2011 The Authors. Child Development © 2011 Society for Research in Child Development, Inc.
Structural Equivalence of Involvement in Problem Behavior by Adolescents across Racial Groups Using Multiple Group Confirmatory Factor Analysis.

ERIC Educational Resources Information Center

Williams, James H.; And Others

1996-01-01

Problem behavior theory predicts that adolescent problem behaviors are manifestations of a single behavioral syndrome. This study tested the validity of the theory across racial groups. Results indicate that multiple pathways are necessary to account for the problem behaviors and they support previous research indicating system response bias in…
Dynamic Assessment of Algebraic Learning in Predicting Third Graders’ Development of Mathematical Problem Solving

PubMed Central

Fuchs, Lynn S.; Compton, Donald L.; Fuchs, Douglas; Hollenbeck, Kurstin N.; Craddock, Caitlin F.; Hamlett, Carol L.

2008-01-01

Dynamic assessment (DA) involves helping students learn a task and indexing responsiveness to that instruction as a measure of learning potential. The purpose of this study was to explore the utility of a DA of algebraic learning in predicting 3rd graders’ development of mathematics problem solving. In the fall, 122 3rd-grade students were assessed on language, nonverbal reasoning, attentive behavior, calculations, word-problem skill, and DA. On the basis of random assignment, students received 16 weeks of validated instruction on word problems or received 16 weeks of conventional instruction on word problems. Then, students were assessed on word-problem measures proximal and distal to instruction. Structural equation measurement models showed that DA measured a distinct dimension of pretreatment ability and that proximal and distal word-problem measures were needed to account for outcome. Structural equation modeling showed that instruction (conventional vs. validated) was sufficient to account for math word-problem outcome proximal to instruction; by contrast, language, pretreatment math skill, and DA were needed to forecast learning on word-problem outcomes more distal to instruction. Findings are discussed in terms of responsiveness-to-intervention models for preventing and identifying learning disabilities. PMID:19884957
Blind prediction of noncanonical RNA structure at atomic accuracy.

PubMed

Watkins, Andrew M; Geniesse, Caleb; Kladwang, Wipapat; Zakrevsky, Paul; Jaeger, Luc; Das, Rhiju

2018-05-01

Prediction of RNA structure from nucleotide sequence remains an unsolved grand challenge of biochemistry and requires distinct concepts from protein structure prediction. Despite extensive algorithmic development in recent years, modeling of noncanonical base pairs of new RNA structural motifs has not been achieved in blind challenges. We report a stepwise Monte Carlo (SWM) method with a unique add-and-delete move set that enables predictions of noncanonical base pairs of complex RNA structures. A benchmark of 82 diverse motifs establishes the method's general ability to recover noncanonical pairs ab initio, including multistrand motifs that have been refractory to prior approaches. In a blind challenge, SWM models predicted nucleotide-resolution chemical mapping and compensatory mutagenesis experiments for three in vitro selected tetraloop/receptors with previously unsolved structures (C7.2, C7.10, and R1). As a final test, SWM blindly and correctly predicted all noncanonical pairs of a Zika virus double pseudoknot during a recent community-wide RNA-Puzzle. Stepwise structure formation, as encoded in the SWM method, enables modeling of noncanonical RNA structure in a variety of previously intractable problems.

A new model for approximating RNA folding trajectories and population kinetics

NASA Astrophysics Data System (ADS)

Kirkpatrick, Bonnie; Hajiaghayi, Monir; Condon, Anne

2013-01-01

RNA participates both in functional aspects of the cell and in gene regulation. The interactions of these molecules are mediated by their secondary structure which can be viewed as a planar circle graph with arcs for all the chemical bonds between pairs of bases in the RNA sequence. The problem of predicting RNA secondary structure, specifically the chemically most probable structure, has many useful and efficient algorithms. This leaves RNA folding, the problem of predicting the dynamic behavior of RNA structure over time, as the main open problem. RNA folding is important for functional understanding because some RNA molecules change secondary structure in response to interactions with the environment. The full RNA folding model on at most O(3n) secondary structures is the gold standard. We present a new subset approximation model for the full model, give methods to analyze its accuracy and discuss the relative merits of our model as compared with a pre-existing subset approximation. The main advantage of our model is that it generates Monte Carlo folding pathways with the same probabilities with which they are generated under the full model. The pre-existing subset approximation does not have this property.
Crystal Structure Predictions Using Adaptive Genetic Algorithm and Motif Search methods

NASA Astrophysics Data System (ADS)

Ho, K. M.; Wang, C. Z.; Zhao, X.; Wu, S.; Lyu, X.; Zhu, Z.; Nguyen, M. C.; Umemoto, K.; Wentzcovitch, R. M. M.

2017-12-01

Material informatics is a new initiative which has attracted a lot of attention in recent scientific research. The basic strategy is to construct comprehensive data sets and use machine learning to solve a wide variety of problems in material design and discovery. In pursuit of this goal, a key element is the quality and completeness of the databases used. Recent advance in the development of crystal structure prediction algorithms has made it a complementary and more efficient approach to explore the structure/phase space in materials using computers. In this talk, we discuss the importance of the structural motifs and motif-networks in crystal structure predictions. Correspondingly, powerful methods are developed to improve the sampling of the low-energy structure landscape.
Modularity of Protein Folds as a Tool for Template-Free Modeling of Structures.

PubMed

Vallat, Brinda; Madrid-Aliste, Carlos; Fiser, Andras

2015-08-01

Predicting the three-dimensional structure of proteins from their amino acid sequences remains a challenging problem in molecular biology. While the current structural coverage of proteins is almost exclusively provided by template-based techniques, the modeling of the rest of the protein sequences increasingly require template-free methods. However, template-free modeling methods are much less reliable and are usually applicable for smaller proteins, leaving much space for improvement. We present here a novel computational method that uses a library of supersecondary structure fragments, known as Smotifs, to model protein structures. The library of Smotifs has saturated over time, providing a theoretical foundation for efficient modeling. The method relies on weak sequence signals from remotely related protein structures to create a library of Smotif fragments specific to the target protein sequence. This Smotif library is exploited in a fragment assembly protocol to sample decoys, which are assessed by a composite scoring function. Since the Smotif fragments are larger in size compared to the ones used in other fragment-based methods, the proposed modeling algorithm, SmotifTF, can employ an exhaustive sampling during decoy assembly. SmotifTF successfully predicts the overall fold of the target proteins in about 50% of the test cases and performs competitively when compared to other state of the art prediction methods, especially when sequence signal to remote homologs is diminishing. Smotif-based modeling is complementary to current prediction methods and provides a promising direction in addressing the structure prediction problem, especially when targeting larger proteins for modeling.
Thermodynamic heuristics with case-based reasoning: combined insights for RNA pseudoknot secondary structure.

PubMed

Al-Khatib, Ra'ed M; Rashid, Nur'Aini Abdul; Abdullah, Rosni

2011-08-01

The secondary structure of RNA pseudoknots has been extensively inferred and scrutinized by computational approaches. Experimental methods for determining RNA structure are time consuming and tedious; therefore, predictive computational approaches are required. Predicting the most accurate and energy-stable pseudoknot RNA secondary structure has been proven to be an NP-hard problem. In this paper, a new RNA folding approach, termed MSeeker, is presented; it includes KnotSeeker (a heuristic method) and Mfold (a thermodynamic algorithm). The global optimization of this thermodynamic heuristic approach was further enhanced by using a case-based reasoning technique as a local optimization method. MSeeker is a proposed algorithm for predicting RNA pseudoknot structure from individual sequences, especially long ones. This research demonstrates that MSeeker improves the sensitivity and specificity of existing RNA pseudoknot structure predictions. The performance and structural results from this proposed method were evaluated against seven other state-of-the-art pseudoknot prediction methods. The MSeeker method had better sensitivity than the DotKnot, FlexStem, HotKnots, pknotsRG, ILM, NUPACK and pknotsRE methods, with 79% of the predicted pseudoknot base-pairs being correct.
CMsearch: simultaneous exploration of protein sequence space and structure space improves not only protein homology detection but also protein structure prediction.

PubMed

Cui, Xuefeng; Lu, Zhiwu; Wang, Sheng; Jing-Yan Wang, Jim; Gao, Xin

2016-06-15

Protein homology detection, a fundamental problem in computational biology, is an indispensable step toward predicting protein structures and understanding protein functions. Despite the advances in recent decades on sequence alignment, threading and alignment-free methods, protein homology detection remains a challenging open problem. Recently, network methods that try to find transitive paths in the protein structure space demonstrate the importance of incorporating network information of the structure space. Yet, current methods merge the sequence space and the structure space into a single space, and thus introduce inconsistency in combining different sources of information. We present a novel network-based protein homology detection method, CMsearch, based on cross-modal learning. Instead of exploring a single network built from the mixture of sequence and structure space information, CMsearch builds two separate networks to represent the sequence space and the structure space. It then learns sequence-structure correlation by simultaneously taking sequence information, structure information, sequence space information and structure space information into consideration. We tested CMsearch on two challenging tasks, protein homology detection and protein structure prediction, by querying all 8332 PDB40 proteins. Our results demonstrate that CMsearch is insensitive to the similarity metrics used to define the sequence and the structure spaces. By using HMM-HMM alignment as the sequence similarity metric, CMsearch clearly outperforms state-of-the-art homology detection methods and the CASP-winning template-based protein structure prediction methods. Our program is freely available for download from http://sfb.kaust.edu.sa/Pages/Software.aspx : xin.gao@kaust.edu.sa Supplementary data are available at Bioinformatics online. © The Author 2016. Published by Oxford University Press.
fRMSDPred: Predicting Local RMSD Between Structural Fragments Using Sequence Information

DTIC Science & Technology

2007-04-04

machine learning approaches for estimating the RMSD value of a pair of protein fragments. These estimated fragment-level RMSD values can be used to construct the alignment, assess the quality of an alignment, and identify high-quality alignment segments. We present algorithms to solve this fragment-level RMSD prediction problem using a supervised learning framework based on support vector regression and classification that incorporates protein profiles, predicted secondary structure, effective information encoding schemes, and novel second-order pairwise exponential kernel
An optimal design of wind turbine and ship structure based on neuro-response surface method

NASA Astrophysics Data System (ADS)

Lee, Jae-Chul; Shin, Sung-Chul; Kim, Soo-Young

2015-07-01

The geometry of engineering systems affects their performances. For this reason, the shape of engineering systems needs to be optimized in the initial design stage. However, engineering system design problems consist of multi-objective optimization and the performance analysis using commercial code or numerical analysis is generally time-consuming. To solve these problems, many engineers perform the optimization using the approximation model (response surface). The Response Surface Method (RSM) is generally used to predict the system performance in engineering research field, but RSM presents some prediction errors for highly nonlinear systems. The major objective of this research is to establish an optimal design method for multi-objective problems and confirm its applicability. The proposed process is composed of three parts: definition of geometry, generation of response surface, and optimization process. To reduce the time for performance analysis and minimize the prediction errors, the approximation model is generated using the Backpropagation Artificial Neural Network (BPANN) which is considered as Neuro-Response Surface Method (NRSM). The optimization is done for the generated response surface by non-dominated sorting genetic algorithm-II (NSGA-II). Through case studies of marine system and ship structure (substructure of floating offshore wind turbine considering hydrodynamics performances and bulk carrier bottom stiffened panels considering structure performance), we have confirmed the applicability of the proposed method for multi-objective side constraint optimization problems.
Free energy minimization to predict RNA secondary structures and computational RNA design.

PubMed

Churkin, Alexander; Weinbrand, Lina; Barash, Danny

2015-01-01

Determining the RNA secondary structure from sequence data by computational predictions is a long-standing problem. Its solution has been approached in two distinctive ways. If a multiple sequence alignment of a collection of homologous sequences is available, the comparative method uses phylogeny to determine conserved base pairs that are more likely to form as a result of billions of years of evolution than by chance. In the case of single sequences, recursive algorithms that compute free energy structures by using empirically derived energy parameters have been developed. This latter approach of RNA folding prediction by energy minimization is widely used to predict RNA secondary structure from sequence. For a significant number of RNA molecules, the secondary structure of the RNA molecule is indicative of its function and its computational prediction by minimizing its free energy is important for its functional analysis. A general method for free energy minimization to predict RNA secondary structures is dynamic programming, although other optimization methods have been developed as well along with empirically derived energy parameters. In this chapter, we introduce and illustrate by examples the approach of free energy minimization to predict RNA secondary structures.
Bibliography of information on mechanics of structural failure

NASA Technical Reports Server (NTRS)

Carpenter, J. L., Jr.; Moya, N.; Shaffer, R. A.; Smith, D. M.

1973-01-01

A bibliography of approximately 1500 reference citations related to six problem areas in the mechanics of failure in aerospace structures is presented. The bibliography represents a search of the literature published in the ten year period 1962-1972 and is largely limited to documents published in the United States. Listings are subdivided into the six problem areas: (1) life prediction of structural materials; (2) fracture toughness data; (3) fracture mechanics analysis; (4) hydrogen embrittlement; (5) protective coatings; and (6) composite materials. An author index is included.
Predicting helix orientation for coiled-coil dimers

PubMed Central

Apgar, James R.; Gutwin, Karl N.; Keating, Amy E.

2008-01-01

The alpha-helical coiled coil is a structurally simple protein oligomerization or interaction motif consisting of two or more alpha helices twisted into a supercoiled bundle. Coiled coils can differ in their stoichiometry, helix orientation and axial alignment. Because of the near degeneracy of many of these variants, coiled coils pose a challenge to fold recognition methods for structure prediction. Whereas distinctions between some protein folds can be discriminated on the basis of hydrophobic/polar patterning or secondary structure propensities, the sequence differences that encode important details of coiled-coil structure can be subtle. This is emblematic of a larger problem in the field of protein structure and interaction prediction: that of establishing specificity between closely similar structures. We tested the behavior of different computational models on the problem of recognizing the correct orientation - parallel vs. antiparallel - of pairs of alpha helices that can form a dimeric coiled coil. For each of 131 examples of known structure, we constructed a large number of both parallel and antiparallel structural models and used these to asses the ability of five energy functions to recognize the correct fold. We also developed and tested three sequenced-based approaches that make use of varying degrees of implicit structural information. The best structural methods performed similarly to the best sequence methods, correctly categorizing ∼81% of dimers. Steric compatibility with the fold was important for some coiled coils we investigated. For many examples, the correct orientation was determined by smaller energy differences between parallel and antiparallel structures distributed over many residues and energy components. Prediction methods that used structure but incorporated varying approximations and assumptions showed quite different behaviors when used to investigate energetic contributions to orientation preference. Sequence based methods were sensitive to the choice of residue-pair interactions scored. PMID:18506779
Puzzle of magnetic moments of Ni clusters revisited using quantum Monte Carlo method.

PubMed

Lee, Hung-Wen; Chang, Chun-Ming; Hsing, Cheng-Rong

2017-02-28

The puzzle of the magnetic moments of small nickel clusters arises from the discrepancy between values predicted using density functional theory (DFT) and experimental measurements. Traditional DFT approaches underestimate the magnetic moments of nickel clusters. Two fundamental problems are associated with this puzzle, namely, calculating the exchange-correlation interaction accurately and determining the global minimum structures of the clusters. Theoretically, the two problems can be solved using quantum Monte Carlo (QMC) calculations and the ab initio random structure searching (AIRSS) method correspondingly. Therefore, we combined the fixed-moment AIRSS and QMC methods to investigate the magnetic properties of Ni n (n = 5-9) clusters. The spin moments of the diffusion Monte Carlo (DMC) ground states are higher than those of the Perdew-Burke-Ernzerhof ground states and, in the case of Ni 8-9 , two new ground-state structures have been discovered using the DMC calculations. The predicted results are closer to the experimental findings, unlike the results predicted in previous standard DFT studies.
Designing and benchmarking the MULTICOM protein structure prediction system

PubMed Central

2013-01-01

Background Predicting protein structure from sequence is one of the most significant and challenging problems in bioinformatics. Numerous bioinformatics techniques and tools have been developed to tackle almost every aspect of protein structure prediction ranging from structural feature prediction, template identification and query-template alignment to structure sampling, model quality assessment, and model refinement. How to synergistically select, integrate and improve the strengths of the complementary techniques at each prediction stage and build a high-performance system is becoming a critical issue for constructing a successful, competitive protein structure predictor. Results Over the past several years, we have constructed a standalone protein structure prediction system MULTICOM that combines multiple sources of information and complementary methods at all five stages of the protein structure prediction process including template identification, template combination, model generation, model assessment, and model refinement. The system was blindly tested during the ninth Critical Assessment of Techniques for Protein Structure Prediction (CASP9) in 2010 and yielded very good performance. In addition to studying the overall performance on the CASP9 benchmark, we thoroughly investigated the performance and contributions of each component at each stage of prediction. Conclusions Our comprehensive and comparative study not only provides useful and practical insights about how to select, improve, and integrate complementary methods to build a cutting-edge protein structure prediction system but also identifies a few new sources of information that may help improve the design of a protein structure prediction system. Several components used in the MULTICOM system are available at: http://sysbio.rnet.missouri.edu/multicom_toolbox/. PMID:23442819
Evaluation of In-Structure Shock Prediction Techniques for Buried Structures

DTIC Science & Technology

1991-10-01

process of modeling this problem necessitated the inclus on of structure- 16 media interaction ( SMk ) for the development of loeds for the structural...shears, moments, and strains are also output. 5.2.1 Free-Field Load Generation The equations used in ISSV3 to characterize the free-field environment are
Goal specificity and knowledge acquisition in statistics problem solving: evidence for attentional focus.

PubMed

Trumpower, David L; Goldsmith, Timothy E; Guynn, Melissa J

2004-12-01

Solving training problems with nonspecific goals (NG; i.e., solving for all possible unknown values) often results in better transfer than solving training problems with standard goals (SG; i.e., solving for one particular unknown value). In this study, we evaluated an attentional focus explanation of the goal specificity effect. According to the attentional focus view, solving NG problems causes attention to be directed to local relations among successive problem states, whereas solving SG problems causes attention to be directed to relations between the various problem states and the goal state. Attention to the former is thought to enhance structural knowledge about the problem domain and thus promote transfer. Results supported this view because structurally different transfer problems were solved faster following NG training than following SG training. Moreover, structural knowledge representations revealed more links depicting local relations following NG training and more links to the training goal following SG training. As predicted, these effects were obtained only by domain novices.
Self-Cognitions, Risk Factors for Alcohol Problems, and Drinking in Preadolescent Urban Youths

ERIC Educational Resources Information Center

Corte, Colleen; Szalacha, Laura

2010-01-01

In this study we examine relationships between self-structure and known precursors for alcohol problems in 9- to 12-year-old primarily black and Latino youths (N = 79). Parental alcohol problems and being female predicted few positive and many negative self-cognitions and a future-oriented self-cognition related to alcohol ("drinking possible…
A predictive structural model for bulk metallic glasses

PubMed Central

Laws, K. J.; Miracle, D. B.; Ferry, M.

2015-01-01

Great progress has been made in understanding the atomic structure of metallic glasses, but there is still no clear connection between atomic structure and glass-forming ability. Here we give new insights into perhaps the most important question in the field of amorphous metals: how can glass-forming ability be predicted from atomic structure? We give a new approach to modelling metallic glass atomic structures by solving three long-standing problems: we discover a new family of structural defects that discourage glass formation; we impose efficient local packing around all atoms simultaneously; and we enforce structural self-consistency. Fewer than a dozen binary structures satisfy these constraints, but extra degrees of freedom in structures with three or more different atom sizes significantly expand the number of relatively stable, ‘bulk' metallic glasses. The present work gives a new approach towards achieving the long-sought goal of a predictive capability for bulk metallic glasses. PMID:26370667
Asymmetric bagging and feature selection for activities prediction of drug molecules.

PubMed

Li, Guo-Zheng; Meng, Hao-Hua; Lu, Wen-Cong; Yang, Jack Y; Yang, Mary Qu

2008-05-28

Activities of drug molecules can be predicted by QSAR (quantitative structure activity relationship) models, which overcomes the disadvantages of high cost and long cycle by employing the traditional experimental method. With the fact that the number of drug molecules with positive activity is rather fewer than that of negatives, it is important to predict molecular activities considering such an unbalanced situation. Here, asymmetric bagging and feature selection are introduced into the problem and asymmetric bagging of support vector machines (asBagging) is proposed on predicting drug activities to treat the unbalanced problem. At the same time, the features extracted from the structures of drug molecules affect prediction accuracy of QSAR models. Therefore, a novel algorithm named PRIFEAB is proposed, which applies an embedded feature selection method to remove redundant and irrelevant features for asBagging. Numerical experimental results on a data set of molecular activities show that asBagging improve the AUC and sensitivity values of molecular activities and PRIFEAB with feature selection further helps to improve the prediction ability. Asymmetric bagging can help to improve prediction accuracy of activities of drug molecules, which can be furthermore improved by performing feature selection to select relevant features from the drug molecules data sets.
Linear regression models for solvent accessibility prediction in proteins.

PubMed

Wagner, Michael; Adamczak, Rafał; Porollo, Aleksey; Meller, Jarosław

2005-04-01

The relative solvent accessibility (RSA) of an amino acid residue in a protein structure is a real number that represents the solvent exposed surface area of this residue in relative terms. The problem of predicting the RSA from the primary amino acid sequence can therefore be cast as a regression problem. Nevertheless, RSA prediction has so far typically been cast as a classification problem. Consequently, various machine learning techniques have been used within the classification framework to predict whether a given amino acid exceeds some (arbitrary) RSA threshold and would thus be predicted to be "exposed," as opposed to "buried." We have recently developed novel methods for RSA prediction using nonlinear regression techniques which provide accurate estimates of the real-valued RSA and outperform classification-based approaches with respect to commonly used two-class projections. However, while their performance seems to provide a significant improvement over previously published approaches, these Neural Network (NN) based methods are computationally expensive to train and involve several thousand parameters. In this work, we develop alternative regression models for RSA prediction which are computationally much less expensive, involve orders-of-magnitude fewer parameters, and are still competitive in terms of prediction quality. In particular, we investigate several regression models for RSA prediction using linear L1-support vector regression (SVR) approaches as well as standard linear least squares (LS) regression. Using rigorously derived validation sets of protein structures and extensive cross-validation analysis, we compare the performance of the SVR with that of LS regression and NN-based methods. In particular, we show that the flexibility of the SVR (as encoded by metaparameters such as the error insensitivity and the error penalization terms) can be very beneficial to optimize the prediction accuracy for buried residues. We conclude that the simple and computationally much more efficient linear SVR performs comparably to nonlinear models and thus can be used in order to facilitate further attempts to design more accurate RSA prediction methods, with applications to fold recognition and de novo protein structure prediction methods.
Individual differences in children's innovative problem-solving are not predicted by divergent thinking or executive functions

PubMed Central

2016-01-01

Recent studies of children's tool innovation have revealed that there is variation in children's success in middle-childhood. In two individual differences studies, we sought to identify personal characteristics that might predict success on an innovation task. In Study 1, we found that although measures of divergent thinking were related to each other they did not predict innovation success. In Study 2, we measured executive functioning including: inhibition, working memory, attentional flexibility and ill-structured problem-solving. None of these measures predicted innovation, but, innovation was predicted by children's performance on a receptive vocabulary scale that may function as a proxy for general intelligence. We did not find evidence that children's innovation was predicted by specific personal characteristics. PMID:26926280
A new similarity measure for link prediction based on local structures in social networks

NASA Astrophysics Data System (ADS)

Aghabozorgi, Farshad; Khayyambashi, Mohammad Reza

2018-07-01

Link prediction is a fundamental problem in social network analysis. There exist a variety of techniques for link prediction which applies the similarity measures to estimate proximity of vertices in the network. Complex networks like social networks contain structural units named network motifs. In this study, a newly developed similarity measure is proposed where these structural units are applied as the source of similarity estimation. This similarity measure is tested through a supervised learning experiment framework, where other similarity measures are compared with this similarity measure. The classification model trained with this similarity measure outperforms others of its kind.

Winnerless competition principle and prediction of the transient dynamics in a Lotka-Volterra model

NASA Astrophysics Data System (ADS)

Afraimovich, Valentin; Tristan, Irma; Huerta, Ramon; Rabinovich, Mikhail I.

2008-12-01

Predicting the evolution of multispecies ecological systems is an intriguing problem. A sufficiently complex model with the necessary predicting power requires solutions that are structurally stable. Small variations of the system parameters should not qualitatively perturb its solutions. When one is interested in just asymptotic results of evolution (as time goes to infinity), then the problem has a straightforward mathematical image involving simple attractors (fixed points or limit cycles) of a dynamical system. However, for an accurate prediction of evolution, the analysis of transient solutions is critical. In this paper, in the framework of the traditional Lotka-Volterra model (generalized in some sense), we show that the transient solution representing multispecies sequential competition can be reproducible and predictable with high probability.
Winnerless competition principle and prediction of the transient dynamics in a Lotka-Volterra model.

PubMed

Afraimovich, Valentin; Tristan, Irma; Huerta, Ramon; Rabinovich, Mikhail I

2008-12-01

Predicting the evolution of multispecies ecological systems is an intriguing problem. A sufficiently complex model with the necessary predicting power requires solutions that are structurally stable. Small variations of the system parameters should not qualitatively perturb its solutions. When one is interested in just asymptotic results of evolution (as time goes to infinity), then the problem has a straightforward mathematical image involving simple attractors (fixed points or limit cycles) of a dynamical system. However, for an accurate prediction of evolution, the analysis of transient solutions is critical. In this paper, in the framework of the traditional Lotka-Volterra model (generalized in some sense), we show that the transient solution representing multispecies sequential competition can be reproducible and predictable with high probability.
Manual for the prediction of blast and fragment loadings on structures

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

Not Available

1980-11-01

The purpose of this manual is to provide Architect-Engineer (AE) firms guidance for the prediction of air blast, ground shock and fragment loadings on structures as a result of accidental explosions in or near these structures. Information in this manual is the result of an extensive literature survey and data gathering effort, supplemented by some original analytical studies on various aspects of blast phenomena. Many prediction equations and graphs are presented, accompanied by numerous example problems illustrating their use. The manual is complementary to existing structural design manuals and is intended to reflect the current state-of-the-art in prediction of blastmore » and fragment loads for accidental explosions of high explosives at the Pantex Plant. In some instances, particularly for explosions within blast-resistant structures of complex geometry, rational estimation of these loads is beyond the current state-of-the-art.« less
Uncluttered Single-Image Visualization of Vascular Structures using GPU and Integer Programming

PubMed Central

Won, Joong-Ho; Jeon, Yongkweon; Rosenberg, Jarrett; Yoon, Sungroh; Rubin, Geoffrey D.; Napel, Sandy

2013-01-01

Direct projection of three-dimensional branching structures, such as networks of cables, blood vessels, or neurons onto a 2D image creates the illusion of intersecting structural parts and creates challenges for understanding and communication. We present a method for visualizing such structures, and demonstrate its utility in visualizing the abdominal aorta and its branches, whose tomographic images might be obtained by computed tomography or magnetic resonance angiography, in a single two-dimensional stylistic image, without overlaps among branches. The visualization method, termed uncluttered single-image visualization (USIV), involves optimization of geometry. This paper proposes a novel optimization technique that utilizes an interesting connection of the optimization problem regarding USIV to the protein structure prediction problem. Adopting the integer linear programming-based formulation for the protein structure prediction problem, we tested the proposed technique using 30 visualizations produced from five patient scans with representative anatomical variants in the abdominal aortic vessel tree. The novel technique can exploit commodity-level parallelism, enabling use of general-purpose graphics processing unit (GPGPU) technology that yields a significant speedup. Comparison of the results with the other optimization technique previously reported elsewhere suggests that, in most aspects, the quality of the visualization is comparable to that of the previous one, with a significant gain in the computation time of the algorithm. PMID:22291148
How evolutionary crystal structure prediction works--and why.

PubMed

Oganov, Artem R; Lyakhov, Andriy O; Valle, Mario

2011-03-15

Once the crystal structure of a chemical substance is known, many properties can be predicted reliably and routinely. Therefore if researchers could predict the crystal structure of a material before it is synthesized, they could significantly accelerate the discovery of new materials. In addition, the ability to predict crystal structures at arbitrary conditions of pressure and temperature is invaluable for the study of matter at extreme conditions, where experiments are difficult. Crystal structure prediction (CSP), the problem of finding the most stable arrangement of atoms given only the chemical composition, has long remained a major unsolved scientific problem. Two problems are entangled here: search, the efficient exploration of the multidimensional energy landscape, and ranking, the correct calculation of relative energies. For organic crystals, which contain a few molecules in the unit cell, search can be quite simple as long as a researcher does not need to include many possible isomers or conformations of the molecules; therefore ranking becomes the main challenge. For inorganic crystals, quantum mechanical methods often provide correct relative energies, making search the most critical problem. Recent developments provide useful practical methods for solving the search problem to a considerable extent. One can use simulated annealing, metadynamics, random sampling, basin hopping, minima hopping, and data mining. Genetic algorithms have been applied to crystals since 1995, but with limited success, which necessitated the development of a very different evolutionary algorithm. This Account reviews CSP using one of the major techniques, the hybrid evolutionary algorithm USPEX (Universal Structure Predictor: Evolutionary Xtallography). Using recent developments in the theory of energy landscapes, we unravel the reasons evolutionary techniques work for CSP and point out their limitations. We demonstrate that the energy landscapes of chemical systems have an overall shape and explore their intrinsic dimensionalities. Because of the inverse relationships between order and energy and between the dimensionality and diversity of an ensemble of crystal structures, the chances that a random search will find the ground state decrease exponentially with increasing system size. A well-designed evolutionary algorithm allows for much greater computational efficiency. We illustrate the power of evolutionary CSP through applications that examine matter at high pressure, where new, unexpected phenomena take place. Evolutionary CSP has allowed researchers to make unexpected discoveries such as a transparent phase of sodium, a partially ionic form of boron, complex superconducting forms of calcium, a novel superhard allotrope of carbon, polymeric modifications of nitrogen, and a new class of compounds, perhydrides. These methods have also led to the discovery of novel hydride superconductors including the "impossible" LiH(n) (n=2, 6, 8) compounds, and CaLi(2). We discuss extensions of the method to molecular crystals, systems of variable composition, and the targeted optimization of specific physical properties. © 2011 American Chemical Society
Accurate Prediction of Contact Numbers for Multi-Spanning Helical Membrane Proteins

PubMed Central

Li, Bian; Mendenhall, Jeffrey; Nguyen, Elizabeth Dong; Weiner, Brian E.; Fischer, Axel W.; Meiler, Jens

2017-01-01

Prediction of the three-dimensional (3D) structures of proteins by computational methods is acknowledged as an unsolved problem. Accurate prediction of important structural characteristics such as contact number is expected to accelerate the otherwise slow progress being made in the prediction of 3D structure of proteins. Here, we present a dropout neural network-based method, TMH-Expo, for predicting the contact number of transmembrane helix (TMH) residues from sequence. Neuronal dropout is a strategy where certain neurons of the network are excluded from back-propagation to prevent co-adaptation of hidden-layer neurons. By using neuronal dropout, overfitting was significantly reduced and performance was noticeably improved. For multi-spanning helical membrane proteins, TMH-Expo achieved a remarkable Pearson correlation coefficient of 0.69 between predicted and experimental values and a mean absolute error of only 1.68. In addition, among those membrane protein–membrane protein interface residues, 76.8% were correctly predicted. Mapping of predicted contact numbers onto structures indicates that contact numbers predicted by TMH-Expo reflect the exposure patterns of TMHs and reveal membrane protein–membrane protein interfaces, reinforcing the potential of predicted contact numbers to be used as restraints for 3D structure prediction and protein–protein docking. TMH-Expo can be accessed via a Web server at www.meilerlab.org. PMID:26804342
Development of a severe local storm prediction system: A 60-day test of a mesoscale primitive equation model

NASA Technical Reports Server (NTRS)

Paine, D. A.; Zack, J. W.; Kaplan, M. L.

1979-01-01

The progress and problems associated with the dynamical forecast system which was developed to predict severe storms are examined. The meteorological problem of severe convective storm forecasting is reviewed. The cascade hypothesis which forms the theoretical core of the nested grid dynamical numerical modelling system is described. The dynamical and numerical structure of the model used during the 1978 test period is presented and a preliminary description of a proposed multigrid system for future experiments and tests is provided. Six cases from the spring of 1978 are discussed to illustrate the model's performance and its problems. Potential solutions to the problems are examined.
Prediction of Human Phenotype Ontology terms by means of hierarchical ensemble methods.

PubMed

Notaro, Marco; Schubach, Max; Robinson, Peter N; Valentini, Giorgio

2017-10-12

The prediction of human gene-abnormal phenotype associations is a fundamental step toward the discovery of novel genes associated with human disorders, especially when no genes are known to be associated with a specific disease. In this context the Human Phenotype Ontology (HPO) provides a standard categorization of the abnormalities associated with human diseases. While the problem of the prediction of gene-disease associations has been widely investigated, the related problem of gene-phenotypic feature (i.e., HPO term) associations has been largely overlooked, even if for most human genes no HPO term associations are known and despite the increasing application of the HPO to relevant medical problems. Moreover most of the methods proposed in literature are not able to capture the hierarchical relationships between HPO terms, thus resulting in inconsistent and relatively inaccurate predictions. We present two hierarchical ensemble methods that we formally prove to provide biologically consistent predictions according to the hierarchical structure of the HPO. The modular structure of the proposed methods, that consists in a "flat" learning first step and a hierarchical combination of the predictions in the second step, allows the predictions of virtually any flat learning method to be enhanced. The experimental results show that hierarchical ensemble methods are able to predict novel associations between genes and abnormal phenotypes with results that are competitive with state-of-the-art algorithms and with a significant reduction of the computational complexity. Hierarchical ensembles are efficient computational methods that guarantee biologically meaningful predictions that obey the true path rule, and can be used as a tool to improve and make consistent the HPO terms predictions starting from virtually any flat learning method. The implementation of the proposed methods is available as an R package from the CRAN repository.
Prediction of Protein-Protein Interaction Sites by Random Forest Algorithm with mRMR and IFS

PubMed Central

Li, Bi-Qing; Feng, Kai-Yan; Chen, Lei; Huang, Tao; Cai, Yu-Dong

2012-01-01

Prediction of protein-protein interaction (PPI) sites is one of the most challenging problems in computational biology. Although great progress has been made by employing various machine learning approaches with numerous characteristic features, the problem is still far from being solved. In this study, we developed a novel predictor based on Random Forest (RF) algorithm with the Minimum Redundancy Maximal Relevance (mRMR) method followed by incremental feature selection (IFS). We incorporated features of physicochemical/biochemical properties, sequence conservation, residual disorder, secondary structure and solvent accessibility. We also included five 3D structural features to predict protein-protein interaction sites and achieved an overall accuracy of 0.672997 and MCC of 0.347977. Feature analysis showed that 3D structural features such as Depth Index (DPX) and surface curvature (SC) contributed most to the prediction of protein-protein interaction sites. It was also shown via site-specific feature analysis that the features of individual residues from PPI sites contribute most to the determination of protein-protein interaction sites. It is anticipated that our prediction method will become a useful tool for identifying PPI sites, and that the feature analysis described in this paper will provide useful insights into the mechanisms of interaction. PMID:22937126
Infant brain structures, executive function, and attention deficit/hyperactivity problems at preschool age. A prospective study.

PubMed

Ghassabian, Akhgar; Herba, Catherine M; Roza, Sabine J; Govaert, Paul; Schenk, Jacqueline J; Jaddoe, Vincent W; Hofman, Albert; White, Tonya; Verhulst, Frank C; Tiemeier, Henning

2013-01-01

Neuroimaging findings have provided evidence for a relation between variations in brain structures and attention deficit/hyperactivity disorder (ADHD). However, longitudinal neuroimaging studies are typically confined to children who have already been diagnosed with ADHD. In a population-based study, we aimed to characterize the prospective association between brain structures measured during infancy and executive function and attention deficit/hyperactivity problems assessed at preschool age. In the Generation R Study, the corpus callosum length, the gangliothalamic ovoid diameter (encompassing the basal ganglia and thalamus), and the ventricular volume were measured in 784 6-week-old children using cranial postnatal ultrasounds. Parents rated executive functioning at 4 years using the behavior rating inventory of executive function-preschool version in five dimensions: inhibition, shifting, emotional control, working memory, and planning/organizing. Attention deficit/hyperactivity problems were assessed at ages 3 and 5 years using the child behavior checklist. A smaller corpus callosum length during infancy was associated with greater deficits in executive functioning at 4 years. This was accounted for by higher problem scores on inhibition and emotional control. The corpus callosum length during infancy did not predict attention deficit/hyperactivity problem at 3 and 5 years, when controlling for the confounders. We did not find any relation between gangliothalamic ovoid diameter and executive function or Attention deficit/hyperactivity problem. Variations in brain structures detectible in infants predicted subtle impairments in inhibition and emotional control. However, in this population-based study, we could not demonstrate that early structural brain variations precede symptoms of ADHD. © 2012 The Authors. Journal of Child Psychology and Psychiatry © 2012 Association for Child and Adolescent Mental Health.
Secondary Structure Predictions for Long RNA Sequences Based on Inversion Excursions and MapReduce.

PubMed

Yehdego, Daniel T; Zhang, Boyu; Kodimala, Vikram K R; Johnson, Kyle L; Taufer, Michela; Leung, Ming-Ying

2013-05-01

Secondary structures of ribonucleic acid (RNA) molecules play important roles in many biological processes including gene expression and regulation. Experimental observations and computing limitations suggest that we can approach the secondary structure prediction problem for long RNA sequences by segmenting them into shorter chunks, predicting the secondary structures of each chunk individually using existing prediction programs, and then assembling the results to give the structure of the original sequence. The selection of cutting points is a crucial component of the segmenting step. Noting that stem-loops and pseudoknots always contain an inversion, i.e., a stretch of nucleotides followed closely by its inverse complementary sequence, we developed two cutting methods for segmenting long RNA sequences based on inversion excursions: the centered and optimized method. Each step of searching for inversions, chunking, and predictions can be performed in parallel. In this paper we use a MapReduce framework, i.e., Hadoop, to extensively explore meaningful inversion stem lengths and gap sizes for the segmentation and identify correlations between chunking methods and prediction accuracy. We show that for a set of long RNA sequences in the RFAM database, whose secondary structures are known to contain pseudoknots, our approach predicts secondary structures more accurately than methods that do not segment the sequence, when the latter predictions are possible computationally. We also show that, as sequences exceed certain lengths, some programs cannot computationally predict pseudoknots while our chunking methods can. Overall, our predicted structures still retain the accuracy level of the original prediction programs when compared with known experimental secondary structure.
Structural Damage Prediction and Analysis for Hypervelocity Impact. BUMPERII Suggestion and Problem Reports

NASA Technical Reports Server (NTRS)

1995-01-01

In the course of preparing the SD_SURF space debris analysis code, several problems and possibilities for improvement of the BUMPERII code were documented and sent to MSFC. These suggestions and problem reports are included here as a part of the contract final report. This includes reducing BUMPERII memory requirements, compiling problems with BUMPERII, FORTRAN-lint analysis of BUMPERII, and error in function PRV in BUMPERII.
[Testing a Model to Predict Problem Gambling in Speculative Game Users].

PubMed

Park, Hyangjin; Kim, Suk Sun

2018-04-01

The purpose of the study was to develop and test a model for predicting problem gambling in speculative game users based on Blaszczynski and Nower's pathways model of problem and pathological gambling. The participants were 262 speculative game users recruited from seven speculative gambling places located in Seoul, Gangwon, and Gyeonggi, Korea. They completed a structured self-report questionnaire comprising measures of problem gambling, negative emotions, attentional impulsivity, motor impulsivity, non-planning impulsivity, gambler's fallacy, and gambling self-efficacy. Structural Equation Modeling was used to test the hypothesized model and to examine the direct and indirect effects on problem gambling in speculative game users using SPSS 22.0 and AMOS 20.0 programs. The hypothetical research model provided a reasonable fit to the data. Negative emotions, motor impulsivity, gambler's fallacy, and gambling self-efficacy had direct effects on problem gambling in speculative game users, while indirect effects were reported for negative emotions, motor impulsivity, and gambler's fallacy. These predictors explained 75.2% problem gambling in speculative game users. The findings suggest that developing intervention programs to reduce negative emotions, motor impulsivity, and gambler's fallacy, and to increase gambling self-efficacy in speculative game users are needed to prevent their problem gambling. © 2018 Korean Society of Nursing Science.
Sparse RNA folding revisited: space-efficient minimum free energy structure prediction.

PubMed

Will, Sebastian; Jabbari, Hosna

2016-01-01

RNA secondary structure prediction by energy minimization is the central computational tool for the analysis of structural non-coding RNAs and their interactions. Sparsification has been successfully applied to improve the time efficiency of various structure prediction algorithms while guaranteeing the same result; however, for many such folding problems, space efficiency is of even greater concern, particularly for long RNA sequences. So far, space-efficient sparsified RNA folding with fold reconstruction was solved only for simple base-pair-based pseudo-energy models. Here, we revisit the problem of space-efficient free energy minimization. Whereas the space-efficient minimization of the free energy has been sketched before, the reconstruction of the optimum structure has not even been discussed. We show that this reconstruction is not possible in trivial extension of the method for simple energy models. Then, we present the time- and space-efficient sparsified free energy minimization algorithm SparseMFEFold that guarantees MFE structure prediction. In particular, this novel algorithm provides efficient fold reconstruction based on dynamically garbage-collected trace arrows. The complexity of our algorithm depends on two parameters, the number of candidates Z and the number of trace arrows T; both are bounded by [Formula: see text], but are typically much smaller. The time complexity of RNA folding is reduced from [Formula: see text] to [Formula: see text]; the space complexity, from [Formula: see text] to [Formula: see text]. Our empirical results show more than 80 % space savings over RNAfold [Vienna RNA package] on the long RNAs from the RNA STRAND database (≥2500 bases). The presented technique is intentionally generalizable to complex prediction algorithms; due to their high space demands, algorithms like pseudoknot prediction and RNA-RNA-interaction prediction are expected to profit even stronger than "standard" MFE folding. SparseMFEFold is free software, available at http://www.bioinf.uni-leipzig.de/~will/Software/SparseMFEFold.
A Multiobjective Approach Applied to the Protein Structure Prediction Problem

DTIC Science & Technology

2002-03-07

like a low energy search landscape . 2.1.1 Symbolic/Formalized Problem Domain Description. Every computer representable problem can also be embodied...method [60]. 3.4 Energy Minimization Methods The energy landscape algorithms are based on the idea that a protein’s final resting conformation is...in our GA used to search the PSP problem energy landscape ). 3.5.1 Simple GA. The main routine in a sGA, after encoding the problem, builds a
Predicting high school truancy among students in the Appalachian south.

PubMed

Hunt, Melissa K; Hopko, Derek R

2009-09-01

Truancy is a considerable problem among adolescents. Considering the historical emphasis on studying truancy in urban regions, a concerted effort is needed to extend this research into rural areas to examine cultural generalizability of findings. The purpose of this study was to assess variables associated with truancy in a rural sample (N = 367) of students attending high school in a southern rural region of the Appalachian Mountains. The primary objective was to assess the relative predictive strength of the following variables: academic performance, religiosity, environmental factors (family structure, parental education, and adolescent perceptions of family functioning), internalizing problems (anxiety, depression, thought problems, attentional problems), externalizing problems (substance use and rule-breaking behaviors), and prosocial overt behaviors (participation in school and leadership activities). Regression analysis indicated that truancy was significantly associated with poor school performance, increased depression, social problems, having a less educated mother, a less structured home environment, higher grade, and decreased participation in school sports. EDITORS' STRATEGIC IMPLICATIONS: These findings are critical for the understanding of truancy in rural areas, and they highlight contextual factors that must be identified and addressed through systematic prevention programs targeting adolescents at risk for truancy.
Damage prognosis: the future of structural health monitoring.

PubMed

Farrar, Charles R; Lieven, Nick A J

2007-02-15

This paper concludes the theme issue on structural health monitoring (SHM) by discussing the concept of damage prognosis (DP). DP attempts to forecast system performance by assessing the current damage state of the system (i.e. SHM), estimating the future loading environments for that system, and predicting through simulation and past experience the remaining useful life of the system. The successful development of a DP capability will require the further development and integration of many technology areas including both measurement/processing/telemetry hardware and a variety of deterministic and probabilistic predictive modelling capabilities, as well as the ability to quantify the uncertainty in these predictions. The multidisciplinary and challenging nature of the DP problem, its current embryonic state of development, and its tremendous potential for life-safety and economic benefits qualify DP as a 'grand challenge' problem for engineers in the twenty-first century.
An elastic-plastic contact model for line contact structures

NASA Astrophysics Data System (ADS)

Zhu, Haibin; Zhao, Yingtao; He, Zhifeng; Zhang, Ruinan; Ma, Shaopeng

2018-06-01

Although numerical simulation tools are now very powerful, the development of analytical models is very important for the prediction of the mechanical behaviour of line contact structures for deeply understanding contact problems and engineering applications. For the line contact structures widely used in the engineering field, few analytical models are available for predicting the mechanical behaviour when the structures deform plastically, as the classic Hertz's theory would be invalid. Thus, the present study proposed an elastic-plastic model for line contact structures based on the understanding of the yield mechanism. A mathematical expression describing the global relationship between load history and contact width evolution of line contact structures was obtained. The proposed model was verified through an actual line contact test and a corresponding numerical simulation. The results confirmed that this model can be used to accurately predict the elastic-plastic mechanical behaviour of a line contact structure.
Dynamic analysis of space structures including elastic, multibody, and control behavior

NASA Technical Reports Server (NTRS)

Pinson, Larry; Soosaar, Keto

1989-01-01

The problem is to develop analysis methods, modeling stategies, and simulation tools to predict with assurance the on-orbit performance and integrity of large complex space structures that cannot be verified on the ground. The problem must incorporate large reliable structural models, multi-body flexible dynamics, multi-tier controller interaction, environmental models including 1g and atmosphere, various on-board disturbances, and linkage to mission-level performance codes. All areas are in serious need of work, but the weakest link is multi-body flexible dynamics.
Early family-based intervention in the path to alcohol problems: rationale and relationship between treatment process characteristics and child and parenting outcomes.

PubMed

Nye, C L; Zucker, R A; Fitzgerald, H E

1999-03-01

Risk for subsequent development of alcohol problems is not uniform across the population of alcoholic families, but varies with parental comorbidity and family history. Recent studies have also identified disruptive child behavior problems in the preschool years as predictive of alcoholism in adulthood. Given the quality of risk structure in highest risk families, prevention programming is more appropriately family based rather than individual. A family-based intervention program for the prevention of conduct problems among preschool-age sons of alcoholic fathers was implemented to change this potential mediating risk structure. A population-based recruitment strategy enrolled 52 alcoholic families in a 10-month intervention involving parent training and marital problem solving. The study examined the interplay between parent treatment investment and parent and therapist expectations and satisfaction in predicting change in child behavior and authoritative parenting style during the program, and for 6 months afterward among the 29 families whose sustained involvement allowed these effects to be evaluated. Parent expectations at pretreatment influenced their early investment in the program, which in turn predicted child and parenting outcomes. Parent and therapist satisfaction ratings during treatment were associated with one another and with expectations that the program would continue to promote changes in their child. Parent investment was a particularly salient influence on outcome, as higher investment throughout the program was associated with improvement in child behavior and authoritative parenting at termination. Findings indicate that treatment process characteristics mediate the influence of baseline parent functioning on treatment success and that treatment changes themselves predict later child outcomes.

RNA-Puzzles Round II: assessment of RNA structure prediction programs applied to three large RNA structures

PubMed Central

Miao, Zhichao; Adamiak, Ryszard W.; Blanchet, Marc-Frédérick; Boniecki, Michal; Bujnicki, Janusz M.; Chen, Shi-Jie; Cheng, Clarence; Chojnowski, Grzegorz; Chou, Fang-Chieh; Cordero, Pablo; Cruz, José Almeida; Ferré-D'Amaré, Adrian R.; Das, Rhiju; Ding, Feng; Dokholyan, Nikolay V.; Dunin-Horkawicz, Stanislaw; Kladwang, Wipapat; Krokhotin, Andrey; Lach, Grzegorz; Magnus, Marcin; Major, François; Mann, Thomas H.; Masquida, Benoît; Matelska, Dorota; Meyer, Mélanie; Peselis, Alla; Popenda, Mariusz; Purzycka, Katarzyna J.; Serganov, Alexander; Stasiewicz, Juliusz; Szachniuk, Marta; Tandon, Arpit; Tian, Siqi; Wang, Jian; Xiao, Yi; Xu, Xiaojun; Zhang, Jinwei; Zhao, Peinan; Zok, Tomasz; Westhof, Eric

2015-01-01

This paper is a report of a second round of RNA-Puzzles, a collective and blind experiment in three-dimensional (3D) RNA structure prediction. Three puzzles, Puzzles 5, 6, and 10, represented sequences of three large RNA structures with limited or no homology with previously solved RNA molecules. A lariat-capping ribozyme, as well as riboswitches complexed to adenosylcobalamin and tRNA, were predicted by seven groups using RNAComposer, ModeRNA/SimRNA, Vfold, Rosetta, DMD, MC-Fold, 3dRNA, and AMBER refinement. Some groups derived models using data from state-of-the-art chemical-mapping methods (SHAPE, DMS, CMCT, and mutate-and-map). The comparisons between the predictions and the three subsequently released crystallographic structures, solved at diffraction resolutions of 2.5–3.2 Å, were carried out automatically using various sets of quality indicators. The comparisons clearly demonstrate the state of present-day de novo prediction abilities as well as the limitations of these state-of-the-art methods. All of the best prediction models have similar topologies to the native structures, which suggests that computational methods for RNA structure prediction can already provide useful structural information for biological problems. However, the prediction accuracy for non-Watson–Crick interactions, key to proper folding of RNAs, is low and some predicted models had high Clash Scores. These two difficulties point to some of the continuing bottlenecks in RNA structure prediction. All submitted models are available for download at http://ahsoka.u-strasbg.fr/rnapuzzles/. PMID:25883046
Countervailing social network influences on problem behaviors among homeless youth.

PubMed

Rice, Eric; Stein, Judith A; Milburn, Norweeta

2008-10-01

The impact of countervailing social network influences (i.e., pro-social, anti-social or HIV risk peers) on problem behaviors (i.e., HIV drug risk, HIV sex risk or anti-social behaviors) among 696 homeless youth was assessed using structural equation modeling. Results revealed that older youth were less likely to report having pro-social peers and were more likely to have HIV risk and anti-social peers. A longer time homeless predicted fewer pro-social peers, more anti-social peers, and more HIV risk peers. Heterosexual youth reported fewer HIV risk peers and more pro-social peers. Youth recruited at agencies were more likely to report pro-social peers. Having pro-social peers predicted less HIV sex risk behavior and less anti-social behavior. Having HIV risk peers predicted all problem behavior outcomes. Anti-social peers predicted more anti-social behavior. Once the association between anti-social and HIV risk peers was accounted for independently, having anti-social peers did not independently predict sex or drug risk behaviors.
Ab initio protein structure assembly using continuous structure fragments and optimized knowledge-based force field.

PubMed

Xu, Dong; Zhang, Yang

2012-07-01

Ab initio protein folding is one of the major unsolved problems in computational biology owing to the difficulties in force field design and conformational search. We developed a novel program, QUARK, for template-free protein structure prediction. Query sequences are first broken into fragments of 1-20 residues where multiple fragment structures are retrieved at each position from unrelated experimental structures. Full-length structure models are then assembled from fragments using replica-exchange Monte Carlo simulations, which are guided by a composite knowledge-based force field. A number of novel energy terms and Monte Carlo movements are introduced and the particular contributions to enhancing the efficiency of both force field and search engine are analyzed in detail. QUARK prediction procedure is depicted and tested on the structure modeling of 145 nonhomologous proteins. Although no global templates are used and all fragments from experimental structures with template modeling score >0.5 are excluded, QUARK can successfully construct 3D models of correct folds in one-third cases of short proteins up to 100 residues. In the ninth community-wide Critical Assessment of protein Structure Prediction experiment, QUARK server outperformed the second and third best servers by 18 and 47% based on the cumulative Z-score of global distance test-total scores in the FM category. Although ab initio protein folding remains a significant challenge, these data demonstrate new progress toward the solution of the most important problem in the field. Copyright © 2012 Wiley Periodicals, Inc.
Using EarthScope magnetotelluric data to improve the resilience of the US power grid: rapid predictions of geomagnetically induced currents

NASA Astrophysics Data System (ADS)

Schultz, A.; Bonner, L. R., IV

2016-12-01

Existing methods to predict Geomagnetically Induced Currents (GICs) in power grids, such as the North American Electric Reliability Corporation standard adopted by the power industry, require explicit knowledge of the electrical resistivity structure of the crust and mantle to solve for ground level electric fields along transmission lines. The current standard is to apply regional 1-D resistivity models to this problem, which facilitates rapid solution of the governing equations. The systematic mapping of continental resistivity structure from projects such as EarthScope reveals several orders of magnitude of lateral variations in resistivity on local, regional and continental scales, resulting in electric field intensifications relative to existing 1-D solutions that can impact GICs to first order. The computational burden on the ground resistivity/GIC problem of coupled 3-D solutions inhibits the prediction of GICs in a timeframe useful to protecting power grids. In this work we reduce the problem to applying a set of filters, recognizing that the magnetotelluric impedance tensors implicitly contain all known information about the resistivity structure beneath a given site, and thus provides the required relationship between electric and magnetic fields at each site. We project real-time magnetic field data from distant magnetic observatories through a robustly calculated multivariate transfer function to locations where magnetotelluric impedance tensors had previously been obtained. This provides a real-time prediction of the magnetic field at each of those points. We then project the predicted magnetic fields through the impedance tensors to obtain predictions of electric fields induced at ground level. Thus, electric field predictions can be generated in real-time for an entire array from real-time observatory data, then interpolated onto points representing a power transmission line contained within the array to produce a combined electric field prediction necessary for GIC prediction along that line. This method produces more accurate predictions of ground electric fields in conductively heterogeneous areas that are not limited by distance from the nearest observatory, while still retaining comparable computational speeds as existing methods.
Pseudoracemic amino acid complexes: blind predictions for flexible two-component crystals.

PubMed

Görbitz, Carl Henrik; Dalhus, Bjørn; Day, Graeme M

2010-08-14

Ab initio prediction of the crystal packing in complexes between two flexible molecules is a particularly challenging computational chemistry problem. In this work we present results of single crystal structure determinations as well as theoretical predictions for three 1 ratio 1 complexes between hydrophobic l- and d-amino acids (pseudoracemates), known from previous crystallographic work to form structures with one of two alternative hydrogen bonding arrangements. These are accurately reproduced in the theoretical predictions together with a series of patterns that have never been observed experimentally. In this bewildering forest of potential polymorphs, hydrogen bonding arrangements and molecular conformations, the theoretical predictions succeeded, for all three complexes, in finding the correct hydrogen bonding pattern. For two of the complexes, the calculations also reproduce the exact space group and side chain orientations in the best ranked predicted structure. This includes one complex for which the observed crystal packing clearly contradicted previous experience based on experimental data for a substantial number of related amino acid complexes. The results highlight the significant recent advances that have been made in computational methods for crystal structure prediction.
The prediction of crystal structure by merging knowledge methods with first principles quantum mechanics

NASA Astrophysics Data System (ADS)

Ceder, Gerbrand

2007-03-01

The prediction of structure is a key problem in computational materials science that forms the platform on which rational materials design can be performed. Finding structure by traditional optimization methods on quantum mechanical energy models is not possible due to the complexity and high dimensionality of the coordinate space. An unusual, but efficient solution to this problem can be obtained by merging ideas from heuristic and ab initio methods: In the same way that scientist build empirical rules by observation of experimental trends, we have developed machine learning approaches that extract knowledge from a large set of experimental information and a database of over 15,000 first principles computations, and used these to rapidly direct accurate quantum mechanical techniques to the lowest energy crystal structure of a material. Knowledge is captured in a Bayesian probability network that relates the probability to find a particular crystal structure at a given composition to structure and energy information at other compositions. We show that this approach is highly efficient in finding the ground states of binary metallic alloys and can be easily generalized to more complex systems.
Observed emotion frequency versus intensity as predictors of socioemotional maladjustment.

PubMed

Hernández, Maciel M; Eisenberg, Nancy; Valiente, Carlos; Spinrad, Tracy L; VanSchyndel, Sarah K; Diaz, Anjolii; Berger, Rebecca H; Silva, Kassondra M; Southworth, Jody; Piña, Armando A

2015-12-01

The purpose of this study was to assess whether observed emotional frequency (the proportion of instances an emotion was observed) and intensity (the strength of an emotion when it was observed) uniquely predicted kindergartners' (N = 301) internalizing and externalizing problems. Analyses were tested in a structural equation modeling (SEM) framework with data from multireporters (reports of problem behaviors from teachers and parents) and naturalistic observations of emotion in the fall semester. For observed positive emotion, both frequency and intensity negatively predicted parent- or teacher-reported internalizing symptoms. Anger frequency positively predicted parent- and teacher-reported externalizing symptoms, whereas anger intensity positively predicted parent- and teacher-reported externalizing and parent-reported internalizing symptoms. The findings support the importance of examining both aspects of emotion when predicting maladjustment. (c) 2015 APA, all rights reserved).
Improved method for predicting protein fold patterns with ensemble classifiers.

PubMed

Chen, W; Liu, X; Huang, Y; Jiang, Y; Zou, Q; Lin, C

2012-01-27

Protein folding is recognized as a critical problem in the field of biophysics in the 21st century. Predicting protein-folding patterns is challenging due to the complex structure of proteins. In an attempt to solve this problem, we employed ensemble classifiers to improve prediction accuracy. In our experiments, 188-dimensional features were extracted based on the composition and physical-chemical property of proteins and 20-dimensional features were selected using a coupled position-specific scoring matrix. Compared with traditional prediction methods, these methods were superior in terms of prediction accuracy. The 188-dimensional feature-based method achieved 71.2% accuracy in five cross-validations. The accuracy rose to 77% when we used a 20-dimensional feature vector. These methods were used on recent data, with 54.2% accuracy. Source codes and dataset, together with web server and software tools for prediction, are available at: http://datamining.xmu.edu.cn/main/~cwc/ProteinPredict.html.
Computational Methods for Structural Mechanics and Dynamics, part 1

NASA Technical Reports Server (NTRS)

Stroud, W. Jefferson (Editor); Housner, Jerrold M. (Editor); Tanner, John A. (Editor); Hayduk, Robert J. (Editor)

1989-01-01

The structural analysis methods research has several goals. One goal is to develop analysis methods that are general. This goal of generality leads naturally to finite-element methods, but the research will also include other structural analysis methods. Another goal is that the methods be amenable to error analysis; that is, given a physical problem and a mathematical model of that problem, an analyst would like to know the probable error in predicting a given response quantity. The ultimate objective is to specify the error tolerances and to use automated logic to adjust the mathematical model or solution strategy to obtain that accuracy. A third goal is to develop structural analysis methods that can exploit parallel processing computers. The structural analysis methods research will focus initially on three types of problems: local/global nonlinear stress analysis, nonlinear transient dynamics, and tire modeling.
Firefly Algorithm for Structural Search.

PubMed

Avendaño-Franco, Guillermo; Romero, Aldo H

2016-07-12

The problem of computational structure prediction of materials is approached using the firefly (FF) algorithm. Starting from the chemical composition and optionally using prior knowledge of similar structures, the FF method is able to predict not only known stable structures but also a variety of novel competitive metastable structures. This article focuses on the strengths and limitations of the algorithm as a multimodal global searcher. The algorithm has been implemented in software package PyChemia ( https://github.com/MaterialsDiscovery/PyChemia ), an open source python library for materials analysis. We present applications of the method to van der Waals clusters and crystal structures. The FF method is shown to be competitive when compared to other population-based global searchers.
A Simple Label Switching Algorithm for Semisupervised Structural SVMs.

PubMed

Balamurugan, P; Shevade, Shirish; Sundararajan, S

2015-10-01

In structured output learning, obtaining labeled data for real-world applications is usually costly, while unlabeled examples are available in abundance. Semisupervised structured classification deals with a small number of labeled examples and a large number of unlabeled structured data. In this work, we consider semisupervised structural support vector machines with domain constraints. The optimization problem, which in general is not convex, contains the loss terms associated with the labeled and unlabeled examples, along with the domain constraints. We propose a simple optimization approach that alternates between solving a supervised learning problem and a constraint matching problem. Solving the constraint matching problem is difficult for structured prediction, and we propose an efficient and effective label switching method to solve it. The alternating optimization is carried out within a deterministic annealing framework, which helps in effective constraint matching and avoiding poor local minima, which are not very useful. The algorithm is simple and easy to implement. Further, it is suitable for any structured output learning problem where exact inference is available. Experiments on benchmark sequence labeling data sets and a natural language parsing data set show that the proposed approach, though simple, achieves comparable generalization performance.
Shaping up the protein folding funnel by local interaction: lesson from a structure prediction study.

PubMed

Chikenji, George; Fujitsuka, Yoshimi; Takada, Shoji

2006-02-28

Predicting protein tertiary structure by folding-like simulations is one of the most stringent tests of how much we understand the principle of protein folding. Currently, the most successful method for folding-based structure prediction is the fragment assembly (FA) method. Here, we address why the FA method is so successful and its lesson for the folding problem. To do so, using the FA method, we designed a structure prediction test of "chimera proteins." In the chimera proteins, local structural preference is specific to the target sequences, whereas nonlocal interactions are only sequence-independent compaction forces. We find that these chimera proteins can find the native folds of the intact sequences with high probability indicating dominant roles of the local interactions. We further explore roles of local structural preference by exact calculation of the HP lattice model of proteins. From these results, we suggest principles of protein folding: For small proteins, compact structures that are fully compatible with local structural preference are few, one of which is the native fold. These local biases shape up the funnel-like energy landscape.
Shaping up the protein folding funnel by local interaction: Lesson from a structure prediction study

PubMed Central

Chikenji, George; Fujitsuka, Yoshimi; Takada, Shoji

2006-01-01

Predicting protein tertiary structure by folding-like simulations is one of the most stringent tests of how much we understand the principle of protein folding. Currently, the most successful method for folding-based structure prediction is the fragment assembly (FA) method. Here, we address why the FA method is so successful and its lesson for the folding problem. To do so, using the FA method, we designed a structure prediction test of “chimera proteins.” In the chimera proteins, local structural preference is specific to the target sequences, whereas nonlocal interactions are only sequence-independent compaction forces. We find that these chimera proteins can find the native folds of the intact sequences with high probability indicating dominant roles of the local interactions. We further explore roles of local structural preference by exact calculation of the HP lattice model of proteins. From these results, we suggest principles of protein folding: For small proteins, compact structures that are fully compatible with local structural preference are few, one of which is the native fold. These local biases shape up the funnel-like energy landscape. PMID:16488978
Protein Structure Prediction by Protein Threading

NASA Astrophysics Data System (ADS)

Xu, Ying; Liu, Zhijie; Cai, Liming; Xu, Dong

The seminal work of Bowie, Lüthy, and Eisenberg (Bowie et al., 1991) on "the inverse protein folding problem" laid the foundation of protein structure prediction by protein threading. By using simple measures for fitness of different amino acid types to local structural environments defined in terms of solvent accessibility and protein secondary structure, the authors derived a simple and yet profoundly novel approach to assessing if a protein sequence fits well with a given protein structural fold. Their follow-up work (Elofsson et al., 1996; Fischer and Eisenberg, 1996; Fischer et al., 1996a,b) and the work by Jones, Taylor, and Thornton (Jones et al., 1992) on protein fold recognition led to the development of a new brand of powerful tools for protein structure prediction, which we now term "protein threading." These computational tools have played a key role in extending the utility of all the experimentally solved structures by X-ray crystallography and nuclear magnetic resonance (NMR), providing structural models and functional predictions for many of the proteins encoded in the hundreds of genomes that have been sequenced up to now.
Advanced Computational Modeling Approaches for Shock Response Prediction

NASA Technical Reports Server (NTRS)

Derkevorkian, Armen; Kolaini, Ali R.; Peterson, Lee

2015-01-01

Motivation: (1) The activation of pyroshock devices such as explosives, separation nuts, pin-pullers, etc. produces high frequency transient structural response, typically from few tens of Hz to several hundreds of kHz. (2) Lack of reliable analytical tools makes the prediction of appropriate design and qualification test levels a challenge. (3) In the past few decades, several attempts have been made to develop methodologies that predict the structural responses to shock environments. (4) Currently, there is no validated approach that is viable to predict shock environments overt the full frequency range (i.e., 100 Hz to 10 kHz). Scope: (1) Model, analyze, and interpret space structural systems with complex interfaces and discontinuities, subjected to shock loads. (2) Assess the viability of a suite of numerical tools to simulate transient, non-linear solid mechanics and structural dynamics problems, such as shock wave propagation.
Accurate prediction of personalized olfactory perception from large-scale chemoinformatic features.

PubMed

Li, Hongyang; Panwar, Bharat; Omenn, Gilbert S; Guan, Yuanfang

2018-02-01

The olfactory stimulus-percept problem has been studied for more than a century, yet it is still hard to precisely predict the odor given the large-scale chemoinformatic features of an odorant molecule. A major challenge is that the perceived qualities vary greatly among individuals due to different genetic and cultural backgrounds. Moreover, the combinatorial interactions between multiple odorant receptors and diverse molecules significantly complicate the olfaction prediction. Many attempts have been made to establish structure-odor relationships for intensity and pleasantness, but no models are available to predict the personalized multi-odor attributes of molecules. In this study, we describe our winning algorithm for predicting individual and population perceptual responses to various odorants in the DREAM Olfaction Prediction Challenge. We find that random forest model consisting of multiple decision trees is well suited to this prediction problem, given the large feature spaces and high variability of perceptual ratings among individuals. Integrating both population and individual perceptions into our model effectively reduces the influence of noise and outliers. By analyzing the importance of each chemical feature, we find that a small set of low- and nondegenerative features is sufficient for accurate prediction. Our random forest model successfully predicts personalized odor attributes of structurally diverse molecules. This model together with the top discriminative features has the potential to extend our understanding of olfactory perception mechanisms and provide an alternative for rational odorant design.
Generalized self-consistent method for predicting the effective elastic properties of composites with random hybrid structures

NASA Astrophysics Data System (ADS)

Pan'kov, A. A.

1997-05-01

The feasibility of using a generalized self-consistent method for predicting the effective elastic properties of composites with random hybrid structures has been examined. Using this method, the problem is reduced to solution of simpler special averaged problems for composites with single inclusions and corresponding transition layers in the medium examined. The dimensions of the transition layers are defined by correlation radii of the composite random structure of the composite, while the heterogeneous elastic properties of the transition layers take account of the probabilities for variation of the size and configuration of the inclusions using averaged special indicator functions. Results are given for a numerical calculation of the averaged indicator functions and analysis of the effect of the micropores in the matrix-fiber interface region on the effective elastic properties of unidirectional fiberglass—epoxy using the generalized self-consistent method and compared with experimental data and reported solutions.
Folding and Stabilization of Native-Sequence-Reversed Proteins

PubMed Central

Zhang, Yuanzhao; Weber, Jeffrey K; Zhou, Ruhong

2016-01-01

Though the problem of sequence-reversed protein folding is largely unexplored, one might speculate that reversed native protein sequences should be significantly more foldable than purely random heteropolymer sequences. In this article, we investigate how the reverse-sequences of native proteins might fold by examining a series of small proteins of increasing structural complexity (α-helix, β-hairpin, α-helix bundle, and α/β-protein). Employing a tandem protein structure prediction algorithmic and molecular dynamics simulation approach, we find that the ability of reverse sequences to adopt native-like folds is strongly influenced by protein size and the flexibility of the native hydrophobic core. For β-hairpins with reverse-sequences that fail to fold, we employ a simple mutational strategy for guiding stable hairpin formation that involves the insertion of amino acids into the β-turn region. This systematic look at reverse sequence duality sheds new light on the problem of protein sequence-structure mapping and may serve to inspire new protein design and protein structure prediction protocols. PMID:27113844
Folding and Stabilization of Native-Sequence-Reversed Proteins

NASA Astrophysics Data System (ADS)

Zhang, Yuanzhao; Weber, Jeffrey K.; Zhou, Ruhong

2016-04-01

Though the problem of sequence-reversed protein folding is largely unexplored, one might speculate that reversed native protein sequences should be significantly more foldable than purely random heteropolymer sequences. In this article, we investigate how the reverse-sequences of native proteins might fold by examining a series of small proteins of increasing structural complexity (α-helix, β-hairpin, α-helix bundle, and α/β-protein). Employing a tandem protein structure prediction algorithmic and molecular dynamics simulation approach, we find that the ability of reverse sequences to adopt native-like folds is strongly influenced by protein size and the flexibility of the native hydrophobic core. For β-hairpins with reverse-sequences that fail to fold, we employ a simple mutational strategy for guiding stable hairpin formation that involves the insertion of amino acids into the β-turn region. This systematic look at reverse sequence duality sheds new light on the problem of protein sequence-structure mapping and may serve to inspire new protein design and protein structure prediction protocols.
De novo protein structure prediction by dynamic fragment assembly and conformational space annealing.

PubMed

Lee, Juyong; Lee, Jinhyuk; Sasaki, Takeshi N; Sasai, Masaki; Seok, Chaok; Lee, Jooyoung

2011-08-01

Ab initio protein structure prediction is a challenging problem that requires both an accurate energetic representation of a protein structure and an efficient conformational sampling method for successful protein modeling. In this article, we present an ab initio structure prediction method which combines a recently suggested novel way of fragment assembly, dynamic fragment assembly (DFA) and conformational space annealing (CSA) algorithm. In DFA, model structures are scored by continuous functions constructed based on short- and long-range structural restraint information from a fragment library. Here, DFA is represented by the full-atom model by CHARMM with the addition of the empirical potential of DFIRE. The relative contributions between various energy terms are optimized using linear programming. The conformational sampling was carried out with CSA algorithm, which can find low energy conformations more efficiently than simulated annealing used in the existing DFA study. The newly introduced DFA energy function and CSA sampling algorithm are implemented into CHARMM. Test results on 30 small single-domain proteins and 13 template-free modeling targets of the 8th Critical Assessment of protein Structure Prediction show that the current method provides comparable and complementary prediction results to existing top methods. Copyright © 2011 Wiley-Liss, Inc.

Prediction of beta-turns and beta-turn types by a novel bidirectional Elman-type recurrent neural network with multiple output layers (MOLEBRNN).

PubMed

Kirschner, Andreas; Frishman, Dmitrij

2008-10-01

Prediction of beta-turns from amino acid sequences has long been recognized as an important problem in structural bioinformatics due to their frequent occurrence as well as their structural and functional significance. Because various structural features of proteins are intercorrelated, secondary structure information has been often employed as an additional input for machine learning algorithms while predicting beta-turns. Here we present a novel bidirectional Elman-type recurrent neural network with multiple output layers (MOLEBRNN) capable of predicting multiple mutually dependent structural motifs and demonstrate its efficiency in recognizing three aspects of protein structure: beta-turns, beta-turn types, and secondary structure. The advantage of our method compared to other predictors is that it does not require any external input except for sequence profiles because interdependencies between different structural features are taken into account implicitly during the learning process. In a sevenfold cross-validation experiment on a standard test dataset our method exhibits the total prediction accuracy of 77.9% and the Mathew's Correlation Coefficient of 0.45, the highest performance reported so far. It also outperforms other known methods in delineating individual turn types. We demonstrate how simultaneous prediction of multiple targets influences prediction performance on single targets. The MOLEBRNN presented here is a generic method applicable in a variety of research fields where multiple mutually depending target classes need to be predicted. http://webclu.bio.wzw.tum.de/predator-web/.
College Students' Drinking and Posting About Alcohol: Forwarding a Model of Motivations, Behaviors, and Consequences.

PubMed

Thompson, Charee M; Romo, Lynsey K

2016-06-01

College drinking continues to remain a public health problem that has been exacerbated by alcohol-related posts on social networking sites (SNSs). Although existing research has linked alcohol consumption, alcohol posts, and adverse consequences to one another, comprehensive explanations for these associations have been largely unexplored. Thus, we reasoned that students' personal motivations (i.e., espousing an alcohol identity, needing entertainment, and adhering to social norms) influence their behaviors (i.e., alcohol consumption and alcohol-related posting on SNSs), which can lead to alcohol problems. Using structural equation modeling, we analyzed data from 364 undergraduate students and found general support for our model. In particular, espousing an alcohol identity predicted alcohol consumption and alcohol-related SNS posting, needing entertainment predicted alcohol consumption but not alcohol-related SNS posting, and adhering to social norms predicted alcohol-related SNS posting but not alcohol consumption. In turn, alcohol consumption and alcohol-related SNS posting predicted alcohol problems. It is surprising that alcohol-related SNS posting was a stronger predictor of alcohol problems than alcohol consumption. We discuss the findings within their applied applications for college student health.
Marital Problems, Maternal Gatekeeping Attitudes, and Father-Child Relationships in Adolescence

PubMed Central

Stevenson, Matthew M.; Fabricius, William V.; Cookston, Jeffrey T.; Parke, Ross D.; Coltrane, Scott; Braver, Sanford L.; Saenz, Delia S.

2013-01-01

We evaluated maternal gatekeeping attitudes as a mediator of the relation between marital problems and father-child relationships in three waves when children were in 7th through 10th grade. We assessed each parent’s contribution to the marital problems experienced by the couple. Findings from mediational and cross-lagged structural equation models revealed that increased marital problem behaviors on the part of mothers at wave 1 predicted increased maternal gatekeeping attitudes at wave 2 which in turn predicted decreased amounts of father-adolescent interaction at wave 3. Decreased amounts of interaction with either parent were associated within each wave with adolescents’ perceptions that they mattered less to that parent. Amount of interaction with fathers at wave 2 positively predicted changes in boys’ perceptions of how much they mattered to their fathers at wave 3, and amount of interaction with mothers at wave 2 positively predicted changes in girls’ perceptions of how much they mattered to their mothers at wave 3. The findings did not differ for European-American versus Mexican-American families, or for biological fathers versus step-fathers. PMID:24364832
Multiple-Instance Regression with Structured Data

NASA Technical Reports Server (NTRS)

Wagstaff, Kiri L.; Lane, Terran; Roper, Alex

2008-01-01

We present a multiple-instance regression algorithm that models internal bag structure to identify the items most relevant to the bag labels. Multiple-instance regression (MIR) operates on a set of bags with real-valued labels, each containing a set of unlabeled items, in which the relevance of each item to its bag label is unknown. The goal is to predict the labels of new bags from their contents. Unlike previous MIR methods, MI-ClusterRegress can operate on bags that are structured in that they contain items drawn from a number of distinct (but unknown) distributions. MI-ClusterRegress simultaneously learns a model of the bag's internal structure, the relevance of each item, and a regression model that accurately predicts labels for new bags. We evaluated this approach on the challenging MIR problem of crop yield prediction from remote sensing data. MI-ClusterRegress provided predictions that were more accurate than those obtained with non-multiple-instance approaches or MIR methods that do not model the bag structure.
A Survey of Computational Intelligence Techniques in Protein Function Prediction

PubMed Central

Tiwari, Arvind Kumar; Srivastava, Rajeev

2014-01-01

During the past, there was a massive growth of knowledge of unknown proteins with the advancement of high throughput microarray technologies. Protein function prediction is the most challenging problem in bioinformatics. In the past, the homology based approaches were used to predict the protein function, but they failed when a new protein was different from the previous one. Therefore, to alleviate the problems associated with homology based traditional approaches, numerous computational intelligence techniques have been proposed in the recent past. This paper presents a state-of-the-art comprehensive review of various computational intelligence techniques for protein function predictions using sequence, structure, protein-protein interaction network, and gene expression data used in wide areas of applications such as prediction of DNA and RNA binding sites, subcellular localization, enzyme functions, signal peptides, catalytic residues, nuclear/G-protein coupled receptors, membrane proteins, and pathway analysis from gene expression datasets. This paper also summarizes the result obtained by many researchers to solve these problems by using computational intelligence techniques with appropriate datasets to improve the prediction performance. The summary shows that ensemble classifiers and integration of multiple heterogeneous data are useful for protein function prediction. PMID:25574395
Cosmology and the weak interaction

NASA Technical Reports Server (NTRS)

Schramm, David N.

1989-01-01

The weak interaction plays a critical role in modern Big Bang cosmology. Two of its most publicized comological connections are emphasized: big bang nucleosynthesis and dark matter. The first of these is connected to the cosmological prediction of neutrine flavors, N(sub nu) is approximately 3 which in now being confirmed. The second is interrelated to the whole problem of galacty and structure formation in the universe. The role of the weak interaction both for dark matter candidates and for the problem of generating seeds to form structure is demonstrated.
A Novel Two-Step Hierarchial Quantitative Structure-Activity Relationship Modeling Workflow for Predicting Acute Toxicity of Chemicals in Rodents

EPA Science Inventory

Background: Accurate prediction of in vivo toxicity from in vitro testing is a challenging problem. Large public–private consortia have been formed with the goal of improving chemical safety assessment by the means of high-throughput screening. Methods and results: A database co...
Structured Kernel Subspace Learning for Autonomous Robot Navigation.

PubMed

Kim, Eunwoo; Choi, Sungjoon; Oh, Songhwai

2018-02-14

This paper considers two important problems for autonomous robot navigation in a dynamic environment, where the goal is to predict pedestrian motion and control a robot with the prediction for safe navigation. While there are several methods for predicting the motion of a pedestrian and controlling a robot to avoid incoming pedestrians, it is still difficult to safely navigate in a dynamic environment due to challenges, such as the varying quality and complexity of training data with unwanted noises. This paper addresses these challenges simultaneously by proposing a robust kernel subspace learning algorithm based on the recent advances in nuclear-norm and l 1 -norm minimization. We model the motion of a pedestrian and the robot controller using Gaussian processes. The proposed method efficiently approximates a kernel matrix used in Gaussian process regression by learning low-rank structured matrix (with symmetric positive semi-definiteness) to find an orthogonal basis, which eliminates the effects of erroneous and inconsistent data. Based on structured kernel subspace learning, we propose a robust motion model and motion controller for safe navigation in dynamic environments. We evaluate the proposed robust kernel learning in various tasks, including regression, motion prediction, and motion control problems, and demonstrate that the proposed learning-based systems are robust against outliers and outperform existing regression and navigation methods.
Predicted facies, sedimentary structures and potential resources of Jurassic petroleum complex in S-E sWestern Siberia (based on well logging data)

NASA Astrophysics Data System (ADS)

Prakojo, F.; Lobova, G.; Abramova, R.

2015-11-01

This paper is devoted to the current problem in petroleum geology and geophysics- prediction of facies sediments for further evaluation of productive layers. Applying the acoustic method and the characterizing sedimentary structure for each coastal-marine-delta type was determined. The summary of sedimentary structure characteristics and reservoir properties (porosity and permeability) of typical facies were described. Logging models SP, EL and GR (configuration, curve range) in interpreting geophysical data for each litho-facies were identified. According to geophysical characteristics these sediments can be classified as coastal-marine-delta. Prediction models for potential Jurassic oil-gas bearing complexes (horizon J11) in one S-E Western Siberian deposit were conducted. Comparing forecasting to actual testing data of layer J11 showed that the prediction is about 85%.
Psychological maltreatment, emotional and behavioral problems in adolescents: The mediating role of resilience and self-esteem.

PubMed

Arslan, Gökmen

2016-02-01

In this study, structural equation modeling was used to examine the mediating role of resilience and self-esteem in the relationships between psychological maltreatment-emotional problems and psychological maltreatment-behavioral problems in adolescents. Participants were 937 adolescents from different high schools in Turkey. The sample included 502 female (53.6%) and 435 male (46.4%) students, 14-19 years old (mean age=16.51, SD=1.15). Results indicated that psychological maltreatment was negatively correlated with resilience and self-esteem, and positively correlated with behavioral problems and emotional problems. Resilience and self-esteem also predicted behavioral problems and emotional problems. Finally, psychological maltreatment predicted emotional and behavioral problems mediated by resilience and self-esteem. Resilience and self-esteem partially mediated the relationship between psychological maltreatment-behavioral and psychological maltreatment-emotional problems in adolescents. Thus, resilience and self-esteem appear to play a protective role in emotional problems and behavioral problems in psychologically maltreated individuals. Implications are discussed and suggestions for psychological counselors and other mental health professionals are presented. Copyright © 2015 Elsevier Ltd. All rights reserved.
Observed Parenting Behavior with Teens: Measurement Invariance and Predictive Validity Across Race

PubMed Central

Skinner, Martie L.; MacKenzie, Elizabeth P.; Haggerty, Kevin P.; Hill, Karl G.; Roberson, Kendra C.

2011-01-01

Previous reports supporting measurement equality between European American and African American families have often focused on self-reported risk factors or observed parent behavior with young children. This study examines equality of measurement of observer ratings of parenting behavior with adolescents during structured tasks; mean levels of observed parenting; and predictive validity of teen self-reports of antisocial behaviors and beliefs using a sample of 163 African American and 168 European American families. Multiple-group confirmatory factor analyses supported measurement invariance across ethnic groups for 4 measures of observed parenting behavior: prosocial rewards, psychological costs, antisocial rewards, and problem solving. Some mean-level differences were found: African American parents exhibited lower levels of prosocial rewards, higher levels of psychological costs, and lower problem solving when compared to European Americans. No significant mean difference was found in rewards for antisocial behavior. Multigroup structural equation models suggested comparable relationships across race (predictive validity) between parenting constructs and youth antisocial constructs (i.e., drug initiation, positive drug attitudes, antisocial attitudes, problem behaviors) in all but one of the tested relationships. This study adds to existing evidence that family-based interventions targeting parenting behaviors can be generalized to African American families. PMID:21787057
Sex differences in the interacting roles of impulsivity and positive alcohol expectancy in problem drinking: A structural brain imaging study.

PubMed

Ide, Jaime S; Zhornitsky, Simon; Hu, Sien; Zhang, Sheng; Krystal, John H; Li, Chiang-Shan R

2017-01-01

Alcohol expectancy and impulsivity are implicated in alcohol misuse. However, how these two risk factors interact to determine problem drinking and whether men and women differ in these risk processes remain unclear. In 158 social drinkers (86 women) assessed for Alcohol Use Disorder Identification Test (AUDIT), positive alcohol expectancy, and Barratt impulsivity, we examined sex differences in these risk processes. Further, with structural brain imaging, we examined the neural bases underlying the relationship between these risk factors and problem drinking. The results of general linear modeling showed that alcohol expectancy best predicted problem drinking in women, whereas in men as well as in the combined group alcohol expectancy and impulsivity interacted to best predict problem drinking. Alcohol expectancy was associated with decreased gray matter volume (GMV) of the right posterior insula in women and the interaction of alcohol expectancy and impulsivity was associated with decreased GMV of the left thalamus in women and men combined and in men alone, albeit less significantly. These risk factors mediated the correlation between GMV and problem drinking. Conversely, models where GMV resulted from problem drinking were not supported. These new findings reveal distinct psychological factors that dispose men and women to problem drinking. Although mediation analyses did not determine a causal link, GMV reduction in the insula and thalamus may represent neural phenotype of these risk processes rather than the consequence of alcohol consumption in non-dependent social drinkers. The results add to the alcohol imaging literature which has largely focused on dependent individuals and help elucidate alterations in brain structures that may contribute to the transition from social to habitual drinking.
Extending RosettaDock with water, sugar, and pH for prediction of complex structures and affinities for CAPRI rounds 20-27.

PubMed

Kilambi, Krishna Praneeth; Pacella, Michael S; Xu, Jianqing; Labonte, Jason W; Porter, Justin R; Muthu, Pravin; Drew, Kevin; Kuroda, Daisuke; Schueler-Furman, Ora; Bonneau, Richard; Gray, Jeffrey J

2013-12-01

Rounds 20-27 of the Critical Assessment of PRotein Interactions (CAPRI) provided a testing platform for computational methods designed to address a wide range of challenges. The diverse targets drove the creation of and new combinations of computational tools. In this study, RosettaDock and other novel Rosetta protocols were used to successfully predict four of the 10 blind targets. For example, for DNase domain of Colicin E2-Im2 immunity protein, RosettaDock and RosettaLigand were used to predict the positions of water molecules at the interface, recovering 46% of the native water-mediated contacts. For α-repeat Rep4-Rep2 and g-type lysozyme-PliG inhibitor complexes, homology models were built and standard and pH-sensitive docking algorithms were used to generate structures with interface RMSD values of 3.3 Å and 2.0 Å, respectively. A novel flexible sugar-protein docking protocol was also developed and used for structure prediction of the BT4661-heparin-like saccharide complex, recovering 71% of the native contacts. Challenges remain in the generation of accurate homology models for protein mutants and sampling during global docking. On proteins designed to bind influenza hemagglutinin, only about half of the mutations were identified that affect binding (T55: 54%; T56: 48%). The prediction of the structure of the xylanase complex involving homology modeling and multidomain docking pushed the limits of global conformational sampling and did not result in any successful prediction. The diversity of problems at hand requires computational algorithms to be versatile; the recent additions to the Rosetta suite expand the capabilities to encompass more biologically realistic docking problems. Copyright © 2013 Wiley Periodicals, Inc.
Ab Initio Protein Structure Assembly Using Continuous Structure Fragments and Optimized Knowledge-based Force Field

PubMed Central

Xu, Dong; Zhang, Yang

2012-01-01

Ab initio protein folding is one of the major unsolved problems in computational biology due to the difficulties in force field design and conformational search. We developed a novel program, QUARK, for template-free protein structure prediction. Query sequences are first broken into fragments of 1–20 residues where multiple fragment structures are retrieved at each position from unrelated experimental structures. Full-length structure models are then assembled from fragments using replica-exchange Monte Carlo simulations, which are guided by a composite knowledge-based force field. A number of novel energy terms and Monte Carlo movements are introduced and the particular contributions to enhancing the efficiency of both force field and search engine are analyzed in detail. QUARK prediction procedure is depicted and tested on the structure modeling of 145 non-homologous proteins. Although no global templates are used and all fragments from experimental structures with template modeling score (TM-score) >0.5 are excluded, QUARK can successfully construct 3D models of correct folds in 1/3 cases of short proteins up to 100 residues. In the ninth community-wide Critical Assessment of protein Structure Prediction (CASP9) experiment, QUARK server outperformed the second and third best servers by 18% and 47% based on the cumulative Z-score of global distance test-total (GDT-TS) scores in the free modeling (FM) category. Although ab initio protein folding remains a significant challenge, these data demonstrate new progress towards the solution of the most important problem in the field. PMID:22411565
Structure and validity of sluggish cognitive tempo using an expanded item pool in children with attention-deficit/hyperactivity disorder.

PubMed

McBurnett, Keith; Villodas, Miguel; Burns, G Leonard; Hinshaw, Stephen P; Beaulieu, Allyson; Pfiffner, Linda J

2014-01-01

We evaluated the latent structure and validity of an expanded pool of Sluggish Cognitive Tempo (SCT) items. An experimental rating scale with 44 candidate SCT items was administered to parents and teachers of 165 children in grades 2-5 (ages 7-11) recruited for a randomized clinical trial of a psychosocial intervention for Attention-Deficit/Hyperactivity Disorder, Predominantly Inattentive Type. Exploratory factor analyses (EFA) were used to extract items with high loadings (>0.59) on primary factors of SCT and low cross-loadings (0.30 or lower) on other SCT factors and on the Inattention factor of ADHD. Items were required to meet these criteria for both informants. This procedure reduced the pool to 15 items. Generally, items representing slowness and low initiative failed these criteria. SCT factors (termed Daydreaming, Working Memory Problems, and Sleepy/Tired) showed good convergent and discriminant validity in EFA and in a confirmatory model with ADHD factors. Simultaneous regressions of impairment and comorbidity on SCT and ADHD factors found that Daydreams was associated with global impairment, and Sleepy/Tired was associated with organizational problems and depression ratings, across both informants. For teachers, Daydreams also predicted ODD (inversely); Sleepy/Tired also predicted poor academic behavior, low social skills, and problem social behavior; and Working Memory Problems predicted organizational problems and anxiety. When depression, rather than ADHD, was included among the predictors, the only SCT-related associations rendered insignificant were the teacher-reported associations of Daydreams with ODD; Working Memory Problems with anxiety, and Sleepy/Tired with poor social skills. SCT appears to be meaningfully associated with impairment, even when controlling for depression. Common behaviors resembling Working Memory problems may represent a previously undescribed factor of SCT.
Improving protein disorder prediction by deep bidirectional long short-term memory recurrent neural networks.

PubMed

Hanson, Jack; Yang, Yuedong; Paliwal, Kuldip; Zhou, Yaoqi

2017-03-01

Capturing long-range interactions between structural but not sequence neighbors of proteins is a long-standing challenging problem in bioinformatics. Recently, long short-term memory (LSTM) networks have significantly improved the accuracy of speech and image classification problems by remembering useful past information in long sequential events. Here, we have implemented deep bidirectional LSTM recurrent neural networks in the problem of protein intrinsic disorder prediction. The new method, named SPOT-Disorder, has steadily improved over a similar method using a traditional, window-based neural network (SPINE-D) in all datasets tested without separate training on short and long disordered regions. Independent tests on four other datasets including the datasets from critical assessment of structure prediction (CASP) techniques and >10 000 annotated proteins from MobiDB, confirmed SPOT-Disorder as one of the best methods in disorder prediction. Moreover, initial studies indicate that the method is more accurate in predicting functional sites in disordered regions. These results highlight the usefulness combining LSTM with deep bidirectional recurrent neural networks in capturing non-local, long-range interactions for bioinformatics applications. SPOT-disorder is available as a web server and as a standalone program at: http://sparks-lab.org/server/SPOT-disorder/index.php . j.hanson@griffith.edu.au or yuedong.yang@griffith.edu.au or yaoqi.zhou@griffith.edu.au. Supplementary data is available at Bioinformatics online. © The Author 2016. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com
Robust distributed model predictive control of linear systems with structured time-varying uncertainties

NASA Astrophysics Data System (ADS)

Zhang, Langwen; Xie, Wei; Wang, Jingcheng

2017-11-01

In this work, synthesis of robust distributed model predictive control (MPC) is presented for a class of linear systems subject to structured time-varying uncertainties. By decomposing a global system into smaller dimensional subsystems, a set of distributed MPC controllers, instead of a centralised controller, are designed. To ensure the robust stability of the closed-loop system with respect to model uncertainties, distributed state feedback laws are obtained by solving a min-max optimisation problem. The design of robust distributed MPC is then transformed into solving a minimisation optimisation problem with linear matrix inequality constraints. An iterative online algorithm with adjustable maximum iteration is proposed to coordinate the distributed controllers to achieve a global performance. The simulation results show the effectiveness of the proposed robust distributed MPC algorithm.
Social skills and behavior problems of urban, African American preschoolers: role of parenting practices, family conflict, and maternal depression.

PubMed

Koblinsky, Sally A; Kuvalanka, Katherine A; Randolph, Suzanne M

2006-10-01

This study examined the role of parenting, family routines, family conflict, and maternal depression in predicting the social skills and behavior problems of low-income African American preschoolers. A sample of 184 African American mothers of Head Start children completed participant and child measures in a structured interview. Results of regression analyses revealed that mothers who utilized more positive parenting practices and engaged in more family routines had children who displayed higher levels of total prosocial skills. Positive parenting and lower levels of maternal depressive symptoms were predictive of fewer externalizing and internalizing child behavior problems. Lower family conflict was linked with fewer externalizing problems. Implications of the study for future research and intervention are discussed. (c) 2007 APA, all rights reserved
Rapid search for tertiary fragments reveals protein sequence–structure relationships

PubMed Central

Zhou, Jianfu; Grigoryan, Gevorg

2015-01-01

Finding backbone substructures from the Protein Data Bank that match an arbitrary query structural motif, composed of multiple disjoint segments, is a problem of growing relevance in structure prediction and protein design. Although numerous protein structure search approaches have been proposed, methods that address this specific task without additional restrictions and on practical time scales are generally lacking. Here, we propose a solution, dubbed MASTER, that is both rapid, enabling searches over the Protein Data Bank in a matter of seconds, and provably correct, finding all matches below a user-specified root-mean-square deviation cutoff. We show that despite the potentially exponential time complexity of the problem, running times in practice are modest even for queries with many segments. The ability to explore naturally plausible structural and sequence variations around a given motif has the potential to synthesize its design principles in an automated manner; so we go on to illustrate the utility of MASTER to protein structural biology. We demonstrate its capacity to rapidly establish structure–sequence relationships, uncover the native designability landscapes of tertiary structural motifs, identify structural signatures of binding, and automatically rewire protein topologies. Given the broad utility of protein tertiary fragment searches, we hope that providing MASTER in an open-source format will enable novel advances in understanding, predicting, and designing protein structure. PMID:25420575
Learning Orthographic Structure with Sequential Generative Neural Networks

ERIC Educational Resources Information Center

Testolin, Alberto; Stoianov, Ivilin; Sperduti, Alessandro; Zorzi, Marco

2016-01-01

Learning the structure of event sequences is a ubiquitous problem in cognition and particularly in language. One possible solution is to learn a probabilistic generative model of sequences that allows making predictions about upcoming events. Though appealing from a neurobiological standpoint, this approach is typically not pursued in…

Prediction of Breakthrough Curves for Conservative and Reactive Transport from the Structural Parameters of Highly Heterogeneous Media

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

Hansen, Scott; Haslauer, Claus P.; Cirpka, Olaf A.

2017-01-05

The key points of this presentation were to approach the problem of linking breakthrough curve shape (RP-CTRW transition distribution) to structural parameters from a Monte Carlo approach and to use the Monte Carlo analysis to determine any empirical error
Structural kinematics based damage zone prediction in gradient structures using vibration database

NASA Astrophysics Data System (ADS)

Talha, Mohammad; Ashokkumar, Chimpalthradi R.

2014-05-01

To explore the applications of functionally graded materials (FGMs) in dynamic structures, structural kinematics based health monitoring technique becomes an important problem. Depending upon the displacements in three dimensions, the health of the material to withstand dynamic loads is inferred in this paper, which is based on the net compressive and tensile displacements that each structural degree of freedom takes. These net displacements at each finite element node predicts damage zones of the FGM where the material is likely to fail due to a vibration response which is categorized according to loading condition. The damage zone prediction of a dynamically active FGMs plate have been accomplished using Reddy's higher-order theory. The constituent material properties are assumed to vary in the thickness direction according to the power-law behavior. The proposed C0 finite element model (FEM) is applied to get net tensile and compressive displacement distributions across the structures. A plate made of Aluminum/Ziconia is considered to illustrate the concept of structural kinematics-based health monitoring aspects of FGMs.
A robust component mode synthesis method for stochastic damped vibroacoustics

NASA Astrophysics Data System (ADS)

Tran, Quang Hung; Ouisse, Morvan; Bouhaddi, Noureddine

2010-01-01

In order to reduce vibrations or sound levels in industrial vibroacoustic problems, the low-cost and efficient way consists in introducing visco- and poro-elastic materials either on the structure or on cavity walls. Depending on the frequency range of interest, several numerical approaches can be used to estimate the behavior of the coupled problem. In the context of low frequency applications related to acoustic cavities with surrounding vibrating structures, the finite elements method (FEM) is one of the most efficient techniques. Nevertheless, industrial problems lead to large FE models which are time-consuming in updating or optimization processes. A classical way to reduce calculation time is the component mode synthesis (CMS) method, whose classical formulation is not always efficient to predict dynamical behavior of structures including visco-elastic and/or poro-elastic patches. Then, to ensure an efficient prediction, the fluid and structural bases used for the model reduction need to be updated as a result of changes in a parametric optimization procedure. For complex models, this leads to prohibitive numerical costs in the optimization phase or for management and propagation of uncertainties in the stochastic vibroacoustic problem. In this paper, the formulation of an alternative CMS method is proposed and compared to classical ( u, p) CMS method: the Ritz basis is completed with static residuals associated to visco-elastic and poro-elastic behaviors. This basis is also enriched by the static response of residual forces due to structural modifications, resulting in a so-called robust basis, also adapted to Monte Carlo simulations for uncertainties propagation using reduced models.
Parenting, attention and externalizing problems: testing mediation longitudinally, repeatedly and reciprocally.

PubMed

Belsky, Jay; Pasco Fearon, R M; Bell, Brian

2007-12-01

Building on prior work, this paper tests, longitudinally and repeatedly, the proposition that attentional control processes mediate the effect of earlier parenting on later externalizing problems. Repeated independent measurements of all three constructs--observed parenting, computer-tested attentional control and adult-reported externalizing problems--were subjected to structural equation modeling using data from the large-scale American study of child care and youth development. Structural equation modeling indicated (a) that greater maternal sensitivity at two different ages (54 months, approximately 6 years) predicted better attentional control on the Continuous Performance Test (CPT) of attention regulation two later ages ( approximately 6/9 years); (2) that better attentional control at three different ages (54 months, approximately 6/9 years) predicted less teacher-reported externalizing problems at three later ages ( approximately 6/8/10 years); and (3) that attentional control partially mediated the effect of parenting on externalizing problems at two different lags (i.e., 54 months--> approximately 6 years--> approximately 8 years; approximately 6 years--> approximately 9 years--> approximately 10 years), though somewhat more strongly for the first. Additionally, (4) some evidence of reciprocal effects of attentional processes on parenting emerged (54 months--> approximately 6 years; approximately 6 years--> approximately 8 years), but not of problem behavior on attention. Because attention control partially mediates the effects of parenting on externalizing problems, intervention efforts could target both parenting and attentional processes.
Mixed time integration methods for transient thermal analysis of structures, appendix 5

NASA Technical Reports Server (NTRS)

Liu, W. K.

1982-01-01

Mixed time integration methods for transient thermal analysis of structures are studied. An efficient solution procedure for predicting the thermal behavior of aerospace vehicle structures was developed. A 2D finite element computer program incorporating these methodologies is being implemented. The performance of these mixed time finite element algorithms can then be evaluated employing the proposed example problem.
High frequency flow-structural interaction in dense subsonic fluids

NASA Technical Reports Server (NTRS)

Liu, Baw-Lin; Ofarrell, J. M.

1995-01-01

Prediction of the detailed dynamic behavior in rocket propellant feed systems and engines and other such high-energy fluid systems requires precise analysis to assure structural performance. Designs sometimes require placement of bluff bodies in a flow passage. Additionally, there are flexibilities in ducts, liners, and piping systems. A design handbook and interactive data base have been developed for assessing flow/structural interactions to be used as a tool in design and development, to evaluate applicable geometries before problems develop, or to eliminate or minimize problems with existing hardware. This is a compilation of analytical/empirical data and techniques to evaluate detailed dynamic characteristics of both the fluid and structures. These techniques have direct applicability to rocket engine internal flow passages, hot gas drive systems, and vehicle propellant feed systems. Organization of the handbook is by basic geometries for estimating Strouhal numbers, added mass effects, mode shapes for various end constraints, critical onset flow conditions, and possible structural response amplitudes. Emphasis is on dense fluids and high structural loading potential for fatigue at low subsonic flow speeds where high-frequency excitations are possible. Avoidance and corrective measure illustrations are presented together with analytical curve fits for predictions compiled from a comprehensive data base.
Classification and Prediction of RF Coupling inside A-320 and A-319 Airplanes using Feed Forward Neural Networks

NASA Technical Reports Server (NTRS)

Jafri, Madiha; Ely, Jay; Vahala, Linda

2006-01-01

Neural Network Modeling is introduced in this paper to classify and predict Interference Path Loss measurements on Airbus 319 and 320 airplanes. Interference patterns inside the aircraft are classified and predicted based on the locations of the doors, windows, aircraft structures and the communication/navigation system-of-concern. Modeled results are compared with measured data and a plan is proposed to enhance the modeling for better prediction of electromagnetic coupling problems inside aircraft.
BetaSCPWeb: side-chain prediction for protein structures using Voronoi diagrams and geometry prioritization

PubMed Central

Ryu, Joonghyun; Lee, Mokwon; Cha, Jehyun; Laskowski, Roman A.; Ryu, Seong Eon; Kim, Deok-Soo

2016-01-01

Many applications, such as protein design, homology modeling, flexible docking, etc. require the prediction of a protein's optimal side-chain conformations from just its amino acid sequence and backbone structure. Side-chain prediction (SCP) is an NP-hard energy minimization problem. Here, we present BetaSCPWeb which efficiently computes a conformation close to optimal using a geometry-prioritization method based on the Voronoi diagram of spherical atoms. Its outputs are visual, textual and PDB file format. The web server is free and open to all users at http://voronoi.hanyang.ac.kr/betascpweb with no login requirement. PMID:27151195
Parenting and children's externalizing problems in substance-abusing families.

PubMed

Stanger, Catherine; Dumenci, Levent; Kamon, Jody; Burstein, Marcy

2004-09-01

This study tested associations in path models among positive and negative parenting and children's rule-breaking behavior, aggressive and oppositional behavior, and attention problems for families with a drug-dependent parent. A structural model tested relations between parenting and children's externalizing problems for 251 families with 399 children between the ages of 6 and 18, controlling for nonindependence of ratings at the family level. The model also tested potential moderators, including child age, gender, and ethnicity (White vs. other), and caregiver gender (families with a female substance-abusing caregiver vs. families with a male substance-abusing caregiver). Results indicated that caregiver ratings of monitoring predicted rule-breaking behavior and use of inconsistent discipline predicted ratings of all 3 externalizing syndromes, after controlling parenting and externalizing problems for the effects of the moderators and after controlling significant relations among types of parenting and types of externalizing problems.
Unified constitutive models for high-temperature structural applications

NASA Technical Reports Server (NTRS)

Lindholm, U. S.; Chan, K. S.; Bodner, S. R.; Weber, R. M.; Walker, K. P.

1988-01-01

Unified constitutive models are characterized by the use of a single inelastic strain rate term for treating all aspects of inelastic deformation, including plasticity, creep, and stress relaxation under monotonic or cyclic loading. The structure of this class of constitutive theory pertinent for high temperature structural applications is first outlined and discussed. The effectiveness of the unified approach for representing high temperature deformation of Ni-base alloys is then evaluated by extensive comparison of experimental data and predictions of the Bodner-Partom and the Walker models. The use of the unified approach for hot section structural component analyses is demonstrated by applying the Walker model in finite element analyses of a benchmark notch problem and a turbine blade problem.
Advances and trends in structural and solid mechanics; Proceedings of the Symposium, Washington, DC, October 4-7, 1982

NASA Technical Reports Server (NTRS)

Noor, A. K. (Editor); Housner, J. M.

1983-01-01

The mechanics of materials and material characterization are considered, taking into account micromechanics, the behavior of steel structures at elevated temperatures, and an anisotropic plasticity model for inelastic multiaxial cyclic deformation. Other topics explored are related to advances and trends in finite element technology, classical analytical techniques and their computer implementation, interactive computing and computational strategies for nonlinear problems, advances and trends in numerical analysis, database management systems and CAD/CAM, space structures and vehicle crashworthiness, beams, plates and fibrous composite structures, design-oriented analysis, artificial intelligence and optimization, contact problems, random waves, and lifetime prediction. Earthquake-resistant structures and other advanced structural applications are also discussed, giving attention to cumulative damage in steel structures subjected to earthquake ground motions, and a mixed domain analysis of nuclear containment structures using impulse functions.
The Minimal Control Principle Predicts Strategy Shifts in the Abstract Decision Making Task

ERIC Educational Resources Information Center

Taatgen, Niels A.

2011-01-01

The minimal control principle (Taatgen, 2007) predicts that people strive for problem-solving strategies that require as few internal control states as possible. In an experiment with the Abstract Decision Making task (ADM task; Joslyn & Hunt, 1998) the reward structure was manipulated to make either a low-control strategy or a high-strategy…
3D Protein structure prediction with genetic tabu search algorithm

PubMed Central

2010-01-01

Background Protein structure prediction (PSP) has important applications in different fields, such as drug design, disease prediction, and so on. In protein structure prediction, there are two important issues. The first one is the design of the structure model and the second one is the design of the optimization technology. Because of the complexity of the realistic protein structure, the structure model adopted in this paper is a simplified model, which is called off-lattice AB model. After the structure model is assumed, optimization technology is needed for searching the best conformation of a protein sequence based on the assumed structure model. However, PSP is an NP-hard problem even if the simplest model is assumed. Thus, many algorithms have been developed to solve the global optimization problem. In this paper, a hybrid algorithm, which combines genetic algorithm (GA) and tabu search (TS) algorithm, is developed to complete this task. Results In order to develop an efficient optimization algorithm, several improved strategies are developed for the proposed genetic tabu search algorithm. The combined use of these strategies can improve the efficiency of the algorithm. In these strategies, tabu search introduced into the crossover and mutation operators can improve the local search capability, the adoption of variable population size strategy can maintain the diversity of the population, and the ranking selection strategy can improve the possibility of an individual with low energy value entering into next generation. Experiments are performed with Fibonacci sequences and real protein sequences. Experimental results show that the lowest energy obtained by the proposed GATS algorithm is lower than that obtained by previous methods. Conclusions The hybrid algorithm has the advantages from both genetic algorithm and tabu search algorithm. It makes use of the advantage of multiple search points in genetic algorithm, and can overcome poor hill-climbing capability in the conventional genetic algorithm by using the flexible memory functions of TS. Compared with some previous algorithms, GATS algorithm has better performance in global optimization and can predict 3D protein structure more effectively. PMID:20522256
NONUNIFORM FOURIER TRANSFORMS FOR RIGID-BODY AND MULTI-DIMENSIONAL ROTATIONAL CORRELATIONS

PubMed Central

BAJAJ, CHANDRAJIT; BAUER, BENEDIKT; BETTADAPURA, RADHAKRISHNA; VOLLRATH, ANTJE

2013-01-01

The task of evaluating correlations is central to computational structural biology. The rigid-body correlation problem seeks the rigid-body transformation (R, t), R ∈ SO(3), t ∈ ℝ3 that maximizes the correlation between a pair of input scalar-valued functions representing molecular structures. Exhaustive solutions to the rigid-body correlation problem take advantage of the fast Fourier transform to achieve a speedup either with respect to the sought translation or rotation. We present PFcorr, a new exhaustive solution, based on the non-equispaced SO(3) Fourier transform, to the rigid-body correlation problem; unlike previous solutions, ours achieves a combination of translational and rotational speedups without requiring equispaced grids. PFcorr can be straightforwardly applied to a variety of problems in protein structure prediction and refinement that involve correlations under rigid-body motions of the protein. Additionally, we show how it applies, along with an appropriate flexibility model, to analogs of the above problems in which the flexibility of the protein is relevant. PMID:24379643
When the lowest energy does not induce native structures: parallel minimization of multi-energy values by hybridizing searching intelligences.

PubMed

Lü, Qiang; Xia, Xiao-Yan; Chen, Rong; Miao, Da-Jun; Chen, Sha-Sha; Quan, Li-Jun; Li, Hai-Ou

2012-01-01

Protein structure prediction (PSP), which is usually modeled as a computational optimization problem, remains one of the biggest challenges in computational biology. PSP encounters two difficult obstacles: the inaccurate energy function problem and the searching problem. Even if the lowest energy has been luckily found by the searching procedure, the correct protein structures are not guaranteed to obtain. A general parallel metaheuristic approach is presented to tackle the above two problems. Multi-energy functions are employed to simultaneously guide the parallel searching threads. Searching trajectories are in fact controlled by the parameters of heuristic algorithms. The parallel approach allows the parameters to be perturbed during the searching threads are running in parallel, while each thread is searching the lowest energy value determined by an individual energy function. By hybridizing the intelligences of parallel ant colonies and Monte Carlo Metropolis search, this paper demonstrates an implementation of our parallel approach for PSP. 16 classical instances were tested to show that the parallel approach is competitive for solving PSP problem. This parallel approach combines various sources of both searching intelligences and energy functions, and thus predicts protein conformations with good quality jointly determined by all the parallel searching threads and energy functions. It provides a framework to combine different searching intelligence embedded in heuristic algorithms. It also constructs a container to hybridize different not-so-accurate objective functions which are usually derived from the domain expertise.
When the Lowest Energy Does Not Induce Native Structures: Parallel Minimization of Multi-Energy Values by Hybridizing Searching Intelligences

PubMed Central

Lü, Qiang; Xia, Xiao-Yan; Chen, Rong; Miao, Da-Jun; Chen, Sha-Sha; Quan, Li-Jun; Li, Hai-Ou

2012-01-01

Background Protein structure prediction (PSP), which is usually modeled as a computational optimization problem, remains one of the biggest challenges in computational biology. PSP encounters two difficult obstacles: the inaccurate energy function problem and the searching problem. Even if the lowest energy has been luckily found by the searching procedure, the correct protein structures are not guaranteed to obtain. Results A general parallel metaheuristic approach is presented to tackle the above two problems. Multi-energy functions are employed to simultaneously guide the parallel searching threads. Searching trajectories are in fact controlled by the parameters of heuristic algorithms. The parallel approach allows the parameters to be perturbed during the searching threads are running in parallel, while each thread is searching the lowest energy value determined by an individual energy function. By hybridizing the intelligences of parallel ant colonies and Monte Carlo Metropolis search, this paper demonstrates an implementation of our parallel approach for PSP. 16 classical instances were tested to show that the parallel approach is competitive for solving PSP problem. Conclusions This parallel approach combines various sources of both searching intelligences and energy functions, and thus predicts protein conformations with good quality jointly determined by all the parallel searching threads and energy functions. It provides a framework to combine different searching intelligence embedded in heuristic algorithms. It also constructs a container to hybridize different not-so-accurate objective functions which are usually derived from the domain expertise. PMID:23028708
Large-scale model quality assessment for improving protein tertiary structure prediction.

PubMed

Cao, Renzhi; Bhattacharya, Debswapna; Adhikari, Badri; Li, Jilong; Cheng, Jianlin

2015-06-15

Sampling structural models and ranking them are the two major challenges of protein structure prediction. Traditional protein structure prediction methods generally use one or a few quality assessment (QA) methods to select the best-predicted models, which cannot consistently select relatively better models and rank a large number of models well. Here, we develop a novel large-scale model QA method in conjunction with model clustering to rank and select protein structural models. It unprecedentedly applied 14 model QA methods to generate consensus model rankings, followed by model refinement based on model combination (i.e. averaging). Our experiment demonstrates that the large-scale model QA approach is more consistent and robust in selecting models of better quality than any individual QA method. Our method was blindly tested during the 11th Critical Assessment of Techniques for Protein Structure Prediction (CASP11) as MULTICOM group. It was officially ranked third out of all 143 human and server predictors according to the total scores of the first models predicted for 78 CASP11 protein domains and second according to the total scores of the best of the five models predicted for these domains. MULTICOM's outstanding performance in the extremely competitive 2014 CASP11 experiment proves that our large-scale QA approach together with model clustering is a promising solution to one of the two major problems in protein structure modeling. The web server is available at: http://sysbio.rnet.missouri.edu/multicom_cluster/human/. © The Author 2015. Published by Oxford University Press.
Recent developments in analysis of crack propagation and fracture of practical materials. [stress analysis in aircraft structures

NASA Technical Reports Server (NTRS)

Hardrath, H. F.; Newman, J. C., Jr.; Elber, W.; Poe, C. C., Jr.

1978-01-01

The limitations of linear elastic fracture mechanics in aircraft design and in the study of fatigue crack propagation in aircraft structures are discussed. NASA-Langley research to extend the capabilities of fracture mechanics to predict the maximum load that can be carried by a cracked part and to deal with aircraft design problems are reported. Achievements include: (1) improved stress intensity solutions for laboratory specimens; (2) fracture criterion for practical materials; (3) crack propagation predictions that account for mean stress and high maximum stress effects; (4) crack propagation predictions for variable amplitude loading; and (5) the prediction of crack growth and residual stress in built-up structural assemblies. These capabilities are incorporated into a first generation computerized analysis that allows for damage tolerance and tradeoffs with other disciplines to produce efficient designs that meet current airworthiness requirements.
Prediction of rodent carcinogenicity bioassays from molecular structure using inductive logic programming

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

King, R.D.; Srinivasan, A.

1996-10-01

The machine learning program Progol was applied to the problem of forming the structure-activity relationship (SAR) for a set of compounds tested for carcinogenicity in rodent bioassays by the U.S. National Toxicology Program (NTP). Progol is the first inductive logic programming (ILP) algorithm to use a fully relational method for describing chemical structure in SARs, based on using atoms and their bond connectivities. Progol is well suited to forming SARs for carcinogenicity as it is designed to produce easily understandable rules (structural alerts) for sets of noncongeneric compounds. The Progol SAR method was tested by prediction of a set ofmore » compounds that have been widely predicted by other SAR methods (the compounds used in the NTP`s first round of carcinogenesis predictions). For these compounds no method (human or machine) was significantly more accurate than Progol. Progol was the most accurate method that did not use data from biological tests on rodents (however, the difference in accuracy is not significant). The Progol predictions were based solely on chemical structure and the results of tests for Salmonella mutagenicity. Using the full NTP database, the prediction accuracy of Progol was estimated to be 63% ({+-}3%) using 5-fold cross validation. A set of structural alerts for carcinogenesis was automatically generated and the chemical rationale for them investigated-these structural alerts are statistically independent of the Salmonella mutagenicity. Carcinogenicity is predicted for the compounds used in the NTP`s second round of carcinogenesis predictions. The results for prediction of carcinogenesis, taken together with the previous successful applications of predicting mutagenicity in nitroaromatic compounds, and inhibition of angiogenesis by suramin analogues, show that Progol has a role to play in understanding the SARs of cancer-related compounds. 29 refs., 2 figs., 4 tabs.« less
Structural Revisions of a Class of Natural Products: Scaffolds of Aglycon Analogues of Fusicoccins and Cotylenins Isolated from Fungi.

PubMed

Tang, Ying; Xue, Yongbo; Du, Guang; Wang, Jianping; Liu, Junjun; Sun, Bin; Li, Xiao-Nian; Yao, Guangmin; Luo, Zengwei; Zhang, Yonghui

2016-03-14

The reisolation and structural revision of brassicicene D is described, and inspired us to reassign the core skeletons of brassicicenes C-H, J and K, ranging from dicyclopenta[a,d]cyclooctane to tricyclo[9.2.1.0(3,7)]tetradecane using quantum-chemical predictions and experimental validation strategies. Three novel, highly modified fusicoccanes, brassicicenes L-N, were also isolated from the fungus Alternaria brassicicola, and their structures were unequivocally established by spectroscopic data, ECD calculations, and crystallography. The reassigned structures represent the first class of bridgehead double-bond-containing natural products with a bicyclo[6.2.1]undecane carbon skeleton. Furthermore, their stabilities were first predicted with olefin strain energy calculations. Collectively, these findings extend our view of the application of computational predictions and biosynthetic logic-based structure elucidation to address problems related to the structure and stability of natural products. © 2016 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

A cross docking pipeline for improving pose prediction and virtual screening performance

NASA Astrophysics Data System (ADS)

Kumar, Ashutosh; Zhang, Kam Y. J.

2018-01-01

Pose prediction and virtual screening performance of a molecular docking method depend on the choice of protein structures used for docking. Multiple structures for a target protein are often used to take into account the receptor flexibility and problems associated with a single receptor structure. However, the use of multiple receptor structures is computationally expensive when docking a large library of small molecules. Here, we propose a new cross-docking pipeline suitable to dock a large library of molecules while taking advantage of multiple target protein structures. Our method involves the selection of a suitable receptor for each ligand in a screening library utilizing ligand 3D shape similarity with crystallographic ligands. We have prospectively evaluated our method in D3R Grand Challenge 2 and demonstrated that our cross-docking pipeline can achieve similar or better performance than using either single or multiple-receptor structures. Moreover, our method displayed not only decent pose prediction performance but also better virtual screening performance over several other methods.
NIAS-Server: Neighbors Influence of Amino acids and Secondary Structures in Proteins.

PubMed

Borguesan, Bruno; Inostroza-Ponta, Mario; Dorn, Márcio

2017-03-01

The exponential growth in the number of experimentally determined three-dimensional protein structures provide a new and relevant knowledge about the conformation of amino acids in proteins. Only a few of probability densities of amino acids are publicly available for use in structure validation and prediction methods. NIAS (Neighbors Influence of Amino acids and Secondary structures) is a web-based tool used to extract information about conformational preferences of amino acid residues and secondary structures in experimental-determined protein templates. This information is useful, for example, to characterize folds and local motifs in proteins, molecular folding, and can help the solution of complex problems such as protein structure prediction, protein design, among others. The NIAS-Server and supplementary data are available at http://sbcb.inf.ufrgs.br/nias .
How can we improve problem solving in undergraduate biology? Applying lessons from 30 years of physics education research.

PubMed

Hoskinson, A-M; Caballero, M D; Knight, J K

2013-06-01

If students are to successfully grapple with authentic, complex biological problems as scientists and citizens, they need practice solving such problems during their undergraduate years. Physics education researchers have investigated student problem solving for the past three decades. Although physics and biology problems differ in structure and content, the instructional purposes align closely: explaining patterns and processes in the natural world and making predictions about physical and biological systems. In this paper, we discuss how research-supported approaches developed by physics education researchers can be adopted by biologists to enhance student problem-solving skills. First, we compare the problems that biology students are typically asked to solve with authentic, complex problems. We then describe the development of research-validated physics curricula emphasizing process skills in problem solving. We show that solving authentic, complex biology problems requires many of the same skills that practicing physicists and biologists use in representing problems, seeking relationships, making predictions, and verifying or checking solutions. We assert that acquiring these skills can help biology students become competent problem solvers. Finally, we propose how biology scholars can apply lessons from physics education in their classrooms and inspire new studies in biology education research.
Learning Activity Predictors from Sensor Data: Algorithms, Evaluation, and Applications.

PubMed

Minor, Bryan; Doppa, Janardhan Rao; Cook, Diane J

2017-12-01

Recent progress in Internet of Things (IoT) platforms has allowed us to collect large amounts of sensing data. However, there are significant challenges in converting this large-scale sensing data into decisions for real-world applications. Motivated by applications like health monitoring and intervention and home automation we consider a novel problem called Activity Prediction , where the goal is to predict future activity occurrence times from sensor data. In this paper, we make three main contributions. First, we formulate and solve the activity prediction problem in the framework of imitation learning and reduce it to a simple regression learning problem. This approach allows us to leverage powerful regression learners that can reason about the relational structure of the problem with negligible computational overhead. Second, we present several metrics to evaluate activity predictors in the context of real-world applications. Third, we evaluate our approach using real sensor data collected from 24 smart home testbeds. We also embed the learned predictor into a mobile-device-based activity prompter and evaluate the app for 9 participants living in smart homes. Our results indicate that our activity predictor performs better than the baseline methods, and offers a simple approach for predicting activities from sensor data.
High Performance Parallel Analysis of Coupled Problems for Aircraft Propulsion

NASA Technical Reports Server (NTRS)

Felippa, C. A.; Farhat, C.; Lanteri, S.; Maman, N.; Piperno, S.; Gumaste, U.

1994-01-01

In order to predict the dynamic response of a flexible structure in a fluid flow, the equations of motion of the structure and the fluid must be solved simultaneously. In this paper, we present several partitioned procedures for time-integrating this focus coupled problem and discuss their merits in terms of accuracy, stability, heterogeneous computing, I/O transfers, subcycling, and parallel processing. All theoretical results are derived for a one-dimensional piston model problem with a compressible flow, because the complete three-dimensional aeroelastic problem is difficult to analyze mathematically. However, the insight gained from the analysis of the coupled piston problem and the conclusions drawn from its numerical investigation are confirmed with the numerical simulation of the two-dimensional transient aeroelastic response of a flexible panel in a transonic nonlinear Euler flow regime.
Combining Vision with Voice: A Learning and Implementation Structure Promoting Teachers' Internalization of Practices Based on Self-Determination Theory

ERIC Educational Resources Information Center

Assor, Avi; Kaplan, Haya; Feinberg, Ofra; Tal, Karen

2009-01-01

We propose that self-determination theory's conceptualization of internalization may help school reformers overcome the recurrent problem of "the predictable failure of educational reform" (Sarason, 1993). Accordingly, we present a detailed learning and implementation structure to promote teachers' internalization and application of ideas and…
A combination of feature extraction methods with an ensemble of different classifiers for protein structural class prediction problem.

PubMed

Dehzangi, Abdollah; Paliwal, Kuldip; Sharma, Alok; Dehzangi, Omid; Sattar, Abdul

2013-01-01

Better understanding of structural class of a given protein reveals important information about its overall folding type and its domain. It can also be directly used to provide critical information on general tertiary structure of a protein which has a profound impact on protein function determination and drug design. Despite tremendous enhancements made by pattern recognition-based approaches to solve this problem, it still remains as an unsolved issue for bioinformatics that demands more attention and exploration. In this study, we propose a novel feature extraction model that incorporates physicochemical and evolutionary-based information simultaneously. We also propose overlapped segmented distribution and autocorrelation-based feature extraction methods to provide more local and global discriminatory information. The proposed feature extraction methods are explored for 15 most promising attributes that are selected from a wide range of physicochemical-based attributes. Finally, by applying an ensemble of different classifiers namely, Adaboost.M1, LogitBoost, naive Bayes, multilayer perceptron (MLP), and support vector machine (SVM) we show enhancement of the protein structural class prediction accuracy for four popular benchmarks.
Some aspects of forecasting the post-mining substratum deformation for evaluation of its influence on constructions

NASA Astrophysics Data System (ADS)

Strzałkowski, Piotr; Ścigała, Roman; Szafulera, Katarzyna

2018-04-01

Some problems have been discussed, connected with performing predictions of post-mining terrain deformations. Especially problems occur with the summation of horizontal strain over long time intervals as well as predictions of linear discontinuous deformations. Of great importance in recent years is the problem of taking into account transient values of deformations associated with the development of extraction field. The exemplary analysis has been presented of planned extraction influences on two characteristic locations of building structure. The proposal has been shown of calculations with using transient deformation model allowing to describe the influence of extraction advance influence on the value of coefficient of extraction rate c (time factor), according to own original empirical formula.
Computational structural mechanics for engine structures

NASA Technical Reports Server (NTRS)

Chamis, Christos C.

1988-01-01

The computational structural mechanics (CSM) program at Lewis encompasses the formulation and solution of structural mechanics problems and the development of integrated software systems to computationally simulate the performance, durability, and life of engine structures. It is structured to supplement, complement, and, whenever possible, replace costly experimental efforts. Specific objectives are to investigate unique advantages of parallel and multiprocessing for reformulating and solving structural mechanics and formulating and solving multidisciplinary mechanics and to develop integrated structural system computational simulators for predicting structural performance, evaluating newly developed methods, and identifying and prioritizing improved or missing methods.
Computational structural mechanics for engine structures

NASA Technical Reports Server (NTRS)

Chamis, Christos C.

1989-01-01

The computational structural mechanics (CSM) program at Lewis encompasses the formulation and solution of structural mechanics problems and the development of integrated software systems to computationally simulate the performance, durability, and life of engine structures. It is structured to supplement, complement, and, whenever possible, replace costly experimental efforts. Specific objectives are to investigate unique advantages of parallel and multiprocessing for reformulating and solving structural mechanics and formulating and solving multidisciplinary mechanics and to develop integrated structural system computational simulators for predicting structural performance, evaluating newly developed methods, and identifying and prioritizing improved or missing methods.
Predicting behavior problems in deaf and hearing children: The influences of language, attention, and parent–child communication

PubMed Central

Barker, David H.; Quittner, Alexandra L.; Fink, Nancy E.; Eisenberg, Laurie S.; Tobey, Emily A.; Niparko, John K.

2009-01-01

The development of language and communication may play an important role in the emergence of behavioral problems in young children, but they are rarely included in predictive models of behavioral development. In this study, cross-sectional relationships between language, attention, and behavior problems were examined using parent report, videotaped observations, and performance measures in a sample of 116 severely and profoundly deaf and 69 normally hearing children ages 1.5 to 5 years. Secondary analyses were performed on data collected as part of the Childhood Development After Cochlear Implantation Study, funded by the National Institutes of Health. Hearing-impaired children showed more language, attention, and behavioral difficulties, and spent less time communicating with their parents than normally hearing children. Structural equation modeling indicated there were significant relationships between language, attention, and child behavior problems. Language was associated with behavior problems both directly and indirectly through effects on attention. Amount of parent–child communication was not related to behavior problems. PMID:19338689
Predicting behavior problems in deaf and hearing children: the influences of language, attention, and parent-child communication.

PubMed

Barker, David H; Quittner, Alexandra L; Fink, Nancy E; Eisenberg, Laurie S; Tobey, Emily A; Niparko, John K

2009-01-01

The development of language and communication may play an important role in the emergence of behavioral problems in young children, but they are rarely included in predictive models of behavioral development. In this study, cross-sectional relationships between language, attention, and behavior problems were examined using parent report, videotaped observations, and performance measures in a sample of 116 severely and profoundly deaf and 69 normally hearing children ages 1.5 to 5 years. Secondary analyses were performed on data collected as part of the Childhood Development After Cochlear Implantation Study, funded by the National Institutes of Health. Hearing-impaired children showed more language, attention, and behavioral difficulties, and spent less time communicating with their parents than normally hearing children. Structural equation modeling indicated there were significant relationships between language, attention, and child behavior problems. Language was associated with behavior problems both directly and indirectly through effects on attention. Amount of parent-child communication was not related to behavior problems.
Predicting beta-turns in proteins using support vector machines with fractional polynomials

PubMed Central

2013-01-01

Background β-turns are secondary structure type that have essential role in molecular recognition, protein folding, and stability. They are found to be the most common type of non-repetitive structures since 25% of amino acids in protein structures are situated on them. Their prediction is considered to be one of the crucial problems in bioinformatics and molecular biology, which can provide valuable insights and inputs for the fold recognition and drug design. Results We propose an approach that combines support vector machines (SVMs) and logistic regression (LR) in a hybrid prediction method, which we call (H-SVM-LR) to predict β-turns in proteins. Fractional polynomials are used for LR modeling. We utilize position specific scoring matrices (PSSMs) and predicted secondary structure (PSS) as features. Our simulation studies show that H-SVM-LR achieves Qtotal of 82.87%, 82.84%, and 82.32% on the BT426, BT547, and BT823 datasets respectively. These values are the highest among other β-turns prediction methods that are based on PSSMs and secondary structure information. H-SVM-LR also achieves favorable performance in predicting β-turns as measured by the Matthew's correlation coefficient (MCC) on these datasets. Furthermore, H-SVM-LR shows good performance when considering shape strings as additional features. Conclusions In this paper, we present a comprehensive approach for β-turns prediction. Experiments show that our proposed approach achieves better performance compared to other competing prediction methods. PMID:24565438
Predicting beta-turns in proteins using support vector machines with fractional polynomials.

PubMed

Elbashir, Murtada; Wang, Jianxin; Wu, Fang-Xiang; Wang, Lusheng

2013-11-07

β-turns are secondary structure type that have essential role in molecular recognition, protein folding, and stability. They are found to be the most common type of non-repetitive structures since 25% of amino acids in protein structures are situated on them. Their prediction is considered to be one of the crucial problems in bioinformatics and molecular biology, which can provide valuable insights and inputs for the fold recognition and drug design. We propose an approach that combines support vector machines (SVMs) and logistic regression (LR) in a hybrid prediction method, which we call (H-SVM-LR) to predict β-turns in proteins. Fractional polynomials are used for LR modeling. We utilize position specific scoring matrices (PSSMs) and predicted secondary structure (PSS) as features. Our simulation studies show that H-SVM-LR achieves Qtotal of 82.87%, 82.84%, and 82.32% on the BT426, BT547, and BT823 datasets respectively. These values are the highest among other β-turns prediction methods that are based on PSSMs and secondary structure information. H-SVM-LR also achieves favorable performance in predicting β-turns as measured by the Matthew's correlation coefficient (MCC) on these datasets. Furthermore, H-SVM-LR shows good performance when considering shape strings as additional features. In this paper, we present a comprehensive approach for β-turns prediction. Experiments show that our proposed approach achieves better performance compared to other competing prediction methods.
Design, implementation and evaluation of a practical pseudoknot folding algorithm based on thermodynamics

PubMed Central

Reeder, Jens; Giegerich, Robert

2004-01-01

Background The general problem of RNA secondary structure prediction under the widely used thermodynamic model is known to be NP-complete when the structures considered include arbitrary pseudoknots. For restricted classes of pseudoknots, several polynomial time algorithms have been designed, where the O(n6)time and O(n4) space algorithm by Rivas and Eddy is currently the best available program. Results We introduce the class of canonical simple recursive pseudoknots and present an algorithm that requires O(n4) time and O(n2) space to predict the energetically optimal structure of an RNA sequence, possible containing such pseudoknots. Evaluation against a large collection of known pseudoknotted structures shows the adequacy of the canonization approach and our algorithm. Conclusions RNA pseudoknots of medium size can now be predicted reliably as well as efficiently by the new algorithm. PMID:15294028
A probabilistic framework to infer brain functional connectivity from anatomical connections.

PubMed

Deligianni, Fani; Varoquaux, Gael; Thirion, Bertrand; Robinson, Emma; Sharp, David J; Edwards, A David; Rueckert, Daniel

2011-01-01

We present a novel probabilistic framework to learn across several subjects a mapping from brain anatomical connectivity to functional connectivity, i.e. the covariance structure of brain activity. This prediction problem must be formulated as a structured-output learning task, as the predicted parameters are strongly correlated. We introduce a model selection framework based on cross-validation with a parametrization-independent loss function suitable to the manifold of covariance matrices. Our model is based on constraining the conditional independence structure of functional activity by the anatomical connectivity. Subsequently, we learn a linear predictor of a stationary multivariate autoregressive model. This natural parameterization of functional connectivity also enforces the positive-definiteness of the predicted covariance and thus matches the structure of the output space. Our results show that functional connectivity can be explained by anatomical connectivity on a rigorous statistical basis, and that a proper model of functional connectivity is essential to assess this link.
The interplay of externalizing problems and physical and inductive discipline during childhood.

PubMed

Choe, Daniel Ewon; Olson, Sheryl L; Sameroff, Arnold J

2013-11-01

Children who are physically disciplined are at elevated risk for externalizing problems. Conversely, maternal reasoning and reminding of rules, or inductive discipline, is associated with fewer child externalizing problems. Few studies have simultaneously examined bidirectional associations between these forms of discipline and child adjustment using cross-informant, multimethod data. We hypothesized that less inductive and more physical discipline would predict more externalizing problems, children would have evocative effects on parenting, and high levels of either form of discipline would predict low levels of the other. In a study of 241 children-spanning ages 3, 5.5, and 10-structural equation modeling indicated that 3-year-olds with higher teacher ratings of externalizing problems received higher mother ratings of physical discipline at age 5.5. Mothers endorsing more inductive discipline at child age 3 reported less physical discipline and had children with fewer externalizing problems at age 5.5. Negative bidirectional associations emerged between physical and inductive discipline from ages 5.5 to 10. Findings suggested children's externalizing problems elicited physical discipline, and maternal inductive discipline might help prevent externalizing problems and physical discipline.
Young Adolescents' Metacognition and Domain Knowledge as Predictors of Hypothesis-Development Performance in a Computer-Supported Context

ERIC Educational Resources Information Center

Kim, Hye Jeong; Pedersen, Susan

2010-01-01

Recently, the importance of ill-structured problem-solving in real-world contexts has become a focus of educational research. Particularly, the hypothesis-development process has been examined as one of the keys to developing a high-quality solution in a problem context. The authors of this study examined predictive relations between young…
Do Demographic Factors, School Functioning, and Quality of Student-Teacher Relationships as Rated by Teachers Predict Internalising and Externalising Problems among Norwegian Schoolchildren?

ERIC Educational Resources Information Center

Drugli, May Britt; Klokner, Christian; Larsson, Bo

2011-01-01

The present study explored the association between child internalising and externalising problems in schools and demographic factors (sex and age), school functioning (academic performance and adaptive functioning) and teacher-reported student-teacher relationship quality in a cross-sectional study using structural equation modelling. The study…
How coping styles, cognitive distortions, and attachment predict problem gambling among adolescents and young adults.

PubMed

Calado, Filipa; Alexandre, Joana; Griffiths, Mark D

2017-12-01

Background and aims Recent research suggests that youth problem gambling is associated with several factors, but little is known how these factors might influence or interact each other in predicting this behavior. Consequently, this is the first study to examine the mediation effect of coping styles in the relationship between attachment to parental figures and problem gambling. Methods A total of 988 adolescents and emerging adults were recruited to participate. The first set of analyses tested the adequacy of a model comprising biological, cognitive, and family variables in predicting youth problem gambling. The second set of analyses explored the relationship between family and individual variables in problem gambling behavior. Results The results of the first set of analyses demonstrated that the individual factors of gender, cognitive distortions, and coping styles showed a significant predictive effect on youth problematic gambling, and the family factors of attachment and family structure did not reveal a significant influence on this behavior. The results of the second set of analyses demonstrated that the attachment dimension of angry distress exerted a more indirect influence on problematic gambling, through emotion-focused coping style. Discussion This study revealed that some family variables can have a more indirect effect on youth gambling behavior and provided some insights in how some factors interact in predicting problem gambling. Conclusion These findings suggest that youth gambling is a multifaceted phenomenon, and that the indirect effects of family variables are important in estimating the complex social forces that might inﬂuence adolescent decisions to gamble.

A conceptual model predicting internalizing problems in middle childhood among children of alcoholic and nonalcoholic fathers: the role of marital aggression.

PubMed

Eiden, Rina D; Molnar, Danielle S; Colder, Craig; Edwards, Ellen P; Leonard, Kenneth E

2009-09-01

The purpose of this study was to test a conceptual model predicting children's anxiety/depression in middle childhood in a community sample of children with parents who had alcohol problems (n = 112) and those without alcohol problems (n = 101). The conceptual model examined the role of parents' alcohol diagnoses, depression, and antisocial behavior among parents of children ages 12 months to kindergarten age in predicting marital aggression and parental aggravation. Higher levels of marital aggression and parental aggravation were hypothesized to predict children's depression/anxiety within time (18 months to kindergarten age and, prospectively, to age during fourth grade). The sample was recruited from New York State birth records when the children were 12 months old. Assessments were conducted at 12, 18, 24, and 36 months; at kindergarten age; and during fourth grade. Children with alcoholic fathers had higher depression/anxiety scores according to parental reports but not self-reports. Structural equations modeling was largely supportive of the conceptual model. Fathers' alcoholism was associated with higher child anxiety via greater levels of marital aggression among families with alcohol problems. Results also indicated that there was a significant indirect association between parents' depression symptoms and child anxiety via marital aggression. The results highlight the nested nature of risk characteristics in alcoholic families and the important role of marital aggression in predicting children's anxiety/depression. Interventions targeting both parents' alcohol problems and associated marital aggression are likely to provide the dual benefits of improving family interactions and lowering risk of children's internalizing behavior problems.
Efficient pairwise RNA structure prediction using probabilistic alignment constraints in Dynalign

PubMed Central

2007-01-01

Background Joint alignment and secondary structure prediction of two RNA sequences can significantly improve the accuracy of the structural predictions. Methods addressing this problem, however, are forced to employ constraints that reduce computation by restricting the alignments and/or structures (i.e. folds) that are permissible. In this paper, a new methodology is presented for the purpose of establishing alignment constraints based on nucleotide alignment and insertion posterior probabilities. Using a hidden Markov model, posterior probabilities of alignment and insertion are computed for all possible pairings of nucleotide positions from the two sequences. These alignment and insertion posterior probabilities are additively combined to obtain probabilities of co-incidence for nucleotide position pairs. A suitable alignment constraint is obtained by thresholding the co-incidence probabilities. The constraint is integrated with Dynalign, a free energy minimization algorithm for joint alignment and secondary structure prediction. The resulting method is benchmarked against the previous version of Dynalign and against other programs for pairwise RNA structure prediction. Results The proposed technique eliminates manual parameter selection in Dynalign and provides significant computational time savings in comparison to prior constraints in Dynalign while simultaneously providing a small improvement in the structural prediction accuracy. Savings are also realized in memory. In experiments over a 5S RNA dataset with average sequence length of approximately 120 nucleotides, the method reduces computation by a factor of 2. The method performs favorably in comparison to other programs for pairwise RNA structure prediction: yielding better accuracy, on average, and requiring significantly lesser computational resources. Conclusion Probabilistic analysis can be utilized in order to automate the determination of alignment constraints for pairwise RNA structure prediction methods in a principled fashion. These constraints can reduce the computational and memory requirements of these methods while maintaining or improving their accuracy of structural prediction. This extends the practical reach of these methods to longer length sequences. The revised Dynalign code is freely available for download. PMID:17445273
Post processing of protein-compound docking for fragment-based drug discovery (FBDD): in-silico structure-based drug screening and ligand-binding pose prediction.

PubMed

Fukunishi, Yoshifumi

2010-01-01

For fragment-based drug development, both hit (active) compound prediction and docking-pose (protein-ligand complex structure) prediction of the hit compound are important, since chemical modification (fragment linking, fragment evolution) subsequent to the hit discovery must be performed based on the protein-ligand complex structure. However, the naïve protein-compound docking calculation shows poor accuracy in terms of docking-pose prediction. Thus, post-processing of the protein-compound docking is necessary. Recently, several methods for the post-processing of protein-compound docking have been proposed. In FBDD, the compounds are smaller than those for conventional drug screening. This makes it difficult to perform the protein-compound docking calculation. A method to avoid this problem has been reported. Protein-ligand binding free energy estimation is useful to reduce the procedures involved in the chemical modification of the hit fragment. Several prediction methods have been proposed for high-accuracy estimation of protein-ligand binding free energy. This paper summarizes the various computational methods proposed for docking-pose prediction and their usefulness in FBDD.
Control of Flexible Structures (COFS) Flight Experiment Background and Description

NASA Technical Reports Server (NTRS)

Hanks, B. R.

1985-01-01

A fundamental problem in designing and delivering large space structures to orbit is to provide sufficient structural stiffness and static configuration precision to meet performance requirements. These requirements are directly related to control requirements and the degree of control system sophistication available to supplement the as-built structure. Background and rationale are presented for a research study in structures, structural dynamics, and controls using a relatively large, flexible beam as a focus. This experiment would address fundamental problems applicable to large, flexible space structures in general and would involve a combination of ground tests, flight behavior prediction, and instrumented orbital tests. Intended to be multidisciplinary but basic within each discipline, the experiment should provide improved understanding and confidence in making design trades between structural conservatism and control system sophistication for meeting static shape and dynamic response/stability requirements. Quantitative results should be obtained for use in improving the validity of ground tests for verifying flight performance analyses.
Reconstruction of the spatial dependence of dielectric and geometrical properties of adhesively bonded structures

NASA Astrophysics Data System (ADS)

Mackay, C.; Hayward, D.; Mulholland, A. J.; McKee, S.; Pethrick, R. A.

2005-06-01

An inverse problem motivated by the nondestructive testing of adhesively bonded structures used in the aircraft industry is studied. Using transmission line theory, a model is developed which, when supplied with electrical and geometrical parameters, accurately predicts the reflection coefficient associated with such structures. Particular attention is paid to modelling the connection between the structures and the equipment used to measure the reflection coefficient. The inverse problem is then studied and an optimization approach employed to recover these electrical and geometrical parameters from experimentally obtained data. In particular the approach focuses on the recovery of spatially varying geometrical parameters as this is paramount to the successful reconstruction of electrical parameters. Reconstructions of structure geometry using this method are found to be in close agreement with experimental observations.
BetaSCPWeb: side-chain prediction for protein structures using Voronoi diagrams and geometry prioritization.

PubMed

Ryu, Joonghyun; Lee, Mokwon; Cha, Jehyun; Laskowski, Roman A; Ryu, Seong Eon; Kim, Deok-Soo

2016-07-08

Many applications, such as protein design, homology modeling, flexible docking, etc. require the prediction of a protein's optimal side-chain conformations from just its amino acid sequence and backbone structure. Side-chain prediction (SCP) is an NP-hard energy minimization problem. Here, we present BetaSCPWeb which efficiently computes a conformation close to optimal using a geometry-prioritization method based on the Voronoi diagram of spherical atoms. Its outputs are visual, textual and PDB file format. The web server is free and open to all users at http://voronoi.hanyang.ac.kr/betascpweb with no login requirement. © The Author(s) 2016. Published by Oxford University Press on behalf of Nucleic Acids Research.
Assessment of Hybrid RANS/LES Turbulence Models for Aeroacoustics Applications

NASA Technical Reports Server (NTRS)

Vatsa, Veer N.; Lockard, David P.

2010-01-01

Predicting the noise from aircraft with exposed landing gear remains a challenging problem for the aeroacoustics community. Although computational fluid dynamics (CFD) has shown promise as a technique that could produce high-fidelity flow solutions, generating grids that can resolve the pertinent physics around complex configurations can be very challenging. Structured grids are often impractical for such configurations. Unstructured grids offer a path forward for simulating complex configurations. However, few unstructured grid codes have been thoroughly tested for unsteady flow problems in the manner needed for aeroacoustic prediction. A widely used unstructured grid code, FUN3D, is examined for resolving the near field in unsteady flow problems. Although the ultimate goal is to compute the flow around complex geometries such as the landing gear, simpler problems that include some of the relevant physics, and are easily amenable to the structured grid approaches are used for testing the unstructured grid approach. The test cases chosen for this study correspond to the experimental work on single and tandem cylinders conducted in the Basic Aerodynamic Research Tunnel (BART) and the Quiet Flow Facility (QFF) at NASA Langley Research Center. These configurations offer an excellent opportunity to assess the performance of hybrid RANS/LES turbulence models that transition from RANS in unresolved regions near solid bodies to LES in the outer flow field. Several of these models have been implemented and tested in both structured and unstructured grid codes to evaluate their dependence on the solver and mesh type. Comparison of FUN3D solutions with experimental data and numerical solutions from a structured grid flow solver are found to be encouraging.
The Inverse Contagion Problem (ICP) vs.. Predicting site contagion in real time, when network links are not observable

NASA Astrophysics Data System (ADS)

Mushkin, I.; Solomon, S.

2017-10-01

We study the inverse contagion problem (ICP). As opposed to the direct contagion problem, in which the network structure is known and the question is when each node will be contaminated, in the inverse problem the links of the network are unknown but a sequence of contagion histories (the times when each node was contaminated) is observed. We consider two versions of the ICP: The strong problem (SICP), which is the reconstruction of the network and has been studied before, and the weak problem (WICP), which requires "only" the prediction (at each time step) of the nodes that will be contaminated at the next time step (this is often the real life situation in which a contagion is observed and predictions are made in real time). Moreover, our focus is on analyzing the increasing accuracy of the solution, as a function of the number of contagion histories already observed. For simplicity, we discuss the simplest (deterministic and synchronous) contagion dynamics and the simplest solution algorithm, which we have applied to different network types. The main result of this paper is that the complex problem of the convergence of the ICP for a network can be reduced to an individual property of pairs of nodes: the "false link difficulty". By definition, given a pair of unlinked nodes i and j, the difficulty of the false link (i,j) is the probability that in a random contagion history, the nodes i and j are not contaminated at the same time step (or at consecutive time steps). In other words, the "false link difficulty" of a non-existing network link is the probability that the observations during a random contagion history would not rule out that link. This probability is relatively straightforward to calculate, and in most instances relies only on the relative positions of the two nodes (i,j) and not on the entire network structure. We have observed the distribution of false link difficulty for various network types, estimated it theoretically and confronted it (successfully) with the numerical simulations. Based on it, we estimated analytically the convergence of the ICP solution (as a function of the number of contagion histories observed), and found it to be in perfect agreement with simulation results. Finally, the most important insight we obtained is that SICP and WICP are have quite different properties: if one in interested only in the operational aspect of predicting how contagion will spread, the links which are most difficult to decide about are the least influential on contagion dynamics. In other words, the parts of the network which are harder to reconstruct are also least important for predicting the contagion dynamics, up to the point where a (large) constant number of false links in the network (i.e. non-convergence of the network reconstruction procedure) implies a zero rate of the node contagion prediction errors (perfect convergence of the WICP). Thus, the contagion prediction problem (WICP) difficulty is very different from the network reconstruction problem (SICP), in as far as links which are difficult to reconstruct are quite harmless in terms of contagion prediction capability (WICP).
Re-Examining the Relationship between Need for Cognition and Creativity: Predicting Creative Problem Solving across Multiple Domains

ERIC Educational Resources Information Center

Watts, Logan L.; Steele, Logan M.; Song, Hairong

2017-01-01

Prior studies have demonstrated inconsistent findings with regard to the relationship between need for cognition and creativity. In our study, measurement issues were explored as a potential source of these inconsistencies. Structural equation modeling techniques were used to examine the factor structure underlying the 18-item need for cognition…
Vibroacoustic Response of Pad Structures to Space Shuttle Launch Acoustic Loads

NASA Technical Reports Server (NTRS)

Margasahayam, R. N.; Caimi, Raoul E.

1995-01-01

This paper presents a deterministic theory for the random vibration problem for predicting the response of structures in the low-frequency range (0 to 20 hertz) of launch transients. Also presented are some innovative ways to characterize noise and highlights of ongoing test-analysis correlation efforts titled the Verification Test Article (VETA) project.
Application of the Spectral Element Method to Interior Noise Problems

NASA Technical Reports Server (NTRS)

Doyle, James F.

1998-01-01

The primary effort of this research project was focused the development of analytical methods for the accurate prediction of structural acoustic noise and response. Of particular interest was the development of curved frame and shell spectral elements for the efficient computational of structural response and of schemes to match this to the surrounding fluid.
Using Deep Learning Model for Meteorological Satellite Cloud Image Prediction

NASA Astrophysics Data System (ADS)

Su, X.

2017-12-01

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

NASA Astrophysics Data System (ADS)

Sih, G. C.; de Oliveira Faria, L.

1984-09-01

The application of fracture mechanics to structural-design problems is discussed in lectures presented in the AGARD Fracture Mechanics Methodology course held in Lisbon, Portugal, in June 1981. The emphasis is on aeronautical design, and chapters are included on fatigue-life prediction for metals and composites, the fracture mechanics of engineering structural components, failure mechanics and damage evaluation of structural components, flaw-acceptance methods, and reliability in probabilistic design. Graphs, diagrams, drawings, and photographs are provided.
Learning To Fold Proteins Using Energy Landscape Theory

PubMed Central

Schafer, N.P.; Kim, B.L.; Zheng, W.; Wolynes, P.G.

2014-01-01

This review is a tutorial for scientists interested in the problem of protein structure prediction, particularly those interested in using coarse-grained molecular dynamics models that are optimized using lessons learned from the energy landscape theory of protein folding. We also present a review of the results of the AMH/AMC/AMW/AWSEM family of coarse-grained molecular dynamics protein folding models to illustrate the points covered in the first part of the article. Accurate coarse-grained structure prediction models can be used to investigate a wide range of conceptual and mechanistic issues outside of protein structure prediction; specifically, the paper concludes by reviewing how AWSEM has in recent years been able to elucidate questions related to the unusual kinetic behavior of artificially designed proteins, multidomain protein misfolding, and the initial stages of protein aggregation. PMID:25308991
Some aspects of control of a large-scale dynamic system

NASA Technical Reports Server (NTRS)

Aoki, M.

1975-01-01

Techniques of predicting and/or controlling the dynamic behavior of large scale systems are discussed in terms of decentralized decision making. Topics discussed include: (1) control of large scale systems by dynamic team with delayed information sharing; (2) dynamic resource allocation problems by a team (hierarchical structure with a coordinator); and (3) some problems related to the construction of a model of reduced dimension.
Introduction to bioinformatics.

PubMed

Can, Tolga

2014-01-01

Bioinformatics is an interdisciplinary field mainly involving molecular biology and genetics, computer science, mathematics, and statistics. Data intensive, large-scale biological problems are addressed from a computational point of view. The most common problems are modeling biological processes at the molecular level and making inferences from collected data. A bioinformatics solution usually involves the following steps: Collect statistics from biological data. Build a computational model. Solve a computational modeling problem. Test and evaluate a computational algorithm. This chapter gives a brief introduction to bioinformatics by first providing an introduction to biological terminology and then discussing some classical bioinformatics problems organized by the types of data sources. Sequence analysis is the analysis of DNA and protein sequences for clues regarding function and includes subproblems such as identification of homologs, multiple sequence alignment, searching sequence patterns, and evolutionary analyses. Protein structures are three-dimensional data and the associated problems are structure prediction (secondary and tertiary), analysis of protein structures for clues regarding function, and structural alignment. Gene expression data is usually represented as matrices and analysis of microarray data mostly involves statistics analysis, classification, and clustering approaches. Biological networks such as gene regulatory networks, metabolic pathways, and protein-protein interaction networks are usually modeled as graphs and graph theoretic approaches are used to solve associated problems such as construction and analysis of large-scale networks.
Research at USAFA 2011

DTIC Science & Technology

2011-01-01

field repair technique for enamel -coated steel used in reinforcing concrete structures. In addition to solving real problems, these efforts provide...projects are varied and range from designing and validating repairs, performing residual life analysis, augmenting the current crack growth prediction
Evolution, Energy Landscapes and the Paradoxes of Protein Folding

PubMed Central

Wolynes, Peter G.

2014-01-01

Protein folding has been viewed as a difficult problem of molecular self-organization. The search problem involved in folding however has been simplified through the evolution of folding energy landscapes that are funneled. The funnel hypothesis can be quantified using energy landscape theory based on the minimal frustration principle. Strong quantitative predictions that follow from energy landscape theory have been widely confirmed both through laboratory folding experiments and from detailed simulations. Energy landscape ideas also have allowed successful protein structure prediction algorithms to be developed. The selection constraint of having funneled folding landscapes has left its imprint on the sequences of existing protein structural families. Quantitative analysis of co-evolution patterns allows us to infer the statistical characteristics of the folding landscape. These turn out to be consistent with what has been obtained from laboratory physicochemical folding experiments signalling a beautiful confluence of genomics and chemical physics. PMID:25530262
Correlation of predicted and measured thermal stresses on an advanced aircraft structure with dissimilar materials. [hypersonic heating simulation

NASA Technical Reports Server (NTRS)

Jenkins, J. M.

1979-01-01

Additional information was added to a growing data base from which estimates of finite element model complexities can be made with respect to thermal stress analysis. The manner in which temperatures were smeared to the finite element grid points was examined from the point of view of the impact on thermal stress calculations. The general comparison of calculated and measured thermal stresses is guite good and there is little doubt that the finite element approach provided by NASTRAN results in correct thermal stress calculations. Discrepancies did exist between measured and calculated values in the skin and the skin/frame junctures. The problems with predicting skin thermal stress were attributed to inadequate temperature inputs to the structural model rather than modeling insufficiencies. The discrepancies occurring at the skin/frame juncture were most likely due to insufficient modeling elements rather than temperature problems.
Predicting the melting temperature of ice-Ih with only electronic structure information as input.

PubMed

Pinnick, Eric R; Erramilli, Shyamsunder; Wang, Feng

2012-07-07

The melting temperature of ice-Ih was calculated with only electronic structure information as input by creating a problem-specific force field. The force field, Water model by AFM for Ice and Liquid (WAIL), was developed with the adaptive force matching (AFM) method by fitting to post-Hartree-Fock quality forces obtained in quantum mechanics∕molecular mechanics calculations. WAIL predicts the ice-Ih melting temperature to be 270 K. The model also predicts the densities of ice and water, the temperature of maximum density of water, the heat of vaporizations, and the radial distribution functions for both ice and water in good agreement with experimental measurements. The non-dissociative WAIL model is very similar to a flexible version of the popular TIP4P potential and has comparable computational cost. By customizing to problem-specific configurations with the AFM approach, the resulting model is remarkably more accurate than any variants of TIP4P for simulating ice-Ih and water in the temperature range from 253 K and 293 K under ambient pressure.

Development of an Evolutionary Algorithm for the ab Initio Discovery of Two-Dimensional Materials

NASA Astrophysics Data System (ADS)

Revard, Benjamin Charles

Crystal structure prediction is an important first step on the path toward computational materials design. Increasingly robust methods have become available in recent years for computing many materials properties, but because properties are largely a function of crystal structure, the structure must be known before these methods can be brought to bear. In addition, structure prediction is particularly useful for identifying low-energy structures of subperiodic materials, such as two-dimensional (2D) materials, which may adopt unexpected structures that differ from those of the corresponding bulk phases. Evolutionary algorithms, which are heuristics for global optimization inspired by biological evolution, have proven to be a fruitful approach for tackling the problem of crystal structure prediction. This thesis describes the development of an improved evolutionary algorithm for structure prediction and several applications of the algorithm to predict the structures of novel low-energy 2D materials. The first part of this thesis contains an overview of evolutionary algorithms for crystal structure prediction and presents our implementation, including details of extending the algorithm to search for clusters, wires, and 2D materials, improvements to efficiency when running in parallel, improved composition space sampling, and the ability to search for partial phase diagrams. We then present several applications of the evolutionary algorithm to 2D systems, including InP, the C-Si and Sn-S phase diagrams, and several group-IV dioxides. This thesis makes use of the Cornell graduate school's "papers" option. Chapters 1 and 3 correspond to the first-author publications of Refs. [131] and [132], respectively, and chapter 2 will soon be submitted as a first-author publication. The material in chapter 4 is taken from Ref. [144], in which I share joint first-authorship. In this case I have included only my own contributions.
How Can We Improve Problem Solving in Undergraduate Biology? Applying Lessons from 30 Years of Physics Education Research

PubMed Central

Hoskinson, A.-M.; Caballero, M. D.; Knight, J. K.

2013-01-01

If students are to successfully grapple with authentic, complex biological problems as scientists and citizens, they need practice solving such problems during their undergraduate years. Physics education researchers have investigated student problem solving for the past three decades. Although physics and biology problems differ in structure and content, the instructional purposes align closely: explaining patterns and processes in the natural world and making predictions about physical and biological systems. In this paper, we discuss how research-supported approaches developed by physics education researchers can be adopted by biologists to enhance student problem-solving skills. First, we compare the problems that biology students are typically asked to solve with authentic, complex problems. We then describe the development of research-validated physics curricula emphasizing process skills in problem solving. We show that solving authentic, complex biology problems requires many of the same skills that practicing physicists and biologists use in representing problems, seeking relationships, making predictions, and verifying or checking solutions. We assert that acquiring these skills can help biology students become competent problem solvers. Finally, we propose how biology scholars can apply lessons from physics education in their classrooms and inspire new studies in biology education research. PMID:23737623
Identification of family-specific residue packing motifs and their use for structure-based protein function prediction: I. Method development.

PubMed

Bandyopadhyay, Deepak; Huan, Jun; Prins, Jan; Snoeyink, Jack; Wang, Wei; Tropsha, Alexander

2009-11-01

Protein function prediction is one of the central problems in computational biology. We present a novel automated protein structure-based function prediction method using libraries of local residue packing patterns that are common to most proteins in a known functional family. Critical to this approach is the representation of a protein structure as a graph where residue vertices (residue name used as a vertex label) are connected by geometrical proximity edges. The approach employs two steps. First, it uses a fast subgraph mining algorithm to find all occurrences of family-specific labeled subgraphs for all well characterized protein structural and functional families. Second, it queries a new structure for occurrences of a set of motifs characteristic of a known family, using a graph index to speed up Ullman's subgraph isomorphism algorithm. The confidence of function inference from structure depends on the number of family-specific motifs found in the query structure compared with their distribution in a large non-redundant database of proteins. This method can assign a new structure to a specific functional family in cases where sequence alignments, sequence patterns, structural superposition and active site templates fail to provide accurate annotation.
Hydrological model parameter dimensionality is a weak measure of prediction uncertainty

NASA Astrophysics Data System (ADS)

Pande, S.; Arkesteijn, L.; Savenije, H.; Bastidas, L. A.

2015-04-01

This paper shows that instability of hydrological system representation in response to different pieces of information and associated prediction uncertainty is a function of model complexity. After demonstrating the connection between unstable model representation and model complexity, complexity is analyzed in a step by step manner. This is done measuring differences between simulations of a model under different realizations of input forcings. Algorithms are then suggested to estimate model complexity. Model complexities of the two model structures, SAC-SMA (Sacramento Soil Moisture Accounting) and its simplified version SIXPAR (Six Parameter Model), are computed on resampled input data sets from basins that span across the continental US. The model complexities for SIXPAR are estimated for various parameter ranges. It is shown that complexity of SIXPAR increases with lower storage capacity and/or higher recession coefficients. Thus it is argued that a conceptually simple model structure, such as SIXPAR, can be more complex than an intuitively more complex model structure, such as SAC-SMA for certain parameter ranges. We therefore contend that magnitudes of feasible model parameters influence the complexity of the model selection problem just as parameter dimensionality (number of parameters) does and that parameter dimensionality is an incomplete indicator of stability of hydrological model selection and prediction problems.
Predicting internalizing problems in Chinese children: the unique and interactive effects of parenting and child temperament.

PubMed

Muhtadie, Luma; Zhou, Qing; Eisenberg, Nancy; Wang, Yun

2013-08-01

The additive and interactive relations of parenting styles (authoritative and authoritarian parenting) and child temperament (anger/frustration, sadness, and effortful control) to children's internalizing problems were examined in a 3.8-year longitudinal study of 425 Chinese children (aged 6-9 years) from Beijing. At Wave 1, parents self-reported on their parenting styles, and parents and teachers rated child temperament. At Wave 2, parents, teachers, and children rated children's internalizing problems. Structural equation modeling indicated that the main effect of authoritative parenting and the interactions of Authoritarian Parenting × Effortful Control and Authoritative Parenting × Anger/Frustration (parents' reports only) prospectively and uniquely predicted internalizing problems. The above results did not vary by child sex and remained significant after controlling for co-occurring externalizing problems. These findings suggest that (a) children with low effortful control may be particularly susceptible to the adverse effect of authoritarian parenting and (b) the benefit of authoritative parenting may be especially important for children with high anger/frustration.
The impact of creative tendency, academic performance, and self-concept on creative science problem-finding.

PubMed

Liu, Mingxin; Hu, Weiping; Adey, Philip; Cheng, Li; Zhang, Xingli

2013-04-01

This study was designed to address the impacts of science performance, science self-concept, and creative tendency on the creative science problem-finding (CSPF) ability of a sample of Chinese middle-school students. Structural equation modeling was used to indicate that CSPF could be directly predicted by creative tendency and academic performance, and indirectly predicted by science self-concept. The findings strongly support the idea that curiosity, imagination, and domain-specific knowledge are important for CSPF, and science self-concept could be mediated by knowledge that affects CSPF. © 2012 The Institute of Psychology, Chinese Academy of Sciences and Blackwell Publishing Asia Pty Ltd.
NASA GRC Fatigue Crack Initiation Life Prediction Models

NASA Technical Reports Server (NTRS)

Arya, Vinod K.; Halford, Gary R.

2002-01-01

Metal fatigue has plagued structural components for centuries, and it remains a critical durability issue in today's aerospace hardware. This is true despite vastly improved and advanced materials, increased mechanistic understanding, and development of accurate structural analysis and advanced fatigue life prediction tools. Each advance is quickly taken advantage of to produce safer, more reliable, more cost effective, and better performing products. In other words, as the envelope is expanded, components are then designed to operate just as close to the newly expanded envelope as they were to the initial one. The problem is perennial. The economic importance of addressing structural durability issues early in the design process is emphasized. Tradeoffs with performance, cost, and legislated restrictions are pointed out. Several aspects of structural durability of advanced systems, advanced materials and advanced fatigue life prediction methods are presented. Specific items include the basic elements of durability analysis, conventional designs, barriers to be overcome for advanced systems, high-temperature life prediction for both creep-fatigue and thermomechanical fatigue, mean stress effects, multiaxial stress-strain states, and cumulative fatigue damage accumulation assessment.
A Primer In Advanced Fatigue Life Prediction Methods

NASA Technical Reports Server (NTRS)

Halford, Gary R.

2000-01-01

Metal fatigue has plagued structural components for centuries, and it remains a critical durability issue in today's aerospace hardware. This is true despite vastly improved and advanced materials, increased mechanistic understanding, and development of accurate structural analysis and advanced fatigue life prediction tools. Each advance is quickly taken advantage of to produce safer, more reliable more cost effective, and better performing products. In other words, as the envelop is expanded, components are then designed to operate just as close to the newly expanded envelop as they were to the initial one. The problem is perennial. The economic importance of addressing structural durability issues early in the design process is emphasized. Tradeoffs with performance, cost, and legislated restrictions are pointed out. Several aspects of structural durability of advanced systems, advanced materials and advanced fatigue life prediction methods are presented. Specific items include the basic elements of durability analysis, conventional designs, barriers to be overcome for advanced systems, high-temperature life prediction for both creep-fatigue and thermomechanical fatigue, mean stress effects, multiaxial stress-strain states, and cumulative fatigue damage accumulation assessment.
Building proteins from C alpha coordinates using the dihedral probability grid Monte Carlo method.

PubMed Central

Mathiowetz, A. M.; Goddard, W. A.

1995-01-01

Dihedral probability grid Monte Carlo (DPG-MC) is a general-purpose method of conformational sampling that can be applied to many problems in peptide and protein modeling. Here we present the DPG-MC method and apply it to predicting complete protein structures from C alpha coordinates. This is useful in such endeavors as homology modeling, protein structure prediction from lattice simulations, or fitting protein structures to X-ray crystallographic data. It also serves as an example of how DPG-MC can be applied to systems with geometric constraints. The conformational propensities for individual residues are used to guide conformational searches as the protein is built from the amino-terminus to the carboxyl-terminus. Results for a number of proteins show that both the backbone and side chain can be accurately modeled using DPG-MC. Backbone atoms are generally predicted with RMS errors of about 0.5 A (compared to X-ray crystal structure coordinates) and all atoms are predicted to an RMS error of 1.7 A or better. PMID:7549885
NASA GRC Fatigue Crack Initiation Life Prediction Models

NASA Astrophysics Data System (ADS)

Arya, Vinod K.; Halford, Gary R.

2002-10-01

Metal fatigue has plagued structural components for centuries, and it remains a critical durability issue in today's aerospace hardware. This is true despite vastly improved and advanced materials, increased mechanistic understanding, and development of accurate structural analysis and advanced fatigue life prediction tools. Each advance is quickly taken advantage of to produce safer, more reliable, more cost effective, and better performing products. In other words, as the envelope is expanded, components are then designed to operate just as close to the newly expanded envelope as they were to the initial one. The problem is perennial. The economic importance of addressing structural durability issues early in the design process is emphasized. Tradeoffs with performance, cost, and legislated restrictions are pointed out. Several aspects of structural durability of advanced systems, advanced materials and advanced fatigue life prediction methods are presented. Specific items include the basic elements of durability analysis, conventional designs, barriers to be overcome for advanced systems, high-temperature life prediction for both creep-fatigue and thermomechanical fatigue, mean stress effects, multiaxial stress-strain states, and cumulative fatigue damage accumulation assessment.
Molecular engineering of colloidal liquid crystals using DNA origami

NASA Astrophysics Data System (ADS)

Siavashpouri, Mahsa; Wachauf, Christian; Zakhary, Mark; Praetorius, Florian; Dietz, Hendrik; Dogic, Zvonimir

Understanding the microscopic origin of cholesteric phase remains a foundational, yet unresolved problem in the field of liquid crystals. Lack of experimental model system that allows for the systematic control of the microscopic chiral structure makes it difficult to investigate this problem for several years. Here, using DNA origami technology, we systematically vary the chirality of the colloidal particles with molecular precision and establish a quantitative relationship between the microscopic structure of particles and the macroscopic cholesteric pitch. Our study presents a new methodology for predicting bulk behavior of diverse phases based on the microscopic architectures of the constituent molecules.
Modeling human target acquisition in ground-to-air weapon systems

NASA Technical Reports Server (NTRS)

Phatak, A. V.; Mohr, R. L.; Vikmanis, M.; Wei, K. C.

1982-01-01

The problems associated with formulating and validating mathematical models for describing and predicting human target acquisition response are considered. In particular, the extension of the human observer model to include the acquisition phase as well as the tracking segment is presented. Relationship of the Observer model structure to the more complex Standard Optimal Control model formulation and to the simpler Transfer Function/Noise representation is discussed. Problems pertinent to structural identifiability and the form of the parameterization are elucidated. A systematic approach toward the identification of the observer acquisition model parameters from ensemble tracking error data is presented.
A Symbiotic Framework for coupling Machine Learning and Geosciences in Prediction and Predictability

NASA Astrophysics Data System (ADS)

Ravela, S.

2017-12-01

In this presentation we review the two directions of a symbiotic relationship between machine learning and the geosciences in relation to prediction and predictability. In the first direction, we develop ensemble, information theoretic and manifold learning framework to adaptively improve state and parameter estimates in nonlinear high-dimensional non-Gaussian problems, showing in particular that tractable variational approaches can be produced. We demonstrate these applications in the context of autonomous mapping of environmental coherent structures and other idealized problems. In the reverse direction, we show that data assimilation, particularly probabilistic approaches for filtering and smoothing offer a novel and useful way to train neural networks, and serve as a better basis than gradient based approaches when we must quantify uncertainty in association with nonlinear, chaotic processes. In many inference problems in geosciences we seek to build reduced models to characterize local sensitivies, adjoints or other mechanisms that propagate innovations and errors. Here, the particular use of neural approaches for such propagation trained using ensemble data assimilation provides a novel framework. Through these two examples of inference problems in the earth sciences, we show that not only is learning useful to broaden existing methodology, but in reverse, geophysical methodology can be used to influence paradigms in learning.
Maternal depression in childhood and aggression in young adulthood: evidence for mediation by offspring amygdala-hippocampal volume ratio.

PubMed

Gilliam, Mary; Forbes, Erika E; Gianaros, Peter J; Erickson, Kirk I; Brennan, Lauretta M; Shaw, Daniel S

2015-10-01

There is abundant evidence that offspring of depressed mothers are at increased risk for persistent behavior problems related to emotion regulation, but the mechanisms by which offspring incur this risk are not entirely clear. Early adverse caregiving experiences have been associated with structural alterations in the amygdala and hippocampus, which parallel findings of cortical regions altered in adults with behavior problems related to emotion regulation. This study examined whether exposure to maternal depression during childhood might predict increased aggression and/or depression in early adulthood, and whether offspring amygdala:hippocampal volume ratio might mediate this relationship. Participants were 258 mothers and sons at socioeconomic risk for behavior problems. Sons' trajectories of exposure to maternal depression were generated from eight reports collected prospectively from offspring ages 18 months to 10 years. Offspring brain structure, aggression, and depression were assessed at age 20 (n = 170). Persistent, moderately high trajectories of maternal depression during childhood predicted increased aggression in adult offspring. In contrast, stable and very elevated trajectories of maternal depression during childhood predicted depression in adult offspring. Increased amygdala: hippocampal volume ratios at age 20 were significantly associated with concurrently increased aggression, but not depression, in adult offspring. Offspring amygdala: hippocampal volume ratio mediated the relationship found between trajectories of moderately elevated maternal depression during childhood and aggression in adult offspring. Alterations in the relative size of brain structures implicated in emotion regulation may be one mechanism by which offspring of depressed mothers incur increased risk for the development of aggression. © 2014 Association for Child and Adolescent Mental Health.
Interior noise prediction methodology: ATDAC theory and validation

NASA Technical Reports Server (NTRS)

Mathur, Gopal P.; Gardner, Bryce K.

1992-01-01

The Acoustical Theory for Design of Aircraft Cabins (ATDAC) is a computer program developed to predict interior noise levels inside aircraft and to evaluate the effects of different aircraft configurations on the aircraft acoustical environment. The primary motivation for development of this program is the special interior noise problems associated with advanced turboprop (ATP) aircraft where there is a tonal, low frequency noise problem. Prediction of interior noise levels requires knowledge of the energy sources, the transmission paths, and the relationship between the energy variable and the sound pressure level. The energy sources include engine noise, both airborne and structure-borne; turbulent boundary layer noise; and interior noise sources such as air conditioner noise and auxiliary power unit noise. Since propeller and engine noise prediction programs are widely available, they are not included in ATDAC. Airborne engine noise from any prediction or measurement may be input to this program. This report describes the theory and equations implemented in the ATDAC program.
Interior noise prediction methodology: ATDAC theory and validation

NASA Astrophysics Data System (ADS)

Mathur, Gopal P.; Gardner, Bryce K.

1992-04-01

The Acoustical Theory for Design of Aircraft Cabins (ATDAC) is a computer program developed to predict interior noise levels inside aircraft and to evaluate the effects of different aircraft configurations on the aircraft acoustical environment. The primary motivation for development of this program is the special interior noise problems associated with advanced turboprop (ATP) aircraft where there is a tonal, low frequency noise problem. Prediction of interior noise levels requires knowledge of the energy sources, the transmission paths, and the relationship between the energy variable and the sound pressure level. The energy sources include engine noise, both airborne and structure-borne; turbulent boundary layer noise; and interior noise sources such as air conditioner noise and auxiliary power unit noise. Since propeller and engine noise prediction programs are widely available, they are not included in ATDAC. Airborne engine noise from any prediction or measurement may be input to this program. This report describes the theory and equations implemented in the ATDAC program.
A sparse autoencoder-based deep neural network for protein solvent accessibility and contact number prediction.

PubMed

Deng, Lei; Fan, Chao; Zeng, Zhiwen

2017-12-28

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

PubMed

Verissimo, Angie Denisse Otiniano; Grella, Christine E

2017-04-01

This study examines reasons why people do not seek help for alcohol or drug problems by gender and race/ethnicity using data from the National Epidemiologic Survey on Alcohol and Related Conditions (NESARC), a nationally representative survey. Multivariate models were fit for 3 barriers to seeking help (structural, attitudinal, and readiness for change) for either alcohol or drug problems, controlling for socio-demographic characteristics and problem severity. Predicted probabilities were generated to evaluate gender differences by racial/ethnic subgroups. Over three quarters of the samples endorsed attitudinal barriers related to either alcohol or drug use. Generally, women were less likely to endorse attitudinal barriers for alcohol problems. African Americans and Latina/os were less likely than Whites to endorse attitudinal barriers for alcohol problems, Latina/os were less likely than Whites to endorse readiness for change barriers for alcohol and drug problems, however, African Americans were more likely to endorse structural barriers for alcohol problems. Comparisons within racial/ethnic subgroups by gender revealed more complex findings, although across all racial/ethnic groups women endorsed attitudinal barriers for alcohol problems more than men. Study findings suggest the need to tailor interventions to increase access to help for alcohol and drug problems that take into consideration both attitudinal and structural barriers and how these vary across groups. Copyright © 2017 Elsevier Inc. All rights reserved.
Influence of Gender and Race/Ethnicity on Perceived Barriers to Help-Seeking for Alcohol or Drug Problems

PubMed Central

Verissimo, Angie Denisse Otiniano

2017-01-01

This study examines reasons why people do not seek help for alcohol or drug problems by gender and race/ethnicity using data from the National Epidemiologic Survey on Alcohol and Related Conditions (NESARC), a nationally representative survey. Multivariate models were fit for 3 barriers to seeking help (structural, attitudinal, and readiness for change) for either alcohol or drug problems, controlling for socio-demographic characteristics and problem severity. Predicted probabilities were generated to evaluate gender differences by racial/ethnic subgroups. Over three quarters of the samples endorsed attitudinal barriers related to either alcohol or drug use. Generally, women were less likely to endorse attitudinal barriers for alcohol problems. African Americans and Latina/os were less likely than Whites to endorse attitudinal barriers for alcohol problems, Latina/os were less likely than Whites to endorse readiness for change barriers for alcohol and drug problems, however, African Americans were more likely to endorse structural barriers for alcohol problems. Comparisons within racial/ethnic subgroups by gender revealed more complex findings, although across all racial/ethnic groups women endorsed attitudinal barriers for alcohol problems more than men. Study findings suggest the need to tailor interventions to increase access to help for alcohol and drug problems that take into consideration both attitudinal and structural barriers and how these vary across groups. PMID:28237055
Flexibility Considerations on the Hydrodynamic Loading on a Vertical Wedge Drop

NASA Astrophysics Data System (ADS)

Ren, Zhongshu; Wang, Zhaoyuan; Judge, Carolyn; Stern, Fred; Ikeda, Christine

2017-11-01

High-speed craft operating at in waves frequently become airborne and slam into the water surface. This fluid-structure interaction problem is important to understand in order to increase the operating envelope of these craft. The goals of the current work are to investigate both the hydrodynamic loads and the resulting structural response on a planing hull. A V-shaped wedge is dropped vertically into calm water. The hydrodynamic pressure is measured using pressure sensors at discrete points on the hull. Two hulls are studied: one is rigid and one is flexible. Predictions of the hydrodynamic loading are made using Wagner's theory, Vorus's theory, and simulations in CFDShip Iowa. These predictions assume the structure is completely rigid. These predictions of the pressure coefficient match well with the rigid hull, as expected. The spray root is tracked in the rigid experimental set and compared with the theoretical and computational models. The pressure coefficient measured on the flexible hull shows discrepancies with the predictions due to the fluid-structure interaction. These discrepancies are quantified and interpreted in light of the structural flexibility. Funding for this work is from the Office of Naval Research Grant Number N00014-16-1-3188.

Exposure to stressful life events during pregnancy predicts psychotic experiences via behaviour problems in childhood.

PubMed

Betts, Kim S; Williams, Gail M; Najman, Jakob M; Scott, James; Alati, Rosa

2014-12-01

Exposure to stressful life events during pregnancy has been associated with later schizophrenia in offspring. We explore how prenatal stress and neurodevelopmental abnormalities in childhood associate to increase the risk of later psychotic experiences. Participants from the Mater University Study of Pregnancy (MUSP), an Australian based, pre-birth cohort study were examined for lifetime DSM-IV positive psychotic experiences at 21 years by a semi-structured interview (n = 2227). Structural equation modelling suggested psychotic experiences were best represented with a bifactor model including a general psychosis factor and two group factors. We tested for an association between prenatal stressful life events with the psychotic experiences, and examined for potential moderation and mediation by behaviour problems and cognitive ability in childhood. Prenatal stressful life events predicted psychotic experiences indirectly via behaviour problems at child age five years, and this relationship was not confounded by maternal stressful life events at child age five. We found no statistical evidence for an interaction between prenatal stressful life events and behaviour problems or cognitive ability. The measurable effect of prenatal stressful life events on later psychotic experiences in offspring manifested as behaviour problems by age 5. By identifying early abnormal behavioural development as an intermediary, this finding further confirms the role of prenatal stress to later psychotic disorders. Copyright © 2014 Elsevier Ltd. All rights reserved.
Geophysical Plasmas and Atmospheric Modeling.

DTIC Science & Technology

1982-01-01

analysis of structuring observed in the STARFISH event and early time structure formation in the LWIR predicted for a standard event, (2) IR structure...for the LWIR problem, the SCORPIO code was coupled to a numerical module which describes the behavior of the Rayleigh-Taylor insta- bility, as discussed...the LWIR C1-14p) induced by sunlight and earthshine. Of the constituents in nuclear debris the uranium atom is of particular interest because it
DOE Office of Scientific and Technical Information (OSTI.GOV)

Jiang, Deen

A profound connection has been found between the structures of thiolated gold clusters and the combinatorial problem of pairing up dots on a surface. The bridge is the concept of staple fitness: the fittest combination corresponds to the experimental structure. This connection has been demonstrated for both Au{sub 25}(SR){sub 18} and Au{sub 38}(SR){sub 24} (-SR being a thiolate group) and applied to predict a promising structure for the recently synthesized Au{sub 19}(SR){sub 13}.
Tenth NASTRAN User's Colloquium

NASA Technical Reports Server (NTRS)

1982-01-01

The development of the NASTRAN computer program, a general purpose finite element computer code for structural analysis, was discussed. The application and development of NASTRAN is presented in the following topics: improvements and enhancements; developments of pre and postprocessors; interactive review system; the use of harmonic expansions in magnetic field problems; improving a dynamic model with test data using Linwood; solution of axisymmetric fluid structure interaction problems; large displacements and stability analysis of nonlinear propeller structures; prediction of bead area contact load at the tire wheel interface; elastic plastic analysis of an overloaded breech ring; finite element solution of torsion and other 2-D Poisson equations; new capability for elastic aircraft airloads; usage of substructuring analysis in the get away special program; solving symmetric structures with nonsymmetric loads; evaluation and reduction of errors induced by Guyan transformation.
Structural dynamics payload loads estimates

NASA Technical Reports Server (NTRS)

Engels, R. C.

1982-01-01

Methods for the prediction of loads on large space structures are discussed. Existing approaches to the problem of loads calculation are surveyed. A full scale version of an alternate numerical integration technique to solve the response part of a load cycle is presented, and a set of short cut versions of the algorithm developed. The implementation of these techniques using the software package developed is discussed.
A Model for Predicting Cognitive and Emotional Health from Structural and Functional Neurocircuitry Following Traumatic Brain Injury

DTIC Science & Technology

2017-10-01

Neuroimaging 2006 Reviewer, Journal of Abnormal Psychology 2006 Reviewer, Psychopharmacology 2006 Reviewer, Developmental Science 2006 Reviewer...This study will address this problem by collecting measures of white matter integrity and concomitant neuropsychological status at five time points...hypothesize that structural white matter tract disintegrity will underlie abnormalities in functional connectivity, neurocognitive performance and
Predicting β-Turns in Protein Using Kernel Logistic Regression

PubMed Central

Elbashir, Murtada Khalafallah; Sheng, Yu; Wang, Jianxin; Wu, FangXiang; Li, Min

2013-01-01

A β-turn is a secondary protein structure type that plays a significant role in protein configuration and function. On average 25% of amino acids in protein structures are located in β-turns. It is very important to develope an accurate and efficient method for β-turns prediction. Most of the current successful β-turns prediction methods use support vector machines (SVMs) or neural networks (NNs). The kernel logistic regression (KLR) is a powerful classification technique that has been applied successfully in many classification problems. However, it is often not found in β-turns classification, mainly because it is computationally expensive. In this paper, we used KLR to obtain sparse β-turns prediction in short evolution time. Secondary structure information and position-specific scoring matrices (PSSMs) are utilized as input features. We achieved Q total of 80.7% and MCC of 50% on BT426 dataset. These results show that KLR method with the right algorithm can yield performance equivalent to or even better than NNs and SVMs in β-turns prediction. In addition, KLR yields probabilistic outcome and has a well-defined extension to multiclass case. PMID:23509793
Predicting β-turns in protein using kernel logistic regression.

PubMed

Elbashir, Murtada Khalafallah; Sheng, Yu; Wang, Jianxin; Wu, Fangxiang; Li, Min

2013-01-01

A β-turn is a secondary protein structure type that plays a significant role in protein configuration and function. On average 25% of amino acids in protein structures are located in β-turns. It is very important to develope an accurate and efficient method for β-turns prediction. Most of the current successful β-turns prediction methods use support vector machines (SVMs) or neural networks (NNs). The kernel logistic regression (KLR) is a powerful classification technique that has been applied successfully in many classification problems. However, it is often not found in β-turns classification, mainly because it is computationally expensive. In this paper, we used KLR to obtain sparse β-turns prediction in short evolution time. Secondary structure information and position-specific scoring matrices (PSSMs) are utilized as input features. We achieved Q total of 80.7% and MCC of 50% on BT426 dataset. These results show that KLR method with the right algorithm can yield performance equivalent to or even better than NNs and SVMs in β-turns prediction. In addition, KLR yields probabilistic outcome and has a well-defined extension to multiclass case.
Link prediction with node clustering coefficient

NASA Astrophysics Data System (ADS)

Wu, Zhihao; Lin, Youfang; Wang, Jing; Gregory, Steve

2016-06-01

Predicting missing links in incomplete complex networks efficiently and accurately is still a challenging problem. The recently proposed Cannistrai-Alanis-Ravai (CAR) index shows the power of local link/triangle information in improving link-prediction accuracy. Inspired by the idea of employing local link/triangle information, we propose a new similarity index with more local structure information. In our method, local link/triangle structure information can be conveyed by clustering coefficient of common-neighbors directly. The reason why clustering coefficient has good effectiveness in estimating the contribution of a common-neighbor is that it employs links existing between neighbors of a common-neighbor and these links have the same structural position with the candidate link to this common-neighbor. In our experiments, three estimators: precision, AUP and AUC are used to evaluate the accuracy of link prediction algorithms. Experimental results on ten tested networks drawn from various fields show that our new index is more effective in predicting missing links than CAR index, especially for networks with low correlation between number of common-neighbors and number of links between common-neighbors.
Do clients' problem-solving appraisals predict career counseling outcomes or vice versa? A reanalysis of Heppner, et al.

PubMed

Lee, Dong-Gwi; Park, Hyun-Joo; Heppner, Mary J

2009-12-01

Using Heppner, et al.'s data from 2004, this study tested career counseling clients in the United States on problem-solving appraisal scores and career-related variables. A cross-lagged panel design with structural equation modeling was used. Results supported the link between clients' precounseling problem-solving appraisal scores and career outcome. This finding held for career decision-making, but not for vocational identity. The study provided further support for Heppner, et al.'s findings, highlighting the influential role of clients' problem-solving appraisals in advancing their career decision-making processes.
Quantifying side-chain conformational variations in protein structure

PubMed Central

Miao, Zhichao; Cao, Yang

2016-01-01

Protein side-chain conformation is closely related to their biological functions. The side-chain prediction is a key step in protein design, protein docking and structure optimization. However, side-chain polymorphism comprehensively exists in protein as various types and has been long overlooked by side-chain prediction. But such conformational variations have not been quantitatively studied and the correlations between these variations and residue features are vague. Here, we performed statistical analyses on large scale data sets and found that the side-chain conformational flexibility is closely related to the exposure to solvent, degree of freedom and hydrophilicity. These analyses allowed us to quantify different types of side-chain variabilities in PDB. The results underscore that protein side-chain conformation prediction is not a single-answer problem, leading us to reconsider the assessment approaches of side-chain prediction programs. PMID:27845406
Quantifying side-chain conformational variations in protein structure

NASA Astrophysics Data System (ADS)

Miao, Zhichao; Cao, Yang

2016-11-01

Protein side-chain conformation is closely related to their biological functions. The side-chain prediction is a key step in protein design, protein docking and structure optimization. However, side-chain polymorphism comprehensively exists in protein as various types and has been long overlooked by side-chain prediction. But such conformational variations have not been quantitatively studied and the correlations between these variations and residue features are vague. Here, we performed statistical analyses on large scale data sets and found that the side-chain conformational flexibility is closely related to the exposure to solvent, degree of freedom and hydrophilicity. These analyses allowed us to quantify different types of side-chain variabilities in PDB. The results underscore that protein side-chain conformation prediction is not a single-answer problem, leading us to reconsider the assessment approaches of side-chain prediction programs.
Finite element analysis of large transient elastic-plastic deformations of simple structures, with application to the engine rotor fragment containment/deflection problem

NASA Technical Reports Server (NTRS)

Wu, R. W.; Witmer, E. A.

1972-01-01

Assumed-displacement versions of the finite-element method are developed to predict large-deformation elastic-plastic transient deformations of structures. Both the conventional and a new improved finite-element variational formulation are derived. These formulations are then developed in detail for straight-beam and curved-beam elements undergoing (1) Bernoulli-Euler-Kirchhoff or (2) Timoshenko deformation behavior, in one plane. For each of these categories, several types of assumed-displacement finite elements are developed, and transient response predictions are compared with available exact solutions for small-deflection, linear-elastic transient responses. The present finite-element predictions for large-deflection elastic-plastic transient responses are evaluated via several beam and ring examples for which experimental measurements of transient strains and large transient deformations and independent finite-difference predictions are available.
Quantifying side-chain conformational variations in protein structure.

PubMed

Miao, Zhichao; Cao, Yang

2016-11-15

Protein side-chain conformation is closely related to their biological functions. The side-chain prediction is a key step in protein design, protein docking and structure optimization. However, side-chain polymorphism comprehensively exists in protein as various types and has been long overlooked by side-chain prediction. But such conformational variations have not been quantitatively studied and the correlations between these variations and residue features are vague. Here, we performed statistical analyses on large scale data sets and found that the side-chain conformational flexibility is closely related to the exposure to solvent, degree of freedom and hydrophilicity. These analyses allowed us to quantify different types of side-chain variabilities in PDB. The results underscore that protein side-chain conformation prediction is not a single-answer problem, leading us to reconsider the assessment approaches of side-chain prediction programs.
The Interplay of Externalizing Problems and Physical and Inductive Discipline during Childhood

PubMed Central

Choe, Daniel Ewon; Olson, Sheryl L.; Sameroff, Arnold J.

2013-01-01

Children who are physically disciplined are at elevated risk for externalizing problems. Conversely, maternal reasoning and reminding of rules, or inductive discipline, is associated with fewer child externalizing problems. Few studies have simultaneously examined bidirectional associations between these forms of discipline and child adjustment using cross-informant, multi-method data. We hypothesized that less inductive and more physical discipline would predict more externalizing problems, children would have evocative effects on parenting, and high levels of either form of discipline would predict low levels of the other. In a study of 241 children–spanning ages 3, 5.5, and 10–structural equation modeling indicated that 3-year-olds with higher teacher ratings of externalizing problems received higher mother ratings of physical discipline at age 5.5. Mothers endorsing more inductive discipline at child age 3 reported less physical discipline and had children with fewer externalizing problems at age 5.5. Negative bidirectional associations emerged between physical and inductive discipline from ages 5.5 to 10. Findings suggested children’s externalizing problems elicited physical discipline, and maternal inductive discipline might help prevent externalizing problems and physical discipline. PMID:23458660
Unscented predictive variable structure filter for satellite attitude estimation with model errors when using low precision sensors

NASA Astrophysics Data System (ADS)

Cao, Lu; Li, Hengnian

2016-10-01

For the satellite attitude estimation problem, the serious model errors always exist and hider the estimation performance of the Attitude Determination and Control System (ACDS), especially for a small satellite with low precision sensors. To deal with this problem, a new algorithm for the attitude estimation, referred to as the unscented predictive variable structure filter (UPVSF) is presented. This strategy is proposed based on the variable structure control concept and unscented transform (UT) sampling method. It can be implemented in real time with an ability to estimate the model errors on-line, in order to improve the state estimation precision. In addition, the model errors in this filter are not restricted only to the Gaussian noises; therefore, it has the advantages to deal with the various kinds of model errors or noises. It is anticipated that the UT sampling strategy can further enhance the robustness and accuracy of the novel UPVSF. Numerical simulations show that the proposed UPVSF is more effective and robustness in dealing with the model errors and low precision sensors compared with the traditional unscented Kalman filter (UKF).
From cheminformatics to structure-based design: Web services and desktop applications based on the NAOMI library.

PubMed

Bietz, Stefan; Inhester, Therese; Lauck, Florian; Sommer, Kai; von Behren, Mathias M; Fährrolfes, Rainer; Flachsenberg, Florian; Meyder, Agnes; Nittinger, Eva; Otto, Thomas; Hilbig, Matthias; Schomburg, Karen T; Volkamer, Andrea; Rarey, Matthias

2017-11-10

Nowadays, computational approaches are an integral part of life science research. Problems related to interpretation of experimental results, data analysis, or visualization tasks highly benefit from the achievements of the digital era. Simulation methods facilitate predictions of physicochemical properties and can assist in understanding macromolecular phenomena. Here, we will give an overview of the methods developed in our group that aim at supporting researchers from all life science areas. Based on state-of-the-art approaches from structural bioinformatics and cheminformatics, we provide software covering a wide range of research questions. Our all-in-one web service platform ProteinsPlus (http://proteins.plus) offers solutions for pocket and druggability prediction, hydrogen placement, structure quality assessment, ensemble generation, protein-protein interaction classification, and 2D-interaction visualization. Additionally, we provide a software package that contains tools targeting cheminformatics problems like file format conversion, molecule data set processing, SMARTS editing, fragment space enumeration, and ligand-based virtual screening. Furthermore, it also includes structural bioinformatics solutions for inverse screening, binding site alignment, and searching interaction patterns across structure libraries. The software package is available at http://software.zbh.uni-hamburg.de. Copyright © 2017 The Authors. Published by Elsevier B.V. All rights reserved.
A Review of Computational Intelligence Methods for Eukaryotic Promoter Prediction.

PubMed

Singh, Shailendra; Kaur, Sukhbir; Goel, Neelam

2015-01-01

In past decades, prediction of genes in DNA sequences has attracted the attention of many researchers but due to its complex structure it is extremely intricate to correctly locate its position. A large number of regulatory regions are present in DNA that helps in transcription of a gene. Promoter is one such region and to find its location is a challenging problem. Various computational methods for promoter prediction have been developed over the past few years. This paper reviews these promoter prediction methods. Several difficulties and pitfalls encountered by these methods are also detailed, along with future research directions.
Bhageerath-H: A homology/ab initio hybrid server for predicting tertiary structures of monomeric soluble proteins

PubMed Central

2014-01-01

Background The advent of human genome sequencing project has led to a spurt in the number of protein sequences in the databanks. Success of structure based drug discovery severely hinges on the availability of structures. Despite significant progresses in the area of experimental protein structure determination, the sequence-structure gap is continually widening. Data driven homology based computational methods have proved successful in predicting tertiary structures for sequences sharing medium to high sequence similarities. With dwindling similarities of query sequences, advanced homology/ ab initio hybrid approaches are being explored to solve structure prediction problem. Here we describe Bhageerath-H, a homology/ ab initio hybrid software/server for predicting protein tertiary structures with advancing drug design attempts as one of the goals. Results Bhageerath-H web-server was validated on 75 CASP10 targets which showed TM-scores ≥0.5 in 91% of the cases and Cα RMSDs ≤5Å from the native in 58% of the targets, which is well above the CASP10 water mark. Comparison with some leading servers demonstrated the uniqueness of the hybrid methodology in effectively sampling conformational space, scoring best decoys and refining low resolution models to high and medium resolution. Conclusion Bhageerath-H methodology is web enabled for the scientific community as a freely accessible web server. The methodology is fielded in the on-going CASP11 experiment. PMID:25521245
Discovery of optimal zeolites for challenging separations and chemical transformations using predictive materials modeling

NASA Astrophysics Data System (ADS)

Bai, Peng; Jeon, Mi Young; Ren, Limin; Knight, Chris; Deem, Michael W.; Tsapatsis, Michael; Siepmann, J. Ilja

2015-01-01

Zeolites play numerous important roles in modern petroleum refineries and have the potential to advance the production of fuels and chemical feedstocks from renewable resources. The performance of a zeolite as separation medium and catalyst depends on its framework structure. To date, 213 framework types have been synthesized and >330,000 thermodynamically accessible zeolite structures have been predicted. Hence, identification of optimal zeolites for a given application from the large pool of candidate structures is attractive for accelerating the pace of materials discovery. Here we identify, through a large-scale, multi-step computational screening process, promising zeolite structures for two energy-related applications: the purification of ethanol from fermentation broths and the hydroisomerization of alkanes with 18-30 carbon atoms encountered in petroleum refining. These results demonstrate that predictive modelling and data-driven science can now be applied to solve some of the most challenging separation problems involving highly non-ideal mixtures and highly articulated compounds.

Numerical and experimental study of expiratory flow in the case of major upper airway obstructions with fluid structure interaction

NASA Astrophysics Data System (ADS)

Chouly, F.; van Hirtum, A.; Lagrée, P.-Y.; Pelorson, X.; Payan, Y.

2008-02-01

This study deals with the numerical prediction and experimental description of the flow-induced deformation in a rapidly convergent divergent geometry which stands for a simplified tongue, in interaction with an expiratory airflow. An original in vitro experimental model is proposed, which allows measurement of the deformation of the artificial tongue, in condition of major initial airway obstruction. The experimental model accounts for asymmetries in geometry and tissue properties which are two major physiological upper airway characteristics. The numerical method for prediction of the fluid structure interaction is described. The theory of linear elasticity in small deformations has been chosen to compute the mechanical behaviour of the tongue. The main features of the flow are taken into account using a boundary layer theory. The overall numerical method entails finite element solving of the solid problem and finite differences solving of the fluid problem. First, the numerical method predicts the deformation of the tongue with an overall error of the order of 20%, which can be seen as a preliminary successful validation of the theory and simulations. Moreover, expiratory flow limitation is predicted in this configuration. As a result, both the physical and numerical models could be useful to understand this phenomenon reported in heavy snorers and apneic patients during sleep.
A population-based evolutionary search approach to the multiple minima problem in de novo protein structure prediction

PubMed Central

2013-01-01

Background Elucidating the native structure of a protein molecule from its sequence of amino acids, a problem known as de novo structure prediction, is a long standing challenge in computational structural biology. Difficulties in silico arise due to the high dimensionality of the protein conformational space and the ruggedness of the associated energy surface. The issue of multiple minima is a particularly troublesome hallmark of energy surfaces probed with current energy functions. In contrast to the true energy surface, these surfaces are weakly-funneled and rich in comparably deep minima populated by non-native structures. For this reason, many algorithms seek to be inclusive and obtain a broad view of the low-energy regions through an ensemble of low-energy (decoy) conformations. Conformational diversity in this ensemble is key to increasing the likelihood that the native structure has been captured. Methods We propose an evolutionary search approach to address the multiple-minima problem in decoy sampling for de novo structure prediction. Two population-based evolutionary search algorithms are presented that follow the basic approach of treating conformations as individuals in an evolving population. Coarse graining and molecular fragment replacement are used to efficiently obtain protein-like child conformations from parents. Potential energy is used both to bias parent selection and determine which subset of parents and children will be retained in the evolving population. The effect on the decoy ensemble of sampling minima directly is measured by additionally mapping a conformation to its nearest local minimum before considering it for retainment. The resulting memetic algorithm thus evolves not just a population of conformations but a population of local minima. Results and conclusions Results show that both algorithms are effective in terms of sampling conformations in proximity of the known native structure. The additional minimization is shown to be key to enhancing sampling capability and obtaining a diverse ensemble of decoy conformations, circumventing premature convergence to sub-optimal regions in the conformational space, and approaching the native structure with proximity that is comparable to state-of-the-art decoy sampling methods. The results are shown to be robust and valid when using two representative state-of-the-art coarse-grained energy functions. PMID:24565020
The impact of children's internalizing and externalizing problems on parenting: Transactional processes and reciprocal change over time.

PubMed

Serbin, Lisa A; Kingdon, Danielle; Ruttle, Paula L; Stack, Dale M

2015-11-01

Most theoretical models of developmental psychopathology involve a transactional, bidirectional relation between parenting and children's behavior problems. The present study utilized a cross-lagged panel, multiple interval design to model change in bidirectional relations between child and parent behavior across successive developmental periods. Two major categories of child behavior problems, internalizing and externalizing, and two aspects of parenting, positive (use of support and structure) and harsh discipline (use of physical punishment), were modeled across three time points spaced 3 years apart. Two successive developmental intervals, from approximately age 7.5 to 10.5 and from 10.5 to 13.5, were included. Mother-child dyads (N = 138; 65 boys) from a lower income longitudinal sample of families participated, with standardized measures of mothers rating their own parenting behavior and teachers reporting on child's behavior. Results revealed different types of reciprocal relations between specific aspects of child and parent behavior, with internalizing problems predicting an increase in positive parenting over time, which subsequently led to a reduction in internalizing problems across the successive 3-year interval. In contrast, externalizing predicted reduced levels of positive parenting in a reciprocal sequence that extended across two successive intervals and predicted increased levels of externalizing over time. Implications for prevention and early intervention are discussed.
Integration of QUARK and I-TASSER for ab initio protein structure prediction in CASP11

PubMed Central

Zhang, Wenxuan; Yang, Jianyi; He, Baoji; Walker, Sara Elizabeth; Zhang, Hongjiu; Govindarajoo, Brandon; Virtanen, Jouko; Xue, Zhidong; Shen, Hong-Bin; Zhang, Yang

2015-01-01

We tested two pipelines developed for template-free protein structure prediction in the CASP11 experiment. First, the QUARK pipeline constructs structure models by reassembling fragments of continuously distributed lengths excised from unrelated proteins. Five free-modeling (FM) targets have the model successfully constructed by QUARK with a TM-score above 0.4, including the first model of T0837-D1, which has a TM-score=0.736 and RMSD=2.9 Å to the native. Detailed analysis showed that the success is partly attributed to the high-resolution contact map prediction derived from fragment-based distance-profiles, which are mainly located between regular secondary structure elements and loops/turns and help guide the orientation of secondary structure assembly. In the Zhang-Server pipeline, weakly scoring threading templates are re-ordered by the structural similarity to the ab initio folding models, which are then reassembled by I-TASSER based structure assembly simulations; 60% more domains with length up to 204 residues, compared to the QUARK pipeline, were successfully modeled by the I-TASSER pipeline with a TM-score above 0.4. The robustness of the I-TASSER pipeline can stem from the composite fragment-assembly simulations that combine structures from both ab initio folding and threading template refinements. Despite the promising cases, challenges still exist in long-range beta-strand folding, domain parsing, and the uncertainty of secondary structure prediction; the latter of which was found to affect nearly all aspects of FM structure predictions, from fragment identification, target classification, structure assembly, to final model selection. Significant efforts are needed to solve these problems before real progress on FM could be made. PMID:26370505
Integration of QUARK and I-TASSER for Ab Initio Protein Structure Prediction in CASP11.

PubMed

Zhang, Wenxuan; Yang, Jianyi; He, Baoji; Walker, Sara Elizabeth; Zhang, Hongjiu; Govindarajoo, Brandon; Virtanen, Jouko; Xue, Zhidong; Shen, Hong-Bin; Zhang, Yang

2016-09-01

We tested two pipelines developed for template-free protein structure prediction in the CASP11 experiment. First, the QUARK pipeline constructs structure models by reassembling fragments of continuously distributed lengths excised from unrelated proteins. Five free-modeling (FM) targets have the model successfully constructed by QUARK with a TM-score above 0.4, including the first model of T0837-D1, which has a TM-score = 0.736 and RMSD = 2.9 Å to the native. Detailed analysis showed that the success is partly attributed to the high-resolution contact map prediction derived from fragment-based distance-profiles, which are mainly located between regular secondary structure elements and loops/turns and help guide the orientation of secondary structure assembly. In the Zhang-Server pipeline, weakly scoring threading templates are re-ordered by the structural similarity to the ab initio folding models, which are then reassembled by I-TASSER based structure assembly simulations; 60% more domains with length up to 204 residues, compared to the QUARK pipeline, were successfully modeled by the I-TASSER pipeline with a TM-score above 0.4. The robustness of the I-TASSER pipeline can stem from the composite fragment-assembly simulations that combine structures from both ab initio folding and threading template refinements. Despite the promising cases, challenges still exist in long-range beta-strand folding, domain parsing, and the uncertainty of secondary structure prediction; the latter of which was found to affect nearly all aspects of FM structure predictions, from fragment identification, target classification, structure assembly, to final model selection. Significant efforts are needed to solve these problems before real progress on FM could be made. Proteins 2016; 84(Suppl 1):76-86. © 2015 Wiley Periodicals, Inc. © 2015 Wiley Periodicals, Inc.
GeneBuilder: interactive in silico prediction of gene structure.

PubMed

Milanesi, L; D'Angelo, D; Rogozin, I B

1999-01-01

Prediction of gene structure in newly sequenced DNA becomes very important in large genome sequencing projects. This problem is complicated due to the exon-intron structure of eukaryotic genes and because gene expression is regulated by many different short nucleotide domains. In order to be able to analyse the full gene structure in different organisms, it is necessary to combine information about potential functional signals (promoter region, splice sites, start and stop codons, 3' untranslated region) together with the statistical properties of coding sequences (coding potential), information about homologous proteins, ESTs and repeated elements. We have developed the GeneBuilder system which is based on prediction of functional signals and coding regions by different approaches in combination with similarity searches in proteins and EST databases. The potential gene structure models are obtained by using a dynamic programming method. The program permits the use of several parameters for gene structure prediction and refinement. During gene model construction, selecting different exon homology levels with a protein sequence selected from a list of homologous proteins can improve the accuracy of the gene structure prediction. In the case of low homology, GeneBuilder is still able to predict the gene structure. The GeneBuilder system has been tested by using the standard set (Burset and Guigo, Genomics, 34, 353-367, 1996) and the performances are: 0.89 sensitivity and 0.91 specificity at the nucleotide level. The total correlation coefficient is 0.88. The GeneBuilder system is implemented as a part of the WebGene a the URL: http://www.itba.mi. cnr.it/webgene and TRADAT (TRAncription Database and Analysis Tools) launcher URL: http://www.itba.mi.cnr.it/tradat.
Influence of Early Onset of Alcohol Use on the Development of Adolescent Alcohol Problems: A Longitudinal Binational Study

PubMed Central

Kim, Min Jung; Mason, W. Alex; Herrenkohl, Todd I.; Catalano, Richard F.; Toumbourou, John W.; Hemphill, Sheryl A.

2016-01-01

This study examined cross-national similarities in a developmental model linking early age of alcohol use onset to frequent drinking and heavy drinking and alcohol problems 1 and 2 years later in a binational sample of 13-year-old students from 2 states: Washington State, United States, and Victoria, Australia (N = 1,833). A range of individual, family, school, and peer influences were included in analyses to investigate their unique and shared contribution to development of early and more serious forms of alcohol use and harms from misuse. Data were collected annually over a 3-year period from ages 13 to 15. Analyses were conducted using multiple-group structural equation modeling. For both states, early use of alcohol predicted frequent drinking, which predicted alcohol problems. Family protective influences had no direct effects on heavy drinking, nor effects on alcohol harm in either state, whereas school protection directly reduced the risk of heavy drinking in both states. Exposure to antisocial peers and siblings predicted a higher likelihood of heavy drinking and alcohol harm for students in both Washington and Victoria. Implications for the prevention of adolescent alcohol problems are discussed. PMID:27699620
NASA Composite Materials Development: Lessons Learned and Future Challenges

NASA Technical Reports Server (NTRS)

Tenney, Darrel R.; Davis, John G., Jr.; Pipes, R. Byron; Johnston, Norman

2009-01-01

Composite materials have emerged as the materials of choice for increasing the performance and reducing the weight and cost of military, general aviation, and transport aircraft and space launch vehicles. Major advancements have been made in the ability to design, fabricate, and analyze large complex aerospace structures. The recent efforts by Boeing and Airbus to incorporate composite into primary load carrying structures of large commercial transports and to certify the airworthiness of these structures is evidence of the significant advancements made in understanding and use of these materials in real world aircraft. NASA has been engaged in research on composites since the late 1960 s and has worked to address many development issues with these materials in an effort to ensure safety, improve performance, and improve affordability of air travel for the public good. This research has ranged from synthesis of advanced resin chemistries to development of mathematical analyses tools to reliably predict the response of built-up structures under combined load conditions. The lessons learned from this research are highlighted with specific examples to illustrate the problems encountered and solutions to these problems. Examples include specific technologies related to environmental effects, processing science, fabrication technologies, nondestructive inspection, damage tolerance, micromechanics, structural mechanics, and residual life prediction. The current state of the technology is reviewed and key issues requiring additional research identified. Also, grand challenges to be solved for expanded use of composites in aero structures are identified.
Methods for evaluating the predictive accuracy of structural dynamic models

NASA Technical Reports Server (NTRS)

Hasselman, T. K.; Chrostowski, Jon D.

1990-01-01

Uncertainty of frequency response using the fuzzy set method and on-orbit response prediction using laboratory test data to refine an analytical model are emphasized with respect to large space structures. Two aspects of the fuzzy set approach were investigated relative to its application to large structural dynamics problems: (1) minimizing the number of parameters involved in computing possible intervals; and (2) the treatment of extrema which may occur in the parameter space enclosed by all possible combinations of the important parameters of the model. Extensive printer graphics were added to the SSID code to help facilitate model verification, and an application of this code to the LaRC Ten Bay Truss is included in the appendix to illustrate this graphics capability.
Predicting activity approach based on new atoms similarity kernel function.

PubMed

Abu El-Atta, Ahmed H; Moussa, M I; Hassanien, Aboul Ella

2015-07-01

Drug design is a high cost and long term process. To reduce time and costs for drugs discoveries, new techniques are needed. Chemoinformatics field implements the informational techniques and computer science like machine learning and graph theory to discover the chemical compounds properties, such as toxicity or biological activity. This is done through analyzing their molecular structure (molecular graph). To overcome this problem there is an increasing need for algorithms to analyze and classify graph data to predict the activity of molecules. Kernels methods provide a powerful framework which combines machine learning with graph theory techniques. These kernels methods have led to impressive performance results in many several chemoinformatics problems like biological activity prediction. This paper presents a new approach based on kernel functions to solve activity prediction problem for chemical compounds. First we encode all atoms depending on their neighbors then we use these codes to find a relationship between those atoms each other. Then we use relation between different atoms to find similarity between chemical compounds. The proposed approach was compared with many other classification methods and the results show competitive accuracy with these methods. Copyright © 2015 Elsevier Inc. All rights reserved.
Problems in air traffic management. VII., Job training performance of air traffic control specialists - measurement, structure, and prediction.

DOT National Transportation Integrated Search

1965-07-01

A statistical study of training- and job-performance measures of several hundred Air Traffic Control Specialists (ATCS) representing Enroute, Terminal, and Flight Service Station specialties revealed that training-performance measures reflected: : 1....
RECURSIVE PROTEIN MODELING: A DIVIDE AND CONQUER STRATEGY FOR PROTEIN STRUCTURE PREDICTION AND ITS CASE STUDY IN CASP9

PubMed Central

CHENG, JIANLIN; EICKHOLT, JESSE; WANG, ZHENG; DENG, XIN

2013-01-01

After decades of research, protein structure prediction remains a very challenging problem. In order to address the different levels of complexity of structural modeling, two types of modeling techniques — template-based modeling and template-free modeling — have been developed. Template-based modeling can often generate a moderate- to high-resolution model when a similar, homologous template structure is found for a query protein but fails if no template or only incorrect templates are found. Template-free modeling, such as fragment-based assembly, may generate models of moderate resolution for small proteins of low topological complexity. Seldom have the two techniques been integrated together to improve protein modeling. Here we develop a recursive protein modeling approach to selectively and collaboratively apply template-based and template-free modeling methods to model template-covered (i.e. certain) and template-free (i.e. uncertain) regions of a protein. A preliminary implementation of the approach was tested on a number of hard modeling cases during the 9th Critical Assessment of Techniques for Protein Structure Prediction (CASP9) and successfully improved the quality of modeling in most of these cases. Recursive modeling can signicantly reduce the complexity of protein structure modeling and integrate template-based and template-free modeling to improve the quality and efficiency of protein structure prediction. PMID:22809379
Particle tracking acceleration via signed distance fields in direct-accelerated geometry Monte Carlo

DOE PAGES

Shriwise, Patrick C.; Davis, Andrew; Jacobson, Lucas J.; ...

2017-08-26

Computer-aided design (CAD)-based Monte Carlo radiation transport is of value to the nuclear engineering community for its ability to conduct transport on high-fidelity models of nuclear systems, but it is more computationally expensive than native geometry representations. This work describes the adaptation of a rendering data structure, the signed distance field, as a geometric query tool for accelerating CAD-based transport in the direct-accelerated geometry Monte Carlo toolkit. Demonstrations of its effectiveness are shown for several problems. The beginnings of a predictive model for the data structure's utilization based on various problem parameters is also introduced.
A Study of Effects of MultiCollinearity in the Multivariable Analysis

PubMed Central

Yoo, Wonsuk; Mayberry, Robert; Bae, Sejong; Singh, Karan; (Peter) He, Qinghua; Lillard, James W.

2015-01-01

A multivariable analysis is the most popular approach when investigating associations between risk factors and disease. However, efficiency of multivariable analysis highly depends on correlation structure among predictive variables. When the covariates in the model are not independent one another, collinearity/multicollinearity problems arise in the analysis, which leads to biased estimation. This work aims to perform a simulation study with various scenarios of different collinearity structures to investigate the effects of collinearity under various correlation structures amongst predictive and explanatory variables and to compare these results with existing guidelines to decide harmful collinearity. Three correlation scenarios among predictor variables are considered: (1) bivariate collinear structure as the most simple collinearity case, (2) multivariate collinear structure where an explanatory variable is correlated with two other covariates, (3) a more realistic scenario when an independent variable can be expressed by various functions including the other variables. PMID:25664257
A Study of Effects of MultiCollinearity in the Multivariable Analysis.

PubMed

Yoo, Wonsuk; Mayberry, Robert; Bae, Sejong; Singh, Karan; Peter He, Qinghua; Lillard, James W

2014-10-01

A multivariable analysis is the most popular approach when investigating associations between risk factors and disease. However, efficiency of multivariable analysis highly depends on correlation structure among predictive variables. When the covariates in the model are not independent one another, collinearity/multicollinearity problems arise in the analysis, which leads to biased estimation. This work aims to perform a simulation study with various scenarios of different collinearity structures to investigate the effects of collinearity under various correlation structures amongst predictive and explanatory variables and to compare these results with existing guidelines to decide harmful collinearity. Three correlation scenarios among predictor variables are considered: (1) bivariate collinear structure as the most simple collinearity case, (2) multivariate collinear structure where an explanatory variable is correlated with two other covariates, (3) a more realistic scenario when an independent variable can be expressed by various functions including the other variables.
Family Turbulence and Child Internalizing and Externalizing Behaviors: Moderation of Effects by Race.

PubMed

Womack, Sean R; Taraban, Lindsay; Shaw, Daniel S; Wilson, Melvin N; Dishion, Thomas J

2018-06-19

This study examined the impact of residential instability and family structure transitions on the development of internalizing and externalizing problems from age 2 through 10.5. Child's race was examined as a moderator. Caregiver reports of internalizing and externalizing behaviors were obtained on 665 children at ages 5 and 10.5. Early-childhood residential and family structure transitions predicted elevated internalizing and externalizing problems at ages 5 and 10.5, but only for Caucasian children. These findings suggest that residential and family structure instability during early childhood independently contribute to children's later emotional and behavioral development, but vary as a function of the child's race. Community organizations (e.g., Women, Infant, and Children) can connect turbulent families with resources to attenuate effects of residential and family structure instability. © 2018 Society for Research in Child Development.
Asymmetry in search.

PubMed

Kaindl, H; Kainz, G; Radda, K

2001-01-01

Most of the work on search in artificial intelligence (AI) deals with one search direction only-mostly forward search-although it is known that a structural asymmetry of the search graph causes differences in the efficiency of searching in the forward or the backward direction, respectively. In the case of symmetrical graph structure, however, current theory would not predict such differences in efficiency. In several classes of job sequencing problems, we observed a phenomenon of asymmetry in search that relates to the distribution of the are costs in the search graph. This phenomenon can be utilized for improving the search efficiency by a new algorithm that automatically selects the search direction. We demonstrate fur a class of job sequencing problems that, through the utilization of this phenomenon, much more difficult problems can be solved-according to our best knowledge-than by the best published approach, and on the same problems, the running time is much reduced. As a consequence, we propose to check given problems for asymmetrical distribution of are costs that may cause asymmetry in search.
Approximating the stress field within the unit cell of a fabric reinforced composite using replacement elements

NASA Technical Reports Server (NTRS)

Foye, R. L.

1993-01-01

This report concerns the prediction of the elastic moduli and the internal stresses within the unit cell of a fabric reinforced composite. In the proposed analysis no restrictions or assumptions are necessary concerning yarn or tow cross-sectional shapes or paths through the unit cell but the unit cell itself must be a right hexagonal parallelepiped. All the unit cell dimensions are assumed to be small with respect to the thickness of the composite structure that it models. The finite element analysis of a unit cell is usually complicated by the mesh generation problems and the non-standard, adjacent-cell boundary conditions. This analysis avoids these problems through the use of preprogrammed boundary conditions and replacement materials (or elements). With replacement elements it is not necessary to match all the constitutional material interfaces with finite element boundaries. Simple brick-shaped elements can be used to model the unit cell structure. The analysis predicts the elastic constants and the average stresses within each constituent material of each brick element. The application and results of this analysis are demonstrated through several example problems which include a number of composite microstructures.
LBSizeCleav: improved support vector machine (SVM)-based prediction of Dicer cleavage sites using loop/bulge length.

PubMed

Bao, Yu; Hayashida, Morihiro; Akutsu, Tatsuya

2016-11-25

Dicer is necessary for the process of mature microRNA (miRNA) formation because the Dicer enzyme cleaves pre-miRNA correctly to generate miRNA with correct seed regions. Nonetheless, the mechanism underlying the selection of a Dicer cleavage site is still not fully understood. To date, several studies have been conducted to solve this problem, for example, a recent discovery indicates that the loop/bulge structure plays a central role in the selection of Dicer cleavage sites. In accordance with this breakthrough, a support vector machine (SVM)-based method called PHDCleav was developed to predict Dicer cleavage sites which outperforms other methods based on random forest and naive Bayes. PHDCleav, however, tests only whether a position in the shift window belongs to a loop/bulge structure. In this paper, we used the length of loop/bulge structures (in addition to their presence or absence) to develop an improved method, LBSizeCleav, for predicting Dicer cleavage sites. To evaluate our method, we used 810 empirically validated sequences of human pre-miRNAs and performed fivefold cross-validation. In both 5p and 3p arms of pre-miRNAs, LBSizeCleav showed greater prediction accuracy than PHDCleav did. This result suggests that the length of loop/bulge structures is useful for prediction of Dicer cleavage sites. We developed a novel algorithm for feature space mapping based on the length of a loop/bulge for predicting Dicer cleavage sites. The better performance of our method indicates the usefulness of the length of loop/bulge structures for such predictions.
An Immersed Boundary Method for Solving the Compressible Navier-Stokes Equations with Fluid Structure Interaction

NASA Technical Reports Server (NTRS)

Brehm, Christoph; Barad, Michael F.; Kiris, Cetin C.

2016-01-01

An immersed boundary method for the compressible Navier-Stokes equation and the additional infrastructure that is needed to solve moving boundary problems and fully coupled fluid-structure interaction is described. All the methods described in this paper were implemented in NASA's LAVA solver framework. The underlying immersed boundary method is based on the locally stabilized immersed boundary method that was previously introduced by the authors. In the present paper this method is extended to account for all aspects that are involved for fluid structure interaction simulations, such as fast geometry queries and stencil computations, the treatment of freshly cleared cells, and the coupling of the computational fluid dynamics solver with a linear structural finite element method. The current approach is validated for moving boundary problems with prescribed body motion and fully coupled fluid structure interaction problems in 2D and 3D. As part of the validation procedure, results from the second AIAA aeroelastic prediction workshop are also presented. The current paper is regarded as a proof of concept study, while more advanced methods for fluid structure interaction are currently being investigated, such as geometric and material nonlinearities, and advanced coupling approaches.

Full-field dynamic strain prediction on a wind turbine using displacements of optical targets measured by stereophotogrammetry

NASA Astrophysics Data System (ADS)

Baqersad, Javad; Niezrecki, Christopher; Avitabile, Peter

2015-10-01

Health monitoring of rotating structures (e.g. wind turbines and helicopter blades) has historically been a challenge due to sensing and data transmission problems. Unfortunately mechanical failure in many structures initiates at components on or inside the structure where there is no sensor located to predict the failure. In this paper, a wind turbine was mounted with a semi-built-in configuration and was excited using a mechanical shaker. A series of optical targets was distributed along the blades and the fixture and the displacement of those targets during excitation was measured using a pair of high speed cameras. Measured displacements with three dimensional point tracking were transformed to all finite element degrees of freedom using a modal expansion algorithm. The expanded displacements were applied to the finite element model to predict the full-field dynamic strain on the surface of the structure as well as within the interior points. To validate the methodology of dynamic strain prediction, the predicted strain was compared to measured strain by using six mounted strain-gages. To verify if a simpler model of the turbine can be used for the expansion, the expansion process was performed both by using the modes of the entire turbine and modes of a single cantilever blade. The results indicate that the expansion approach can accurately predict the strain throughout the turbine blades from displacements measured by using stereophotogrammetry.
Structural alignment of protein descriptors - a combinatorial model.

PubMed

Antczak, Maciej; Kasprzak, Marta; Lukasiak, Piotr; Blazewicz, Jacek

2016-09-17

Structural alignment of proteins is one of the most challenging problems in molecular biology. The tertiary structure of a protein strictly correlates with its function and computationally predicted structures are nowadays a main premise for understanding the latter. However, computationally derived 3D models often exhibit deviations from the native structure. A way to confirm a model is a comparison with other structures. The structural alignment of a pair of proteins can be defined with the use of a concept of protein descriptors. The protein descriptors are local substructures of protein molecules, which allow us to divide the original problem into a set of subproblems and, consequently, to propose a more efficient algorithmic solution. In the literature, one can find many applications of the descriptors concept that prove its usefulness for insight into protein 3D structures, but the proposed approaches are presented rather from the biological perspective than from the computational or algorithmic point of view. Efficient algorithms for identification and structural comparison of descriptors can become crucial components of methods for structural quality assessment as well as tertiary structure prediction. In this paper, we propose a new combinatorial model and new polynomial-time algorithms for the structural alignment of descriptors. The model is based on the maximum-size assignment problem, which we define here and prove that it can be solved in polynomial time. We demonstrate suitability of this approach by comparison with an exact backtracking algorithm. Besides a simplification coming from the combinatorial modeling, both on the conceptual and complexity level, we gain with this approach high quality of obtained results, in terms of 3D alignment accuracy and processing efficiency. All the proposed algorithms were developed and integrated in a computationally efficient tool descs-standalone, which allows the user to identify and structurally compare descriptors of biological molecules, such as proteins and RNAs. Both PDB (Protein Data Bank) and mmCIF (macromolecular Crystallographic Information File) formats are supported. The proposed tool is available as an open source project stored on GitHub ( https://github.com/mantczak/descs-standalone ).
From molecular chaperones to membrane motors: through the lens of a mass spectrometrist.

PubMed

Robinson, Carol V

2017-02-08

Twenty-five years ago, we obtained our first mass spectra of molecular chaperones in complex with protein ligands and entered a new field of gas-phase structural biology. It is perhaps now time to pause and reflect, and to ask how many of our initial structure predictions and models derived from mass spectrometry (MS) datasets were correct. With recent advances in structure determination, many of the most challenging complexes that we studied over the years have become tractable by other structural biology approaches enabling such comparisons to be made. Moreover, in the light of powerful new electron microscopy methods, what role is there now for MS? In considering these questions, I will give my personal view on progress and problems as well as my predictions for future directions. © 2017 The Author(s).
The inverse problem of using the information of historical data to estimate model errors is one of the science frontier research topics. In this study, we investigate such a problem using the classic Lorenz (1963) equation as a prediction model.

NASA Astrophysics Data System (ADS)

Wan, S.; He, W.

2016-12-01

The inverse problem of using the information of historical data to estimate model errors is one of the science frontier research topics. In this study, we investigate such a problem using the classic Lorenz (1963) equation as a prediction model and the Lorenz equation with a periodic evolutionary function as an accurate representation of reality to generate "observational data." On the basis of the intelligent features of evolutionary modeling (EM), including self-organization, self-adaptive and self-learning, the dynamic information contained in the historical data can be identified and extracted by computer automatically. Thereby, a new approach is proposed to estimate model errors based on EM in the present paper. Numerical tests demonstrate the ability of the new approach to correct model structural errors. In fact, it can actualize the combination of the statistics and dynamics to certain extent.
Stability and Predictive Validity of the Parent-Child Sleep Interactions Scale: A Longitudinal Study Among Preschoolers.

PubMed

Barrios, Chelsey S; Jay, Samantha Y; Smith, Victoria C; Alfano, Candice A; Dougherty, Lea R

2018-01-01

Little research has examined the processes underlying children's persistent sleep problems and links with later psychopathology. The current study examined the stability of parent-child sleep interactions as assessed with the parent-reported Parent-Child Sleep Interactions Scale (PSIS) and examined whether sleep interactions in preschool-age children predict sleep problems and psychiatric symptoms later in childhood. Participants included 108 preschool-age children (50% female) and their parents. Parents completed the PSIS when children were 3-5 years (T1) and again when they were 6-9 years (T2). The PSIS includes three subscales-Sleep Reinforcement (reassurance of child sleep behaviors), Sleep Conflict (parent-child conflict at bedtime), Sleep Dependence (difficulty going to sleep without parent)-and a total score. Higher scores indicate more problematic bedtime interactions. Children's sleep problems and psychiatric symptoms at T1 and T2 were assessed with a clinical interview. PSIS scores were moderately stable from T1 to T2, and the factor structure of the PSIS remained relatively consistent over time. Higher total PSIS scores at T1 predicted increases in children's sleep problems at T2. Higher PSIS Sleep Conflict scores at T1 predicted increases in oppositional defiant disorder symptoms at T2. Children with more sleep problems and higher PSIS Sleep Reinforcement scores at T1 showed increases in attention deficit/hyperactivity disorder, depressive, and anxiety symptoms at T2. These findings provide evidence for the predictive validity of the PSIS and highlight the importance of early parent-child sleep interactions in the development of sleep and psychiatric symptoms in childhood. Parent-child sleep interactions may serve as a useful target for interventions.
Spontaneous gestures influence strategy choices in problem solving.

PubMed

Alibali, Martha W; Spencer, Robert C; Knox, Lucy; Kita, Sotaro

2011-09-01

Do gestures merely reflect problem-solving processes, or do they play a functional role in problem solving? We hypothesized that gestures highlight and structure perceptual-motor information, and thereby make such information more likely to be used in problem solving. Participants in two experiments solved problems requiring the prediction of gear movement, either with gesture allowed or with gesture prohibited. Such problems can be correctly solved using either a perceptual-motor strategy (simulation of gear movements) or an abstract strategy (the parity strategy). Participants in the gesture-allowed condition were more likely to use perceptual-motor strategies than were participants in the gesture-prohibited condition. Gesture promoted use of perceptual-motor strategies both for participants who talked aloud while solving the problems (Experiment 1) and for participants who solved the problems silently (Experiment 2). Thus, spontaneous gestures influence strategy choices in problem solving.
A study of aeroelastic and structural dynamic effects in multi-rotor systems with application to hybrid heavy lift vehicles

NASA Technical Reports Server (NTRS)

Friedmann, P. P.

1984-01-01

An aeroelastic model suitable for the study of aeroelastic and structural dynamic effects in multirotor vehicles simulating a hybrid heavy lift vehicle was developed and applied to the study of a number of diverse problems. The analytical model developed proved capable of modeling a number of aeroelastic problems, namely: (1) isolated blade aeroelastic stability in hover and forward flight, (2) coupled rotor/fuselage aeromechanical problem in air or ground resonance, (3) tandem rotor coupled rotor/fuselage problems, and (4) the aeromechanical stability of a multirotor vehicle model representing a hybrid heavy lift airship (HHLA). The model was used to simulate the ground resonance boundaries of a three bladed hingeless rotor model, including the effect of aerodynamic loads, and the theoretical predictions compared well with experimental results. Subsequently the model was used to study the aeromechanical stability of a vehicle representing a hybrid heavy lift airship, and potential instabilities which could occur for this type of vehicle were identified. The coupling between various blade, supporting structure and rigid body modes was identified.
Predicting Internalizing Problems in Chinese Children: the Unique and Interactive Effects of Parenting and Child Temperament

PubMed Central

Muhtadie, Luma; Zhou, Qing; Eisenberg, Nancy; Wang, Yun

2012-01-01

The additive and interactive relations of parenting styles (authoritative and authoritarian parenting) and child temperament (anger/frustration, sadness, and effortful control) to children’s internalizing problems were examined in a 3.8-year longitudinal study of 425 Chinese children (6 – 9 years) from Beijing. At Wave 1, parents self-reported on their parenting styles, and parents and teachers rated child temperament. At Wave 2, parents, teachers, and children rated children’s internalizing problems. Structural equation modeling indicated that the main effect of authoritative parenting, and the interactions of authoritarian parenting × effortful control and authoritative parenting × anger/frustration (parents’ reports only) prospectively and uniquely predicted internalizing problems. The above results did not vary by child sex and remained significant after controlling for co-occurring externalizing problems. These findings suggest that: a) children with low effortful control may be particularly susceptible to the adverse effect of authoritarian parenting, and b) the benefit of authoritative parenting may be especially important for children with high anger/frustration. PMID:23880383
Role of conformational sampling in computing mutation-induced changes in protein structure and stability.

PubMed

Kellogg, Elizabeth H; Leaver-Fay, Andrew; Baker, David

2011-03-01

The prediction of changes in protein stability and structure resulting from single amino acid substitutions is both a fundamental test of macromolecular modeling methodology and an important current problem as high throughput sequencing reveals sequence polymorphisms at an increasing rate. In principle, given the structure of a wild-type protein and a point mutation whose effects are to be predicted, an accurate method should recapitulate both the structural changes and the change in the folding-free energy. Here, we explore the performance of protocols which sample an increasing diversity of conformations. We find that surprisingly similar performances in predicting changes in stability are achieved using protocols that involve very different amounts of conformational sampling, provided that the resolution of the force field is matched to the resolution of the sampling method. Methods involving backbone sampling can in some cases closely recapitulate the structural changes accompanying mutations but not surprisingly tend to do more harm than good in cases where structural changes are negligible. Analysis of the outliers in the stability change calculations suggests areas needing particular improvement; these include the balance between desolvation and the formation of favorable buried polar interactions, and unfolded state modeling. Copyright © 2010 Wiley-Liss, Inc.
Structural Dynamics of Electronic Systems

NASA Astrophysics Data System (ADS)

Suhir, E.

2013-03-01

The published work on analytical ("mathematical") and computer-aided, primarily finite-element-analysis (FEA) based, predictive modeling of the dynamic response of electronic systems to shocks and vibrations is reviewed. While understanding the physics of and the ability to predict the response of an electronic structure to dynamic loading has been always of significant importance in military, avionic, aeronautic, automotive and maritime electronics, during the last decade this problem has become especially important also in commercial, and, particularly, in portable electronics in connection with accelerated testing of various surface mount technology (SMT) systems on the board level. The emphasis of the review is on the nonlinear shock-excited vibrations of flexible printed circuit boards (PCBs) experiencing shock loading applied to their support contours during drop tests. At the end of the review we provide, as a suitable and useful illustration, the exact solution to a highly nonlinear problem of the dynamic response of a "flexible-and-heavy" PCB to an impact load applied to its support contour during drop testing.
Early risk factors for depressive symptoms among Korean adolescents: a 6-to-8 year follow-up study.

PubMed

Shin, Kyoung Min; Cho, Sun-Mi; Shin, Yun Mi; Park, Kyung Soon

2013-11-01

Depression during adolescence is critical to the individual's own development. Hence, identifying individuals with high-risk depression at an early stage is necessary. This study aimed to identify childhood emotional and behavioral risk factors related to depressive symptoms in Korean adolescents through a longitudinal study. The first survey took place from 1998 to 2000, and a follow-up assessment conducted in 2006, as the original participants reached 13-15 yr of age. The first assessment used the Korean version of Child Behavior Checklist and a general questionnaire on family structure, parental education, and economic status to evaluate the participants. The follow-up assessment administered the Korean Children's Depression Inventory. Multiple regression analysis revealed that childhood attention problems predicted depressive symptoms during adolescence for both boys and girls. For boys, family structure also predicted adolescent depressive symptoms. This study suggests that adolescents with attention problems during childhood are more likely to experience depressive symptoms.
A stochastic vortex structure method for interacting particles in turbulent shear flows

NASA Astrophysics Data System (ADS)

Dizaji, Farzad F.; Marshall, Jeffrey S.; Grant, John R.

2018-01-01

In a recent study, we have proposed a new synthetic turbulence method based on stochastic vortex structures (SVSs), and we have demonstrated that this method can accurately predict particle transport, collision, and agglomeration in homogeneous, isotropic turbulence in comparison to direct numerical simulation results. The current paper extends the SVS method to non-homogeneous, anisotropic turbulence. The key element of this extension is a new inversion procedure, by which the vortex initial orientation can be set so as to generate a prescribed Reynolds stress field. After validating this inversion procedure for simple problems, we apply the SVS method to the problem of interacting particle transport by a turbulent planar jet. Measures of the turbulent flow and of particle dispersion, clustering, and collision obtained by the new SVS simulations are shown to compare well with direct numerical simulation results. The influence of different numerical parameters, such as number of vortices and vortex lifetime, on the accuracy of the SVS predictions is also examined.
Ab-initio conformational epitope structure prediction using genetic algorithm and SVM for vaccine design.

PubMed

Moghram, Basem Ameen; Nabil, Emad; Badr, Amr

2018-01-01

T-cell epitope structure identification is a significant challenging immunoinformatic problem within epitope-based vaccine design. Epitopes or antigenic peptides are a set of amino acids that bind with the Major Histocompatibility Complex (MHC) molecules. The aim of this process is presented by Antigen Presenting Cells to be inspected by T-cells. MHC-molecule-binding epitopes are responsible for triggering the immune response to antigens. The epitope's three-dimensional (3D) molecular structure (i.e., tertiary structure) reflects its proper function. Therefore, the identification of MHC class-II epitopes structure is a significant step towards epitope-based vaccine design and understanding of the immune system. In this paper, we propose a new technique using a Genetic Algorithm for Predicting the Epitope Structure (GAPES), to predict the structure of MHC class-II epitopes based on their sequence. The proposed Elitist-based genetic algorithm for predicting the epitope's tertiary structure is based on Ab-Initio Empirical Conformational Energy Program for Peptides (ECEPP) Force Field Model. The developed secondary structure prediction technique relies on Ramachandran Plot. We used two alignment algorithms: the ROSS alignment and TM-Score alignment. We applied four different alignment approaches to calculate the similarity scores of the dataset under test. We utilized the support vector machine (SVM) classifier as an evaluation of the prediction performance. The prediction accuracy and the Area Under Receiver Operating Characteristic (ROC) Curve (AUC) were calculated as measures of performance. The calculations are performed on twelve similarity-reduced datasets of the Immune Epitope Data Base (IEDB) and a large dataset of peptide-binding affinities to HLA-DRB1*0101. The results showed that GAPES was reliable and very accurate. We achieved an average prediction accuracy of 93.50% and an average AUC of 0.974 in the IEDB dataset. Also, we achieved an accuracy of 95.125% and an AUC of 0.987 on the HLA-DRB1*0101 allele of the Wang benchmark dataset. The results indicate that the proposed prediction technique "GAPES" is a promising technique that will help researchers and scientists to predict the protein structure and it will assist them in the intelligent design of new epitope-based vaccines. Copyright © 2017 Elsevier B.V. All rights reserved.
A Case Study Using Modeling and Simulation to Predict Logistics Supply Chain Issues

NASA Technical Reports Server (NTRS)

Tucker, David A.

2007-01-01

Optimization of critical supply chains to deliver thousands of parts, materials, sub-assemblies, and vehicle structures as needed is vital to the success of the Constellation Program. Thorough analysis needs to be performed on the integrated supply chain processes to plan, source, make, deliver, and return critical items efficiently. Process modeling provides simulation technology-based, predictive solutions for supply chain problems which enable decision makers to reduce costs, accelerate cycle time and improve business performance. For example, United Space Alliance, LLC utilized this approach in late 2006 to build simulation models that recreated shuttle orbiter thruster failures and predicted the potential impact of thruster removals on logistics spare assets. The main objective was the early identification of possible problems in providing thruster spares for the remainder of the Shuttle Flight Manifest. After extensive analysis the model results were used to quantify potential problems and led to improvement actions in the supply chain. Similarly the proper modeling and analysis of Constellation parts, materials, operations, and information flows will help ensure the efficiency of the critical logistics supply chains and the overall success of the program.
How preschool executive functioning predicts several aspects of math achievement in Grades 1 and 3: A longitudinal study.

PubMed

Viterbori, Paola; Usai, M Carmen; Traverso, Laura; De Franchis, Valentina

2015-12-01

This longitudinal study analyzes whether selected components of executive function (EF) measured during the preschool period predict several indices of math achievement in primary school. Six EF measures were assessed in a sample of 5-year-old children (N = 175). The math achievement of the same children was then tested in Grades 1 and 3 using both a composite math score and three single indices of written calculation, arithmetical facts, and problem solving. Using previous results obtained from the same sample of children, a confirmatory factor analysis examining the latent EF structure in kindergarten indicated that a two-factor model provided the best fit for the data. In this model, inhibition and working memory (WM)-flexibility were separate dimensions. A full structural equation model was then used to test the hypothesis that math achievement (the composite math score and single math scores) in Grades 1 and 3 could be explained by the two EF components comprising the kindergarten model. The results indicate that the WM-flexibility component measured during the preschool period substantially predicts mathematical achievement, especially in Grade 3. The math composite scores were predicted by the WM-flexibility factor at both grade levels. In Grade 3, both problem solving and arithmetical facts were predicted by the WM-flexibility component. The results empirically support interventions that target EF as an important component of early childhood mathematics education. Copyright © 2015 Elsevier Inc. All rights reserved.
Evaluating model structure adequacy: The case of the Maggia Valley groundwater system, southern Switzerland

USGS Publications Warehouse

Hill, Mary C.; L. Foglia,; S. W. Mehl,; P. Burlando,

2013-01-01

Model adequacy is evaluated with alternative models rated using model selection criteria (AICc, BIC, and KIC) and three other statistics. Model selection criteria are tested with cross-validation experiments and insights for using alternative models to evaluate model structural adequacy are provided. The study is conducted using the computer codes UCODE_2005 and MMA (MultiModel Analysis). One recharge alternative is simulated using the TOPKAPI hydrological model. The predictions evaluated include eight heads and three flows located where ecological consequences and model precision are of concern. Cross-validation is used to obtain measures of prediction accuracy. Sixty-four models were designed deterministically and differ in representation of river, recharge, bedrock topography, and hydraulic conductivity. Results include: (1) What may seem like inconsequential choices in model construction may be important to predictions. Analysis of predictions from alternative models is advised. (2) None of the model selection criteria consistently identified models with more accurate predictions. This is a disturbing result that suggests to reconsider the utility of model selection criteria, and/or the cross-validation measures used in this work to measure model accuracy. (3) KIC displayed poor performance for the present regression problems; theoretical considerations suggest that difficulties are associated with wide variations in the sensitivity term of KIC resulting from the models being nonlinear and the problems being ill-posed due to parameter correlations and insensitivity. The other criteria performed somewhat better, and similarly to each other. (4) Quantities with high leverage are more difficult to predict. The results are expected to be generally applicable to models of environmental systems.
Modeling and Analysis of Structural Dynamics for a One-Tenth Scale Model NGST Sunshield

NASA Technical Reports Server (NTRS)

Johnston, John; Lienard, Sebastien; Brodeur, Steve (Technical Monitor)

2001-01-01

New modeling and analysis techniques have been developed for predicting the dynamic behavior of the Next Generation Space Telescope (NGST) sunshield. The sunshield consists of multiple layers of pretensioned, thin-film membranes supported by deployable booms. Modeling the structural dynamic behavior of the sunshield is a challenging aspect of the problem due to the effects of membrane wrinkling. A finite element model of the sunshield was developed using an approximate engineering approach, the cable network method, to account for membrane wrinkling effects. Ground testing of a one-tenth scale model of the NGST sunshield were carried out to provide data for validating the analytical model. A series of analyses were performed to predict the behavior of the sunshield under the ground test conditions. Modal analyses were performed to predict the frequencies and mode shapes of the test article and transient response analyses were completed to simulate impulse excitation tests. Comparison was made between analytical predictions and test measurements for the dynamic behavior of the sunshield. In general, the results show good agreement with the analytical model correctly predicting the approximate frequency and mode shapes for the significant structural modes.
A multivariate model of parent-adolescent relationship variables in early adolescence.

PubMed

McKinney, Cliff; Renk, Kimberly

2011-08-01

Given the importance of predicting outcomes for early adolescents, this study examines a multivariate model of parent-adolescent relationship variables, including parenting, family environment, and conflict. Participants, who completed measures assessing these variables, included 710 culturally diverse 11-14-year-olds who were attending a middle school in a Southeastern state. The parents of a subset of these adolescents (i.e., 487 mother-father pairs) participated in this study as well. Correlational analyses indicate that authoritative and authoritarian parenting, family cohesion and adaptability, and conflict are significant predictors of early adolescents' internalizing and externalizing problems. Structural equation modeling analyses indicate that fathers' parenting may not predict directly externalizing problems in male and female adolescents but instead may act through conflict. More direct relationships exist when examining mothers' parenting. The impact of parenting, family environment, and conflict on early adolescents' internalizing and externalizing problems and the importance of both gender and cross-informant ratings are emphasized.
The roles of birth inputs and outputs in predicting health, behaviour and test scores in early childhood.

PubMed

Li, Kai; Poirier, Dale J

2003-11-30

The goal of this study is to address directly the predictive value of birth inputs and outputs, particularly birth weight, for measures of early childhood development in a simultaneous equations modelling framework. Strikingly, birth outputs have virtually no structural/causal effects on early childhood developmental outcomes, and only maternal smoking and drinking during pregnancy have some effects on child height. Not surprisingly, family child-rearing environment has sizeable negative and positive effects on a behavioural problems index and a mathematics/reading test score, respectively, and a mildly surprising negative effect on child height. Despite little evidence of a structural/causal effect of birth weight on early childhood developmental outcomes, our results demonstrate that birth weight nonetheless has strong predictive effects on early childhood outcomes. Furthermore, these effects are largely invariant to whether family child-rearing environment is taken into account. Family child-rearing environment has both structural and predictive effects on early childhood outcomes, but they are largely orthogonal and in addition to the effects of birth weight. Copyright 2003 John Wiley & Sons, Ltd.
Factor Structure and Predictive Validity of a Homework Motivation Measure for Use With Middle School Students With Attention-Deficit/Hyperactivity Disorder (ADHD).

PubMed

Langberg, Joshua M; Smith, Zoe R; Dvorsky, Melissa R; Molitor, Stephen J; Bourchtein, Elizaveta; Eddy, Laura D; Eadeh, Hana-May; Oddo, Lauren E

2017-08-31

Many students with attention-deficit/hyperactivity disorder (ADHD) exhibit deficits in motivation to pursue long-term goals. Students with ADHD have particular difficulty with motivation to complete homework-related tasks and often fail to complete assignments. Although these problems are common and may impact academic performance, no homework-motivation measures have been validated for use with students with ADHD. The primary goal of the present study was to evaluate the factor structure and predictive validity of a homework-motivation measure based upon the expectancy-value theory of achievement motivation. A sample of 285 middle school students with ADHD completed the measure, and confirmatory factor analysis was used to evaluate the proposed factor structure and associations with parent and teacher ratings of homework performance. A 2-factor structure emerged, and model fit was excellent. Further, student-rated ability-expectancy beliefs demonstrated significant associations with parent-rated homework problems and performance and with teacher-rated homework performance and percentage of assignments turned in above and beyond ADHD symptoms. Future directions for studying the importance of motivation in students with ADHD are provided, with particular attention to the role that reward sensitivity may play in motivation. (PsycINFO Database Record (c) 2017 APA, all rights reserved).

Externalizing symptoms, effortful control, and intrusive parenting: A test of bidirectional longitudinal relations during early childhood.

PubMed

Eisenberg, Nancy; Taylor, Zoe E; Widaman, Keith F; Spinrad, Tracy L

2015-11-01

At approximately 30, 42, and 54 months of age (N = 231), the relations among children's externalizing symptoms, intrusive maternal parenting, and children's effortful control (EC) were examined. Both intrusive parenting and low EC have been related to psychopathology, but children's externalizing problems and low EC might affect the quality of parenting and one another. Mothers' intrusive behavior with their children was assessed with observations, children's EC was measured with mothers' and caregivers' reports, and children's externalizing symptoms were assessed with mothers', fathers', and caregivers' reports. In a structural equation panel model, bidirectional relations between intrusive parenting and EC were found: EC at 30 and 42 months predicted low levels of intrusive parenting a year later, controlling for prior levels of parenting and vice versa. Moreover, high levels of children's externalizing problems at both 30 and 42 months negatively predicted EC a year later, controlling for prior levels of EC. Although externalizing problems positively predicted high EC over time, this appeared to be a suppression effect because these variables had a strong negative pattern in the zero-order correlations. Moreover, when controlling for the stability of intrusive parenting, EC, and externalizing (all exhibited significant stability across time) and the aforementioned cross-lagged predictive paths, EC and externalizing problems were still negatively related within the 54-month assessment. The findings are consistent with the view that children's externalizing behavior undermines their EC and contributes to intrusive mothering and that relations between intrusive parenting and EC are bidirectional across time. Thus, interventions that focus on modifying children's externalizing problems (as well as the quality of parenting) might affect the quality of parenting they receive and, hence, subsequent problems with adjustment.
Effects and limitations of a nucleobase-driven backmapping procedure for nucleic acids using steered molecular dynamics.

PubMed

Poblete, Simón; Bottaro, Sandro; Bussi, Giovanni

2018-03-29

Coarse-grained models can be of great help to address the problem of structure prediction in nucleic acids. On one hand they can make the prediction more efficient, while on the other hand they can also help to identify the essential degrees of freedom and interactions for the description of a number of structures. With the aim to provide an all-atom representation in an explicit solvent to the predictions of our SPlit and conQueR (SPQR) coarse-grained model of RNA, we recently introduced a backmapping procedure which enforces the predicted structure into an atomistic one by means of steered molecular dynamics. These simulations minimize the ERMSD, a particular metric which deals exclusively with the relative arrangement of nucleobases, between the atomistic representation and the target structure. In this paper, we explore the effects of this approach on the resulting interaction networks and backbone conformations by applying it on a set of fragments using as a target their native structure. We find that the geometry of the target structures can be reliably recovered, with limitations in the regions with unpaired bases such as bulges. In addition, we observe that the folding pathway can also change depending on the parameters used in the definition of the ERMSD and the use of other metrics such as the RMSD. Copyright © 2017 Elsevier Inc. All rights reserved.
RNA Secondary Structure Prediction by Using Discrete Mathematics: An Interdisciplinary Research Experience for Undergraduate Students

PubMed Central

Ellington, Roni; Wachira, James

2010-01-01

The focus of this Research Experience for Undergraduates (REU) project was on RNA secondary structure prediction by using a lattice walk approach. The lattice walk approach is a combinatorial and computational biology method used to enumerate possible secondary structures and predict RNA secondary structure from RNA sequences. The method uses discrete mathematical techniques and identifies specified base pairs as parameters. The goal of the REU was to introduce upper-level undergraduate students to the principles and challenges of interdisciplinary research in molecular biology and discrete mathematics. At the beginning of the project, students from the biology and mathematics departments of a mid-sized university received instruction on the role of secondary structure in the function of eukaryotic RNAs and RNA viruses, RNA related to combinatorics, and the National Center for Biotechnology Information resources. The student research projects focused on RNA secondary structure prediction on a regulatory region of the yellow fever virus RNA genome and on an untranslated region of an mRNA of a gene associated with the neurological disorder epilepsy. At the end of the project, the REU students gave poster and oral presentations, and they submitted written final project reports to the program director. The outcome of the REU was that the students gained transferable knowledge and skills in bioinformatics and an awareness of the applications of discrete mathematics to biological research problems. PMID:20810968
RNA secondary structure prediction by using discrete mathematics: an interdisciplinary research experience for undergraduate students.

PubMed

Ellington, Roni; Wachira, James; Nkwanta, Asamoah

2010-01-01

The focus of this Research Experience for Undergraduates (REU) project was on RNA secondary structure prediction by using a lattice walk approach. The lattice walk approach is a combinatorial and computational biology method used to enumerate possible secondary structures and predict RNA secondary structure from RNA sequences. The method uses discrete mathematical techniques and identifies specified base pairs as parameters. The goal of the REU was to introduce upper-level undergraduate students to the principles and challenges of interdisciplinary research in molecular biology and discrete mathematics. At the beginning of the project, students from the biology and mathematics departments of a mid-sized university received instruction on the role of secondary structure in the function of eukaryotic RNAs and RNA viruses, RNA related to combinatorics, and the National Center for Biotechnology Information resources. The student research projects focused on RNA secondary structure prediction on a regulatory region of the yellow fever virus RNA genome and on an untranslated region of an mRNA of a gene associated with the neurological disorder epilepsy. At the end of the project, the REU students gave poster and oral presentations, and they submitted written final project reports to the program director. The outcome of the REU was that the students gained transferable knowledge and skills in bioinformatics and an awareness of the applications of discrete mathematics to biological research problems.
Advanced computational techniques for incompressible/compressible fluid-structure interactions

NASA Astrophysics Data System (ADS)

Kumar, Vinod

2005-07-01

Fluid-Structure Interaction (FSI) problems are of great importance to many fields of engineering and pose tremendous challenges to numerical analyst. This thesis addresses some of the hurdles faced for both 2D and 3D real life time-dependent FSI problems with particular emphasis on parachute systems. The techniques developed here would help improve the design of parachutes and are of direct relevance to several other FSI problems. The fluid system is solved using the Deforming-Spatial-Domain/Stabilized Space-Time (DSD/SST) finite element formulation for the Navier-Stokes equations of incompressible and compressible flows. The structural dynamics solver is based on a total Lagrangian finite element formulation. Newton-Raphson method is employed to linearize the otherwise nonlinear system resulting from the fluid and structure formulations. The fluid and structural systems are solved in decoupled fashion at each nonlinear iteration. While rigorous coupling methods are desirable for FSI simulations, the decoupled solution techniques provide sufficient convergence in the time-dependent problems considered here. In this thesis, common problems in the FSI simulations of parachutes are discussed and possible remedies for a few of them are presented. Further, the effects of the porosity model on the aerodynamic forces of round parachutes are analyzed. Techniques for solving compressible FSI problems are also discussed. Subsequently, a better stabilization technique is proposed to efficiently capture and accurately predict the shocks in supersonic flows. The numerical examples simulated here require high performance computing. Therefore, numerical tools using distributed memory supercomputers with message passing interface (MPI) libraries were developed.
I-TASSER: fully automated protein structure prediction in CASP8.

PubMed

Zhang, Yang

2009-01-01

The I-TASSER algorithm for 3D protein structure prediction was tested in CASP8, with the procedure fully automated in both the Server and Human sections. The quality of the server models is close to that of human ones but the human predictions incorporate more diverse templates from other servers which improve the human predictions in some of the distant homology targets. For the first time, the sequence-based contact predictions from machine learning techniques are found helpful for both template-based modeling (TBM) and template-free modeling (FM). In TBM, although the accuracy of the sequence based contact predictions is on average lower than that from template-based ones, the novel contacts in the sequence-based predictions, which are complementary to the threading templates in the weakly or unaligned regions, are important to improve the global and local packing in these regions. Moreover, the newly developed atomic structural refinement algorithm was tested in CASP8 and found to improve the hydrogen-bonding networks and the overall TM-score, which is mainly due to its ability of removing steric clashes so that the models can be generated from cluster centroids. Nevertheless, one of the major issues of the I-TASSER pipeline is the model selection where the best models could not be appropriately recognized when the correct templates are detected only by the minority of the threading algorithms. There are also problems related with domain-splitting and mirror image recognition which mainly influences the performance of I-TASSER modeling in the FM-based structure predictions. Copyright 2009 Wiley-Liss, Inc.
Integration of Structural Dynamics and Molecular Evolution via Protein Interaction Networks: A New Era in Genomic Medicine

PubMed Central

Kumar, Avishek; Butler, Brandon M.; Kumar, Sudhir; Ozkan, S. Banu

2016-01-01

Summary Sequencing technologies are revealing many new non-synonymous single nucleotide variants (nsSNVs) in each personal exome. To assess their functional impacts, comparative genomics is frequently employed to predict if they are benign or not. However, evolutionary analysis alone is insufficient, because it misdiagnoses many disease-associated nsSNVs, such as those at positions involved in protein interfaces, and because evolutionary predictions do not provide mechanistic insights into functional change or loss. Structural analyses can aid in overcoming both of these problems by incorporating conformational dynamics and allostery in nSNV diagnosis. Finally, protein-protein interaction networks using systems-level methodologies shed light onto disease etiology and pathogenesis. Bridging these network approaches with structurally resolved protein interactions and dynamics will advance genomic medicine. PMID:26684487
Contact and Impact Dynamic Modeling Capabilities of LS-DYNA for Fluid-Structure Interaction Problems

DTIC Science & Technology

2010-12-02

rigid sphere in a vertical water entry,” Applied Ocean Research, 13(1), pp. 43-48. Monaghan, J.J., 1994. “ Simulating free surface flows with SPH ...The kinematic free surface condition was used to determine the intersection between the free surface and the body in the outer flow domain...and the results were compared with analytical and numerical predictions. The predictive capability of ALE and SPH features of LS-DYNA for simulation
Improved predictions of atmospheric icing in Norway

NASA Astrophysics Data System (ADS)

Engdahl, Bjørg Jenny; Nygaard, Bjørn Egil; Thompson, Gregory; Bengtsson, Lisa; Berntsen, Terje

2017-04-01

Atmospheric icing of ground structures is a problem in cold climate locations such as Norway. During the 2013/2014 winter season two major power lines in southern Norway suffered severe damage due to ice loads exceeding their design values by two to three times. Better methods are needed to estimate the ice loads that affect various infrastructure, and better models are needed to improve the prediction of severe icing events. The Wind, Ice and Snow loads Impact on Infrastructure and the Natural Environment (WISLINE) project, was initiated to address this problem and to explore how a changing climate may affect the ice loads in Norway. Creating better forecasts of icing requires a proper simulation of supercooled liquid water (SLW). Preliminary results show that the operational numerical weather prediction model (HARMONIE-AROME) at MET-Norway generates considerably lower values of SLW as compared with the WRF model when run with the Thompson microphysics scheme. Therefore, we are piecewise implementing specific processes found in the Thompson scheme into the AROME model and testing the resulting impacts to prediction of SLW and structural icing. Both idealized and real icing cases are carried out to test the newly modified AROME microphysics scheme. Besides conventional observations, a unique set of specialized instrumentation for icing measurements are used for validation. Initial results of this investigation will be presented at the conference.
Design of Biomedical Robots for Phenotype Prediction Problems

PubMed Central

deAndrés-Galiana, Enrique J.; Sonis, Stephen T.

2016-01-01

Abstract Genomics has been used with varying degrees of success in the context of drug discovery and in defining mechanisms of action for diseases like cancer and neurodegenerative and rare diseases in the quest for orphan drugs. To improve its utility, accuracy, and cost-effectiveness optimization of analytical methods, especially those that translate to clinically relevant outcomes, is critical. Here we define a novel tool for genomic analysis termed a biomedical robot in order to improve phenotype prediction, identifying disease pathogenesis and significantly defining therapeutic targets. Biomedical robot analytics differ from historical methods in that they are based on melding feature selection methods and ensemble learning techniques. The biomedical robot mathematically exploits the structure of the uncertainty space of any classification problem conceived as an ill-posed optimization problem. Given a classifier, there exist different equivalent small-scale genetic signatures that provide similar predictive accuracies. We perform the sensitivity analysis to noise of the biomedical robot concept using synthetic microarrays perturbed by different kinds of noises in expression and class assignment. Finally, we show the application of this concept to the analysis of different diseases, inferring the pathways and the correlation networks. The final aim of a biomedical robot is to improve knowledge discovery and provide decision systems to optimize diagnosis, treatment, and prognosis. This analysis shows that the biomedical robots are robust against different kinds of noises and particularly to a wrong class assignment of the samples. Assessing the uncertainty that is inherent to any phenotype prediction problem is the right way to address this kind of problem. PMID:27347715
Design of Biomedical Robots for Phenotype Prediction Problems.

PubMed

deAndrés-Galiana, Enrique J; Fernández-Martínez, Juan Luis; Sonis, Stephen T

2016-08-01

Genomics has been used with varying degrees of success in the context of drug discovery and in defining mechanisms of action for diseases like cancer and neurodegenerative and rare diseases in the quest for orphan drugs. To improve its utility, accuracy, and cost-effectiveness optimization of analytical methods, especially those that translate to clinically relevant outcomes, is critical. Here we define a novel tool for genomic analysis termed a biomedical robot in order to improve phenotype prediction, identifying disease pathogenesis and significantly defining therapeutic targets. Biomedical robot analytics differ from historical methods in that they are based on melding feature selection methods and ensemble learning techniques. The biomedical robot mathematically exploits the structure of the uncertainty space of any classification problem conceived as an ill-posed optimization problem. Given a classifier, there exist different equivalent small-scale genetic signatures that provide similar predictive accuracies. We perform the sensitivity analysis to noise of the biomedical robot concept using synthetic microarrays perturbed by different kinds of noises in expression and class assignment. Finally, we show the application of this concept to the analysis of different diseases, inferring the pathways and the correlation networks. The final aim of a biomedical robot is to improve knowledge discovery and provide decision systems to optimize diagnosis, treatment, and prognosis. This analysis shows that the biomedical robots are robust against different kinds of noises and particularly to a wrong class assignment of the samples. Assessing the uncertainty that is inherent to any phenotype prediction problem is the right way to address this kind of problem.
Statistical energy analysis computer program, user's guide

NASA Technical Reports Server (NTRS)

Trudell, R. W.; Yano, L. I.

1981-01-01

A high frequency random vibration analysis, (statistical energy analysis (SEA) method) is examined. The SEA method accomplishes high frequency prediction of arbitrary structural configurations. A general SEA computer program is described. A summary of SEA theory, example problems of SEA program application, and complete program listing are presented.
Structural mechanics of seed deterioration: Standing the test of time

USDA-ARS?s Scientific Manuscript database

Seeds die unexpectedly during storage and current understanding of seed quality and storage conditions does not allow reliable means to predict or prevent this critical problem. Chemical degradation of seed components likely occurs through oxidative damage, but the rate of these reactions is domina...
DeepQA: improving the estimation of single protein model quality with deep belief networks.

PubMed

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

2016-12-05

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

NASA Astrophysics Data System (ADS)

Keizer, Richard L.; Jun, Heesung; Dunn, Stanley M.

1991-04-01

We have developed a structured light system for noncontact 3-D measurement of human body surface areas and volumes. We illustrate the image processing steps and algorithms used to recover range data from a single camera image, reconstruct a complete surface from one or more sets of range data, and measure areas and volumes. The development of a working system required the solution to a number of practical problems in image processing and grid labeling (the stereo correspondence problem for structured light). In many instances we found that the standard cookbook techniques for image processing failed. This was due in part to the domain (human body), the restrictive assumptions of the models underlying the cookbook techniques, and the inability to consistently predict the outcome of the image processing operations. In this paper, we will discuss some of our successes and failures in two key steps in acquiring range data using structured light: First, the problem of detecting intersections in the structured light grid, and secondly, the problem of establishing correspondence between projected and detected intersections. We will outline the problems and solutions we have arrived at after several years of trial and error. We can now measure range data with an r.m.s. relative error of 0.3% and measure areas on the human body surface within 3% and volumes within 10%. We have found that the solution to building a working vision system requires the right combination of theory and experimental verification.
HEAP: Heat Energy Analysis Program, a computer model simulating solar receivers. [solving the heat transfer problem

NASA Technical Reports Server (NTRS)

Lansing, F. L.

1979-01-01

A computer program which can distinguish between different receiver designs, and predict transient performance under variable solar flux, or ambient temperatures, etc. has a basic structure that fits a general heat transfer problem, but with specific features that are custom-made for solar receivers. The code is written in MBASIC computer language. The methodology followed in solving the heat transfer problem is explained. A program flow chart, an explanation of input and output tables, and an example of the simulation of a cavity-type solar receiver are included.
Geography program, design, structure and operational strategy

NASA Technical Reports Server (NTRS)

Alexander, R. H.

1970-01-01

The geography program is designed to move systematically toward a capability to increase remote sensing data into operational systems for monitoring land use and related environmental change. The problems of environmental imbalance arising from rapid urbanization and other dramatic changes in land use are considered. These overall problems translate into working level problems of establishing the validity of various sensor-data combinations that will best obtain the regional land use and environmental information. The goal, to better understand, predict, and assist policy makers to regulate urban and regional land use changes resulting from population growth and technological advancement, is put forth.
Proteins and Their Interacting Partners: An Introduction to Protein-Ligand Binding Site Prediction Methods.

PubMed

Roche, Daniel Barry; Brackenridge, Danielle Allison; McGuffin, Liam James

2015-12-15

Elucidating the biological and biochemical roles of proteins, and subsequently determining their interacting partners, can be difficult and time consuming using in vitro and/or in vivo methods, and consequently the majority of newly sequenced proteins will have unknown structures and functions. However, in silico methods for predicting protein-ligand binding sites and protein biochemical functions offer an alternative practical solution. The characterisation of protein-ligand binding sites is essential for investigating new functional roles, which can impact the major biological research spheres of health, food, and energy security. In this review we discuss the role in silico methods play in 3D modelling of protein-ligand binding sites, along with their role in predicting biochemical functionality. In addition, we describe in detail some of the key alternative in silico prediction approaches that are available, as well as discussing the Critical Assessment of Techniques for Protein Structure Prediction (CASP) and the Continuous Automated Model EvaluatiOn (CAMEO) projects, and their impact on developments in the field. Furthermore, we discuss the importance of protein function prediction methods for tackling 21st century problems.
Comparative Protein Structure Modeling Using MODELLER.

PubMed

Webb, Benjamin; Sali, Andrej

2014-09-08

Functional characterization of a protein sequence is one of the most frequent problems in biology. This task is usually facilitated by accurate three-dimensional (3-D) structure of the studied protein. In the absence of an experimentally determined structure, comparative or homology modeling can sometimes provide a useful 3-D model for a protein that is related to at least one known protein structure. Comparative modeling predicts the 3-D structure of a given protein sequence (target) based primarily on its alignment to one or more proteins of known structure (templates). The prediction process consists of fold assignment, target-template alignment, model building, and model evaluation. This unit describes how to calculate comparative models using the program MODELLER and discusses all four steps of comparative modeling, frequently observed errors, and some applications. Modeling lactate dehydrogenase from Trichomonas vaginalis (TvLDH) is described as an example. The download and installation of the MODELLER software is also described. Copyright © 2014 John Wiley & Sons, Inc.
Novel nonlinear knowledge-based mean force potentials based on machine learning.

PubMed

Dong, Qiwen; Zhou, Shuigeng

2011-01-01

The prediction of 3D structures of proteins from amino acid sequences is one of the most challenging problems in molecular biology. An essential task for solving this problem with coarse-grained models is to deduce effective interaction potentials. The development and evaluation of new energy functions is critical to accurately modeling the properties of biological macromolecules. Knowledge-based mean force potentials are derived from statistical analysis of proteins of known structures. Current knowledge-based potentials are almost in the form of weighted linear sum of interaction pairs. In this study, a class of novel nonlinear knowledge-based mean force potentials is presented. The potential parameters are obtained by nonlinear classifiers, instead of relative frequencies of interaction pairs against a reference state or linear classifiers. The support vector machine is used to derive the potential parameters on data sets that contain both native structures and decoy structures. Five knowledge-based mean force Boltzmann-based or linear potentials are introduced and their corresponding nonlinear potentials are implemented. They are the DIH potential (single-body residue-level Boltzmann-based potential), the DFIRE-SCM potential (two-body residue-level Boltzmann-based potential), the FS potential (two-body atom-level Boltzmann-based potential), the HR potential (two-body residue-level linear potential), and the T32S3 potential (two-body atom-level linear potential). Experiments are performed on well-established decoy sets, including the LKF data set, the CASP7 data set, and the Decoys “R”Us data set. The evaluation metrics include the energy Z score and the ability of each potential to discriminate native structures from a set of decoy structures. Experimental results show that all nonlinear potentials significantly outperform the corresponding Boltzmann-based or linear potentials, and the proposed discriminative framework is effective in developing knowledge-based mean force potentials. The nonlinear potentials can be widely used for ab initio protein structure prediction, model quality assessment, protein docking, and other challenging problems in computational biology.

Langley's CSI evolutionary model: Phase 2

NASA Technical Reports Server (NTRS)

Horta, Lucas G.; Reaves, Mercedes C.; Elliott, Kenny B.; Belvin, W. Keith; Teter, John E.

1995-01-01

Phase 2 testbed is part of a sequence of laboratory models, developed at NASA Langley Research Center, to enhance our understanding on how to model, control, and design structures for space applications. A key problem with structures that must perform in space is the appearance of unwanted vibrations during operations. Instruments, design independently by different scientists, must share the same vehicle causing them to interact with each other. Once in space, these problems are difficult to correct and therefore, prediction via analysis design, and experiments is very important. Phase 2 laboratory model and its predecessors are designed to fill a gap between theory and practice and to aid in understanding important aspects in modeling, sensor and actuator technology, ground testing techniques, and control design issues. This document provides detailed information on the truss structure and its main components, control computer architecture, and structural models generated along with corresponding experimental results.
A matrix-free implicit unstructured multigrid finite volume method for simulating structural dynamics and fluid structure interaction

NASA Astrophysics Data System (ADS)

Lv, X.; Zhao, Y.; Huang, X. Y.; Xia, G. H.; Su, X. H.

2007-07-01

A new three-dimensional (3D) matrix-free implicit unstructured multigrid finite volume (FV) solver for structural dynamics is presented in this paper. The solver is first validated using classical 2D and 3D cantilever problems. It is shown that very accurate predictions of the fundamental natural frequencies of the problems can be obtained by the solver with fast convergence rates. This method has been integrated into our existing FV compressible solver [X. Lv, Y. Zhao, et al., An efficient parallel/unstructured-multigrid preconditioned implicit method for simulating 3d unsteady compressible flows with moving objects, Journal of Computational Physics 215(2) (2006) 661-690] based on the immersed membrane method (IMM) [X. Lv, Y. Zhao, et al., as mentioned above]. Results for the interaction between the fluid and an immersed fixed-free cantilever are also presented to demonstrate the potential of this integrated fluid-structure interaction approach.
A methodology for the assessment of manned flight simulator fidelity

NASA Technical Reports Server (NTRS)

Hess, Ronald A.; Malsbury, Terry N.

1989-01-01

A relatively simple analytical methodology for assessing the fidelity of manned flight simulators for specific vehicles and tasks is offered. The methodology is based upon an application of a structural model of the human pilot, including motion cue effects. In particular, predicted pilot/vehicle dynamic characteristics are obtained with and without simulator limitations. A procedure for selecting model parameters can be implemented, given a probable pilot control strategy. In analyzing a pair of piloting tasks for which flight and simulation data are available, the methodology correctly predicted the existence of simulator fidelity problems. The methodology permitted the analytical evaluation of a change in simulator characteristics and indicated that a major source of the fidelity problems was a visual time delay in the simulation.
A benchmark testing ground for integrating homology modeling and protein docking.

PubMed

Bohnuud, Tanggis; Luo, Lingqi; Wodak, Shoshana J; Bonvin, Alexandre M J J; Weng, Zhiping; Vajda, Sandor; Schueler-Furman, Ora; Kozakov, Dima

2017-01-01

Protein docking procedures carry out the task of predicting the structure of a protein-protein complex starting from the known structures of the individual protein components. More often than not, however, the structure of one or both components is not known, but can be derived by homology modeling on the basis of known structures of related proteins deposited in the Protein Data Bank (PDB). Thus, the problem is to develop methods that optimally integrate homology modeling and docking with the goal of predicting the structure of a complex directly from the amino acid sequences of its component proteins. One possibility is to use the best available homology modeling and docking methods. However, the models built for the individual subunits often differ to a significant degree from the bound conformation in the complex, often much more so than the differences observed between free and bound structures of the same protein, and therefore additional conformational adjustments, both at the backbone and side chain levels need to be modeled to achieve an accurate docking prediction. In particular, even homology models of overall good accuracy frequently include localized errors that unfavorably impact docking results. The predicted reliability of the different regions in the model can also serve as a useful input for the docking calculations. Here we present a benchmark dataset that should help to explore and solve combined modeling and docking problems. This dataset comprises a subset of the experimentally solved 'target' complexes from the widely used Docking Benchmark from the Weng Lab (excluding antibody-antigen complexes). This subset is extended to include the structures from the PDB related to those of the individual components of each complex, and hence represent potential templates for investigating and benchmarking integrated homology modeling and docking approaches. Template sets can be dynamically customized by specifying ranges in sequence similarity and in PDB release dates, or using other filtering options, such as excluding sets of specific structures from the template list. Multiple sequence alignments, as well as structural alignments of the templates to their corresponding subunits in the target are also provided. The resource is accessible online or can be downloaded at http://cluspro.org/benchmark, and is updated on a weekly basis in synchrony with new PDB releases. Proteins 2016; 85:10-16. © 2016 Wiley Periodicals, Inc. © 2016 Wiley Periodicals, Inc.
A Novel Information-Theoretic Approach for Variable Clustering and Predictive Modeling Using Dirichlet Process Mixtures

PubMed Central

Chen, Yun; Yang, Hui

2016-01-01

In the era of big data, there are increasing interests on clustering variables for the minimization of data redundancy and the maximization of variable relevancy. Existing clustering methods, however, depend on nontrivial assumptions about the data structure. Note that nonlinear interdependence among variables poses significant challenges on the traditional framework of predictive modeling. In the present work, we reformulate the problem of variable clustering from an information theoretic perspective that does not require the assumption of data structure for the identification of nonlinear interdependence among variables. Specifically, we propose the use of mutual information to characterize and measure nonlinear correlation structures among variables. Further, we develop Dirichlet process (DP) models to cluster variables based on the mutual-information measures among variables. Finally, orthonormalized variables in each cluster are integrated with group elastic-net model to improve the performance of predictive modeling. Both simulation and real-world case studies showed that the proposed methodology not only effectively reveals the nonlinear interdependence structures among variables but also outperforms traditional variable clustering algorithms such as hierarchical clustering. PMID:27966581
A Novel Information-Theoretic Approach for Variable Clustering and Predictive Modeling Using Dirichlet Process Mixtures.

PubMed

Chen, Yun; Yang, Hui

2016-12-14

In the era of big data, there are increasing interests on clustering variables for the minimization of data redundancy and the maximization of variable relevancy. Existing clustering methods, however, depend on nontrivial assumptions about the data structure. Note that nonlinear interdependence among variables poses significant challenges on the traditional framework of predictive modeling. In the present work, we reformulate the problem of variable clustering from an information theoretic perspective that does not require the assumption of data structure for the identification of nonlinear interdependence among variables. Specifically, we propose the use of mutual information to characterize and measure nonlinear correlation structures among variables. Further, we develop Dirichlet process (DP) models to cluster variables based on the mutual-information measures among variables. Finally, orthonormalized variables in each cluster are integrated with group elastic-net model to improve the performance of predictive modeling. Both simulation and real-world case studies showed that the proposed methodology not only effectively reveals the nonlinear interdependence structures among variables but also outperforms traditional variable clustering algorithms such as hierarchical clustering.
Application of firefly algorithm to the dynamic model updating problem

NASA Astrophysics Data System (ADS)

Shabbir, Faisal; Omenzetter, Piotr

2015-04-01

Model updating can be considered as a branch of optimization problems in which calibration of the finite element (FE) model is undertaken by comparing the modal properties of the actual structure with these of the FE predictions. The attainment of a global solution in a multi dimensional search space is a challenging problem. The nature-inspired algorithms have gained increasing attention in the previous decade for solving such complex optimization problems. This study applies the novel Firefly Algorithm (FA), a global optimization search technique, to a dynamic model updating problem. This is to the authors' best knowledge the first time FA is applied to model updating. The working of FA is inspired by the flashing characteristics of fireflies. Each firefly represents a randomly generated solution which is assigned brightness according to the value of the objective function. The physical structure under consideration is a full scale cable stayed pedestrian bridge with composite bridge deck. Data from dynamic testing of the bridge was used to correlate and update the initial model by using FA. The algorithm aimed at minimizing the difference between the natural frequencies and mode shapes of the structure. The performance of the algorithm is analyzed in finding the optimal solution in a multi dimensional search space. The paper concludes with an investigation of the efficacy of the algorithm in obtaining a reference finite element model which correctly represents the as-built original structure.
Accelerated probabilistic inference of RNA structure evolution

PubMed Central

Holmes, Ian

2005-01-01

Background Pairwise stochastic context-free grammars (Pair SCFGs) are powerful tools for evolutionary analysis of RNA, including simultaneous RNA sequence alignment and secondary structure prediction, but the associated algorithms are intensive in both CPU and memory usage. The same problem is faced by other RNA alignment-and-folding algorithms based on Sankoff's 1985 algorithm. It is therefore desirable to constrain such algorithms, by pre-processing the sequences and using this first pass to limit the range of structures and/or alignments that can be considered. Results We demonstrate how flexible classes of constraint can be imposed, greatly reducing the computational costs while maintaining a high quality of structural homology prediction. Any score-attributed context-free grammar (e.g. energy-based scoring schemes, or conditionally normalized Pair SCFGs) is amenable to this treatment. It is now possible to combine independent structural and alignment constraints of unprecedented general flexibility in Pair SCFG alignment algorithms. We outline several applications to the bioinformatics of RNA sequence and structure, including Waterman-Eggert N-best alignments and progressive multiple alignment. We evaluate the performance of the algorithm on test examples from the RFAM database. Conclusion A program, Stemloc, that implements these algorithms for efficient RNA sequence alignment and structure prediction is available under the GNU General Public License. PMID:15790387
Towards Practical Carbonation Prediction and Modelling for Service Life Design of Reinforced Concrete Structures

NASA Astrophysics Data System (ADS)

Ekolu, O. S.

2015-11-01

Amongst the scientific community, the interest in durability of concrete structures has been high for quite a long time of over 40 years. Of the various causes of degradation of concrete structures, corrosion is the most widespread durability problem and carbonation is one of the two causes of steel reinforcement corrosion. While much scientific understanding has been gained from the numerous carbonation studies undertaken over the past years, it is still presently not possible to accurately predict carbonation and apply it in design of structures. This underscores the complex nature of the mechanisms as influenced by several interactive factors. Based on critical literature and some experience of the author, it is found that there still exist major challenges in establishing a mathematical constitutive relation for realistic carbonation prediction. While most current models employ permeability /diffusion as the main model property, analysis shows that the most practical material property would be compressive strength, which has a low coefficient of variation of 20% compared to 30 to 50% for permeability. This important characteristic of compressive strength, combined with its merit of simplicity and data availability at all stages of a structure's life, promote its potential use in modelling over permeability. By using compressive strength in carbonation prediction, the need for accelerated testing and permeability measurement can be avoided. This paper attempts to examine the issues associated with carbonation prediction, which could underlie the current lack of a sound established prediction method. Suggestions are then made for possible employment of different or alternative approaches.
Guiding Conformation Space Search with an All-Atom Energy Potential

PubMed Central

Brunette, TJ; Brock, Oliver

2009-01-01

The most significant impediment for protein structure prediction is the inadequacy of conformation space search. Conformation space is too large and the energy landscape too rugged for existing search methods to consistently find near-optimal minima. To alleviate this problem, we present model-based search, a novel conformation space search method. Model-based search uses highly accurate information obtained during search to build an approximate, partial model of the energy landscape. Model-based search aggregates information in the model as it progresses, and in turn uses this information to guide exploration towards regions most likely to contain a near-optimal minimum. We validate our method by predicting the structure of 32 proteins, ranging in length from 49 to 213 amino acids. Our results demonstrate that model-based search is more effective at finding low-energy conformations in high-dimensional conformation spaces than existing search methods. The reduction in energy translates into structure predictions of increased accuracy. PMID:18536015
Classification of Chemicals Based On Structured Toxicity ...

EPA Pesticide Factsheets

Thirty years and millions of dollars worth of pesticide registration toxicity studies, historically stored as hardcopy and scanned documents, have been digitized into highly standardized and structured toxicity data within the Toxicity Reference Database (ToxRefDB). Toxicity-based classifications of chemicals were performed as a model application of ToxRefDB. These endpoints will ultimately provide the anchoring toxicity information for the development of predictive models and biological signatures utilizing in vitro assay data. Utilizing query and structured data mining approaches, toxicity profiles were uniformly generated for greater than 300 chemicals. Based on observation rate, species concordance and regulatory relevance, individual and aggregated effects have been selected to classify the chemicals providing a set of predictable endpoints. ToxRefDB exhibits the utility of transforming unstructured toxicity data into structured data and, furthermore, into computable outputs, and serves as a model for applying such data to address modern toxicological problems.
Predicting Gene Structures from Multiple RT-PCR Tests

NASA Astrophysics Data System (ADS)

Kováč, Jakub; Vinař, Tomáš; Brejová, Broňa

It has been demonstrated that the use of additional information such as ESTs and protein homology can significantly improve accuracy of gene prediction. However, many sources of external information are still being omitted from consideration. Here, we investigate the use of product lengths from RT-PCR experiments in gene finding. We present hardness results and practical algorithms for several variants of the problem and apply our methods to a real RT-PCR data set in the Drosophila genome. We conclude that the use of RT-PCR data can improve the sensitivity of gene prediction and locate novel splicing variants.
Interference Path Loss Prediction in A319/320 Airplanes Using Modulated Fuzzy Logic and Neural Networks

NASA Technical Reports Server (NTRS)

Jafri, Madiha J.; Ely, Jay J.; Vahala, Linda L.

2007-01-01

In this paper, neural network (NN) modeling is combined with fuzzy logic to estimate Interference Path Loss measurements on Airbus 319 and 320 airplanes. Interference patterns inside the aircraft are classified and predicted based on the locations of the doors, windows, aircraft structures and the communication/navigation system-of-concern. Modeled results are compared with measured data. Combining fuzzy logic and NN modeling is shown to improve estimates of measured data over estimates obtained with NN alone. A plan is proposed to enhance the modeling for better prediction of electromagnetic coupling problems inside aircraft.
Integrated identification, modeling and control with applications

NASA Astrophysics Data System (ADS)

Shi, Guojun

This thesis deals with the integration of system design, identification, modeling and control. In particular, six interdisciplinary engineering problems are addressed and investigated. Theoretical results are established and applied to structural vibration reduction and engine control problems. First, the data-based LQG control problem is formulated and solved. It is shown that a state space model is not necessary to solve this problem; rather a finite sequence from the impulse response is the only model data required to synthesize an optimal controller. The new theory avoids unnecessary reliance on a model, required in the conventional design procedure. The infinite horizon model predictive control problem is addressed for multivariable systems. The basic properties of the receding horizon implementation strategy is investigated and the complete framework for solving the problem is established. The new theory allows the accommodation of hard input constraints and time delays. The developed control algorithms guarantee the closed loop stability. A closed loop identification and infinite horizon model predictive control design procedure is established for engine speed regulation. The developed algorithms are tested on the Cummins Engine Simulator and desired results are obtained. A finite signal-to-noise ratio model is considered for noise signals. An information quality index is introduced which measures the essential information precision required for stabilization. The problems of minimum variance control and covariance control are formulated and investigated. Convergent algorithms are developed for solving the problems of interest. The problem of the integrated passive and active control design is addressed in order to improve the overall system performance. A design algorithm is developed, which simultaneously finds: (i) the optimal values of the stiffness and damping ratios for the structure, and (ii) an optimal output variance constrained stabilizing controller such that the active control energy is minimized. A weighted q-Markov COVER method is introduced for identification with measurement noise. The result is use to develop an iterative closed loop identification/control design algorithm. The effectiveness of the algorithm is illustrated by experimental results.
Predicting Gene Structure Changes Resulting from Genetic Variants via Exon Definition Features.

PubMed

Majoros, William H; Holt, Carson; Campbell, Michael S; Ware, Doreen; Yandell, Mark; Reddy, Timothy E

2018-04-25

Genetic variation that disrupts gene function by altering gene splicing between individuals can substantially influence traits and disease. In those cases, accurately predicting the effects of genetic variation on splicing can be highly valuable for investigating the mechanisms underlying those traits and diseases. While methods have been developed to generate high quality computational predictions of gene structures in reference genomes, the same methods perform poorly when used to predict the potentially deleterious effects of genetic changes that alter gene splicing between individuals. Underlying that discrepancy in predictive ability are the common assumptions by reference gene finding algorithms that genes are conserved, well-formed, and produce functional proteins. We describe a probabilistic approach for predicting recent changes to gene structure that may or may not conserve function. The model is applicable to both coding and noncoding genes, and can be trained on existing gene annotations without requiring curated examples of aberrant splicing. We apply this model to the problem of predicting altered splicing patterns in the genomes of individual humans, and we demonstrate that performing gene-structure prediction without relying on conserved coding features is feasible. The model predicts an unexpected abundance of variants that create de novo splice sites, an observation supported by both simulations and empirical data from RNA-seq experiments. While these de novo splice variants are commonly misinterpreted by other tools as coding or noncoding variants of little or no effect, we find that in some cases they can have large effects on splicing activity and protein products, and we propose that they may commonly act as cryptic factors in disease. The software is available from geneprediction.org/SGRF. bmajoros@duke.edu. Supplementary information is available at Bioinformatics online.
Development and application of a mark-recapture model incorporating predicted sex and transitory behaviour

USGS Publications Warehouse

Conroy, M.J.; Senar, J.C.; Hines, J.E.; Domenech, J.

1999-01-01

We developed an extension of Cormack-Jolly-Seber models to handle a complex mark-recapture problem in which (a) the sex of birds cannot be determined prior to first moult, but can be predicted on the basis of body measurements, and (b) a significant portion of captured birds appear to be transients (i.e. are captured once but leave the area or otherwise become ' untrappable'). We applied this methodology to a data set of 4184 serins (Serinus serinus) trapped in northeastern Spain during 1985-96, in order to investigate age-, sex-, and time-specific variation in survival rates. Using this approach, we were able to successfully incorporate the majority of ringings of serins. Had we eliminated birds not previously captured (as has been advocated to avoid the problem of transience) we would have reduced our sample sizes by >2000 releases. In addition, we were able to include 1610 releases of birds of unknown (but predicted) sex; these data contributed to the precision of our estimates and the power of statistical tests. We discuss problems with data structure, encoding of the algorithms to compute parameter estimates, model selection, identifiability of parameters, and goodness-of-fit, and make recommendations for the design and analysis of future studies facing similar problems.
Development and application of a mark-recapture model incorporating predicted sex and transitory behaviour

USGS Publications Warehouse

Conroy, M.J.; Senar, J.C.; Hines, J.E.; Domenech, J.

1999-01-01

We developed an extension of Cormack-Jolly-Seber models to handle a complex mark-recapture problem in which (a) the sex of birds cannot be determined prior to first moult, but can be predicted on the basis of body measurements, and (b) a significant portion of captured birds appear to be transients (i.e. are captured once but leave the area or otherwise become 'untrappable'). We applied this methodology to a data set of 4184 serins (Serinus serinus) trapped in northeastern Spain during 1985-96, in order to investigate age-, sex-, and time-specific variation in survival rates. Using this approach, we were able to successfully incorporate the majority of ringings of serins. Had we eliminated birds not previously captured (as has been advocated to avoid the problem of transience) we would have reduced our sample sizes by >2000 releases. In addition, we were able to include 1610 releases of birds of unknown (but predicted) sex; these data contributed to the precision of our estimates and the power of statistical tests. We discuss problems with data structure, encoding of the algorithms to compute parameter estimates, model selection, identifiability of parameters, and goodness-of-fit, and make recommendations for the design and analysis of future studies facing similar problems.
CNNH_PSS: protein 8-class secondary structure prediction by convolutional neural network with highway.

PubMed

Zhou, Jiyun; Wang, Hongpeng; Zhao, Zhishan; Xu, Ruifeng; Lu, Qin

2018-05-08

Protein secondary structure is the three dimensional form of local segments of proteins and its prediction is an important problem in protein tertiary structure prediction. Developing computational approaches for protein secondary structure prediction is becoming increasingly urgent. We present a novel deep learning based model, referred to as CNNH_PSS, by using multi-scale CNN with highway. In CNNH_PSS, any two neighbor convolutional layers have a highway to deliver information from current layer to the output of the next one to keep local contexts. As lower layers extract local context while higher layers extract long-range interdependencies, the highways between neighbor layers allow CNNH_PSS to have ability to extract both local contexts and long-range interdependencies. We evaluate CNNH_PSS on two commonly used datasets: CB6133 and CB513. CNNH_PSS outperforms the multi-scale CNN without highway by at least 0.010 Q8 accuracy and also performs better than CNF, DeepCNF and SSpro8, which cannot extract long-range interdependencies, by at least 0.020 Q8 accuracy, demonstrating that both local contexts and long-range interdependencies are indeed useful for prediction. Furthermore, CNNH_PSS also performs better than GSM and DCRNN which need extra complex model to extract long-range interdependencies. It demonstrates that CNNH_PSS not only cost less computer resource, but also achieves better predicting performance. CNNH_PSS have ability to extracts both local contexts and long-range interdependencies by combing multi-scale CNN and highway network. The evaluations on common datasets and comparisons with state-of-the-art methods indicate that CNNH_PSS is an useful and efficient tool for protein secondary structure prediction.
Density functional theory in the solid state

PubMed Central

Hasnip, Philip J.; Refson, Keith; Probert, Matt I. J.; Yates, Jonathan R.; Clark, Stewart J.; Pickard, Chris J.

2014-01-01

Density functional theory (DFT) has been used in many fields of the physical sciences, but none so successfully as in the solid state. From its origins in condensed matter physics, it has expanded into materials science, high-pressure physics and mineralogy, solid-state chemistry and more, powering entire computational subdisciplines. Modern DFT simulation codes can calculate a vast range of structural, chemical, optical, spectroscopic, elastic, vibrational and thermodynamic phenomena. The ability to predict structure–property relationships has revolutionized experimental fields, such as vibrational and solid-state NMR spectroscopy, where it is the primary method to analyse and interpret experimental spectra. In semiconductor physics, great progress has been made in the electronic structure of bulk and defect states despite the severe challenges presented by the description of excited states. Studies are no longer restricted to known crystallographic structures. DFT is increasingly used as an exploratory tool for materials discovery and computational experiments, culminating in ex nihilo crystal structure prediction, which addresses the long-standing difficult problem of how to predict crystal structure polymorphs from nothing but a specified chemical composition. We present an overview of the capabilities of solid-state DFT simulations in all of these topics, illustrated with recent examples using the CASTEP computer program. PMID:24516184
Wind erosion in semiarid landscapes: Predictive models and remote sensing methods for the influence of vegetation

NASA Technical Reports Server (NTRS)

Musick, H. Brad; Truman, C. Randall; Trujillo, Steven M.

1992-01-01

Wind erosion in semi-arid regions is a significant problem for which the sheltering effect of rangeland vegetation is poorly understood. Individual plants may be considered as porous roughness elements which absorb or redistribute the wind's momentum. The saltation threshold is the minimum wind velocity at which soil movement begins. The dependence of the saltation threshold on geometrical parameters of a uniform roughness array was studied in a wind tunnel. Both solid and porous elements were used to determine relationships between canopy structure and the threshold velocity for soil transport. The development of a predictive relation for the influence of vegetation canopy structure on wind erosion of soil is discussed.

Immersed Boundary Methods for Optimization of Strongly Coupled Fluid-Structure Systems

NASA Astrophysics Data System (ADS)

Jenkins, Nicholas J.

Conventional methods for design of tightly coupled multidisciplinary systems, such as fluid-structure interaction (FSI) problems, traditionally rely on manual revisions informed by a loosely coupled linearized analysis. These approaches are both inaccurate for a multitude of applications, and they require an intimate understanding of the assumptions and limitations of the procedure in order to soundly optimize the design. Computational optimization, in particular topology optimization, has been shown to yield remarkable results for problems in solid mechanics using density interpolations schemes. In the context of FSI, however, well defined boundaries play a key role in both the design problem and the mechanical model. Density methods neither accurately represent the material boundary, nor provide a suitable platform to apply appropriate interface conditions. This thesis presents a new framework for shape and topology optimization of FSI problems that uses for the design problem the Level Set method (LSM) to describe the geometry evolution in the optimization process. The Extended Finite Element method (XFEM) is combined with a fictitiously deforming fluid domain (stationary arbitrary Lagrangian-Eulerian method) to predict the FSI response. The novelty of the proposed approach lies in the fact that the XFEM explicitly captures the material boundary defined by the level set iso-surface. Moreover, the XFEM provides a means to discretize the governing equations, and weak immersed boundary conditions are applied with Nitsche's Method to couple the fields. The flow is predicted by the incompressible Navier-Stokes equations, and a finite-deformation solid model is developed and tested for both hyperelastic and linear elastic problems. Transient and stationary numerical examples are presented to validate the FSI model and numerical solver approach. Pertaining to the optimization of FSI problems, the parameters of the discretized level set function are defined as explicit functions of the optimization variables, and the parameteric optimization problem is solved by nonlinear programming methods. The gradients of the objective and constrains are computed by the adjoint method for the global monolithic fluid-solid system. Two types of design problems are explored for optimization of the fluid-structure response: 1) the internal structural topology is varied, preserving the fluid-solid interface geometry, and 2) the fluid-solid interface is manipulated directly, which leads to simultaneously configuring both internal structural topology and outer mold shape. The numerical results show that the LSM-XFEM approach is well suited for designing practical applications, while at the same time reducing the requirement on highly refined mesh resolution compared to traditional density methods. However, these results also emphasize the need for a more robust embedded boundary condition framework. Further, the LSM can exhibit greater dependence on initial design seeding, and can impede design convergence. In particular for the strongly coupled FSI analysis developed here, the thinning and eventual removal of structural members can cause jumps in the evolution of the optimization functions.
Motivation to Change as a Predictor of Treatment Outcome for Adolescent Substance Abusers.

ERIC Educational Resources Information Center

Cady, Mary E.; And Others

1996-01-01

The Problem Recognition Questionnaire (PRQ), administered to 234 adolescents undergoing chemical dependency treatment, was examined with respect to its reliability, factor structure, and predictive validity. Results indicate that the PRQ (a measure of adolescent motivation to change substance-use behaviors) has acceptable reliability and potential…
Predict Dem Bones!

ERIC Educational Resources Information Center

Gray, John S.

1994-01-01

A detailed analysis and computer-based solution to a puzzle addressing the arrangement of dominoes on a grid is presented. The problem is one used in a college-level data structures or algorithms course. The solution uses backtracking to generate all possible answers. Details of the use of backtracking and techniques for mapping abstract problems…
On Correlations, Distances and Error Rates.

ERIC Educational Resources Information Center

Dorans, Neil J.

The nature of the criterion (dependent) variable may play a useful role in structuring a list of classification/prediction problems. Such criteria are continuous in nature, binary dichotomous, or multichotomous. In this paper, discussion is limited to the continuous normally distributed criterion scenarios. For both cases, it is assumed that the…
ANSYS tools in modeling tires

NASA Technical Reports Server (NTRS)

Ali, Ashraf; Lovell, Michael

1995-01-01

This presentation summarizes the capabilities in the ANSYS program that relate to the computational modeling of tires. The power and the difficulties associated with modeling nearly incompressible rubber-like materials using hyperelastic constitutive relationships are highlighted from a developer's point of view. The topics covered include a hyperelastic material constitutive model for rubber-like materials, a general overview of contact-friction capabilities, and the acoustic fluid-structure interaction problem for noise prediction. Brief theoretical development and example problems are presented for each topic.
A class-based link prediction using Distance Dependent Chinese Restaurant Process

NASA Astrophysics Data System (ADS)

Andalib, Azam; Babamir, Seyed Morteza

2016-08-01

One of the important tasks in relational data analysis is link prediction which has been successfully applied on many applications such as bioinformatics, information retrieval, etc. The link prediction is defined as predicting the existence or absence of edges between nodes of a network. In this paper, we propose a novel method for link prediction based on Distance Dependent Chinese Restaurant Process (DDCRP) model which enables us to utilize the information of the topological structure of the network such as shortest path and connectivity of the nodes. We also propose a new Gibbs sampling algorithm for computing the posterior distribution of the hidden variables based on the training data. Experimental results on three real-world datasets show the superiority of the proposed method over other probabilistic models for link prediction problem.
Prediction of bead area contact load at the tire-wheel interface using NASTRAN

NASA Technical Reports Server (NTRS)

Chen, C. H. S.

1982-01-01

The theoretical prediction of the bead area contact load at the tire wheel interface using NASTRAN is reported. The application of the linear code to a basically nonlinear problem results in excessive deformation of the structure and the tire-wheel contact conditions become impossible to achieve. A psuedo-nonlinear approach was adopted in which the moduli of the cord reinforced composite are increased so that the computed key deformations matched that of the experiment. Numerical results presented are discussed.
Large-eddy simulation, fuel rod vibration and grid-to-rod fretting in pressurized water reactors

DOE PAGES

Christon, Mark A.; Lu, Roger; Bakosi, Jozsef; ...

2016-10-01

Grid-to-rod fretting (GTRF) in pressurized water reactors is a flow-induced vibration phenomenon that results in wear and fretting of the cladding material on fuel rods. GTRF is responsible for over 70% of the fuel failures in pressurized water reactors in the United States. Predicting the GTRF wear and concomitant interval between failures is important because of the large costs associated with reactor shutdown and replacement of fuel rod assemblies. The GTRF-induced wear process involves turbulent flow, mechanical vibration, tribology, and time-varying irradiated material properties in complex fuel assembly geometries. This paper presents a new approach for predicting GTRF induced fuelmore » rod wear that uses high-resolution implicit large-eddy simulation to drive nonlinear transient dynamics computations. The GTRF fluid–structure problem is separated into the simulation of the turbulent flow field in the complex-geometry fuel-rod bundles using implicit large-eddy simulation, the calculation of statistics of the resulting fluctuating structural forces, and the nonlinear transient dynamics analysis of the fuel rod. Ultimately, the methods developed here, can be used, in conjunction with operational management, to improve reactor core designs in which fuel rod failures are minimized or potentially eliminated. Furthermore, robustness of the behavior of both the structural forces computed from the turbulent flow simulations and the results from the transient dynamics analyses highlight the progress made towards achieving a predictive simulation capability for the GTRF problem.« less
Large-eddy simulation, fuel rod vibration and grid-to-rod fretting in pressurized water reactors

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

Christon, Mark A.; Lu, Roger; Bakosi, Jozsef

Grid-to-rod fretting (GTRF) in pressurized water reactors is a flow-induced vibration phenomenon that results in wear and fretting of the cladding material on fuel rods. GTRF is responsible for over 70% of the fuel failures in pressurized water reactors in the United States. Predicting the GTRF wear and concomitant interval between failures is important because of the large costs associated with reactor shutdown and replacement of fuel rod assemblies. The GTRF-induced wear process involves turbulent flow, mechanical vibration, tribology, and time-varying irradiated material properties in complex fuel assembly geometries. This paper presents a new approach for predicting GTRF induced fuelmore » rod wear that uses high-resolution implicit large-eddy simulation to drive nonlinear transient dynamics computations. The GTRF fluid–structure problem is separated into the simulation of the turbulent flow field in the complex-geometry fuel-rod bundles using implicit large-eddy simulation, the calculation of statistics of the resulting fluctuating structural forces, and the nonlinear transient dynamics analysis of the fuel rod. Ultimately, the methods developed here, can be used, in conjunction with operational management, to improve reactor core designs in which fuel rod failures are minimized or potentially eliminated. Furthermore, robustness of the behavior of both the structural forces computed from the turbulent flow simulations and the results from the transient dynamics analyses highlight the progress made towards achieving a predictive simulation capability for the GTRF problem.« less
Computational structural mechanics for engine structures

NASA Technical Reports Server (NTRS)

Chamis, C. C.

1989-01-01

The computational structural mechanics (CSM) program at Lewis encompasses: (1) fundamental aspects for formulating and solving structural mechanics problems, and (2) development of integrated software systems to computationally simulate the performance/durability/life of engine structures. It is structured to mainly supplement, complement, and whenever possible replace, costly experimental efforts which are unavoidable during engineering research and development programs. Specific objectives include: investigate unique advantages of parallel and multiprocesses for: reformulating/solving structural mechanics and formulating/solving multidisciplinary mechanics and develop integrated structural system computational simulators for: predicting structural performances, evaluating newly developed methods, and for identifying and prioritizing improved/missing methods needed. Herein the CSM program is summarized with emphasis on the Engine Structures Computational Simulator (ESCS). Typical results obtained using ESCS are described to illustrate its versatility.
Robust Feedback Control of Flow Induced Structural Radiation of Sound

NASA Technical Reports Server (NTRS)

Heatwole, Craig M.; Bernhard, Robert J.; Franchek, Matthew A.

1997-01-01

A significant component of the interior noise of aircraft and automobiles is a result of turbulent boundary layer excitation of the vehicular structure. In this work, active robust feedback control of the noise due to this non-predictable excitation is investigated. Both an analytical model and experimental investigations are used to determine the characteristics of the flow induced structural sound radiation problem. The problem is shown to be broadband in nature with large system uncertainties associated with the various operating conditions. Furthermore the delay associated with sound propagation is shown to restrict the use of microphone feedback. The state of the art control methodologies, IL synthesis and adaptive feedback control, are evaluated and shown to have limited success for solving this problem. A robust frequency domain controller design methodology is developed for the problem of sound radiated from turbulent flow driven plates. The control design methodology uses frequency domain sequential loop shaping techniques. System uncertainty, sound pressure level reduction performance, and actuator constraints are included in the design process. Using this design method, phase lag was added using non-minimum phase zeros such that the beneficial plant dynamics could be used. This general control approach has application to lightly damped vibration and sound radiation problems where there are high bandwidth control objectives requiring a low controller DC gain and controller order.
Validation of a coupled core-transport, pedestal-structure, current-profile and equilibrium model

NASA Astrophysics Data System (ADS)

Meneghini, O.

2015-11-01

The first workflow capable of predicting the self-consistent solution to the coupled core-transport, pedestal structure, and equilibrium problems from first-principles and its experimental tests are presented. Validation with DIII-D discharges in high confinement regimes shows that the workflow is capable of robustly predicting the kinetic profiles from on axis to the separatrix and matching the experimental measurements to within their uncertainty, with no prior knowledge of the pedestal height nor of any measurement of the temperature or pressure. Self-consistent coupling has proven to be essential to match the experimental results, and capture the non-linear physics that governs the core and pedestal solutions. In particular, clear stabilization of the pedestal peeling ballooning instabilities by the global Shafranov shift and destabilization by additional edge bootstrap current, and subsequent effect on the core plasma profiles, have been clearly observed and documented. In our model, self-consistency is achieved by iterating between the TGYRO core transport solver (with NEO and TGLF for neoclassical and turbulent flux), and the pedestal structure predicted by the EPED model. A self-consistent equilibrium is calculated by EFIT, while the ONETWO transport package evolves the current profile and calculates the particle and energy sources. The capabilities of such workflow are shown to be critical for the design of future experiments such as ITER and FNSF, which operate in a regime where the equilibrium, the pedestal, and the core transport problems are strongly coupled, and for which none of these quantities can be assumed to be known. Self-consistent core-pedestal predictions for ITER, as well as initial optimizations, will be presented. Supported by the US Department of Energy under DE-FC02-04ER54698, DE-SC0012652.
ClusPro: an automated docking and discrimination method for the prediction of protein complexes.

PubMed

Comeau, Stephen R; Gatchell, David W; Vajda, Sandor; Camacho, Carlos J

2004-01-01

Predicting protein interactions is one of the most challenging problems in functional genomics. Given two proteins known to interact, current docking methods evaluate billions of docked conformations by simple scoring functions, and in addition to near-native structures yield many false positives, i.e. structures with good surface complementarity but far from the native. We have developed a fast algorithm for filtering docked conformations with good surface complementarity, and ranking them based on their clustering properties. The free energy filters select complexes with lowest desolvation and electrostatic energies. Clustering is then used to smooth the local minima and to select the ones with the broadest energy wells-a property associated with the free energy at the binding site. The robustness of the method was tested on sets of 2000 docked conformations generated for 48 pairs of interacting proteins. In 31 of these cases, the top 10 predictions include at least one near-native complex, with an average RMSD of 5 A from the native structure. The docking and discrimination method also provides good results for a number of complexes that were used as targets in the Critical Assessment of PRedictions of Interactions experiment. The fully automated docking and discrimination server ClusPro can be found at http://structure.bu.edu
Predicting crystal structures and properties of matter under extreme conditions via quantum mechanics: The pressure is on

DOE PAGES

Zurek, Eva; Grochala, Wojciech

2014-11-27

Experimental studies of compressed matter are now routinely conducted at pressures exceeding 1 mln atm (100 GPa) and occasionally they even surpass 10 mln atm (1 TPa). The structure and properties of solids that have been so significantly squeezed differ considerably from those know at ambient pressures (1 atm), often times leading to new and unexpected physics. Chemical reactivity is also substantially altered in the extreme pressure regime. In this feature paper we describe how synergy between theory and experiment can pave the road towards new experimental discoveries. Because chemical rules-of-thumb established at 1 atm often fail to predict themore » structures of solids under high pressure, automated crystal structure prediction (CSP) methods have been increasingly employed. After outlining the most important CSP techniques, we showcase a few examples from the recent literature that exemplify just how useful theory can be as an aid in the interpretation of experimental data, describe exciting theoretical predictions that are guiding experiment, and discuss when the computational methods that are currently routinely employed fail. Lastly, we forecast important problems that will be targeted by theory as theoretical methods undergo rapid development, along with the simultaneous increase of computational power.« less
The discovery of indicator variables for QSAR using inductive logic programming

NASA Astrophysics Data System (ADS)

King, Ross D.; Srinivasan, Ashwin

1997-11-01

A central problem in forming accurate regression equations in QSAR studies isthe selection of appropriate descriptors for the compounds under study. Wedescribe a novel procedure for using inductive logic programming (ILP) todiscover new indicator variables (attributes) for QSAR problems, and show thatthese improve the accuracy of the derived regression equations. ILP techniqueshave previously been shown to work well on drug design problems where thereis a large structural component or where clear comprehensible rules arerequired. However, ILP techniques have had the disadvantage of only being ableto make qualitative predictions (e.g. active, inactive) and not to predictreal numbers (regression). We unify ILP and linear regression techniques togive a QSAR method that has the strength of ILP at describing stericstructure, with the familiarity and power of linear regression. We evaluatedthe utility of this new QSAR technique by examining the prediction ofbiological activity with and without the addition of new structural indicatorvariables formed by ILP. In three out of five datasets examined the additionof ILP variables produced statistically better results (P < 0.01) over theoriginal description. The new ILP variables did not increase the overallcomplexity of the derived QSAR equations and added insight into possiblemechanisms of action. We conclude that ILP can aid in the process of drugdesign.
Analysis of whisker-toughened CMC structural components using an interactive reliability model

NASA Technical Reports Server (NTRS)

Duffy, Stephen F.; Palko, Joseph L.

1992-01-01

Realizing wider utilization of ceramic matrix composites (CMC) requires the development of advanced structural analysis technologies. This article focuses on the use of interactive reliability models to predict component probability of failure. The deterministic William-Warnke failure criterion serves as theoretical basis for the reliability model presented here. The model has been implemented into a test-bed software program. This computer program has been coupled to a general-purpose finite element program. A simple structural problem is presented to illustrate the reliability model and the computer algorithm.
A generative, probabilistic model of local protein structure.

PubMed

Boomsma, Wouter; Mardia, Kanti V; Taylor, Charles C; Ferkinghoff-Borg, Jesper; Krogh, Anders; Hamelryck, Thomas

2008-07-01

Despite significant progress in recent years, protein structure prediction maintains its status as one of the prime unsolved problems in computational biology. One of the key remaining challenges is an efficient probabilistic exploration of the structural space that correctly reflects the relative conformational stabilities. Here, we present a fully probabilistic, continuous model of local protein structure in atomic detail. The generative model makes efficient conformational sampling possible and provides a framework for the rigorous analysis of local sequence-structure correlations in the native state. Our method represents a significant theoretical and practical improvement over the widely used fragment assembly technique by avoiding the drawbacks associated with a discrete and nonprobabilistic approach.
SSME lifetime prediction and verification, integrating environments, structures, materials: The challenge

NASA Technical Reports Server (NTRS)

Ryan, R. S.; Salter, L. D.; Young, G. M., III; Munafo, P. M.

1985-01-01

The planned missions for the space shuttle dictated a unique and technology-extending rocket engine. The high specific impulse requirements in conjunction with a 55-mission lifetime, plus volume and weight constraints, produced unique structural design, manufacturing, and verification requirements. Operations from Earth to orbit produce severe dynamic environments, which couple with the extreme pressure and thermal environments associated with the high performance, creating large low cycle loads and high alternating stresses above endurance limit which result in high sensitivity to alternating stresses. Combining all of these effects resulted in the requirements for exotic materials, which are more susceptible to manufacturing problems, and the use of an all-welded structure. The challenge of integrating environments, dynamics, structures, and materials into a verified SSME structure is discussed. The verification program and developmental flight results are included. The first six shuttle flights had engine performance as predicted with no failures. The engine system has met the basic design challenges.
Support Vector Machines Trained with Evolutionary Algorithms Employing Kernel Adatron for Large Scale Classification of Protein Structures.

PubMed

Arana-Daniel, Nancy; Gallegos, Alberto A; López-Franco, Carlos; Alanís, Alma Y; Morales, Jacob; López-Franco, Adriana

2016-01-01

With the increasing power of computers, the amount of data that can be processed in small periods of time has grown exponentially, as has the importance of classifying large-scale data efficiently. Support vector machines have shown good results classifying large amounts of high-dimensional data, such as data generated by protein structure prediction, spam recognition, medical diagnosis, optical character recognition and text classification, etc. Most state of the art approaches for large-scale learning use traditional optimization methods, such as quadratic programming or gradient descent, which makes the use of evolutionary algorithms for training support vector machines an area to be explored. The present paper proposes an approach that is simple to implement based on evolutionary algorithms and Kernel-Adatron for solving large-scale classification problems, focusing on protein structure prediction. The functional properties of proteins depend upon their three-dimensional structures. Knowing the structures of proteins is crucial for biology and can lead to improvements in areas such as medicine, agriculture and biofuels.
On vital aid: the why, what and how of validation

PubMed Central

Kleywegt, Gerard J.

2009-01-01

Limitations to the data and subjectivity in the structure-determination process may cause errors in macromolecular crystal structures. Appropriate validation techniques may be used to reveal problems in structures, ideally before they are analysed, published or deposited. Additionally, such techniques may be used a posteriori to assess the (relative) merits of a model by potential users. Weak validation methods and statistics assess how well a model reproduces the information that was used in its construction (i.e. experimental data and prior knowledge). Strong methods and statistics, on the other hand, test how well a model predicts data or information that were not used in the structure-determination process. These may be data that were excluded from the process on purpose, general knowledge about macromolecular structure, information about the biological role and biochemical activity of the molecule under study or its mutants or complexes and predictions that are based on the model and that can be tested experimentally. PMID:19171968

Building machine learning force fields for nanoclusters

NASA Astrophysics Data System (ADS)

Zeni, Claudio; Rossi, Kevin; Glielmo, Aldo; Fekete, Ádám; Gaston, Nicola; Baletto, Francesca; De Vita, Alessandro

2018-06-01

We assess Gaussian process (GP) regression as a technique to model interatomic forces in metal nanoclusters by analyzing the performance of 2-body, 3-body, and many-body kernel functions on a set of 19-atom Ni cluster structures. We find that 2-body GP kernels fail to provide faithful force estimates, despite succeeding in bulk Ni systems. However, both 3- and many-body kernels predict forces within an ˜0.1 eV/Å average error even for small training datasets and achieve high accuracy even on out-of-sample, high temperature structures. While training and testing on the same structure always provide satisfactory accuracy, cross-testing on dissimilar structures leads to higher prediction errors, posing an extrapolation problem. This can be cured using heterogeneous training on databases that contain more than one structure, which results in a good trade-off between versatility and overall accuracy. Starting from a 3-body kernel trained this way, we build an efficient non-parametric 3-body force field that allows accurate prediction of structural properties at finite temperatures, following a newly developed scheme [A. Glielmo et al., Phys. Rev. B 95, 214302 (2017)]. We use this to assess the thermal stability of Ni19 nanoclusters at a fractional cost of full ab initio calculations.
Integration of structural dynamics and molecular evolution via protein interaction networks: a new era in genomic medicine.

PubMed

Kumar, Avishek; Butler, Brandon M; Kumar, Sudhir; Ozkan, S Banu

2015-12-01

Sequencing technologies are revealing many new non-synonymous single nucleotide variants (nsSNVs) in each personal exome. To assess their functional impacts, comparative genomics is frequently employed to predict if they are benign or not. However, evolutionary analysis alone is insufficient, because it misdiagnoses many disease-associated nsSNVs, such as those at positions involved in protein interfaces, and because evolutionary predictions do not provide mechanistic insights into functional change or loss. Structural analyses can aid in overcoming both of these problems by incorporating conformational dynamics and allostery in nSNV diagnosis. Finally, protein-protein interaction networks using systems-level methodologies shed light onto disease etiology and pathogenesis. Bridging these network approaches with structurally resolved protein interactions and dynamics will advance genomic medicine. Copyright © 2015 Elsevier Ltd. All rights reserved.
Social Trust Prediction Using Heterogeneous Networks

PubMed Central

HUANG, JIN; NIE, FEIPING; HUANG, HENG; TU, YI-CHENG; LEI, YU

2014-01-01

Along with increasing popularity of social websites, online users rely more on the trustworthiness information to make decisions, extract and filter information, and tag and build connections with other users. However, such social network data often suffer from severe data sparsity and are not able to provide users with enough information. Therefore, trust prediction has emerged as an important topic in social network research. Traditional approaches are primarily based on exploring trust graph topology itself. However, research in sociology and our life experience suggest that people who are in the same social circle often exhibit similar behaviors and tastes. To take advantage of the ancillary information for trust prediction, the challenge then becomes what to transfer and how to transfer. In this article, we address this problem by aggregating heterogeneous social networks and propose a novel joint social networks mining (JSNM) method. Our new joint learning model explores the user-group-level similarity between correlated graphs and simultaneously learns the individual graph structure; therefore, the shared structures and patterns from multiple social networks can be utilized to enhance the prediction tasks. As a result, we not only improve the trust prediction in the target graph but also facilitate other information retrieval tasks in the auxiliary graphs. To optimize the proposed objective function, we use the alternative technique to break down the objective function into several manageable subproblems. We further introduce the auxiliary function to solve the optimization problems with rigorously proved convergence. The extensive experiments have been conducted on both synthetic and real- world data. All empirical results demonstrate the effectiveness of our method. PMID:24729776
Social Trust Prediction Using Heterogeneous Networks.

PubMed

Huang, Jin; Nie, Feiping; Huang, Heng; Tu, Yi-Cheng; Lei, Yu

2013-11-01

Along with increasing popularity of social websites, online users rely more on the trustworthiness information to make decisions, extract and filter information, and tag and build connections with other users. However, such social network data often suffer from severe data sparsity and are not able to provide users with enough information. Therefore, trust prediction has emerged as an important topic in social network research. Traditional approaches are primarily based on exploring trust graph topology itself. However, research in sociology and our life experience suggest that people who are in the same social circle often exhibit similar behaviors and tastes. To take advantage of the ancillary information for trust prediction, the challenge then becomes what to transfer and how to transfer. In this article, we address this problem by aggregating heterogeneous social networks and propose a novel joint social networks mining (JSNM) method. Our new joint learning model explores the user-group-level similarity between correlated graphs and simultaneously learns the individual graph structure; therefore, the shared structures and patterns from multiple social networks can be utilized to enhance the prediction tasks. As a result, we not only improve the trust prediction in the target graph but also facilitate other information retrieval tasks in the auxiliary graphs. To optimize the proposed objective function, we use the alternative technique to break down the objective function into several manageable subproblems. We further introduce the auxiliary function to solve the optimization problems with rigorously proved convergence. The extensive experiments have been conducted on both synthetic and real- world data. All empirical results demonstrate the effectiveness of our method.
Scoring of Side-Chain Packings: An Analysis of Weight Factors and Molecular Dynamics Structures.

PubMed

Colbes, Jose; Aguila, Sergio A; Brizuela, Carlos A

2018-02-26

The protein side-chain packing problem (PSCPP) is a central task in computational protein design. The problem is usually modeled as a combinatorial optimization problem, which consists of searching for a set of rotamers, from a given rotamer library, that minimizes a scoring function (SF). The SF is a weighted sum of terms, that can be decomposed in physics-based and knowledge-based terms. Although there are many methods to obtain approximate solutions for this problem, all of them have similar performances and there has not been a significant improvement in recent years. Studies on protein structure prediction and protein design revealed the limitations of current SFs to achieve further improvements for these two problems. In the same line, a recent work reported a similar result for the PSCPP. In this work, we ask whether or not this negative result regarding further improvements in performance is due to (i) an incorrect weighting of the SFs terms or (ii) the constrained conformation resulting from the protein crystallization process. To analyze these questions, we (i) model the PSCPP as a bi-objective combinatorial optimization problem, optimizing, at the same time, the two most important terms of two SFs of state-of-the-art algorithms and (ii) performed a preprocessing relaxation of the crystal structure through molecular dynamics to simulate the protein in the solvent and evaluated the performance of these two state-of-the-art SFs under these conditions. Our results indicate that (i) no matter what combination of weight factors we use the current SFs will not lead to better performances and (ii) the evaluated SFs will not be able to improve performance on relaxed structures. Furthermore, the experiments revealed that the SFs and the methods are biased toward crystallized structures.
A model for the progressive failure of laminated composite structural components

NASA Technical Reports Server (NTRS)

Allen, D. H.; Lo, D. C.

1991-01-01

Laminated continuous fiber polymeric composites are capable of sustaining substantial load induced microstructural damage prior to component failure. Because this damage eventually leads to catastrophic failure, it is essential to capture the mechanics of progressive damage in any cogent life prediction model. For the past several years the authors have been developing one solution approach to this problem. In this approach the mechanics of matrix cracking and delamination are accounted for via locally averaged internal variables which account for the kinematics of microcracking. Damage progression is predicted by using phenomenologically based damage evolution laws which depend on the load history. The result is a nonlinear and path dependent constitutive model which has previously been implemented to a finite element computer code for analysis of structural components. Using an appropriate failure model, this algorithm can be used to predict component life. In this paper the model will be utilized to demonstrate the ability to predict the load path dependence of the damage and stresses in plates subjected to fatigue loading.
Sphinx: merging knowledge-based and ab initio approaches to improve protein loop prediction

PubMed Central

Marks, Claire; Nowak, Jaroslaw; Klostermann, Stefan; Georges, Guy; Dunbar, James; Shi, Jiye; Kelm, Sebastian

2017-01-01

Abstract Motivation: Loops are often vital for protein function, however, their irregular structures make them difficult to model accurately. Current loop modelling algorithms can mostly be divided into two categories: knowledge-based, where databases of fragments are searched to find suitable conformations and ab initio, where conformations are generated computationally. Existing knowledge-based methods only use fragments that are the same length as the target, even though loops of slightly different lengths may adopt similar conformations. Here, we present a novel method, Sphinx, which combines ab initio techniques with the potential extra structural information contained within loops of a different length to improve structure prediction. Results: We show that Sphinx is able to generate high-accuracy predictions and decoy sets enriched with near-native loop conformations, performing better than the ab initio algorithm on which it is based. In addition, it is able to provide predictions for every target, unlike some knowledge-based methods. Sphinx can be used successfully for the difficult problem of antibody H3 prediction, outperforming RosettaAntibody, one of the leading H3-specific ab initio methods, both in accuracy and speed. Availability and Implementation: Sphinx is available at http://opig.stats.ox.ac.uk/webapps/sphinx. Contact: deane@stats.ox.ac.uk Supplementary information: Supplementary data are available at Bioinformatics online. PMID:28453681
Sphinx: merging knowledge-based and ab initio approaches to improve protein loop prediction.

PubMed

Marks, Claire; Nowak, Jaroslaw; Klostermann, Stefan; Georges, Guy; Dunbar, James; Shi, Jiye; Kelm, Sebastian; Deane, Charlotte M

2017-05-01

Loops are often vital for protein function, however, their irregular structures make them difficult to model accurately. Current loop modelling algorithms can mostly be divided into two categories: knowledge-based, where databases of fragments are searched to find suitable conformations and ab initio, where conformations are generated computationally. Existing knowledge-based methods only use fragments that are the same length as the target, even though loops of slightly different lengths may adopt similar conformations. Here, we present a novel method, Sphinx, which combines ab initio techniques with the potential extra structural information contained within loops of a different length to improve structure prediction. We show that Sphinx is able to generate high-accuracy predictions and decoy sets enriched with near-native loop conformations, performing better than the ab initio algorithm on which it is based. In addition, it is able to provide predictions for every target, unlike some knowledge-based methods. Sphinx can be used successfully for the difficult problem of antibody H3 prediction, outperforming RosettaAntibody, one of the leading H3-specific ab initio methods, both in accuracy and speed. Sphinx is available at http://opig.stats.ox.ac.uk/webapps/sphinx. deane@stats.ox.ac.uk. Supplementary data are available at Bioinformatics online. © The Author 2017. Published by Oxford University Press.
Thermomechanical deformation in the presence of metallurgical changes

NASA Technical Reports Server (NTRS)

Robinson, D. N.

1985-01-01

Nonisothermal testing that can be used as a basis of a nonisothermal representation is discussed. Related tests regarding metallurgical changes that occur in other high temperature structural alloys are discussed. A viscoplastic constitutive model capable of qualitatively representing the behavioral features was formulated. This model is used to assess the differences in ultimate life prediction in some typical nonisothermal structural problems when the constitutive model does or does not account for metallurgically induced thermomechanical history dependence.
Global-local methodologies and their application to nonlinear analysis. [for structural postbuckling study

NASA Technical Reports Server (NTRS)

Noor, Ahmed K.

1986-01-01

An assessment is made of the potential of different global-local analysis strategies for predicting the nonlinear and postbuckling responses of structures. Two postbuckling problems of composite panels are used as benchmarks and the application of different global-local methodologies to these benchmarks is outlined. The key elements of each of the global-local strategies are discussed and future research areas needed to realize the full potential of global-local methodologies are identified.
Stratospheric and Mesospheric Trace Gas Studies Using Ground-Based mm-Wave Receivers

NASA Technical Reports Server (NTRS)

daZafra, Robert L.

1997-01-01

The goal of the proposed work was to understand the latitude structure of nitric oxide in the mesosphere and lower thermosphere. The problem was portrayed by a clear difference between predictions of the nitric oxide distribution from chemical/dynamical models and data from observations made by the Solar Mesosphere Explorer (SMEE) in the early to mid eighties. The data exhibits a flat latitude structure of NO, the models tend to produce at equatorial maximum.
A homogenization-based quasi-discrete method for the fracture of heterogeneous materials

NASA Astrophysics Data System (ADS)

Berke, P. Z.; Peerlings, R. H. J.; Massart, T. J.; Geers, M. G. D.

2014-05-01

The understanding and the prediction of the failure behaviour of materials with pronounced microstructural effects is of crucial importance. This paper presents a novel computational methodology for the handling of fracture on the basis of the microscale behaviour. The basic principles presented here allow the incorporation of an adaptive discretization scheme of the structure as a function of the evolution of strain localization in the underlying microstructure. The proposed quasi-discrete methodology bridges two scales: the scale of the material microstructure, modelled with a continuum type description; and the structural scale, where a discrete description of the material is adopted. The damaging material at the structural scale is divided into unit volumes, called cells, which are represented as a discrete network of points. The scale transition is inspired by computational homogenization techniques; however it does not rely on classical averaging theorems. The structural discrete equilibrium problem is formulated in terms of the underlying fine scale computations. Particular boundary conditions are developed on the scale of the material microstructure to address damage localization problems. The performance of this quasi-discrete method with the enhanced boundary conditions is assessed using different computational test cases. The predictions of the quasi-discrete scheme agree well with reference solutions obtained through direct numerical simulations, both in terms of crack patterns and load versus displacement responses.
Family caregivers' attributions about care-recipient behaviour: does caregiver relationship satisfaction mediate the attribution-distress relationship?

PubMed

Hui, Siu-Kuen Azor; Elliott, Timothy R; Martin, Roy; Uswatte, Gitendra

2011-09-01

The relations of caregiver attributions about care-recipient's problem behaviour to caregiving relationship satisfaction and caregiver distress were examined. This is a cross sectional study. Seventy-five family caregivers of individuals diagnosed with various disabling health conditions were recruited and interviewed. Caregiver attributions (internality, intentionality, responsibility, and controllability), caregiving relationship satisfaction, and caregiver distress variables were measured. Structural equation techniques tested an a priori model of the latent constructs of caregiver attributions and caregiver relationship satisfaction to caregiver distress. Maladaptive caregiver attributions (i.e., more trait, higher intentionality, higher responsibility, and higher controllability) about care-recipients' problem behaviours predicted lower caregiving relationship satisfaction, which in turn was predictive of higher caregiver distress. Unexpectedly, caregiver attributions were not directly related to caregiver distress. However, attributions had an indirect effect on distress through relationship satisfaction. Younger caregivers experienced higher caregiver distress. Caregivers' explanations about care-recipient's problem behaviour are indicative of their satisfaction in the relationship with the care recipient, and poor caregiving relationship satisfaction is predictive of caregiver distress. Caregiver attributions and relationship quality may be considered in interventions with family caregivers. ©2010 The British Psychological Society.
Hydroelastic response of a floating runway to cnoidal waves

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

Ertekin, R. C., E-mail: ertekin@hawaii.edu; Xia, Dingwu

2014-02-15

The hydroelastic response of mat-type Very Large Floating Structures (VLFSs) to severe sea conditions, such as tsunamis and hurricanes, must be assessed for safety and survivability. An efficient and robust nonlinear hydroelastic model is required to predict accurately the motion of and the dynamic loads on a VLFS due to such large waves. We develop a nonlinear theory to predict the hydroelastic response of a VLFS in the presence of cnoidal waves and compare the predictions with the linear theory that is also developed here. This hydroelastic problem is formulated by directly coupling the structure with the fluid, by usemore » of the Level I Green-Naghdi theory for the fluid motion and the Kirchhoff thin plate theory for the runway. The coupled fluid structure system, together with the appropriate jump conditions are solved in two-dimensions by the finite-difference method. The numerical model is used to study the nonlinear response of a VLFS to storm waves which are modeled by use of the cnoidal-wave theory. Parametric studies show that the nonlinearity of the waves is very important in accurately predicting the dynamic bending moment and wave run-up on a VLFS in high seas.« less
An integrated simulator of structure and anisotropic flow in gas diffusion layers with hydrophobic additives

NASA Astrophysics Data System (ADS)

Burganos, Vasilis N.; Skouras, Eugene D.; Kalarakis, Alexandros N.

2017-10-01

The lattice-Boltzmann (LB) method is used in this work to reproduce the controlled addition of binder and hydrophobicity-promoting agents, like polytetrafluoroethylene (PTFE), into gas diffusion layers (GDLs) and to predict flow permeabilities in the through- and in-plane directions. The present simulator manages to reproduce spreading of binder and hydrophobic additives, sequentially, into the neat fibrous layer using a two-phase flow model. Gas flow simulation is achieved by the same code, sidestepping the need for a post-processing flow code and avoiding the usual input/output and data interface problems that arise in other techniques. Compression effects on flow anisotropy of the impregnated GDL are also studied. The permeability predictions for different compression levels and for different binder or PTFE loadings are found to compare well with experimental data for commercial GDL products and with computational fluid dynamics (CFD) predictions. Alternatively, the PTFE-impregnated structure is reproduced from Scanning Electron Microscopy (SEM) images using an independent, purely geometrical approach. A comparison of the two approaches is made regarding their adequacy to reproduce correctly the main structural features of the GDL and to predict anisotropic flow permeabilities at different volume fractions of binder and hydrophobic additives.
Improving Predictions of Multiple Binary Models in ILP

PubMed Central

2014-01-01

Despite the success of ILP systems in learning first-order rules from small number of examples and complexly structured data in various domains, they struggle in dealing with multiclass problems. In most cases they boil down a multiclass problem into multiple black-box binary problems following the one-versus-one or one-versus-rest binarisation techniques and learn a theory for each one. When evaluating the learned theories of multiple class problems in one-versus-rest paradigm particularly, there is a bias caused by the default rule toward the negative classes leading to an unrealistic high performance beside the lack of prediction integrity between the theories. Here we discuss the problem of using one-versus-rest binarisation technique when it comes to evaluating multiclass data and propose several methods to remedy this problem. We also illustrate the methods and highlight their link to binary tree and Formal Concept Analysis (FCA). Our methods allow learning of a simple, consistent, and reliable multiclass theory by combining the rules of the multiple one-versus-rest theories into one rule list or rule set theory. Empirical evaluation over a number of data sets shows that our proposed methods produce coherent and accurate rule models from the rules learned by the ILP system of Aleph. PMID:24696657
Efficient first-principles prediction of solid stability: Towards chemical accuracy

NASA Astrophysics Data System (ADS)

Zhang, Yubo; Kitchaev, Daniil A.; Yang, Julia; Chen, Tina; Dacek, Stephen T.; Sarmiento-Pérez, Rafael A.; Marques, Maguel A. L.; Peng, Haowei; Ceder, Gerbrand; Perdew, John P.; Sun, Jianwei

2018-03-01

The question of material stability is of fundamental importance to any analysis of system properties in condensed matter physics and materials science. The ability to evaluate chemical stability, i.e., whether a stoichiometry will persist in some chemical environment, and structure selection, i.e. what crystal structure a stoichiometry will adopt, is critical to the prediction of materials synthesis, reactivity and properties. Here, we demonstrate that density functional theory, with the recently developed strongly constrained and appropriately normed (SCAN) functional, has advanced to a point where both facets of the stability problem can be reliably and efficiently predicted for main group compounds, while transition metal compounds are improved but remain a challenge. SCAN therefore offers a robust model for a significant portion of the periodic table, presenting an opportunity for the development of novel materials and the study of fine phase transformations even in largely unexplored systems with little to no experimental data.
Prediction based Greedy Perimeter Stateless Routing Protocol for Vehicular Self-organizing Network

NASA Astrophysics Data System (ADS)

Wang, Chunlin; Fan, Quanrun; Chen, Xiaolin; Xu, Wanjin

2018-03-01

PGPSR (Prediction based Greedy Perimeter Stateless Routing) is based on and extended the GPSR protocol to adapt to the high speed mobility of the vehicle auto organization network (VANET) and the changes in the network topology. GPSR is used in the VANET network environment, the network loss rate and throughput are not ideal, even cannot work. Aiming at the problems of the GPSR, the proposed PGPSR routing protocol, it redefines the hello and query packet structure, in the structure of the new node speed and direction information, which received the next update before you can take advantage of its speed and direction to predict the position of node and new network topology, select the right the next hop routing and path. Secondly, the update of the outdated node information of the neighbor’s table is deleted in time. The simulation experiment shows the performance of PGPSR is better than that of GPSR.
Computational modeling of RNA 3D structures, with the aid of experimental restraints

PubMed Central

Magnus, Marcin; Matelska, Dorota; Łach, Grzegorz; Chojnowski, Grzegorz; Boniecki, Michal J; Purta, Elzbieta; Dawson, Wayne; Dunin-Horkawicz, Stanislaw; Bujnicki, Janusz M

2014-01-01

In addition to mRNAs whose primary function is transmission of genetic information from DNA to proteins, numerous other classes of RNA molecules exist, which are involved in a variety of functions, such as catalyzing biochemical reactions or performing regulatory roles. In analogy to proteins, the function of RNAs depends on their structure and dynamics, which are largely determined by the ribonucleotide sequence. Experimental determination of high-resolution RNA structures is both laborious and difficult, and therefore, the majority of known RNAs remain structurally uncharacterized. To address this problem, computational structure prediction methods were developed that simulate either the physical process of RNA structure formation (“Greek science” approach) or utilize information derived from known structures of other RNA molecules (“Babylonian science” approach). All computational methods suffer from various limitations that make them generally unreliable for structure prediction of long RNA sequences. However, in many cases, the limitations of computational and experimental methods can be overcome by combining these two complementary approaches with each other. In this work, we review computational approaches for RNA structure prediction, with emphasis on implementations (particular programs) that can utilize restraints derived from experimental analyses. We also list experimental approaches, whose results can be relatively easily used by computational methods. Finally, we describe case studies where computational and experimental analyses were successfully combined to determine RNA structures that would remain out of reach for each of these approaches applied separately. PMID:24785264
Life Outside the Golden Window: Statistical Angles on the Signal-to-Noise Problem

NASA Astrophysics Data System (ADS)

Wagman, Michael

2018-03-01

Lattice QCD simulations of multi-baryon correlation functions can predict the structure and reactions of nuclei without encountering the baryon chemical potential sign problem. However, they suffer from a signal-to-noise problem where Monte Carlo estimates of observables have quantum fluctuations that are exponentially larger than their average values. Recent lattice QCD results demonstrate that the complex phase of baryon correlations functions relates the baryon signal-to-noise problem to a sign problem and exhibits unexpected statistical behavior resembling a heavy-tailed random walk on the unit circle. Estimators based on differences of correlation function phases evaluated at different Euclidean times are discussed that avoid the usual signal-to-noise problem, instead facing a signal-to-noise problem as the time interval associated with the phase difference is increased, and allow hadronic observables to be determined from arbitrarily large-time correlation functions.

Cumulative contextual risk at birth in relation to adolescent substance use, conduct problems, and risky sex: General and specific predictive associations in a Finnish birth cohort.

PubMed

Mason, W Alex; January, Stacy-Ann A; Chmelka, Mary B; Parra, Gilbert R; Savolainen, Jukka; Miettunen, Jouko; Järvelin, Marjo-Riitta; Taanila, Anja; Moilanen, Irma

2016-07-01

Research indicates that risk factors cluster in the most vulnerable youth, increasing their susceptibility for adverse developmental outcomes. However, most studies of cumulative risk are cross-sectional or short-term longitudinal, and have been based on data from the United States or the United Kingdom. Using data from the Northern Finland Birth Cohort 1986 Study (NFBC1986), we examined cumulative contextual risk (CCR) at birth as a predictor of adolescent substance use and co-occurring conduct problems and risky sex to determine the degree to which CCR predicts specific outcomes over-and-above its effect on general problem behavior, while testing for moderation of associations by gender. Analyses of survey data from 6963 participants of the NFBC1986 followed from the prenatal/birth period into adolescence were conducted using structural equation modeling. CCR had long-term positive associations with first-order substance use, conduct problems, and risky sex factors, and, in a separate analysis, with a second-order general problem behavior factor. Further analyses showed that there was a positive specific effect of CCR on risky sex, over-and-above general problem behavior, for girls only. This study, conducted within the Finnish context, showed that CCR at birth had long-term general and specific predictive associations with substance use and co-occurring problem behaviors in adolescence; effects on risky sex were stronger for girls. Results are consistent with the hypothesis that early exposure to CCR can have lasting adverse consequences, suggesting the need for early identification and intervention efforts for vulnerable children. Copyright © 2016 Elsevier Ltd. All rights reserved.
A Structural Equation Model Explaining 8th Grade Students' Mathematics Achievements

ERIC Educational Resources Information Center

Yurt, Eyüp; Sünbül, Ali Murat

2014-01-01

The purpose of this study is to investigate, via a model, the explanatory and predictive relationships among the following variables: Mathematical Problem Solving and Reasoning Skills, Sources of Mathematics Self-Efficacy, Spatial Ability, and Mathematics Achievements of Secondary School 8th Grade Students. The sample group of the study, itself…
Realism, Instrumentalism, and Scientific Symbiosis: Psychological Theory as a Search for Truth and the Discovery of Solutions

ERIC Educational Resources Information Center

Cacioppo, John T.; Semin, Gun R.; Berntson, Gary G.

2004-01-01

Scientific realism holds that scientific theories are approximations of universal truths about reality, whereas scientific instrumentalism posits that scientific theories are intellectual structures that provide adequate predictions of what is observed and useful frameworks for answering questions and solving problems in a given domain. These…
The Politics of Scarcity: A Consideration of Futurist Models of Boom and Doom.

ERIC Educational Resources Information Center

Johnston, Barry V.

The works of 20 futurists and their predictions for the year 2000 and beyond are examined according to four perspectives: Malthusianism, Utopianism (based on theories of William Godwin), Marxism, and social structuralism. Futurists may be grouped into one of the categories according to their theories about the interdependent problems of…
A Question of Trust: Predictive Conditions for Adaptive and Technical Leadership in Educational Contexts

ERIC Educational Resources Information Center

Daly, Alan J.; Chrispeels, Janet

2008-01-01

Recent studies have suggested that educational leaders enacting a balance of technical and adaptive leadership have an effect on increasing student achievement. Technical leadership focuses on problem-solving or first-order changes within existing structures and paradigms. Adaptive leadership involves deep or second-order changes that alter…
Outstanding problems in the band structures of 152Sm

NASA Astrophysics Data System (ADS)

Gupta, J. B.; Hamilton, J. H.

2017-09-01

The recent data on B (E 2 ) values, deduced from the multi-Coulex excitation of the low spin states in the decay of 152Sm, and other experimental findings in the last two decades are compared with the predictions from the microscopic dynamic pairing plus quadrupole model of Kumar and Baranger. The 1292.8 keV 2+ state is assigned to the 03 + band, and the K =2 assignment of the 1769 keV 2+ state is confirmed. The anomaly of the shape coexistence of the assumed spherical β band versus the deformed ground band is resolved. The values from the critical point symmetry X(5) support the collective character of the β band. The problem with the two-term interacting boson model Hamiltonian in predicting β and γ bands in 152Sm leads to interesting consequences. The collective features of the second excited Kπ=03 + band are preferred over the "pairing isomer" view. Also the multiphonon nature of the higher lying Kπ=22 +β γ band and Kπ=4+ band are illustrated vis-à-vis the new data and the nuclear structure theory.
A study on the social behavior and social isolation of the elderly Korea.

PubMed

Yi, Eun-Surk; Hwang, Hee-Joung

2015-06-01

This study aimed at presenting what factors are to predict the social isolation of the elderly as an element to prevent the problem of why various matters related to old people are inevitably taking place by carefully examining the meaning of social isolation and the conditions of social isolation that the South Korean senior citizens go through after working on previous studies. This section discusses the results obtained through document analysis. First, the aspects of the elderly's social isolation arising from the changes of the South Korean society are changes of family relationship, the social structure, the economic structure and the culture. Second, the social isolation and social activity of the elderly are problems (suicide, criminals, dementia, depression and medical costs) of the elderly, change trend of the elderly issues related to social isolation and prediction factors that personal and regional. Lastly, as a role and challenges of the field of rehabilitation exercise aimed at resolving social isolation should be vitalized such as the development and provision of various relationship-building programs.
Bayesian calibration of the Community Land Model using surrogates

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

Ray, Jaideep; Hou, Zhangshuan; Huang, Maoyi

2014-02-01

We present results from the Bayesian calibration of hydrological parameters of the Community Land Model (CLM), which is often used in climate simulations and Earth system models. A statistical inverse problem is formulated for three hydrological parameters, conditional on observations of latent heat surface fluxes over 48 months. Our calibration method uses polynomial and Gaussian process surrogates of the CLM, and solves the parameter estimation problem using a Markov chain Monte Carlo sampler. Posterior probability densities for the parameters are developed for two sites with different soil and vegetation covers. Our method also allows us to examine the structural errormore » in CLM under two error models. We find that surrogate models can be created for CLM in most cases. The posterior distributions are more predictive than the default parameter values in CLM. Climatologically averaging the observations does not modify the parameters' distributions significantly. The structural error model reveals a correlation time-scale which can be used to identify the physical process that could be contributing to it. While the calibrated CLM has a higher predictive skill, the calibration is under-dispersive.« less
Exact calculation of distributions on integers, with application to sequence alignment.

PubMed

Newberg, Lee A; Lawrence, Charles E

2009-01-01

Computational biology is replete with high-dimensional discrete prediction and inference problems. Dynamic programming recursions can be applied to several of the most important of these, including sequence alignment, RNA secondary-structure prediction, phylogenetic inference, and motif finding. In these problems, attention is frequently focused on some scalar quantity of interest, a score, such as an alignment score or the free energy of an RNA secondary structure. In many cases, score is naturally defined on integers, such as a count of the number of pairing differences between two sequence alignments, or else an integer score has been adopted for computational reasons, such as in the test of significance of motif scores. The probability distribution of the score under an appropriate probabilistic model is of interest, such as in tests of significance of motif scores, or in calculation of Bayesian confidence limits around an alignment. Here we present three algorithms for calculating the exact distribution of a score of this type; then, in the context of pairwise local sequence alignments, we apply the approach so as to find the alignment score distribution and Bayesian confidence limits.
A structural model decomposition framework for systems health management

NASA Astrophysics Data System (ADS)

Roychoudhury, I.; Daigle, M.; Bregon, A.; Pulido, B.

Systems health management (SHM) is an important set of technologies aimed at increasing system safety and reliability by detecting, isolating, and identifying faults; and predicting when the system reaches end of life (EOL), so that appropriate fault mitigation and recovery actions can be taken. Model-based SHM approaches typically make use of global, monolithic system models for online analysis, which results in a loss of scalability and efficiency for large-scale systems. Improvement in scalability and efficiency can be achieved by decomposing the system model into smaller local submodels and operating on these submodels instead. In this paper, the global system model is analyzed offline and structurally decomposed into local submodels. We define a common model decomposition framework for extracting submodels from the global model. This framework is then used to develop algorithms for solving model decomposition problems for the design of three separate SHM technologies, namely, estimation (which is useful for fault detection and identification), fault isolation, and EOL prediction. We solve these model decomposition problems using a three-tank system as a case study.
Application of soft computing based hybrid models in hydrological variables modeling: a comprehensive review

NASA Astrophysics Data System (ADS)

Fahimi, Farzad; Yaseen, Zaher Mundher; El-shafie, Ahmed

2017-05-01

Since the middle of the twentieth century, artificial intelligence (AI) models have been used widely in engineering and science problems. Water resource variable modeling and prediction are the most challenging issues in water engineering. Artificial neural network (ANN) is a common approach used to tackle this problem by using viable and efficient models. Numerous ANN models have been successfully developed to achieve more accurate results. In the current review, different ANN models in water resource applications and hydrological variable predictions are reviewed and outlined. In addition, recent hybrid models and their structures, input preprocessing, and optimization techniques are discussed and the results are compared with similar previous studies. Moreover, to achieve a comprehensive view of the literature, many articles that applied ANN models together with other techniques are included. Consequently, coupling procedure, model evaluation, and performance comparison of hybrid models with conventional ANN models are assessed, as well as, taxonomy and hybrid ANN models structures. Finally, current challenges and recommendations for future researches are indicated and new hybrid approaches are proposed.
A Structural Model Decomposition Framework for Systems Health Management

NASA Technical Reports Server (NTRS)

Roychoudhury, Indranil; Daigle, Matthew J.; Bregon, Anibal; Pulido, Belamino

2013-01-01

Systems health management (SHM) is an important set of technologies aimed at increasing system safety and reliability by detecting, isolating, and identifying faults; and predicting when the system reaches end of life (EOL), so that appropriate fault mitigation and recovery actions can be taken. Model-based SHM approaches typically make use of global, monolithic system models for online analysis, which results in a loss of scalability and efficiency for large-scale systems. Improvement in scalability and efficiency can be achieved by decomposing the system model into smaller local submodels and operating on these submodels instead. In this paper, the global system model is analyzed offline and structurally decomposed into local submodels. We define a common model decomposition framework for extracting submodels from the global model. This framework is then used to develop algorithms for solving model decomposition problems for the design of three separate SHM technologies, namely, estimation (which is useful for fault detection and identification), fault isolation, and EOL prediction. We solve these model decomposition problems using a three-tank system as a case study.
A study on the social behavior and social isolation of the elderly Korea

PubMed Central

Yi, Eun-Surk; Hwang, Hee-Joung

2015-01-01

This study aimed at presenting what factors are to predict the social isolation of the elderly as an element to prevent the problem of why various matters related to old people are inevitably taking place by carefully examining the meaning of social isolation and the conditions of social isolation that the South Korean senior citizens go through after working on previous studies. This section discusses the results obtained through document analysis. First, the aspects of the elderly’s social isolation arising from the changes of the South Korean society are changes of family relationship, the social structure, the economic structure and the culture. Second, the social isolation and social activity of the elderly are problems (suicide, criminals, dementia, depression and medical costs) of the elderly, change trend of the elderly issues related to social isolation and prediction factors that personal and regional. Lastly, as a role and challenges of the field of rehabilitation exercise aimed at resolving social isolation should be vitalized such as the development and provision of various relationship-building programs. PMID:26171377
Resolvent analysis of shear flows using One-Way Navier-Stokes equations

NASA Astrophysics Data System (ADS)

Rigas, Georgios; Schmidt, Oliver; Towne, Aaron; Colonius, Tim

2017-11-01

For three-dimensional flows, questions of stability, receptivity, secondary flows, and coherent structures require the solution of large partial-derivative eigenvalue problems. Reduced-order approximations are thus required for engineering prediction since these problems are often computationally intractable or prohibitively expensive. For spatially slowly evolving flows, such as jets and boundary layers, the One-Way Navier-Stokes (OWNS) equations permit a fast spatial marching procedure that results in a huge reduction in computational cost. Here, an adjoint-based optimization framework is proposed and demonstrated for calculating optimal boundary conditions and optimal volumetric forcing. The corresponding optimal response modes are validated against modes obtained in terms of global resolvent analysis. For laminar base flows, the optimal modes reveal modal and non-modal transition mechanisms. For turbulent base flows, they predict the evolution of coherent structures in a statistical sense. Results from the application of the method to three-dimensional laminar wall-bounded flows and turbulent jets will be presented. This research was supported by the Office of Naval Research (N00014-16-1-2445) and Boeing Company (CT-BA-GTA-1).
Revising explanatory models to accommodate anomalous genetic phenomena: Problem solving in the context of discovery

NASA Astrophysics Data System (ADS)

Hafner, Robert; Stewart, Jim

Past problem-solving research has provided a basis for helping students structure their knowledge and apply appropriate problem-solving strategies to solve problems for which their knowledge (or mental models) of scientific phenomena is adequate (model-using problem solving). This research examines how problem solving in the domain of Mendelian genetics proceeds in situations where solvers' mental models are insufficient to solve problems at hand (model-revising problem solving). Such situations require solvers to use existing models to recognize anomalous data and to revise those models to accommodate the data. The study was conducted in the context of 9-week high school genetics course and addressed: the heuristics charactenstic of successful model-revising problem solving: the nature of the model revisions, made by students as well as the nature of model development across problem types; and the basis upon which solvers decide that a revised model is sufficient (that t has both predictive and explanatory power).
ASPIC: a novel method to predict the exon-intron structure of a gene that is optimally compatible to a set of transcript sequences.

PubMed

Bonizzoni, Paola; Rizzi, Raffaella; Pesole, Graziano

2005-10-05

Currently available methods to predict splice sites are mainly based on the independent and progressive alignment of transcript data (mostly ESTs) to the genomic sequence. Apart from often being computationally expensive, this approach is vulnerable to several problems--hence the need to develop novel strategies. We propose a method, based on a novel multiple genome-EST alignment algorithm, for the detection of splice sites. To avoid limitations of splice sites prediction (mainly, over-predictions) due to independent single EST alignments to the genomic sequence our approach performs a multiple alignment of transcript data to the genomic sequence based on the combined analysis of all available data. We recast the problem of predicting constitutive and alternative splicing as an optimization problem, where the optimal multiple transcript alignment minimizes the number of exons and hence of splice site observations. We have implemented a splice site predictor based on this algorithm in the software tool ASPIC (Alternative Splicing PredICtion). It is distinguished from other methods based on BLAST-like tools by the incorporation of entirely new ad hoc procedures for accurate and computationally efficient transcript alignment and adopts dynamic programming for the refinement of intron boundaries. ASPIC also provides the minimal set of non-mergeable transcript isoforms compatible with the detected splicing events. The ASPIC web resource is dynamically interconnected with the Ensembl and Unigene databases and also implements an upload facility. Extensive bench marking shows that ASPIC outperforms other existing methods in the detection of novel splicing isoforms and in the minimization of over-predictions. ASPIC also requires a lower computation time for processing a single gene and an EST cluster. The ASPIC web resource is available at http://aspic.algo.disco.unimib.it/aspic-devel/.
Exploiting Information Diffusion Feature for Link Prediction in Sina Weibo

NASA Astrophysics Data System (ADS)

Li, Dong; Zhang, Yongchao; Xu, Zhiming; Chu, Dianhui; Li, Sheng

2016-01-01

The rapid development of online social networks (e.g., Twitter and Facebook) has promoted research related to social networks in which link prediction is a key problem. Although numerous attempts have been made for link prediction based on network structure, node attribute and so on, few of the current studies have considered the impact of information diffusion on link creation and prediction. This paper mainly addresses Sina Weibo, which is the largest microblog platform with Chinese characteristics, and proposes the hypothesis that information diffusion influences link creation and verifies the hypothesis based on real data analysis. We also detect an important feature from the information diffusion process, which is used to promote link prediction performance. Finally, the experimental results on Sina Weibo dataset have demonstrated the effectiveness of our methods.
Exploiting Information Diffusion Feature for Link Prediction in Sina Weibo.

PubMed

Li, Dong; Zhang, Yongchao; Xu, Zhiming; Chu, Dianhui; Li, Sheng

2016-01-28

The rapid development of online social networks (e.g., Twitter and Facebook) has promoted research related to social networks in which link prediction is a key problem. Although numerous attempts have been made for link prediction based on network structure, node attribute and so on, few of the current studies have considered the impact of information diffusion on link creation and prediction. This paper mainly addresses Sina Weibo, which is the largest microblog platform with Chinese characteristics, and proposes the hypothesis that information diffusion influences link creation and verifies the hypothesis based on real data analysis. We also detect an important feature from the information diffusion process, which is used to promote link prediction performance. Finally, the experimental results on Sina Weibo dataset have demonstrated the effectiveness of our methods.
Towards Automated Structure-Based NMR Resonance Assignment

NASA Astrophysics Data System (ADS)

Jang, Richard; Gao, Xin; Li, Ming

We propose a general framework for solving the structure-based NMR backbone resonance assignment problem. The core is a novel 0-1 integer programming model that can start from a complete or partial assignment, generate multiple assignments, and model not only the assignment of spins to residues, but also pairwise dependencies consisting of pairs of spins to pairs of residues. It is still a challenge for automated resonance assignment systems to perform the assignment directly from spectra without any manual intervention. To test the feasibility of this for structure-based assignment, we integrated our system with our automated peak picking and sequence-based resonance assignment system to obtain an assignment for the protein TM1112 with 91% recall and 99% precision without manual intervention. Since using a known structure has the potential to allow one to use only N-labeled NMR data and avoid the added expense of using C-labeled data, we work towards the goal of automated structure-based assignment using only such labeled data. Our system reduced the assignment error of Xiong-Pandurangan-Bailey-Kellogg's contact replacement (CR) method, which to our knowledge is the most error-tolerant method for this problem, by 5 folds on average. By using an iterative algorithm, our system has the added capability of using the NOESY data to correct assignment errors due to errors in predicting the amino acid and secondary structure type of each spin system. On a publicly available data set for Ubiquitin, where the type prediction accuracy is 83%, we achieved 91% assignment accuracy, compared to the 59% accuracy that was obtained without correcting for typing errors.
Protein structure-structure alignment with discrete Fréchet distance.

PubMed

Jiang, Minghui; Xu, Ying; Zhu, Binhai

2008-02-01

Matching two geometric objects in two-dimensional (2D) and three-dimensional (3D) spaces is a central problem in computer vision, pattern recognition, and protein structure prediction. In particular, the problem of aligning two polygonal chains under translation and rotation to minimize their distance has been studied using various distance measures. It is well known that the Hausdorff distance is useful for matching two point sets, and that the Fréchet distance is a superior measure for matching two polygonal chains. The discrete Fréchet distance closely approximates the (continuous) Fréchet distance, and is a natural measure for the geometric similarity of the folded 3D structures of biomolecules such as proteins. In this paper, we present new algorithms for matching two polygonal chains in two dimensions to minimize their discrete Fréchet distance under translation and rotation, and an effective heuristic for matching two polygonal chains in three dimensions. We also describe our empirical results on the application of the discrete Fréchet distance to protein structure-structure alignment.

Real-Time Ligand Binding Pocket Database Search Using Local Surface Descriptors

PubMed Central

Chikhi, Rayan; Sael, Lee; Kihara, Daisuke

2010-01-01

Due to the increasing number of structures of unknown function accumulated by ongoing structural genomics projects, there is an urgent need for computational methods for characterizing protein tertiary structures. As functions of many of these proteins are not easily predicted by conventional sequence database searches, a legitimate strategy is to utilize structure information in function characterization. Of a particular interest is prediction of ligand binding to a protein, as ligand molecule recognition is a major part of molecular function of proteins. Predicting whether a ligand molecule binds a protein is a complex problem due to the physical nature of protein-ligand interactions and the flexibility of both binding sites and ligand molecules. However, geometric and physicochemical complementarity is observed between the ligand and its binding site in many cases. Therefore, ligand molecules which bind to a local surface site in a protein can be predicted by finding similar local pockets of known binding ligands in the structure database. Here, we present two representations of ligand binding pockets and utilize them for ligand binding prediction by pocket shape comparison. These representations are based on mapping of surface properties of binding pockets, which are compactly described either by the two dimensional pseudo-Zernike moments or the 3D Zernike descriptors. These compact representations allow a fast real-time pocket searching against a database. Thorough benchmark study employing two different datasets show that our representations are competitive with the other existing methods. Limitations and potentials of the shape-based methods as well as possible improvements are discussed. PMID:20455259
Electrostatics, structure prediction, and the energy landscapes for protein folding and binding.

PubMed

Tsai, Min-Yeh; Zheng, Weihua; Balamurugan, D; Schafer, Nicholas P; Kim, Bobby L; Cheung, Margaret S; Wolynes, Peter G

2016-01-01

While being long in range and therefore weakly specific, electrostatic interactions are able to modulate the stability and folding landscapes of some proteins. The relevance of electrostatic forces for steering the docking of proteins to each other is widely acknowledged, however, the role of electrostatics in establishing specifically funneled landscapes and their relevance for protein structure prediction are still not clear. By introducing Debye-Hückel potentials that mimic long-range electrostatic forces into the Associative memory, Water mediated, Structure, and Energy Model (AWSEM), a transferable protein model capable of predicting tertiary structures, we assess the effects of electrostatics on the landscapes of thirteen monomeric proteins and four dimers. For the monomers, we find that adding electrostatic interactions does not improve structure prediction. Simulations of ribosomal protein S6 show, however, that folding stability depends monotonically on electrostatic strength. The trend in predicted melting temperatures of the S6 variants agrees with experimental observations. Electrostatic effects can play a range of roles in binding. The binding of the protein complex KIX-pKID is largely assisted by electrostatic interactions, which provide direct charge-charge stabilization of the native state and contribute to the funneling of the binding landscape. In contrast, for several other proteins, including the DNA-binding protein FIS, electrostatics causes frustration in the DNA-binding region, which favors its binding with DNA but not with its protein partner. This study highlights the importance of long-range electrostatics in functional responses to problems where proteins interact with their charged partners, such as DNA, RNA, as well as membranes. © 2015 The Protein Society.
Real-time ligand binding pocket database search using local surface descriptors.

PubMed

Chikhi, Rayan; Sael, Lee; Kihara, Daisuke

2010-07-01

Because of the increasing number of structures of unknown function accumulated by ongoing structural genomics projects, there is an urgent need for computational methods for characterizing protein tertiary structures. As functions of many of these proteins are not easily predicted by conventional sequence database searches, a legitimate strategy is to utilize structure information in function characterization. Of particular interest is prediction of ligand binding to a protein, as ligand molecule recognition is a major part of molecular function of proteins. Predicting whether a ligand molecule binds a protein is a complex problem due to the physical nature of protein-ligand interactions and the flexibility of both binding sites and ligand molecules. However, geometric and physicochemical complementarity is observed between the ligand and its binding site in many cases. Therefore, ligand molecules which bind to a local surface site in a protein can be predicted by finding similar local pockets of known binding ligands in the structure database. Here, we present two representations of ligand binding pockets and utilize them for ligand binding prediction by pocket shape comparison. These representations are based on mapping of surface properties of binding pockets, which are compactly described either by the two-dimensional pseudo-Zernike moments or the three-dimensional Zernike descriptors. These compact representations allow a fast real-time pocket searching against a database. Thorough benchmark studies employing two different datasets show that our representations are competitive with the other existing methods. Limitations and potentials of the shape-based methods as well as possible improvements are discussed.
Discriminative motif discovery via simulated evolution and random under-sampling.

PubMed

Song, Tao; Gu, Hong

2014-01-01

Conserved motifs in biological sequences are closely related to their structure and functions. Recently, discriminative motif discovery methods have attracted more and more attention. However, little attention has been devoted to the data imbalance problem, which is one of the main reasons affecting the performance of the discriminative models. In this article, a simulated evolution method is applied to solve the multi-class imbalance problem at the stage of data preprocessing, and at the stage of Hidden Markov Models (HMMs) training, a random under-sampling method is introduced for the imbalance between the positive and negative datasets. It is shown that, in the task of discovering targeting motifs of nine subcellular compartments, the motifs found by our method are more conserved than the methods without considering data imbalance problem and recover the most known targeting motifs from Minimotif Miner and InterPro. Meanwhile, we use the found motifs to predict protein subcellular localization and achieve higher prediction precision and recall for the minority classes.
Job satisfaction among hospital nurses: a longitudinal study.

PubMed Central

Weisman, C S; Alexander, C S; Chase, G A

1980-01-01

Data from a two-wave panel study of staff nurses in two hospitals are used to assess the relative importance of several types of independent variables as determinants of job satisfaction. Both organizational and nonorganizational determinants are examined, with the formed including both perceptual and structural measures. Job satisfaction is measured in two ways using both Overall and Multi-Facet indicators. The independent variables were measured five months before the dependent variables were measured in order to attenuate contamination problems. Findings indicate that perceptions of job and nursing unit attributes, particularly autonomy and task delegation, predict satisfaction most strongly. In addition, a nurse's own characteristics are found to be more important than either structural attributes of nursing units or job characteristics in predicting job satisfaction. PMID:7461970
A model of insulin fibrils derived from the x-ray crystal structure of a monomeric insulin (despentapeptide insulin).

PubMed

Brange, J; Dodson, G G; Edwards, D J; Holden, P H; Whittingham, J L

1997-04-01

The crystal structure of despentapeptide insulin, a monomeric insulin, has been refined at 1.3 A spacing and subsequently used to predict and model the organization in the insulin fibril. The model makes use of the contacts in the densely packed despentapeptide insulin crystal, and takes into account other experimental evidence, including binding studies with Congo red. The dimensions of this model fibril correspond well with those measured experimentally, and the monomer-monomer contacts within the fibril are in accordance with the known physical chemistry of insulin fibrils. Using this model, it may be possible to predict mutations in insulin that might alleviate problems associated with fibril formation during insulin therapy.
Dynamic analysis of space-related linear and non-linear structures

NASA Technical Reports Server (NTRS)

Bosela, Paul A.; Shaker, Francis J.; Fertis, Demeter G.

1990-01-01

In order to be cost effective, space structures must be extremely light weight, and subsequently, very flexible structures. The power system for Space Station Freedom is such a structure. Each array consists of a deployable truss mast and a split blanket of photo-voltaic solar collectors. The solar arrays are deployed in orbit, and the blanket is stretched into position as the mast is extended. Geometric stiffness due to the preload make this an interesting non-linear problem. The space station will be subjected to various dynamic loads, during shuttle docking, solar tracking, attitude adjustment, etc. Accurate prediction of the natural frequencies and mode shapes of the space station components, including the solar arrays, is critical for determining the structural adequacy of the components, and for designing a dynamic control system. The process used in developing and verifying the finite element dynamic model of the photo-voltaic arrays is documented. Various problems were identified, such as grounding effects due to geometric stiffness, large displacement effects, and pseudo-stiffness (grounding) due to lack of required rigid body modes. Analysis techniques, such as development of rigorous solutions using continuum mechanics, finite element solution sequence altering, equivalent systems using a curvature basis, Craig-Bampton superelement approach, and modal ordering schemes were utilized. The grounding problems associated with the geometric stiffness are emphasized.
Dynamic analysis of space-related linear and non-linear structures

NASA Technical Reports Server (NTRS)

Bosela, Paul A.; Shaker, Francis J.; Fertis, Demeter G.

1990-01-01

In order to be cost effective, space structures must be extremely light weight, and subsequently, very flexible structures. The power system for Space Station Freedom is such a structure. Each array consists of a deployable truss mast and a split blanket of photovoltaic solar collectors. The solar arrays are deployed in orbit, and the blanket is stretched into position as the mast is extended. Geometric stiffness due to the preload make this an interesting non-linear problem. The space station will be subjected to various dynamic loads, during shuttle docking, solar tracking, attitude adjustment, etc. Accurate prediction of the natural frequencies and mode shapes of the space station components, including the solar arrays, is critical for determining the structural adequacy of the components, and for designing a dynamic controls system. The process used in developing and verifying the finite element dynamic model of the photo-voltaic arrays is documented. Various problems were identified, such as grounding effects due to geometric stiffness, large displacement effects, and pseudo-stiffness (grounding) due to lack of required rigid body modes. Analysis techniques, such as development of rigorous solutions using continuum mechanics, finite element solution sequence altering, equivalent systems using a curvature basis, Craig-Bampton superelement approach, and modal ordering schemes were utilized. The grounding problems associated with the geometric stiffness are emphasized.
Assessing the Liquidity of Firms: Robust Neural Network Regression as an Alternative to the Current Ratio

NASA Astrophysics Data System (ADS)

de Andrés, Javier; Landajo, Manuel; Lorca, Pedro; Labra, Jose; Ordóñez, Patricia

Artificial neural networks have proven to be useful tools for solving financial analysis problems such as financial distress prediction and audit risk assessment. In this paper we focus on the performance of robust (least absolute deviation-based) neural networks on measuring liquidity of firms. The problem of learning the bivariate relationship between the components (namely, current liabilities and current assets) of the so-called current ratio is analyzed, and the predictive performance of several modelling paradigms (namely, linear and log-linear regressions, classical ratios and neural networks) is compared. An empirical analysis is conducted on a representative data base from the Spanish economy. Results indicate that classical ratio models are largely inadequate as a realistic description of the studied relationship, especially when used for predictive purposes. In a number of cases, especially when the analyzed firms are microenterprises, the linear specification is improved by considering the flexible non-linear structures provided by neural networks.
Theory and computer simulation of hard-core Yukawa mixtures: thermodynamical, structural and phase coexistence properties.

PubMed

Mkanya, Anele; Pellicane, Giuseppe; Pini, Davide; Caccamo, Carlo

2017-09-13

We report extensive calculations, based on the modified hypernetted chain (MHNC) theory, on the hierarchical reference theory (HRT), and on Monte Carlo simulations, of thermodynamical, structural and phase coexistence properties of symmetric binary hard-core Yukawa mixtures (HCYM) with attractive interactions at equal species concentration. The obtained results are throughout compared with those available in the literature for the same systems. It turns out that the MHNC predictions for thermodynamic and structural quantities are quite accurate in comparison with the MC data. The HRT is equally accurate for thermodynamics, and slightly less accurate for structure. Liquid-vapor (LV) and liquid-liquid (LL) consolute coexistence conditions as emerging from simulations, are also highly satisfactorily reproduced by both the MHNC and HRT for relatively long ranged potentials. When the potential range reduces, the MHNC faces problems in determining the LV binodal line; however, the LL consolute line and the critical end point (CEP) temperature and density turn out to be still satisfactorily predicted within this theory. The HRT also predicts with good accuracy the CEP position. The possibility of employing liquid state theories HCYM for the purpose of reliably determining phase equilibria in multicomponent colloidal fluids of current technological interest, is discussed.
Theory and computer simulation of hard-core Yukawa mixtures: thermodynamical, structural and phase coexistence properties

NASA Astrophysics Data System (ADS)

Mkanya, Anele; Pellicane, Giuseppe; Pini, Davide; Caccamo, Carlo

2017-09-01

We report extensive calculations, based on the modified hypernetted chain (MHNC) theory, on the hierarchical reference theory (HRT), and on Monte Carlo simulations, of thermodynamical, structural and phase coexistence properties of symmetric binary hard-core Yukawa mixtures (HCYM) with attractive interactions at equal species concentration. The obtained results are throughout compared with those available in the literature for the same systems. It turns out that the MHNC predictions for thermodynamic and structural quantities are quite accurate in comparison with the MC data. The HRT is equally accurate for thermodynamics, and slightly less accurate for structure. Liquid-vapor (LV) and liquid-liquid (LL) consolute coexistence conditions as emerging from simulations, are also highly satisfactorily reproduced by both the MHNC and HRT for relatively long ranged potentials. When the potential range reduces, the MHNC faces problems in determining the LV binodal line; however, the LL consolute line and the critical end point (CEP) temperature and density turn out to be still satisfactorily predicted within this theory. The HRT also predicts with good accuracy the CEP position. The possibility of employing liquid state theories HCYM for the purpose of reliably determining phase equilibria in multicomponent colloidal fluids of current technological interest, is discussed.
Using kaizen to improve employee well-being: Results from two organizational intervention studies.

PubMed

von Thiele Schwarz, Ulrica; Nielsen, Karina M; Stenfors-Hayes, Terese; Hasson, Henna

2017-08-01

Participatory intervention approaches that are embedded in existing organizational structures may improve the efficiency and effectiveness of organizational interventions, but concrete tools are lacking. In the present article, we use a realist evaluation approach to explore the role of kaizen, a lean tool for participatory continuous improvement, in improving employee well-being in two cluster-randomized, controlled participatory intervention studies. Case 1 is from the Danish Postal Service, where kaizen boards were used to implement action plans. The results of multi-group structural equation modeling showed that kaizen served as a mechanism that increased the level of awareness of and capacity to manage psychosocial issues, which, in turn, predicted increased job satisfaction and mental health. Case 2 is from a regional hospital in Sweden that integrated occupational health processes with a pre-existing kaizen system. Multi-group structural equation modeling revealed that, in the intervention group, kaizen work predicted better integration of organizational and employee objectives after 12 months, which, in turn, predicted increased job satisfaction and decreased discomfort at 24 months. The findings suggest that participatory and structured problem-solving approaches that are familiar and visual to employees can facilitate organizational interventions.
Using kaizen to improve employee well-being: Results from two organizational intervention studies

PubMed Central

von Thiele Schwarz, Ulrica; Nielsen, Karina M; Stenfors-Hayes, Terese; Hasson, Henna

2016-01-01

Participatory intervention approaches that are embedded in existing organizational structures may improve the efficiency and effectiveness of organizational interventions, but concrete tools are lacking. In the present article, we use a realist evaluation approach to explore the role of kaizen, a lean tool for participatory continuous improvement, in improving employee well-being in two cluster-randomized, controlled participatory intervention studies. Case 1 is from the Danish Postal Service, where kaizen boards were used to implement action plans. The results of multi-group structural equation modeling showed that kaizen served as a mechanism that increased the level of awareness of and capacity to manage psychosocial issues, which, in turn, predicted increased job satisfaction and mental health. Case 2 is from a regional hospital in Sweden that integrated occupational health processes with a pre-existing kaizen system. Multi-group structural equation modeling revealed that, in the intervention group, kaizen work predicted better integration of organizational and employee objectives after 12 months, which, in turn, predicted increased job satisfaction and decreased discomfort at 24 months. The findings suggest that participatory and structured problem-solving approaches that are familiar and visual to employees can facilitate organizational interventions. PMID:28736455
Galvanic Liquid Applied Coating Development for Protection of Steel in Concrete

NASA Technical Reports Server (NTRS)

Curran, Joseph John; Curran, Jerry; MacDowell, Louis

2004-01-01

Corrosion of reinforcing steel in concrete is a major problem affecting NASA facilities at Kennedy Space Center (KSC), other government agencies, and the general public. Problems include damage to KSC launch support structures, transportation and marine infrastructures, as well as building structures. A galvanic liquid applied coating was developed at KSC in order to address this problem. The coating is a non-epoxy metal rich ethyl silicate liquid coating. The coating is applied as a liquid from initial stage to final stage. Preliminary data shows that this coating system exceeds the NACE 100 millivolt shift criterion. The remainder of the paper details the development of the coating system through the following phases: Phase I: Development of multiple formulations of the coating to achieve easy application characteristics, predictable galvanic activity, long-term protection, and minimum environmental impact. Phase II: Improvement of the formulations tested in Phase I including optimization of metallic loading as well as incorporation of humectants for continuous activation. Phase III: Application and testing of improved formulations on the test blocks. Phase IV: Incorporation of the final formulation upgrades onto large instrumented structures (slabs).
A novel time series link prediction method: Learning automata approach

NASA Astrophysics Data System (ADS)

Moradabadi, Behnaz; Meybodi, Mohammad Reza

2017-09-01

Link prediction is a main social network challenge that uses the network structure to predict future links. The common link prediction approaches to predict hidden links use a static graph representation where a snapshot of the network is analyzed to find hidden or future links. For example, similarity metric based link predictions are a common traditional approach that calculates the similarity metric for each non-connected link and sort the links based on their similarity metrics and label the links with higher similarity scores as the future links. Because people activities in social networks are dynamic and uncertainty, and the structure of the networks changes over time, using deterministic graphs for modeling and analysis of the social network may not be appropriate. In the time-series link prediction problem, the time series link occurrences are used to predict the future links In this paper, we propose a new time series link prediction based on learning automata. In the proposed algorithm for each link that must be predicted there is one learning automaton and each learning automaton tries to predict the existence or non-existence of the corresponding link. To predict the link occurrence in time T, there is a chain consists of stages 1 through T - 1 and the learning automaton passes from these stages to learn the existence or non-existence of the corresponding link. Our preliminary link prediction experiments with co-authorship and email networks have provided satisfactory results when time series link occurrences are considered.
[Formula: see text]Executive functions and social information processing in adolescents with severe behavior problems.

PubMed

Van Nieuwenhuijzen, M; Van Rest, M M; Embregts, P J C M; Vriens, A; Oostermeijer, S; Van Bokhoven, I; Matthys, W

2017-02-01

One tradition in research for explaining aggression and antisocial behavior has focused on social information processing (SIP). Aggression and antisocial behavior have also been studied from the perspective of executive functions (EFs), the higher-order cognitive abilities that affect other cognitive processes, such as social cognitive processes. The main goal of the present study is to provide insight into the relation between EFs and SIP in adolescents with severe behavior problems. Because of the hierarchical relation between EFs and SIP, we examined EFs as predictors of SIP. We hypothesized that, first, focused attention predicts encoding and interpretation, second, inhibition predicts interpretation, response generation, evaluation, and selection, and third, working memory predicts response generation and selection. The participants consisted of 94 respondents living in residential facilities aged 12-20 years, all showing behavior problems in the clinical range according to care staff. EFs were assessed using subtests from the Amsterdam Neuropsychological Test battery. Focused attention was measured by the Flanker task, inhibition by the GoNoGo task, and working memory by the Visual Spatial Sequencing task. SIP was measured by video vignettes and a structured interview. The results indicate that positive evaluation of aggressive responses is predicted by impaired inhibition and selection of aggressive responses by a combination of impaired focused attention and inhibition. It is concluded that different components of EFs as higher-order cognitive abilities affect SIP.
Efficient Computation of Info-Gap Robustness for Finite Element Models

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

Stull, Christopher J.; Hemez, Francois M.; Williams, Brian J.

2012-07-05

A recent research effort at LANL proposed info-gap decision theory as a framework by which to measure the predictive maturity of numerical models. Info-gap theory explores the trade-offs between accuracy, that is, the extent to which predictions reproduce the physical measurements, and robustness, that is, the extent to which predictions are insensitive to modeling assumptions. Both accuracy and robustness are necessary to demonstrate predictive maturity. However, conducting an info-gap analysis can present a formidable challenge, from the standpoint of the required computational resources. This is because a robustness function requires the resolution of multiple optimization problems. This report offers anmore » alternative, adjoint methodology to assess the info-gap robustness of Ax = b-like numerical models solved for a solution x. Two situations that can arise in structural analysis and design are briefly described and contextualized within the info-gap decision theory framework. The treatments of the info-gap problems, using the adjoint methodology are outlined in detail, and the latter problem is solved for four separate finite element models. As compared to statistical sampling, the proposed methodology offers highly accurate approximations of info-gap robustness functions for the finite element models considered in the report, at a small fraction of the computational cost. It is noted that this report considers only linear systems; a natural follow-on study would extend the methodologies described herein to include nonlinear systems.« less
Realizing drug repositioning by adapting a recommendation system to handle the process.

PubMed

Ozsoy, Makbule Guclin; Özyer, Tansel; Polat, Faruk; Alhajj, Reda

2018-04-12

Drug repositioning is the process of identifying new targets for known drugs. It can be used to overcome problems associated with traditional drug discovery by adapting existing drugs to treat new discovered diseases. Thus, it may reduce associated risk, cost and time required to identify and verify new drugs. Nowadays, drug repositioning has received more attention from industry and academia. To tackle this problem, researchers have applied many different computational methods and have used various features of drugs and diseases. In this study, we contribute to the ongoing research efforts by combining multiple features, namely chemical structures, protein interactions and side-effects to predict new indications of target drugs. To achieve our target, we realize drug repositioning as a recommendation process and this leads to a new perspective in tackling the problem. The utilized recommendation method is based on Pareto dominance and collaborative filtering. It can also integrate multiple data-sources and multiple features. For the computation part, we applied several settings and we compared their performance. Evaluation results show that the proposed method can achieve more concentrated predictions with high precision, where nearly half of the predictions are true. Compared to other state of the art methods described in the literature, the proposed method is better at making right predictions by having higher precision. The reported results demonstrate the applicability and effectiveness of recommendation methods for drug repositioning.
Accurate secondary structure prediction and fold recognition for circular dichroism spectroscopy

PubMed Central

Micsonai, András; Wien, Frank; Kernya, Linda; Lee, Young-Ho; Goto, Yuji; Réfrégiers, Matthieu; Kardos, József

2015-01-01

Circular dichroism (CD) spectroscopy is a widely used technique for the study of protein structure. Numerous algorithms have been developed for the estimation of the secondary structure composition from the CD spectra. These methods often fail to provide acceptable results on α/β-mixed or β-structure–rich proteins. The problem arises from the spectral diversity of β-structures, which has hitherto been considered as an intrinsic limitation of the technique. The predictions are less reliable for proteins of unusual β-structures such as membrane proteins, protein aggregates, and amyloid fibrils. Here, we show that the parallel/antiparallel orientation and the twisting of the β-sheets account for the observed spectral diversity. We have developed a method called β-structure selection (BeStSel) for the secondary structure estimation that takes into account the twist of β-structures. This method can reliably distinguish parallel and antiparallel β-sheets and accurately estimates the secondary structure for a broad range of proteins. Moreover, the secondary structure components applied by the method are characteristic to the protein fold, and thus the fold can be predicted to the level of topology in the CATH classification from a single CD spectrum. By constructing a web server, we offer a general tool for a quick and reliable structure analysis using conventional CD or synchrotron radiation CD (SRCD) spectroscopy for the protein science research community. The method is especially useful when X-ray or NMR techniques fail. Using BeStSel on data collected by SRCD spectroscopy, we investigated the structure of amyloid fibrils of various disease-related proteins and peptides. PMID:26038575
Some Unsolved Problems, Questions, and Applications of the Brightsen Nucleon Cluster Model

NASA Astrophysics Data System (ADS)

Smarandache, Florentin

2010-10-01

Brightsen Model is opposite to the Standard Model, and it was build on John Weeler's Resonating Group Structure Model and on Linus Pauling's Close-Packed Spheron Model. Among Brightsen Model's predictions and applications we cite the fact that it derives the average number of prompt neutrons per fission event, it provides a theoretical way for understanding the low temperature / low energy reactions and for approaching the artificially induced fission, it predicts that forces within nucleon clusters are stronger than forces between such clusters within isotopes; it predicts the unmatter entities inside nuclei that result from stable and neutral union of matter and antimatter, and so on. But these predictions have to be tested in the future at the new CERN laboratory.

Maximum likelihood Bayesian model averaging and its predictive analysis for groundwater reactive transport models

USGS Publications Warehouse

Curtis, Gary P.; Lu, Dan; Ye, Ming

2015-01-01

While Bayesian model averaging (BMA) has been widely used in groundwater modeling, it is infrequently applied to groundwater reactive transport modeling because of multiple sources of uncertainty in the coupled hydrogeochemical processes and because of the long execution time of each model run. To resolve these problems, this study analyzed different levels of uncertainty in a hierarchical way, and used the maximum likelihood version of BMA, i.e., MLBMA, to improve the computational efficiency. This study demonstrates the applicability of MLBMA to groundwater reactive transport modeling in a synthetic case in which twenty-seven reactive transport models were designed to predict the reactive transport of hexavalent uranium (U(VI)) based on observations at a former uranium mill site near Naturita, CO. These reactive transport models contain three uncertain model components, i.e., parameterization of hydraulic conductivity, configuration of model boundary, and surface complexation reactions that simulate U(VI) adsorption. These uncertain model components were aggregated into the alternative models by integrating a hierarchical structure into MLBMA. The modeling results of the individual models and MLBMA were analyzed to investigate their predictive performance. The predictive logscore results show that MLBMA generally outperforms the best model, suggesting that using MLBMA is a sound strategy to achieve more robust model predictions relative to a single model. MLBMA works best when the alternative models are structurally distinct and have diverse model predictions. When correlation in model structure exists, two strategies were used to improve predictive performance by retaining structurally distinct models or assigning smaller prior model probabilities to correlated models. Since the synthetic models were designed using data from the Naturita site, the results of this study are expected to provide guidance for real-world modeling. Limitations of applying MLBMA to the synthetic study and future real-world modeling are discussed.
Generalized self-adjustment method for statistical mechanics of composite materials

NASA Astrophysics Data System (ADS)

Pan'kov, A. A.

1997-03-01

A new method is developed for the statistical mechanics of composite materials — the generalized selfadjustment method — which makes it possible to reduce the problem of predicting effective elastic properties of composites with random structures to the solution of two simpler "averaged" problems of an inclusion with transitional layers in a medium with the desired effective elastic properties. The inhomogeneous elastic properties and dimensions of the transitional layers take into account both the "approximate" order of mutual positioning, and also the variation in the dimensions and elastics properties of inclusions through appropriate special averaged indicator functions of the random structure of the composite. A numerical calculation of averaged indicator functions and effective elastic characteristics is performed by the generalized self-adjustment method for a unidirectional fiberglass on the basis of various models of actual random structures in the plane of isotropy.
Temperament and Parenting during the First Year of Life Predict Future Child Conduct Problems

PubMed Central

Lahey, Benjamin B.; Van Hulle, Carol A.; Keenan, Kate; Rathouz, Paul J.; D’Onofrio, Brian M.; Rodgers, Joseph Lee; Waldman, Irwin D.

2010-01-01

Predictive associations between parenting and temperament during the first year of life and child conduct problems were assessed longitudinally in 1,863 offspring of a representative sample of women. Maternal ratings of infant fussiness, activity level, predictability, and positive affect each independently predicted maternal ratings of conduct problems during ages 4–13 years. Furthermore, a significant interaction indicated that infants who were both low in fussiness and high in predictability were at very low risk for future conduct problems. Fussiness was a stronger predictor of conduct problems in boys whereas fearfulness was a stronger predictor in girls. Conduct problems also were robustly predicted by low levels of early mother-report cognitive stimulation. Interviewer-rated maternal responsiveness was a robust predictor of conduct problems, but only among infants low in fearfulness. Spanking during infancy predicted slightly more severe conduct problems, but the prediction was moderated by infant fussiness and positive affect. Thus, individual differences in risk for mother-rated conduct problems across childhood are already partly evident in maternal ratings of temperament during the first year of life and are predicted by early parenting and parenting-by-temperament interactions. PMID:18568397
Comprehension by learning-disabled and nondisabled adolescents of personal/social problems presented in text.

PubMed

Williams, J P

1991-01-01

Four groups of 14-year-olds, differing in reading level, learning disability status, and socioeconomic status, read and retold short problem narratives and answered questions. The pattern of reporting components of the problem schema (goal/obstacles/choices) differed for problems presented with or without a statement of the character's priority for action, suggesting that including priorities adds another level of information to the problem text and changes its macrostructure. Even the poorest readers showed this sensitivity to text structure. Three of the four measures of problem representation (idea units recalled, problem-schema components reported, and error rate) reflected overall reading ability. However, the degree to which extraneous information was incorporated into problem representations did not. Learning-disabled students made more importations, and more implausible importations, than did non-disabled students. Moreover, this pattern was associated with poor problem solving. Only proficient readers showed awareness of the source of the information (text or extratext) on which their predictions were based.
RNA 3D Structure Modeling by Combination of Template-Based Method ModeRNA, Template-Free Folding with SimRNA, and Refinement with QRNAS.

PubMed

Piatkowski, Pawel; Kasprzak, Joanna M; Kumar, Deepak; Magnus, Marcin; Chojnowski, Grzegorz; Bujnicki, Janusz M

2016-01-01

RNA encompasses an essential part of all known forms of life. The functions of many RNA molecules are dependent on their ability to form complex three-dimensional (3D) structures. However, experimental determination of RNA 3D structures is laborious and challenging, and therefore, the majority of known RNAs remain structurally uncharacterized. To address this problem, computational structure prediction methods were developed that either utilize information derived from known structures of other RNA molecules (by way of template-based modeling) or attempt to simulate the physical process of RNA structure formation (by way of template-free modeling). All computational methods suffer from various limitations that make theoretical models less reliable than high-resolution experimentally determined structures. This chapter provides a protocol for computational modeling of RNA 3D structure that overcomes major limitations by combining two complementary approaches: template-based modeling that is capable of predicting global architectures based on similarity to other molecules but often fails to predict local unique features, and template-free modeling that can predict the local folding, but is limited to modeling the structure of relatively small molecules. Here, we combine the use of a template-based method ModeRNA with a template-free method SimRNA. ModeRNA requires a sequence alignment of the target RNA sequence to be modeled with a template of the known structure; it generates a model that predicts the structure of a conserved core and provides a starting point for modeling of variable regions. SimRNA can be used to fold small RNAs (<80 nt) without any additional structural information, and to refold parts of models for larger RNAs that have a correctly modeled core. ModeRNA can be either downloaded, compiled and run locally or run through a web interface at http://genesilico.pl/modernaserver/ . SimRNA is currently available to download for local use as a precompiled software package at http://genesilico.pl/software/stand-alone/simrna and as a web server at http://genesilico.pl/SimRNAweb . For model optimization we use QRNAS, available at http://genesilico.pl/qrnas .
Vibrations and structureborne noise in space station

NASA Technical Reports Server (NTRS)

Vaicaitis, R.

1985-01-01

Theoretical models were developed capable of predicting structural response and noise transmission to random point mechanical loads. Fiber reinforced composite and aluminum materials were considered. Cylindrical shells and circular plates were taken as typical representatives of structural components for space station habitability modules. Analytical formulations include double wall and single wall constructions. Pressurized and unpressurized models were considered. Parametric studies were conducted to determine the effect on structural response and noise transmission due to fiber orientation, point load location, damping in the core and the main load carrying structure, pressurization, interior acoustic absorption, etc. These analytical models could serve as preliminary tools for assessing noise related problems, for space station applications.
Cohesive phase-field fracture and a PDE constrained optimization approach to fracture inverse problems

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

Tupek, Michael R.

2016-06-30

In recent years there has been a proliferation of modeling techniques for forward predictions of crack propagation in brittle materials, including: phase-field/gradient damage models, peridynamics, cohesive-zone models, and G/XFEM enrichment techniques. However, progress on the corresponding inverse problems has been relatively lacking. Taking advantage of key features of existing modeling approaches, we propose a parabolic regularization of Barenblatt cohesive models which borrows extensively from previous phase-field and gradient damage formulations. An efficient explicit time integration strategy for this type of nonlocal fracture model is then proposed and justified. In addition, we present a C++ computational framework for computing in- putmore » parameter sensitivities efficiently for explicit dynamic problems using the adjoint method. This capability allows for solving inverse problems involving crack propagation to answer interesting engineering questions such as: 1) what is the optimal design topology and material placement for a heterogeneous structure to maximize fracture resistance, 2) what loads must have been applied to a structure for it to have failed in an observed way, 3) what are the existing cracks in a structure given various experimental observations, etc. In this work, we focus on the first of these engineering questions and demonstrate a capability to automatically and efficiently compute optimal designs intended to minimize crack propagation in structures.« less
Optical Variability and Bottom Classification in Turbid Waters: HyMOM Predictions of the Light Field in Ports and Beneath Ship Hulls

DTIC Science & Technology

2006-09-30

dealing with the bleaching of corals and foraminifera and the photosynthesis of benthic plants. OBJECTIVES The initial objective of this work...of the structural light field around coral heads and other vertical structures should be included in future studies of bleaching of coral and... coral bleaching –Perceptibility problem begun for AUV @2m and @8m above 10m bottom –Higher resolution and higher-speed calculations (e.g. more
Foraging on the potential energy surface: a swarm intelligence-based optimizer for molecular geometry.

PubMed

Wehmeyer, Christoph; Falk von Rudorff, Guido; Wolf, Sebastian; Kabbe, Gabriel; Schärf, Daniel; Kühne, Thomas D; Sebastiani, Daniel

2012-11-21

We present a stochastic, swarm intelligence-based optimization algorithm for the prediction of global minima on potential energy surfaces of molecular cluster structures. Our optimization approach is a modification of the artificial bee colony (ABC) algorithm which is inspired by the foraging behavior of honey bees. We apply our modified ABC algorithm to the problem of global geometry optimization of molecular cluster structures and show its performance for clusters with 2-57 particles and different interatomic interaction potentials.
Foraging on the potential energy surface: A swarm intelligence-based optimizer for molecular geometry

NASA Astrophysics Data System (ADS)

Wehmeyer, Christoph; Falk von Rudorff, Guido; Wolf, Sebastian; Kabbe, Gabriel; Schärf, Daniel; Kühne, Thomas D.; Sebastiani, Daniel

2012-11-01

We present a stochastic, swarm intelligence-based optimization algorithm for the prediction of global minima on potential energy surfaces of molecular cluster structures. Our optimization approach is a modification of the artificial bee colony (ABC) algorithm which is inspired by the foraging behavior of honey bees. We apply our modified ABC algorithm to the problem of global geometry optimization of molecular cluster structures and show its performance for clusters with 2-57 particles and different interatomic interaction potentials.
Protein structure prediction with local adjust tabu search algorithm

PubMed Central

2014-01-01

Background Protein folding structure prediction is one of the most challenging problems in the bioinformatics domain. Because of the complexity of the realistic protein structure, the simplified structure model and the computational method should be adopted in the research. The AB off-lattice model is one of the simplification models, which only considers two classes of amino acids, hydrophobic (A) residues and hydrophilic (B) residues. Results The main work of this paper is to discuss how to optimize the lowest energy configurations in 2D off-lattice model and 3D off-lattice model by using Fibonacci sequences and real protein sequences. In order to avoid falling into local minimum and faster convergence to the global minimum, we introduce a novel method (SATS) to the protein structure problem, which combines simulated annealing algorithm and tabu search algorithm. Various strategies, such as the new encoding strategy, the adaptive neighborhood generation strategy and the local adjustment strategy, are adopted successfully for high-speed searching the optimal conformation corresponds to the lowest energy of the protein sequences. Experimental results show that some of the results obtained by the improved SATS are better than those reported in previous literatures, and we can sure that the lowest energy folding state for short Fibonacci sequences have been found. Conclusions Although the off-lattice models is not very realistic, they can reflect some important characteristics of the realistic protein. It can be found that 3D off-lattice model is more like native folding structure of the realistic protein than 2D off-lattice model. In addition, compared with some previous researches, the proposed hybrid algorithm can more effectively and more quickly search the spatial folding structure of a protein chain. PMID:25474708
Structural Inelasticity (28th), A Finite Element ’Vectorization’ Method for the Solution of Crack Growth Problems in Two or Three Dimensions.

DTIC Science & Technology

1986-02-01

analitic and numerical paws which have aieared In the literature. A more detail accbunt is contained In the review article by Rice [91. The...where Y is the initial yield stress. Based on the stress change A011O) we predict thA the element o for which...solution predicts an applied load of 0.93, which is 7% greater than the measured value. The plastic zones at different leves of lied load are shown
Perspectives on multifield models

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

Banerjee, S.

1997-07-01

Multifield models for prediction of nuclear reactor thermalhydraulics are reviewed from the viewpoint of their structure and requirements for closure relationships. Their strengths and weaknesses are illustrated with examples, indicating that they are effective in predicting separated and distributed flow regimes, but have problems for flows with large oscillations. Needs for multifield models are also discussed in the context of reactor operations and accident simulations. The highest priorities for future developments appear to relate to closure relationships for three-dimensional multifield models with emphasis on those needed for calculations of phase separation and entrainment/de-entrainment in complex geometries.
First-principles theory of cation- and intercalation-ordering in Li_xCoO_2

NASA Astrophysics Data System (ADS)

Wolverton, C.; Zunger, Alex

1998-03-01

Using a combination of first-principles total energies, a cluster expansion technique, and Monte Carlo simulations, we present a first-principles theory which can predict both cation- and intercalation-ordering patterns at both zero and finite temperatures, and can provide first-principles predictions of battery voltages of Li_xCoO_2/Li cells. The classes of ordering problems that we study are the following: (i) The LiMO2 oxides (M=3d metal) form a series of structures based on an octahedrally-coordinated network with anions (O) on one fcc sublattice and cations (Li and M) on the other, leading to Li/Co ordering in LiCoO2 (x=1). We find the ground state is the CuPt or (111)-layered cation arrangment, in agreement with the observed structure. (ii) In battery applications, Li is (de)intercalated from the compound, creating a vacancy (denoted Box) that can be positioned in different lattice locations; Thus, Box/Co ordering in BoxCoO2 (x=0) is also of interest. We find the ground state for BoxCoO2 is also a (111)-layered structure, although a different stacking sequence (AAA) of close-packed layers is preferred. (iii) The vacancies left behind by Li extraction can form ordered vacancy compounds in partially de-lithiated Li_xCoO_2, leading to a Box/Li ordering problem (0<=x<=1). Our calculations agree with the observed voltage profiles in these systems, and predict the existence of new intercalation-ordered compounds. Supported by BES/OER/DMS under contract DE-AC36-83CH10093.
Partial unfolding and refolding for structure refinement: A unified approach of geometric simulations and molecular dynamics.

PubMed

Kumar, Avishek; Campitelli, Paul; Thorpe, M F; Ozkan, S Banu

2015-12-01

The most successful protein structure prediction methods to date have been template-based modeling (TBM) or homology modeling, which predicts protein structure based on experimental structures. These high accuracy predictions sometimes retain structural errors due to incorrect templates or a lack of accurate templates in the case of low sequence similarity, making these structures inadequate in drug-design studies or molecular dynamics simulations. We have developed a new physics based approach to the protein refinement problem by mimicking the mechanism of chaperons that rehabilitate misfolded proteins. The template structure is unfolded by selectively (targeted) pulling on different portions of the protein using the geometric based technique FRODA, and then refolded using hierarchically restrained replica exchange molecular dynamics simulations (hr-REMD). FRODA unfolding is used to create a diverse set of topologies for surveying near native-like structures from a template and to provide a set of persistent contacts to be employed during re-folding. We have tested our approach on 13 previous CASP targets and observed that this method of folding an ensemble of partially unfolded structures, through the hierarchical addition of contact restraints (that is, first local and then nonlocal interactions), leads to a refolding of the structure along with refinement in most cases (12/13). Although this approach yields refined models through advancement in sampling, the task of blind selection of the best refined models still needs to be solved. Overall, the method can be useful for improved sampling for low resolution models where certain of the portions of the structure are incorrectly modeled. © 2015 Wiley Periodicals, Inc.
PDB-wide identification of biological assemblies from conserved quaternary structure geometry.

PubMed

Dey, Sucharita; Ritchie, David W; Levy, Emmanuel D

2018-01-01

Protein structures are key to understanding biomolecular mechanisms and diseases, yet their interpretation is hampered by limited knowledge of their biologically relevant quaternary structure (QS). A critical challenge in inferring QS information from crystallographic data is distinguishing biological interfaces from fortuitous crystal-packing contacts. Here, we tackled this problem by developing strategies for aligning and comparing QS states across both homologs and data repositories. QS conservation across homologs proved remarkably strong at predicting biological relevance and is implemented in two methods, QSalign and anti-QSalign, for annotating homo-oligomers and monomers, respectively. QS conservation across repositories is implemented in QSbio (http://www.QSbio.org), which approaches the accuracy of manual curation and allowed us to predict >100,000 QS states across the Protein Data Bank. Based on this high-quality data set, we analyzed pairs of structurally conserved interfaces, and this analysis revealed a striking plasticity whereby evolutionary distant interfaces maintain similar interaction geometries through widely divergent chemical properties.
Detection of bondline delaminations in multilayer structures with lossy components

NASA Technical Reports Server (NTRS)

Madaras, Eric I.; Winfree, William P.; Smith, B. T.; Heyman, Joseph H.

1988-01-01

The detection of bondline delaminations in multilayer structures using ultrasonic reflection techniques is a generic problem in adhesively bonded composite structures such as the Space Shuttles's Solid Rocket Motors (SRM). Standard pulse echo ultrasonic techniques do not perform well for a composite resonator composed of a resonant layer combined with attenuating layers. Excessive ringing in the resonant layer tends to mask internal echoes emanating from the attenuating layers. The SRM is made up of a resonant steel layer backed by layers of adhesive, rubber, liner and fuel, which are ultrasonically attenuating. The structure's response is modeled as a lossy ultrasonic transmission line. The model predicts that the acoustic response of the system is sensitive to delaminations at the interior bondlines in a few narrow frequency bands. These predictions are verified by measurements on a fabricated system. Successful imaging of internal delaminations is sensitive to proper selection of the interrogating frequency. Images of fabricated bondline delaminations are presented based on these studies.
The use of subjective expert opinions in cost optimum design of aerospace structures. [probabilistic failure models

NASA Technical Reports Server (NTRS)

Thomas, J. M.; Hanagud, S.

1975-01-01

The results of two questionnaires sent to engineering experts are statistically analyzed and compared with objective data from Saturn V design and testing. Engineers were asked how likely it was for structural failure to occur at load increments above and below analysts' stress limit predictions. They were requested to estimate the relative probabilities of different failure causes, and of failure at each load increment given a specific cause. Three mathematical models are constructed based on the experts' assessment of causes. The experts' overall assessment of prediction strength fits the Saturn V data better than the models do, but a model test option (T-3) based on the overall assessment gives more design change likelihood to overstrength structures than does an older standard test option. T-3 compares unfavorably with the standard option in a cost optimum structural design problem. The report reflects a need for subjective data when objective data are unavailable.
Automated antibody structure prediction using Accelrys tools: Results and best practices

PubMed Central

Fasnacht, Marc; Butenhof, Ken; Goupil-Lamy, Anne; Hernandez-Guzman, Francisco; Huang, Hongwei; Yan, Lisa

2014-01-01

We describe the methodology and results from our participation in the second Antibody Modeling Assessment experiment. During the experiment we predicted the structure of eleven unpublished antibody Fv fragments. Our prediction methods centered on template-based modeling; potential templates were selected from an antibody database based on their sequence similarity to the target in the framework regions. Depending on the quality of the templates, we constructed models of the antibody framework regions either using a single, chimeric or multiple template approach. The hypervariable loop regions in the initial models were rebuilt by grafting the corresponding regions from suitable templates onto the model. For the H3 loop region, we further refined models using ab initio methods. The final models were subjected to constrained energy minimization to resolve severe local structural problems. The analysis of the models submitted show that Accelrys tools allow for the construction of quite accurate models for the framework and the canonical CDR regions, with RMSDs to the X-ray structure on average below 1 Å for most of these regions. The results show that accurate prediction of the H3 hypervariable loops remains a challenge. Furthermore, model quality assessment of the submitted models show that the models are of quite high quality, with local geometry assessment scores similar to that of the target X-ray structures. Proteins 2014; 82:1583–1598. © 2014 The Authors. Proteins published by Wiley Periodicals, Inc. PMID:24833271
Method for protein structure alignment

DOEpatents

Blankenbecler, Richard; Ohlsson, Mattias; Peterson, Carsten; Ringner, Markus

2005-02-22

This invention provides a method for protein structure alignment. More particularly, the present invention provides a method for identification, classification and prediction of protein structures. The present invention involves two key ingredients. First, an energy or cost function formulation of the problem simultaneously in terms of binary (Potts) assignment variables and real-valued atomic coordinates. Second, a minimization of the energy or cost function by an iterative method, where in each iteration (1) a mean field method is employed for the assignment variables and (2) exact rotation and/or translation of atomic coordinates is performed, weighted with the corresponding assignment variables.

Physics and chemistry-driven artificial neural network for predicting bioactivity of peptides and proteins and their design.

PubMed

Huang, Ri-Bo; Du, Qi-Shi; Wei, Yu-Tuo; Pang, Zong-Wen; Wei, Hang; Chou, Kuo-Chen

2009-02-07

Predicting the bioactivity of peptides and proteins is an important challenge in drug development and protein engineering. In this study we introduce a novel approach, the so-called "physics and chemistry-driven artificial neural network (Phys-Chem ANN)", to deal with such a problem. Unlike the existing ANN approaches, which were designed under the inspiration of biological neural system, the Phys-Chem ANN approach is based on the physical and chemical principles, as well as the structural features of proteins. In the Phys-Chem ANN model the "hidden layers" are no longer virtual "neurons", but real structural units of proteins and peptides. It is a hybridization approach, which combines the linear free energy concept of quantitative structure-activity relationship (QSAR) with the advanced mathematical technique of ANN. The Phys-Chem ANN approach has adopted an iterative and feedback procedure, incorporating both machine-learning and artificial intelligence capabilities. In addition to making more accurate predictions for the bioactivities of proteins and peptides than is possible with the traditional QSAR approach, the Phys-Chem ANN approach can also provide more insights about the relationship between bioactivities and the structures involved than the ANN approach does. As an example of the application of the Phys-Chem ANN approach, a predictive model for the conformational stability of human lysozyme is presented.
Examining the Latent Structure of the Delis-Kaplan Executive Function System.

PubMed

Karr, Justin E; Hofer, Scott M; Iverson, Grant L; Garcia-Barrera, Mauricio A

2018-05-04

The current study aimed to determine whether the Delis-Kaplan Executive Function System (D-KEFS) taps into three executive function factors (inhibition, shifting, fluency) and to assess the relationship between these factors and tests of executive-related constructs less often measured in latent variable research: reasoning, abstraction, and problem solving. Participants included 425 adults from the D-KEFS standardization sample (20-49 years old; 50.1% female; 70.1% White). Eight alternative measurement models were compared based on model fit, with test scores assigned a priori to three factors: inhibition (Color-Word Interference, Tower), shifting (Trail Making, Sorting, Design Fluency), and fluency (Verbal/Design Fluency). The Twenty Questions, Word Context, and Proverb Tests were predicted in separate structural models. The three-factor model fit the data well (CFI = 0.938; RMSEA = 0.047), although a two-factor model, with shifting and fluency merged, fit similarly well (CFI = 0.929; RMSEA = 0.048). A bifactor model fit best (CFI = 0.977; RMSEA = 0.032) and explained the most variance in shifting indicators, but rarely converged among 5,000 bootstrapped samples. When the three first-order factors simultaneously predicted the criterion variables, only shifting was uniquely predictive (p < .05; R2 = 0.246-0.408). The bifactor significantly predicted all three criterion variables (p < .001; R2 = 0.141-242). Results supported a three-factor D-KEFS model (i.e., inhibition, shifting, and fluency), although shifting and fluency were highly related (r = 0.696). The bifactor showed superior fit, but converged less often than other models. Shifting best predicted tests of reasoning, abstraction, and problem solving. These findings support the validity of D-KEFS scores for measuring executive-related constructs and provide a framework through which clinicians can interpret D-KEFS results.
Can Learning Style Predict Student Satisfaction with Different Instruction Methods and Academic Achievement in Medical Education?

ERIC Educational Resources Information Center

Gurpinar, Erol; Alimoglu, Mustafa Kemal; Mamakli, Sumer; Aktekin, Mehmet

2010-01-01

The curriculum of our medical school has a hybrid structure including both traditional training (lectures) and problem-based learning (PBL) applications. The purpose of this study was to determine the learning styles of our medical students and investigate the relation of learning styles with each of satisfaction with different instruction methods…
The problem of ecological scaling in spatially complex, nonequilibrium ecological systems [chapter 3

Treesearch

Samuel A. Cushman; Jeremy Littell; Kevin McGarigal

2010-01-01

In the previous chapter we reviewed the challenges posed by spatial complexity and temporal disequilibrium to efforts to understand and predict the structure and dynamics of ecological systems. The central theme was that spatial variability in the environment and population processes fundamentally alters the interactions between species and their environments, largely...
Turbine Engine Hot Section Technology (HOST)

NASA Technical Reports Server (NTRS)

1982-01-01

Research and plans concerning aircraft gas turbine engine hot section durability problems were discussed. Under the topics of structural analysis, fatigue and fracture, surface protective coatings, combustion, turbine heat transfer, and instrumentation specific points addressed were the thermal and fluid environment around liners, blades, and vanes, material coatings, constitutive behavior, stress-strain response, and life prediction methods for the three components.
Genetic and environmental influences on externalizing behavior and alcohol problems in adolescence: A female twin study

PubMed Central

Knopik, Valerie S.; Heath, Andrew C.; Bucholz, Kathleen K.; Madden, Pamela A.F.; Waldron, Mary

2009-01-01

Genetic and environmental contributions to the observed correlations among DSM-IV ADHD problems [inattentive (INATT) and hyperactive/impulsive (HYP/IMP) behaviors], conduct problems (CDP) and alcohol problems (AlcProb) were examined by fitting multivariate structural equation models to data from the Missouri Adolescent Female Twin Study [N=2892 twins (831 monozygotic pairs, 615 dizygotic pairs)]. Based on results of preliminary regression models, we modified the structural model to jointly estimate (i) the regression of each phenotype on significant familial/prenatal predictors, and (ii) genetic and environmental contributions to the residual variance and covariance. Results suggested that (i) parental risk factors, such as parental alcohol dependence and regular smoking, increase risk for externalizing behavior; (ii) prenatal exposures predicted increased symptomatology for HYP/IMP (smoking during pregnancy), INATT and CDP (prenatal alcohol exposure); (iii) after adjusting for measured familial/prenatal risk factors, genetic influences were significant for HYP/IMP, INATT, and CDP; however, similar to earlier reports, genetic effects on alcohol dependence symptoms were negligible; and (iv) in adolescence, correlated liabilities for conduct and alcohol problems are found in environmental factors common to both phenotypes, while covariation among impulsivity, inattention, and conduct problems is primarily due to genetic influences common to these three behaviors. Thus, while a variety of adolescent problem behaviors are significantly correlated, the structure of that association may differ as a function of phenotype (e.g., comorbid HYP/IMP and CDP vs. comorbid CDP and AlcProb), a finding that could inform different approaches to treatment and prevention. PMID:19341765
A structural equation modeling of executive functions, IQ and mathematical skills in primary students: Differential effects on number production, mental calculus and arithmetical problems.

PubMed

Arán Filippetti, Vanessa; Richaud, María Cristina

2017-10-01

Though the relationship between executive functions (EFs) and mathematical skills has been well documented, little is known about how both EFs and IQ differentially support diverse math domains in primary students. Inconsistency of results may be due to the statistical techniques employed, specifically, if the analysis is conducted with observed variables, i.e., regression analysis, or at the latent level, i.e., structural equation modeling (SEM). The current study explores the contribution of both EFs and IQ in mathematics through an SEM approach. A total of 118 8- to 12-year-olds were administered measures of EFs, crystallized (Gc) and fluid (Gf) intelligence, and math abilities (i.e., number production, mental calculus and arithmetical problem-solving). Confirmatory factor analysis (CFA) offered support for the three-factor solution of EFs: (1) working memory (WM), (2) shifting, and (3) inhibition. Regarding the relationship among EFs, IQ and math abilities, the results of the SEM analysis showed that (i) WM and age predict number production and mental calculus, and (ii) shifting and sex predict arithmetical problem-solving. In all of the SEM models, EFs partially or totally mediated the relationship between IQ, age and math achievement. These results suggest that EFs differentially supports math abilities in primary-school children and is a more significant predictor of math achievement than IQ level.
Manipulation of M demand of chemistry problems and its effect on student performance: A neo-piagetian study

NASA Astrophysics Data System (ADS)

Niaz, Mansoor

It has been shown that student performance in chemistry problems decreases as the M demand of the problem increases, thus emphasizing the role of information processing in problem solving. It was hypothesized that manipulation (increase or decrease) of the M demand of a problem can affect student performance. Increasing the M demand of a problem would affect more the performance of subjects with a limited functional M capacity. The objective of this study is to investigate the effect of manipulation (increase) of the M demand of chemistry problems, having the same logical structure, on performance of students having different functional M capacity, cognitive style, and formal operational reasoning patterns. As predicted the performance of one group of students was lower after the manipulation (increase) in the M demand of the problem. This shows how even small changes in the amount of information required for processing can lead to working memory overload, as a consequence of a poor capacity for mobilization of M power.
Space and time in the quantum universe.

NASA Astrophysics Data System (ADS)

Smolin, L.

This paper is devoted to the problem of constructing a quantum theory that could describe a closed system - a quantum cosmology. The author argues that this problem is an aspect of a much older problem - that of how to eliminate from the physical theories "ideal elements", which are elements of the mathematical structure whose interpretation requires the existence of things outside the dynamical system described by the theory. This discussion is aimed at uncovering criteria that a theory of quantum cosmology must satisfy, if it is to give physically sensible predictions. The author proposes three such criteria and shows that conventional quantum cosmology can only satisfy them, if there is an intrinsic time coordinate on the phase space of the theory. It is shown that approaches based on correlations in the wave function, that do not use an inner product, cannot satisfy these criteria. As example, the author discusses the problem of quantizing a class of relational dynamical models invented by Barbour and Bertotti. The dynamical structure of these theories is closely analogous to general relativity, and the problem of their measurement theory is also similar. It is concluded that these theories can only be sensibly quantized if they contain an intrinsic time.
Computer vision system for egg volume prediction using backpropagation neural network

NASA Astrophysics Data System (ADS)

Siswantoro, J.; Hilman, M. Y.; Widiasri, M.

2017-11-01

Volume is one of considered aspects in egg sorting process. A rapid and accurate volume measurement method is needed to develop an egg sorting system. Computer vision system (CVS) provides a promising solution for volume measurement problem. Artificial neural network (ANN) has been used to predict the volume of egg in several CVSs. However, volume prediction from ANN could have less accuracy due to inappropriate input features or inappropriate ANN structure. This paper proposes a CVS for predicting the volume of egg using ANN. The CVS acquired an image of egg from top view and then processed the image to extract its 1D and 2 D size features. The features were used as input for ANN in predicting the volume of egg. The experiment results show that the proposed CSV can predict the volume of egg with a good accuracy and less computation time.
Testing a model of depression among Thai adolescents.

PubMed

Vatanasin, Duangjai; Thapinta, Darawan; Thompson, Elaine Adams; Thungjaroenkul, Petsunee

2012-11-01

This predictive correlational study was designed to test a comprehensive model of depression for Thai adolescents. This sample included 800 high school students in Chiang Mai, Thailand. Data were collected using self-reported measures of depression, negative automatic thoughts, effective social problem solving, ineffective social problem solving, rumination, parental care, parental overprotection, and negative life events. Structural equation modeling revealed that negative automatic thoughts, effective and ineffective social problem solving mediated the effects of rumination, negative life events, and parental care and overprotection on adolescent depression. These findings provide new knowledge about identified factors and the mechanisms of their influence on depression among Thai adolescents, which are appropriate for targeting preventive interventions. © 2012 Wiley Periodicals, Inc.
INFO-RNA--a fast approach to inverse RNA folding.

PubMed

Busch, Anke; Backofen, Rolf

2006-08-01

The structure of RNA molecules is often crucial for their function. Therefore, secondary structure prediction has gained much interest. Here, we consider the inverse RNA folding problem, which means designing RNA sequences that fold into a given structure. We introduce a new algorithm for the inverse folding problem (INFO-RNA) that consists of two parts; a dynamic programming method for good initial sequences and a following improved stochastic local search that uses an effective neighbor selection method. During the initialization, we design a sequence that among all sequences adopts the given structure with the lowest possible energy. For the selection of neighbors during the search, we use a kind of look-ahead of one selection step applying an additional energy-based criterion. Afterwards, the pre-ordered neighbors are tested using the actual optimization criterion of minimizing the structure distance between the target structure and the mfe structure of the considered neighbor. We compared our algorithm to RNAinverse and RNA-SSD for artificial and biological test sets. Using INFO-RNA, we performed better than RNAinverse and in most cases, we gained better results than RNA-SSD, the probably best inverse RNA folding tool on the market. www.bioinf.uni-freiburg.de?Subpages/software.html.
Empirical comparison of local structural similarity indices for collaborative-filtering-based recommender systems

NASA Astrophysics Data System (ADS)

Zhang, Qian-Ming; Shang, Ming-Sheng; Zeng, Wei; Chen, Yong; Lü, Linyuan

2010-08-01

Collaborative filtering is one of the most successful recommendation techniques, which can effectively predict the possible future likes of users based on their past preferences. The key problem of this method is how to define the similarity between users. A standard approach is using the correlation between the ratings that two users give to a set of objects, such as Cosine index and Pearson correlation coefficient. However, the costs of computing this kind of indices are relatively high, and thus it is impossible to be applied in the huge-size systems. To solve this problem, in this paper, we introduce six local-structure-based similarity indices and compare their performances with the above two benchmark indices. Experimental results on two data sets demonstrate that the structure-based similarity indices overall outperform the Pearson correlation coefficient. When the data is dense, the structure-based indices can perform competitively good as Cosine index, while with lower computational complexity. Furthermore, when the data is sparse, the structure-based indices give even better results than Cosine index.
Preschoolers’ Psychopathology and Temperament Predict Mothers’ Later Mood Disorders

PubMed Central

Allmann, Anna E.S.; Kopala-Sibley, Daniel C.; Klein, Daniel N.

2017-01-01

Considerable research exists documenting the relationship between maternal mood disorders, primarily major depressive disorder (MDD), and a variety of negative child outcomes. By contrast, research exploring the reverse pathway whereby child traits are associated with later maternal mood disorders is much more limited. We examined whether young children’s temperament and psychopathology predicted maternal mood disorders approximately 6 years later. Child temperament and symptoms were assessed at age three using semi-structured diagnostic interviews and parent-report inventories. Maternal psychopathology was assessed with semi-structured interviews when children were three and nine years old. Mothers also reported on their marital satisfaction when children were three and six years old. Child temperamental negative affectivity (NA), depressive symptoms, and externalizing behavior problems significantly predicted maternal mood disorders over and above prior maternal mood, anxiety, and substance disorders. The link between children’s early externalizing symptoms and maternal mood disorders 6 years later was mediated by maternal marital satisfaction 3 years after the initial assessment. These findings suggest that early child temperament and psychopathology contribute to risk for later maternal mood disorders both directly and through their impact on the marital system. Research indicates that effective treatment of maternal depression is associated with positive outcomes for children; however, this study suggests that treating early child problems may mitigate the risk of later maternal psychopathology. PMID:26219263
Using Conversation Topics for Predicting Therapy Outcomes in Schizophrenia

PubMed Central

Howes, Christine; Purver, Matthew; McCabe, Rose

2013-01-01

Previous research shows that aspects of doctor-patient communication in therapy can predict patient symptoms, satisfaction and future adherence to treatment (a significant problem with conditions such as schizophrenia). However, automatic prediction has so far shown success only when based on low-level lexical features, and it is unclear how well these can generalize to new data, or whether their effectiveness is due to their capturing aspects of style, structure or content. Here, we examine the use of topic as a higher-level measure of content, more likely to generalize and to have more explanatory power. Investigations show that while topics predict some important factors such as patient satisfaction and ratings of therapy quality, they lack the full predictive power of lower-level features. For some factors, unsupervised methods produce models comparable to manual annotation. PMID:23943658
Accurate De Novo Prediction of Protein Contact Map by Ultra-Deep Learning Model.

PubMed

Wang, Sheng; Sun, Siqi; Li, Zhen; Zhang, Renyu; Xu, Jinbo

2017-01-01

Protein contacts contain key information for the understanding of protein structure and function and thus, contact prediction from sequence is an important problem. Recently exciting progress has been made on this problem, but the predicted contacts for proteins without many sequence homologs is still of low quality and not very useful for de novo structure prediction. This paper presents a new deep learning method that predicts contacts by integrating both evolutionary coupling (EC) and sequence conservation information through an ultra-deep neural network formed by two deep residual neural networks. The first residual network conducts a series of 1-dimensional convolutional transformation of sequential features; the second residual network conducts a series of 2-dimensional convolutional transformation of pairwise information including output of the first residual network, EC information and pairwise potential. By using very deep residual networks, we can accurately model contact occurrence patterns and complex sequence-structure relationship and thus, obtain higher-quality contact prediction regardless of how many sequence homologs are available for proteins in question. Our method greatly outperforms existing methods and leads to much more accurate contact-assisted folding. Tested on 105 CASP11 targets, 76 past CAMEO hard targets, and 398 membrane proteins, the average top L long-range prediction accuracy obtained by our method, one representative EC method CCMpred and the CASP11 winner MetaPSICOV is 0.47, 0.21 and 0.30, respectively; the average top L/10 long-range accuracy of our method, CCMpred and MetaPSICOV is 0.77, 0.47 and 0.59, respectively. Ab initio folding using our predicted contacts as restraints but without any force fields can yield correct folds (i.e., TMscore>0.6) for 203 of the 579 test proteins, while that using MetaPSICOV- and CCMpred-predicted contacts can do so for only 79 and 62 of them, respectively. Our contact-assisted models also have much better quality than template-based models especially for membrane proteins. The 3D models built from our contact prediction have TMscore>0.5 for 208 of the 398 membrane proteins, while those from homology modeling have TMscore>0.5 for only 10 of them. Further, even if trained mostly by soluble proteins, our deep learning method works very well on membrane proteins. In the recent blind CAMEO benchmark, our fully-automated web server implementing this method successfully folded 6 targets with a new fold and only 0.3L-2.3L effective sequence homologs, including one β protein of 182 residues, one α+β protein of 125 residues, one α protein of 140 residues, one α protein of 217 residues, one α/β of 260 residues and one α protein of 462 residues. Our method also achieved the highest F1 score on free-modeling targets in the latest CASP (Critical Assessment of Structure Prediction), although it was not fully implemented back then. http://raptorx.uchicago.edu/ContactMap/.
Accurate De Novo Prediction of Protein Contact Map by Ultra-Deep Learning Model

PubMed Central

Li, Zhen; Zhang, Renyu

2017-01-01

Motivation Protein contacts contain key information for the understanding of protein structure and function and thus, contact prediction from sequence is an important problem. Recently exciting progress has been made on this problem, but the predicted contacts for proteins without many sequence homologs is still of low quality and not very useful for de novo structure prediction. Method This paper presents a new deep learning method that predicts contacts by integrating both evolutionary coupling (EC) and sequence conservation information through an ultra-deep neural network formed by two deep residual neural networks. The first residual network conducts a series of 1-dimensional convolutional transformation of sequential features; the second residual network conducts a series of 2-dimensional convolutional transformation of pairwise information including output of the first residual network, EC information and pairwise potential. By using very deep residual networks, we can accurately model contact occurrence patterns and complex sequence-structure relationship and thus, obtain higher-quality contact prediction regardless of how many sequence homologs are available for proteins in question. Results Our method greatly outperforms existing methods and leads to much more accurate contact-assisted folding. Tested on 105 CASP11 targets, 76 past CAMEO hard targets, and 398 membrane proteins, the average top L long-range prediction accuracy obtained by our method, one representative EC method CCMpred and the CASP11 winner MetaPSICOV is 0.47, 0.21 and 0.30, respectively; the average top L/10 long-range accuracy of our method, CCMpred and MetaPSICOV is 0.77, 0.47 and 0.59, respectively. Ab initio folding using our predicted contacts as restraints but without any force fields can yield correct folds (i.e., TMscore>0.6) for 203 of the 579 test proteins, while that using MetaPSICOV- and CCMpred-predicted contacts can do so for only 79 and 62 of them, respectively. Our contact-assisted models also have much better quality than template-based models especially for membrane proteins. The 3D models built from our contact prediction have TMscore>0.5 for 208 of the 398 membrane proteins, while those from homology modeling have TMscore>0.5 for only 10 of them. Further, even if trained mostly by soluble proteins, our deep learning method works very well on membrane proteins. In the recent blind CAMEO benchmark, our fully-automated web server implementing this method successfully folded 6 targets with a new fold and only 0.3L-2.3L effective sequence homologs, including one β protein of 182 residues, one α+β protein of 125 residues, one α protein of 140 residues, one α protein of 217 residues, one α/β of 260 residues and one α protein of 462 residues. Our method also achieved the highest F1 score on free-modeling targets in the latest CASP (Critical Assessment of Structure Prediction), although it was not fully implemented back then. Availability http://raptorx.uchicago.edu/ContactMap/ PMID:28056090
Designing collective behavior in a termite-inspired robot construction team.

PubMed

Werfel, Justin; Petersen, Kirstin; Nagpal, Radhika

2014-02-14

Complex systems are characterized by many independent components whose low-level actions produce collective high-level results. Predicting high-level results given low-level rules is a key open challenge; the inverse problem, finding low-level rules that give specific outcomes, is in general still less understood. We present a multi-agent construction system inspired by mound-building termites, solving such an inverse problem. A user specifies a desired structure, and the system automatically generates low-level rules for independent climbing robots that guarantee production of that structure. Robots use only local sensing and coordinate their activity via the shared environment. We demonstrate the approach via a physical realization with three autonomous climbing robots limited to onboard sensing. This work advances the aim of engineering complex systems that achieve specific human-designed goals.
Dynamic analysis of rotor flex-structure based on nonlinear anisotropic shell models

NASA Astrophysics Data System (ADS)

Bauchau, Olivier A.; Chiang, Wuying

1991-05-01

In this paper an anisotropic shallow shell model is developed that accommodates transverse shearing deformations and arbitrarily large displacements and rotations, but strains are assumed to remain small. Two kinematic models are developed, the first using two DOF to locate the direction of the normal to the shell's midplane, the second using three. The latter model allows for an automatic compatibility of the shell model with beam models. The shell model is validated by comparing its predictions with several benchmark problems. In actual helicopter rotor blade problems, the shell model of the flex structure is shown to give very different results shown compared to beam models. The lead-lag and torsion modes in particular are strongly affected, whereas flapping modes seem to be less affected.
MQAPRank: improved global protein model quality assessment by learning-to-rank.

PubMed

Jing, Xiaoyang; Dong, Qiwen

2017-05-25

Protein structure prediction has achieved a lot of progress during the last few decades and a greater number of models for a certain sequence can be predicted. Consequently, assessing the qualities of predicted protein models in perspective is one of the key components of successful protein structure prediction. Over the past years, a number of methods have been developed to address this issue, which could be roughly divided into three categories: single methods, quasi-single methods and clustering (or consensus) methods. Although these methods achieve much success at different levels, accurate protein model quality assessment is still an open problem. Here, we present the MQAPRank, a global protein model quality assessment program based on learning-to-rank. The MQAPRank first sorts the decoy models by using single method based on learning-to-rank algorithm to indicate their relative qualities for the target protein. And then it takes the first five models as references to predict the qualities of other models by using average GDT_TS scores between reference models and other models. Benchmarked on CASP11 and 3DRobot datasets, the MQAPRank achieved better performances than other leading protein model quality assessment methods. Recently, the MQAPRank participated in the CASP12 under the group name FDUBio and achieved the state-of-the-art performances. The MQAPRank provides a convenient and powerful tool for protein model quality assessment with the state-of-the-art performances, it is useful for protein structure prediction and model quality assessment usages.

A Message Passing Approach to Side Chain Positioning with Applications in Protein Docking Refinement *

PubMed Central

Moghadasi, Mohammad; Kozakov, Dima; Mamonov, Artem B.; Vakili, Pirooz; Vajda, Sandor; Paschalidis, Ioannis Ch.

2013-01-01

We introduce a message-passing algorithm to solve the Side Chain Positioning (SCP) problem. SCP is a crucial component of protein docking refinement, which is a key step of an important class of problems in computational structural biology called protein docking. We model SCP as a combinatorial optimization problem and formulate it as a Maximum Weighted Independent Set (MWIS) problem. We then employ a modified and convergent belief-propagation algorithm to solve a relaxation of MWIS and develop randomized estimation heuristics that use the relaxed solution to obtain an effective MWIS feasible solution. Using a benchmark set of protein complexes we demonstrate that our approach leads to more accurate docking predictions compared to a baseline algorithm that does not solve the SCP. PMID:23515575
Why are some plant-pollinator networks more nested than others?

PubMed

Song, Chuliang; Rohr, Rudolf P; Saavedra, Serguei

2017-10-01

Empirical studies have found that the mutualistic interactions forming the structure of plant-pollinator networks are typically more nested than expected by chance alone. Additionally, theoretical studies have shown a positive association between the nested structure of mutualistic networks and community persistence. Yet, it has been shown that some plant-pollinator networks may be more nested than others, raising the interesting question of which factors are responsible for such enhanced nested structure. It has been argued that ordered network structures may increase the persistence of ecological communities under less predictable environments. This suggests that nested structures of plant-pollinator networks could be more advantageous under highly seasonal environments. While several studies have investigated the link between nestedness and various environmental variables, unfortunately, there has been no unified answer to validate these predictions. Here, we move from the problem of describing network structures to the problem of comparing network structures. We develop comparative statistics, and apply them to investigate the association between the nested structure of 59 plant-pollinator networks and the temperature seasonality present in their locations. We demonstrate that higher levels of nestedness are associated with a higher temperature seasonality. We show that the previous lack of agreement came from an extended practice of using standardized measures of nestedness that cannot be compared across different networks. Importantly, our observations complement theory showing that more nested network structures can increase the range of environmental conditions compatible with species coexistence in mutualistic systems, also known as structural stability. This increase in nestedness should be more advantageous and occur more often in locations subject to random environmental perturbations, which could be driven by highly changing or seasonal environments. This synthesis of theory and observations could prove relevant for a better understanding of the ecological processes driving the assembly and persistence of ecological communities. © 2017 The Authors. Journal of Animal Ecology © 2017 British Ecological Society.
Numerical study of the electronic structure, elastic and optical properties of defect quaternary semiconductor CuGaSnSe4

NASA Astrophysics Data System (ADS)

Shen, Kesheng; Lu, Hai; Zhang, Xianzhou; Jiao, Zhaoyong

2018-06-01

The electronic structure, elastic and optical properties of the defect quaternary semiconductor CuGaSnSe4 in I 4 bar structure are systematically investigated using first-principles calculations. We summarize and discuss some of the studies on CuGaSnSe4 in partially ordered chalcopyrite structure and find that there are three atomic arrangements so far, but it is still uncertain which is the most stable. Through detailed simulation and comparison with the corresponding literature, we get three models and predict that M1 model should be the most stable. The band structure and optical properties of compound CuGaSnSe4, including dielectric constant, refractive index and absorption spectrum, are drawn for a more intuitive understanding. The elastic constants are also calculated, which not only prove that CuGaSnSe4 in I 4 bar structure is stable naturally but also help solve the problem of no data to accurately predict axial thermal expansion coefficients. The calculated values of the zero frequency dielectric constant and refractive index are comparable to those of the corresponding chalcopyrite structure but slightly larger.
Early childhood precursors and adolescent sequelae of grade school peer rejection and victimization.

PubMed

Bierman, Karen L; Kalvin, Carla B; Heinrichs, Brenda S

2015-01-01

This study examined the early childhood precursors and adolescent outcomes associated with grade school peer rejection and victimization among children oversampled for aggressive-disruptive behaviors. A central goal was to better understand the common and unique developmental correlates associated with these two types of peer adversity. There were 754 participants (46% African American, 50% European American, 4% other; 58% male; average age=5.65 at kindergarten entry) followed into seventh grade. Six waves of data were included in structural models focused on three developmental periods. Parents and teachers rated aggressive behavior, emotion dysregulation, and internalizing problems in kindergarten and Grade 1 (Waves 1-2); peer sociometric nominations tracked "least liked" and victimization in Grades 2, 3, and 4 (Waves 3-5); and youth reported on social problems, depressed mood, school adjustment difficulties, and delinquent activities in early adolescence (Grade 7, Wave 6). Structural models revealed that early aggression and emotion dysregulation (but not internalizing behavior) made unique contributions to grade school peer rejection; only emotion dysregulation made unique contributions to grade school victimization. Early internalizing problems and grade school victimization uniquely predicted adolescent social problems and depressed mood. Early aggression and grade school peer rejection uniquely predicted adolescent school adjustment difficulties and delinquent activities. Aggression and emotion dysregulation at school entry increased risk for peer rejection and victimization, and these two types of peer adversity had distinct as well as shared risk and adjustment correlates. Results suggest that the emotional functioning and peer experiences of aggressive-disruptive children deserve further attention in developmental and clinical research.
Early Childhood Precursors and Adolescent Sequelae of Gradeschool Peer Rejection and Victimization

PubMed Central

Bierman, Karen L.; Kalvin, Carla B.; Heinrichs, Brenda S.

2014-01-01

Objective This study examined the early childhood precursors and adolescent outcomes associated with gradeschool peer rejection and victimization among children oversampled for aggressive-disruptive behaviors. A central goal was to better understand the common and unique developmental correlates associated with these two types of peer adversity. Method 754 participants (46% African American, 50% European American, 4% other; 58% male; average age 5.65 at kindergarten entry) were followed into seventh grade. Six waves of data were included in structural models focused on three developmental periods. Parents and teachers rated aggressive behavior, emotion dysregulation, and internalizing problems in kindergarten and grade 1 (waves 1–2); peer sociometric nominations tracked “least liked” and victimization in grades 2, 3, and 4 (waves 3–5); and youth reported on social problems, depressed mood, school adjustment difficulties, and delinquent activities in early adolescence (grade 7, wave 6). Results Structural models revealed that early aggression and emotion dysregulation (but not internalizing behavior) made unique contributions to gradeschool peer rejection; only emotion dysregulation made unique contributions to gradeschool victimization. Early internalizing problems and gradeschool victimization uniquely predicted adolescent social problems and depressed mood. Early aggression and gradeschool peer rejection uniquely predicted adolescent school adjustment difficulties and delinquent activities. Conclusions Aggression and emotion dysregulation at school entry increased risk for peer rejection and victimization, and these two types of peer adversity had distinct, as well as shared risk and adjustment correlates. Results suggest that the emotional functioning and peer experiences of aggressive-disruptive children deserve further attention in developmental and clinical research. PMID:24527989
Longitudinal Pathways from Cumulative Contextual Risk at Birth to School Functioning in Adolescence: Analysis of Mediation Effects and Gender Moderation.

PubMed

January, Stacy-Ann A; Mason, W Alex; Savolainen, Jukka; Solomon, Starr; Chmelka, Mary B; Miettunen, Jouko; Veijola, Juha; Moilanen, Irma; Taanila, Anja; Järvelin, Marjo-Riitta

2017-01-01

Children and adolescents exposed to multiple contextual risks are more likely to have academic difficulties and externalizing behavior problems than those who experience fewer risks. This study used data from the Northern Finland Birth Cohort 1986 (a population-based study; N = 6961; 51 % female) to investigate (a) the impact of cumulative contextual risk at birth on adolescents' academic performance and misbehavior in school, (b) learning difficulties and/or externalizing behavior problems in childhood as intervening mechanisms in the association of cumulative contextual risk with functioning in adolescence, and (c) potential gender differences in the predictive associations of cumulative contextual risk at birth with functioning in childhood or adolescence. The results of the structural equation modeling analysis suggested that exposure to cumulative contextual risk at birth had negative associations with functioning 16 years later, and academic difficulties and externalizing behavior problems in childhood mediated some of the predictive relations. Gender, however, did not moderate any of the associations. Therefore, the findings of this study have implications for the prevention of learning and conduct problems in youth and future research on the impact of cumulative risk exposure.
Longitudinal Pathways from Cumulative Contextual Risk at Birth to School Functioning in Adolescence: Analysis of Mediation Effects and Gender Moderation

PubMed Central

January, Stacy-Ann A.; Mason, W. Alex; Savolainen, Jukka; Solomon, Starr; Chmelka, Mary B.; Miettunen, Jouko; Veijola, Juha; Moilanen, Irma; Taanila, Anja; Järvelin, Marjo-Riitta

2016-01-01

Children and adolescents exposed to multiple contextual risks are more likely to have academic difficulties and externalizing behavior problems than those who experience fewer risks. This study used data from the Northern Finland Birth Cohort 1986 (a population-based study; N = 6,961; 51% female) to investigate (a) the impact of cumulative contextual risk at birth on adolescents’ academic performance and misbehavior in school, (b) learning difficulties and/or externalizing behavior problems in childhood as intervening mechanisms in the association of cumulative contextual risk with functioning in adolescence, and (c) potential gender differences in the predictive associations of cumulative contextual risk at birth with functioning in childhood or adolescence. The results of the structural equation modeling analysis suggested that exposure to cumulative contextual risk at birth had negative associations with functioning 16 years later, and academic difficulties and externalizing behavior problems in childhood mediated some of the predictive relations. Gender, however, did not moderate any of the associations. Therefore, the findings of this study have implications for the prevention of learning and conduct problems in youth and future research on the impact of cumulative risk exposure. PMID:27665276
DockTrina: docking triangular protein trimers.

PubMed

Popov, Petr; Ritchie, David W; Grudinin, Sergei

2014-01-01

In spite of the abundance of oligomeric proteins within a cell, the structural characterization of protein-protein interactions is still a challenging task. In particular, many of these interactions involve heteromeric complexes, which are relatively difficult to determine experimentally. Hence there is growing interest in using computational techniques to model such complexes. However, assembling large heteromeric complexes computationally is a highly combinatorial problem. Nonetheless the problem can be simplified greatly by considering interactions between protein trimers. After dimers and monomers, triangular trimers (i.e. trimers with pair-wise contacts between all three pairs of proteins) are the most frequently observed quaternary structural motifs according to the three-dimensional (3D) complex database. This article presents DockTrina, a novel protein docking method for modeling the 3D structures of nonsymmetrical triangular trimers. The method takes as input pair-wise contact predictions from a rigid body docking program. It then scans and scores all possible combinations of pairs of monomers using a very fast root mean square deviation test. Finally, it ranks the predictions using a scoring function which combines triples of pair-wise contact terms and a geometric clash penalty term. The overall approach takes less than 2 min per complex on a modern desktop computer. The method is tested and validated using a benchmark set of 220 bound and seven unbound protein trimer structures. DockTrina will be made available at http://nano-d.inrialpes.fr/software/docktrina. Copyright © 2013 Wiley Periodicals, Inc.
Relationship between behavioural problems and use of mental health services in patients with severe mental illness and the mediating role of the perceived burden of care.

PubMed

Bellido-Zanin, Gloria; Vázquez-Morejón, Antonio J; Pérez-San-Gregorio, Maria Ángeles; Martín-Rodríguez, Agustín

2017-10-01

Mental health models proposed for predicting more use of mental health resources by patients with severe mental illness are including a wider variety of predictor variables, but there are still many more remaining to be explored for a complete model. The purpose of this study was to enquire into the relationship between two variables, behaviour problems and burden of care, and the use of mental health resources in patients with severe mental illness. Our hypothesis was that perceived burden of care mediates between behaviour problems of patients with serious mental illness and the use of mental health resources. The Behaviour Problem Inventory, which was filled out by the main caregiver, was used to evaluate 179 patients cared for in a community mental health unit. They also answered a questionnaire on perceived family burden. A structural equation analysis was done to test our hypothesis. The results showed that both the behaviour problems and perceived burden of care are good predictors of the use of mental health resources, where perceived burden of care mediates between behaviour problems and use of resources. These variables seem to be relevant for inclusion in complete models for predicting use of mental health resources. Copyright © 2017 Elsevier B.V. All rights reserved.
Beyond Warmth and Conflict: The Developmental Utility of a Boundary Conceptualization of Sibling Relationship Processes

PubMed Central

Bascoe, Sonnette M.; Davies, Patrick T.; Cummings, E. Mark

2012-01-01

Translating relationship boundaries conceptualizations to the study of sibling relationships, this study examined the utility of sibling enmeshment and disengagement in predicting child adjustment difficulties in a sample of 282 mothers and adolescents (Mean age = 12.7 years). Mothers completed a semi-structured interview at the first measurement occasion to assess sibling interaction patterns. Adolescents, mothers, and teachers reported on children’s adjustment problems across two annual waves of assessment. Supporting the incremental utility of a boundary conceptualization of sibling relationships, results of latent difference score analyses indicated that coder ratings of sibling enmeshment and disengagement uniquely predicted greater adolescent adjustment difficulties even after taking into account standard indices of sibling relationship quality (i.e., warmth, conflict) and sibling structural characteristics (e.g., sex). PMID:22862542
φ-evo: A program to evolve phenotypic models of biological networks.

PubMed

Henry, Adrien; Hemery, Mathieu; François, Paul

2018-06-01

Molecular networks are at the core of most cellular decisions, but are often difficult to comprehend. Reverse engineering of network architecture from their functions has proved fruitful to classify and predict the structure and function of molecular networks, suggesting new experimental tests and biological predictions. We present φ-evo, an open-source program to evolve in silico phenotypic networks performing a given biological function. We include implementations for evolution of biochemical adaptation, adaptive sorting for immune recognition, metazoan development (somitogenesis, hox patterning), as well as Pareto evolution. We detail the program architecture based on C, Python 3, and a Jupyter interface for project configuration and network analysis. We illustrate the predictive power of φ-evo by first recovering the asymmetrical structure of the lac operon regulation from an objective function with symmetrical constraints. Second, we use the problem of hox-like embryonic patterning to show how a single effective fitness can emerge from multi-objective (Pareto) evolution. φ-evo provides an efficient approach and user-friendly interface for the phenotypic prediction of networks and the numerical study of evolution itself.
Drug Repositioning by Kernel-Based Integration of Molecular Structure, Molecular Activity, and Phenotype Data

PubMed Central

Wang, Yongcui; Chen, Shilong; Deng, Naiyang; Wang, Yong

2013-01-01

Computational inference of novel therapeutic values for existing drugs, i.e., drug repositioning, offers the great prospect for faster and low-risk drug development. Previous researches have indicated that chemical structures, target proteins, and side-effects could provide rich information in drug similarity assessment and further disease similarity. However, each single data source is important in its own way and data integration holds the great promise to reposition drug more accurately. Here, we propose a new method for drug repositioning, PreDR (Predict Drug Repositioning), to integrate molecular structure, molecular activity, and phenotype data. Specifically, we characterize drug by profiling in chemical structure, target protein, and side-effects space, and define a kernel function to correlate drugs with diseases. Then we train a support vector machine (SVM) to computationally predict novel drug-disease interactions. PreDR is validated on a well-established drug-disease network with 1,933 interactions among 593 drugs and 313 diseases. By cross-validation, we find that chemical structure, drug target, and side-effects information are all predictive for drug-disease relationships. More experimentally observed drug-disease interactions can be revealed by integrating these three data sources. Comparison with existing methods demonstrates that PreDR is competitive both in accuracy and coverage. Follow-up database search and pathway analysis indicate that our new predictions are worthy of further experimental validation. Particularly several novel predictions are supported by clinical trials databases and this shows the significant prospects of PreDR in future drug treatment. In conclusion, our new method, PreDR, can serve as a useful tool in drug discovery to efficiently identify novel drug-disease interactions. In addition, our heterogeneous data integration framework can be applied to other problems. PMID:24244318
Prediction of Ras-effector interactions using position energy matrices.

PubMed

Kiel, Christina; Serrano, Luis

2007-09-01

One of the more challenging problems in biology is to determine the cellular protein interaction network. Progress has been made to predict protein-protein interactions based on structural information, assuming that structural similar proteins interact in a similar way. In a previous publication, we have determined a genome-wide Ras-effector interaction network based on homology models, with a high accuracy of predicting binding and non-binding domains. However, for a prediction on a genome-wide scale, homology modelling is a time-consuming process. Therefore, we here successfully developed a faster method using position energy matrices, where based on different Ras-effector X-ray template structures, all amino acids in the effector binding domain are sequentially mutated to all other amino acid residues and the effect on binding energy is calculated. Those pre-calculated matrices can then be used to score for binding any Ras or effector sequences. Based on position energy matrices, the sequences of putative Ras-binding domains can be scanned quickly to calculate an energy sum value. By calibrating energy sum values using quantitative experimental binding data, thresholds can be defined and thus non-binding domains can be excluded quickly. Sequences which have energy sum values above this threshold are considered to be potential binding domains, and could be further analysed using homology modelling. This prediction method could be applied to other protein families sharing conserved interaction types, in order to determine in a fast way large scale cellular protein interaction networks. Thus, it could have an important impact on future in silico structural genomics approaches, in particular with regard to increasing structural proteomics efforts, aiming to determine all possible domain folds and interaction types. All matrices are deposited in the ADAN database (http://adan-embl.ibmc.umh.es/). Supplementary data are available at Bioinformatics online.
Neural predictors of individual differences in response to math tutoring in primary-grade school children

PubMed Central

Supekar, Kaustubh; Swigart, Anna G.; Tenison, Caitlin; Jolles, Dietsje D.; Rosenberg-Lee, Miriam; Fuchs, Lynn; Menon, Vinod

2013-01-01

Now, more than ever, the ability to acquire mathematical skills efficiently is critical for academic and professional success, yet little is known about the behavioral and neural mechanisms that drive some children to acquire these skills faster than others. Here we investigate the behavioral and neural predictors of individual differences in arithmetic skill acquisition in response to 8-wk of one-to-one math tutoring. Twenty-four children in grade 3 (ages 8–9 y), a critical period for acquisition of basic mathematical skills, underwent structural and resting-state functional MRI scans pretutoring. A significant shift in arithmetic problem-solving strategies from counting to fact retrieval was observed with tutoring. Notably, the speed and accuracy of arithmetic problem solving increased with tutoring, with some children improving significantly more than others. Next, we examined whether pretutoring behavioral and brain measures could predict individual differences in arithmetic performance improvements with tutoring. No behavioral measures, including intelligence quotient, working memory, or mathematical abilities, predicted performance improvements. In contrast, pretutoring hippocampal volume predicted performance improvements. Furthermore, pretutoring intrinsic functional connectivity of the hippocampus with dorsolateral and ventrolateral prefrontal cortices and the basal ganglia also predicted performance improvements. Our findings provide evidence that individual differences in morphometry and connectivity of brain regions associated with learning and memory, and not regions typically involved in arithmetic processing, are strong predictors of responsiveness to math tutoring in children. More generally, our study suggests that quantitative measures of brain structure and intrinsic brain organization can provide a more sensitive marker of skill acquisition than behavioral measures. PMID:23630286
Neural predictors of individual differences in response to math tutoring in primary-grade school children.

PubMed

Supekar, Kaustubh; Swigart, Anna G; Tenison, Caitlin; Jolles, Dietsje D; Rosenberg-Lee, Miriam; Fuchs, Lynn; Menon, Vinod

2013-05-14

Now, more than ever, the ability to acquire mathematical skills efficiently is critical for academic and professional success, yet little is known about the behavioral and neural mechanisms that drive some children to acquire these skills faster than others. Here we investigate the behavioral and neural predictors of individual differences in arithmetic skill acquisition in response to 8-wk of one-to-one math tutoring. Twenty-four children in grade 3 (ages 8-9 y), a critical period for acquisition of basic mathematical skills, underwent structural and resting-state functional MRI scans pretutoring. A significant shift in arithmetic problem-solving strategies from counting to fact retrieval was observed with tutoring. Notably, the speed and accuracy of arithmetic problem solving increased with tutoring, with some children improving significantly more than others. Next, we examined whether pretutoring behavioral and brain measures could predict individual differences in arithmetic performance improvements with tutoring. No behavioral measures, including intelligence quotient, working memory, or mathematical abilities, predicted performance improvements. In contrast, pretutoring hippocampal volume predicted performance improvements. Furthermore, pretutoring intrinsic functional connectivity of the hippocampus with dorsolateral and ventrolateral prefrontal cortices and the basal ganglia also predicted performance improvements. Our findings provide evidence that individual differences in morphometry and connectivity of brain regions associated with learning and memory, and not regions typically involved in arithmetic processing, are strong predictors of responsiveness to math tutoring in children. More generally, our study suggests that quantitative measures of brain structure and intrinsic brain organization can provide a more sensitive marker of skill acquisition than behavioral measures.
Towards Inferring Protein Interactions: Challenges and Solutions

NASA Astrophysics Data System (ADS)

Zhang, Ya; Zha, Hongyuan; Chu, Chao-Hsien; Ji, Xiang

2006-12-01

Discovering interacting proteins has been an essential part of functional genomics. However, existing experimental techniques only uncover a small portion of any interactome. Furthermore, these data often have a very high false rate. By conceptualizing the interactions at domain level, we provide a more abstract representation of interactome, which also facilitates the discovery of unobserved protein-protein interactions. Although several domain-based approaches have been proposed to predict protein-protein interactions, they usually assume that domain interactions are independent on each other for the convenience of computational modeling. A new framework to predict protein interactions is proposed in this paper, where no assumption is made about domain interactions. Protein interactions may be the result of multiple domain interactions which are dependent on each other. A conjunctive norm form representation is used to capture the relationships between protein interactions and domain interactions. The problem of interaction inference is then modeled as a constraint satisfiability problem and solved via linear programing. Experimental results on a combined yeast data set have demonstrated the robustness and the accuracy of the proposed algorithm. Moreover, we also map some predicted interacting domains to three-dimensional structures of protein complexes to show the validity of our predictions.
Power prediction in mobile communication systems using an optimal neural-network structure.

PubMed

Gao, X M; Gao, X Z; Tanskanen, J A; Ovaska, S J

1997-01-01

Presents a novel neural-network-based predictor for received power level prediction in direct sequence code division multiple access (DS/CDMA) systems. The predictor consists of an adaptive linear element (Adaline) followed by a multilayer perceptron (MLP). An important but difficult problem in designing such a cascade predictor is to determine the complexity of the networks. We solve this problem by using the predictive minimum description length (PMDL) principle to select the optimal numbers of input and hidden nodes. This approach results in a predictor with both good noise attenuation and excellent generalization capability. The optimized neural networks are used for predictive filtering of very noisy Rayleigh fading signals with 1.8 GHz carrier frequency. Our results show that the optimal neural predictor can provide smoothed in-phase and quadrature signals with signal-to-noise ratio (SNR) gains of about 12 and 7 dB at the urban mobile speeds of 5 and 50 km/h, respectively. The corresponding power signal SNR gains are about 11 and 5 dB. Therefore, the neural predictor is well suitable for power control applications where ldquodelaylessrdquo noise attenuation and efficient reduction of fast fading are required.
Unified force-level theory of multiscale transient localization and emergent elasticity in polymer solutions and melts

NASA Astrophysics Data System (ADS)

Dell, Zachary E.; Schweizer, Kenneth S.

A unified, microscopic, theoretical understanding of polymer dynamics in concentrated liquids from segmental to macromolecular scales remains an open problem. We have formulated a statistical mechanical theory for this problem that explicitly accounts for intra- and inter-molecular forces at the Kuhn segment level. The theory is self-consistently closed at the level of a matrix of dynamical second moments of a tagged chain. Two distinct regimes of isotropic transient localization are predicted. In semidilute solutions, weak localization is predicted on a mesoscopic length scale between segment and chain scales which is a power law function of the invariant packing length. This is consistent with the breakdown of Rouse dynamics and the emergence of entanglements. The chain structural correlations in the dynamically arrested state are also computed. In dense melts, strong localization is predicted on a scale much smaller than the segment size which is weakly dependent on chain connectivity and signals the onset of glassy dynamics. Predictions of the dynamic plateau shear modulus are consistent with the known features of emergent rubbery and glassy elasticity. Generalizations to treat the effects of chemical crosslinking and physical bond formation in polymer gels are possible.
Predicting explorative motor learning using decision-making and motor noise.

PubMed

Chen, Xiuli; Mohr, Kieran; Galea, Joseph M

2017-04-01

A fundamental problem faced by humans is learning to select motor actions based on noisy sensory information and incomplete knowledge of the world. Recently, a number of authors have asked whether this type of motor learning problem might be very similar to a range of higher-level decision-making problems. If so, participant behaviour on a high-level decision-making task could be predictive of their performance during a motor learning task. To investigate this question, we studied performance during an explorative motor learning task and a decision-making task which had a similar underlying structure with the exception that it was not subject to motor (execution) noise. We also collected an independent measurement of each participant's level of motor noise. Our analysis showed that explorative motor learning and decision-making could be modelled as the (approximately) optimal solution to a Partially Observable Markov Decision Process bounded by noisy neural information processing. The model was able to predict participant performance in motor learning by using parameters estimated from the decision-making task and the separate motor noise measurement. This suggests that explorative motor learning can be formalised as a sequential decision-making process that is adjusted for motor noise, and raises interesting questions regarding the neural origin of explorative motor learning.
Predicting explorative motor learning using decision-making and motor noise

PubMed Central

Galea, Joseph M.

2017-01-01

A fundamental problem faced by humans is learning to select motor actions based on noisy sensory information and incomplete knowledge of the world. Recently, a number of authors have asked whether this type of motor learning problem might be very similar to a range of higher-level decision-making problems. If so, participant behaviour on a high-level decision-making task could be predictive of their performance during a motor learning task. To investigate this question, we studied performance during an explorative motor learning task and a decision-making task which had a similar underlying structure with the exception that it was not subject to motor (execution) noise. We also collected an independent measurement of each participant’s level of motor noise. Our analysis showed that explorative motor learning and decision-making could be modelled as the (approximately) optimal solution to a Partially Observable Markov Decision Process bounded by noisy neural information processing. The model was able to predict participant performance in motor learning by using parameters estimated from the decision-making task and the separate motor noise measurement. This suggests that explorative motor learning can be formalised as a sequential decision-making process that is adjusted for motor noise, and raises interesting questions regarding the neural origin of explorative motor learning. PMID:28437451

Reliability analysis of composite structures

NASA Technical Reports Server (NTRS)

Kan, Han-Pin

1992-01-01

A probabilistic static stress analysis methodology has been developed to estimate the reliability of a composite structure. Closed form stress analysis methods are the primary analytical tools used in this methodology. These structural mechanics methods are used to identify independent variables whose variations significantly affect the performance of the structure. Once these variables are identified, scatter in their values is evaluated and statistically characterized. The scatter in applied loads and the structural parameters are then fitted to appropriate probabilistic distribution functions. Numerical integration techniques are applied to compute the structural reliability. The predicted reliability accounts for scatter due to variability in material strength, applied load, fabrication and assembly processes. The influence of structural geometry and mode of failure are also considerations in the evaluation. Example problems are given to illustrate various levels of analytical complexity.
Predicting the global spread range via small subnetworks

NASA Astrophysics Data System (ADS)

Sun, Jiachen; Dong, Junyou; Ma, Xiao; Feng, Ling; Hu, Yanqing

2017-04-01

Modern online social network platforms are replacing traditional media due to their effectiveness in both spreading information and communicating opinions. One of the key problems in these online platforms is to predict the global spread range of any given information. Due to its gigantic size as well as time-varying dynamics, an online social network's global structure, however, is usually inaccessible to most researchers. Thus, it raises the very important issue of how to use solely small subnetworks to predict the global influence. In this paper, based on percolation theory, we show that the global spread range can be predicted well from only two small subnetworks. We test our methods in an artificial network and three empirical online social networks, such as the full Sina Weibo network with 99546027 nodes.
PHENOstruct: Prediction of human phenotype ontology terms using heterogeneous data sources.

PubMed

Kahanda, Indika; Funk, Christopher; Verspoor, Karin; Ben-Hur, Asa

2015-01-01

The human phenotype ontology (HPO) was recently developed as a standardized vocabulary for describing the phenotype abnormalities associated with human diseases. At present, only a small fraction of human protein coding genes have HPO annotations. But, researchers believe that a large portion of currently unannotated genes are related to disease phenotypes. Therefore, it is important to predict gene-HPO term associations using accurate computational methods. In this work we demonstrate the performance advantage of the structured SVM approach which was shown to be highly effective for Gene Ontology term prediction in comparison to several baseline methods. Furthermore, we highlight a collection of informative data sources suitable for the problem of predicting gene-HPO associations, including large scale literature mining data.
RF-Phos: A Novel General Phosphorylation Site Prediction Tool Based on Random Forest.

PubMed

Ismail, Hamid D; Jones, Ahoi; Kim, Jung H; Newman, Robert H; Kc, Dukka B

2016-01-01

Protein phosphorylation is one of the most widespread regulatory mechanisms in eukaryotes. Over the past decade, phosphorylation site prediction has emerged as an important problem in the field of bioinformatics. Here, we report a new method, termed Random Forest-based Phosphosite predictor 2.0 (RF-Phos 2.0), to predict phosphorylation sites given only the primary amino acid sequence of a protein as input. RF-Phos 2.0, which uses random forest with sequence and structural features, is able to identify putative sites of phosphorylation across many protein families. In side-by-side comparisons based on 10-fold cross validation and an independent dataset, RF-Phos 2.0 compares favorably to other popular mammalian phosphosite prediction methods, such as PhosphoSVM, GPS2.1, and Musite.
Validation of Design and Analysis Techniques of Tailored Composite Structures

NASA Technical Reports Server (NTRS)

Jegley, Dawn C. (Technical Monitor); Wijayratne, Dulnath D.

2004-01-01

Aeroelasticity is the relationship between the elasticity of an aircraft structure and its aerodynamics. This relationship can cause instabilities such as flutter in a wing. Engineers have long studied aeroelasticity to ensure such instabilities do not become a problem within normal operating conditions. In recent decades structural tailoring has been used to take advantage of aeroelasticity. It is possible to tailor an aircraft structure to respond favorably to multiple different flight regimes such as takeoff, landing, cruise, 2-g pull up, etc. Structures can be designed so that these responses provide an aerodynamic advantage. This research investigates the ability to design and analyze tailored structures made from filamentary composites. Specifically the accuracy of tailored composite analysis must be verified if this design technique is to become feasible. To pursue this idea, a validation experiment has been performed on a small-scale filamentary composite wing box. The box is tailored such that its cover panels induce a global bend-twist coupling under an applied load. Two types of analysis were chosen for the experiment. The first is a closed form analysis based on a theoretical model of a single cell tailored box beam and the second is a finite element analysis. The predicted results are compared with the measured data to validate the analyses. The comparison of results show that the finite element analysis is capable of predicting displacements and strains to within 10% on the small-scale structure. The closed form code is consistently able to predict the wing box bending to 25% of the measured value. This error is expected due to simplifying assumptions in the closed form analysis. Differences between the closed form code representation and the wing box specimen caused large errors in the twist prediction. The closed form analysis prediction of twist has not been validated from this test.
Materials Discovery via CALYPSO Methodology

NASA Astrophysics Data System (ADS)

Ma, Yanming

2014-03-01

Materials design has been the subject of topical interests in materials and physical sciences for long. Atomistic structures of materials occupy a central and often critical role, when establishing a correspondence between materials performance and their basic compositions. Theoretical prediction of atomistic structures of materials with the only given information of chemical compositions becomes crucially important, but it is extremely difficult as it basically involves in classifying a huge number of energy minima on the lattice energy surface. To tackle the problems, we have developed an efficient CALYPSO (Crystal structural AnLYsis by Particle Swarm Optimization) approach for structure prediction from scratch based on particle swarm optimization algorithm by taking the advantage of swarm intelligence and the spirit of structures smart learning. The method has been coded into CALYPSO software (http://www.calypso.cn) which is free for academic use. Currently, CALYPSO method is able to predict structures of three-dimensional crystals, isolated clusters or molecules, surface reconstructions, and two-dimensional layers. The applications of CALYPSO into purposed materials design of layered materials, high-pressure superconductors, and superhard materials were successfully made. Our design of superhard materials introduced a useful scheme, where the hardness value has been employed as the fitness function. This strategy might also be applicable into design of materials with other desired functional properties (e.g., thermoelectric figure of merit, topological Z2 number, etc.). For such a structural design, a well-understood structure to property formulation is required, by which functional properties of materials can be easily acquired at given structures. An emergent application is seen on design of photocatalyst materials.
Experimental issues related to frequency response function measurements for frequency-based substructuring

NASA Astrophysics Data System (ADS)

Nicgorski, Dana; Avitabile, Peter

2010-07-01

Frequency-based substructuring is a very popular approach for the generation of system models from component measured data. Analytically the approach has been shown to produce accurate results. However, implementation with actual test data can cause difficulties and cause problems with the system response prediction. In order to produce good results, extreme care is needed in the measurement of the drive point and transfer impedances of the structure as well as observe all the conditions for a linear time invariant system. Several studies have been conducted to show the sensitivity of the technique to small variations that often occur during typical testing of structures. These variations have been observed in actual tested configurations and have been substantiated with analytical models to replicate the problems typically encountered. The use of analytically simulated issues helps to clearly see the effects of typical measurement difficulties often observed in test data. This paper presents some of these common problems observed and provides guidance and recommendations for data to be used for this modeling approach.
Job stress, unwinding and drinking in transit operators.

PubMed

Delaney, William P; Grube, Joel W; Greiner, Birgit; Fisher, June M; Ragland, David R

2002-07-01

This study tests the spillover model of the effects of work stress on after-work drinking, using the variable "length of time to unwind" as a mediator. A total of 1,974 transit operators were contacted and 1,553 (79%) of them participated in a personal interview. Complete data on the variables in this analysis were available for 1,208 respondents (84% men). Using latent variable structural equation modeling, a model was tested that predicted that daily job problems, skipped meals and less social support from supervisor would increase alcohol consumption through the mediator, length of time to unwind and relax after work. Increased alcohol consumption was, in turn, hypothesized to increase drinking problems. As predicted, skipped meals and daily job problems increased length of time to unwind and had an indirect positive relationship with overall drinking, even when controlling for drinking norms and demographic variables. Overall drinking was positively associated with drinking problems. Supervisor support at work, however, did not significantly influence length of time to unwind. Difficulty unwinding (longer time to unwind) did not have direct effects on drinking problems; however, indirect effects through overall drinking were observed. These results provide preliminary support for the mediating role of length of time to unwind and relax after work in a spillover model of the stress-drinking relationship. This research introduces a new mediator and empirical links between job problems, length of time to unwind, drinking and drinking problems, which ground more substantively the domains of work stress and alcohol consumption.
Lessons learned in induced fit docking and metadynamics in the Drug Design Data Resource Grand Challenge 2

NASA Astrophysics Data System (ADS)

Baumgartner, Matthew P.; Evans, David A.

2018-01-01

Two of the major ongoing challenges in computational drug discovery are predicting the binding pose and affinity of a compound to a protein. The Drug Design Data Resource Grand Challenge 2 was developed to address these problems and to drive development of new methods. The challenge provided the 2D structures of compounds for which the organizers help blinded data in the form of 35 X-ray crystal structures and 102 binding affinity measurements and challenged participants to predict the binding pose and affinity of the compounds. We tested a number of pose prediction methods as part of the challenge; we found that docking methods that incorporate protein flexibility (Induced Fit Docking) outperformed methods that treated the protein as rigid. We also found that using binding pose metadynamics, a molecular dynamics based method, to score docked poses provided the best predictions of our methods with an average RMSD of 2.01 Å. We tested both structure-based (e.g. docking) and ligand-based methods (e.g. QSAR) in the affinity prediction portion of the competition. We found that our structure-based methods based on docking with Smina (Spearman ρ = 0.614), performed slightly better than our ligand-based methods (ρ = 0.543), and had equivalent performance with the other top methods in the competition. Despite the overall good performance of our methods in comparison to other participants in the challenge, there exists significant room for improvement especially in cases such as these where protein flexibility plays such a large role.
Protein-protein structure prediction by scoring molecular dynamics trajectories of putative poses.

PubMed

Sarti, Edoardo; Gladich, Ivan; Zamuner, Stefano; Correia, Bruno E; Laio, Alessandro

2016-09-01

The prediction of protein-protein interactions and their structural configuration remains a largely unsolved problem. Most of the algorithms aimed at finding the native conformation of a protein complex starting from the structure of its monomers are based on searching the structure corresponding to the global minimum of a suitable scoring function. However, protein complexes are often highly flexible, with mobile side chains and transient contacts due to thermal fluctuations. Flexibility can be neglected if one aims at finding quickly the approximate structure of the native complex, but may play a role in structure refinement, and in discriminating solutions characterized by similar scores. We here benchmark the capability of some state-of-the-art scoring functions (BACH-SixthSense, PIE/PISA and Rosetta) in discriminating finite-temperature ensembles of structures corresponding to the native state and to non-native configurations. We produce the ensembles by running thousands of molecular dynamics simulations in explicit solvent starting from poses generated by rigid docking and optimized in vacuum. We find that while Rosetta outperformed the other two scoring functions in scoring the structures in vacuum, BACH-SixthSense and PIE/PISA perform better in distinguishing near-native ensembles of structures generated by molecular dynamics in explicit solvent. Proteins 2016; 84:1312-1320. © 2016 Wiley Periodicals, Inc. © 2016 Wiley Periodicals, Inc.
A Prediction Method of Binding Free Energy of Protein and Ligand

NASA Astrophysics Data System (ADS)

Yang, Kun; Wang, Xicheng

2010-05-01

Predicting the binding free energy is an important problem in bimolecular simulation. Such prediction would be great benefit in understanding protein functions, and may be useful for computational prediction of ligand binding strengths, e.g., in discovering pharmaceutical drugs. Free energy perturbation (FEP)/thermodynamics integration (TI) is a classical method to explicitly predict free energy. However, this method need plenty of time to collect datum, and that attempts to deal with some simple systems and small changes of molecular structures. Another one for estimating ligand binding affinities is linear interaction energy (LIE) method. This method employs averages of interaction potential energy terms from molecular dynamics simulations or other thermal conformational sampling techniques. Incorporation of systematic deviations from electrostatic linear response, derived from free energy perturbation studies, into the absolute binding free energy expression significantly enhances the accuracy of the approach. However, it also is time-consuming work. In this paper, a new prediction method based on steered molecular dynamics (SMD) with direction optimization is developed to compute binding free energy. Jarzynski's equality is used to derive the PMF or free-energy. The results for two numerical examples are presented, showing that the method has good accuracy and efficiency. The novel method can also simulate whole binding proceeding and give some important structural information about development of new drugs.
Dynamic and fluid-structure interaction simulations of bioprosthetic heart valves using parametric design with T-splines and Fung-type material models

NASA Astrophysics Data System (ADS)

Hsu, Ming-Chen; Kamensky, David; Xu, Fei; Kiendl, Josef; Wang, Chenglong; Wu, Michael C. H.; Mineroff, Joshua; Reali, Alessandro; Bazilevs, Yuri; Sacks, Michael S.

2015-06-01

This paper builds on a recently developed immersogeometric fluid-structure interaction (FSI) methodology for bioprosthetic heart valve (BHV) modeling and simulation. It enhances the proposed framework in the areas of geometry design and constitutive modeling. With these enhancements, BHV FSI simulations may be performed with greater levels of automation, robustness and physical realism. In addition, the paper presents a comparison between FSI analysis and standalone structural dynamics simulation driven by prescribed transvalvular pressure, the latter being a more common modeling choice for this class of problems. The FSI computation achieved better physiological realism in predicting the valve leaflet deformation than its standalone structural dynamics counterpart.
The prospect of modern thermomechanics in structural integrity calculations of large-scale pressure vessels

NASA Astrophysics Data System (ADS)

Fekete, Tamás

2018-05-01

Structural integrity calculations play a crucial role in designing large-scale pressure vessels. Used in the electric power generation industry, these kinds of vessels undergo extensive safety analyses and certification procedures before deemed feasible for future long-term operation. The calculations are nowadays directed and supported by international standards and guides based on state-of-the-art results of applied research and technical development. However, their ability to predict a vessel's behavior under accidental circumstances after long-term operation is largely limited by the strong dependence of the analysis methodology on empirical models that are correlated to the behavior of structural materials and their changes during material aging. Recently a new scientific engineering paradigm, structural integrity has been developing that is essentially a synergistic collaboration between a number of scientific and engineering disciplines, modeling, experiments and numerics. Although the application of the structural integrity paradigm highly contributed to improving the accuracy of safety evaluations of large-scale pressure vessels, the predictive power of the analysis methodology has not yet improved significantly. This is due to the fact that already existing structural integrity calculation methodologies are based on the widespread and commonly accepted 'traditional' engineering thermal stress approach, which is essentially based on the weakly coupled model of thermomechanics and fracture mechanics. Recently, a research has been initiated in MTA EK with the aim to review and evaluate current methodologies and models applied in structural integrity calculations, including their scope of validity. The research intends to come to a better understanding of the physical problems that are inherently present in the pool of structural integrity problems of reactor pressure vessels, and to ultimately find a theoretical framework that could serve as a well-grounded theoretical foundation for a new modeling framework of structural integrity. This paper presents the first findings of the research project.
Proteus: a random forest classifier to predict disorder-to-order transitioning binding regions in intrinsically disordered proteins

NASA Astrophysics Data System (ADS)

Basu, Sankar; Söderquist, Fredrik; Wallner, Björn

2017-05-01

The focus of the computational structural biology community has taken a dramatic shift over the past one-and-a-half decades from the classical protein structure prediction problem to the possible understanding of intrinsically disordered proteins (IDP) or proteins containing regions of disorder (IDPR). The current interest lies in the unraveling of a disorder-to-order transitioning code embedded in the amino acid sequences of IDPs/IDPRs. Disordered proteins are characterized by an enormous amount of structural plasticity which makes them promiscuous in binding to different partners, multi-functional in cellular activity and atypical in folding energy landscapes resembling partially folded molten globules. Also, their involvement in several deadly human diseases (e.g. cancer, cardiovascular and neurodegenerative diseases) makes them attractive drug targets, and important for a biochemical understanding of the disease(s). The study of the structural ensemble of IDPs is rather difficult, in particular for transient interactions. When bound to a structured partner, an IDPR adapts an ordered conformation in the complex. The residues that undergo this disorder-to-order transition are called protean residues, generally found in short contiguous stretches and the first step in understanding the modus operandi of an IDP/IDPR would be to predict these residues. There are a few available methods which predict these protean segments from their amino acid sequences; however, their performance reported in the literature leaves clear room for improvement. With this background, the current study presents `Proteus', a random forest classifier that predicts the likelihood of a residue undergoing a disorder-to-order transition upon binding to a potential partner protein. The prediction is based on features that can be calculated using the amino acid sequence alone. Proteus compares favorably with existing methods predicting twice as many true positives as the second best method (55 vs. 27%) with a much higher precision on an independent data set. The current study also sheds some light on a possible `disorder-to-order' transitioning consensus, untangled, yet embedded in the amino acid sequence of IDPs. Some guidelines have also been suggested for proceeding with a real-life structural modeling involving an IDPR using Proteus.
Early Parenting, Represented Family Relationships, and Externalizing Behavior Problems in Children Born Preterm

PubMed Central

Poehlmann, Julie; Burnson, Cynthia; Weymouth, Lindsay A.

2015-01-01

Through assessment of 173 preterm infants and their mothers at hospital discharge and at 9, 16, 24, 36, and 72 months, the study examined early parenting, attachment security, effortful control, and children’s representations of family relationships in relation to subsequent externalizing behavior problems. Less intrusive early parenting predicted more secure attachment, better effortful control skills, and fewer early behavior problems, although it did not directly relate to the structural or content characteristics of children’s represented family relationships. Children with higher effortful control scores at 24 months had more coherent family representations at 36 months. Moreover, children who exhibited less avoidance in their family representations at 36 months had fewer mother-reported externalizing behavior problems at 72 months. The study suggests that early parenting quality and avoidance in children’s represented relationships are important for the development of externalizing behavior problems in children born preterm. PMID:24580068
Development of Mastery during Adolescence: The Role of Family Problem Solving*

PubMed Central

Conger, Katherine Jewsbury; Williams, Shannon Tierney; Little, Wendy M.; Masyn, Katherine E.; Shebloski, Barbara

2009-01-01

A sense of mastery is an important component of psychological health and well-being across the life-span; however, relatively little is known about the development of mastery during childhood and adolescence. Utilizing prospective, longitudinal data from 444 adolescent sibling pairs and their parents, our conceptual model proposes that family SES in the form of parental education promotes effective family problem solving which, in turn, fosters adolescent mastery. Results show: (1) a significant increase in mastery for younger and older siblings, (2) parental education promoted effective problem solving between parents and adolescents and between siblings but not between the parents themselves, and (3) all forms of effective family problem solving predicted greater adolescent mastery. Parental education had a direct effect on adolescent mastery as well as the hypothesized indirect effect through problem solving effectiveness, suggesting both a social structural and social process influence on the development of mastery during adolescence. PMID:19413137
Early parenting, represented family relationships, and externalizing behavior problems in children born preterm.

PubMed

Poehlmann, Julie; Burnson, Cynthia; Weymouth, Lindsay A

2014-01-01

Through assessment of 173 preterm infants and their mothers at hospital discharge and at 9, 16, 24, 36, and 72 months, the study examined early parenting, attachment security, effortful control, and children's representations of family relationships in relation to subsequent externalizing behavior problems. Less intrusive early parenting predicted more secure attachment, better effortful control skills, and fewer early behavior problems, although it did not directly relate to the structural or content characteristics of children's represented family relationships. Children with higher effortful control scores at 24 months had more coherent family representations at 36 months. Moreover, children who exhibited less avoidance in their family representations at 36 months had fewer mother-reported externalizing behavior problems at 72 months. The study suggests that early parenting quality and avoidance in children's represented relationships are important for the development of externalizing behavior problems in children born preterm.
Segmented-memory recurrent neural networks.

PubMed

Chen, Jinmiao; Chaudhari, Narendra S

2009-08-01

Conventional recurrent neural networks (RNNs) have difficulties in learning long-term dependencies. To tackle this problem, we propose an architecture called segmented-memory recurrent neural network (SMRNN). A symbolic sequence is broken into segments and then presented as inputs to the SMRNN one symbol per cycle. The SMRNN uses separate internal states to store symbol-level context, as well as segment-level context. The symbol-level context is updated for each symbol presented for input. The segment-level context is updated after each segment. The SMRNN is trained using an extended real-time recurrent learning algorithm. We test the performance of SMRNN on the information latching problem, the "two-sequence problem" and the problem of protein secondary structure (PSS) prediction. Our implementation results indicate that SMRNN performs better on long-term dependency problems than conventional RNNs. Besides, we also theoretically analyze how the segmented memory of SMRNN helps learning long-term temporal dependencies and study the impact of the segment length.
Development of mastery during adolescence: the role of family problem-solving.

PubMed

Conger, Katherine Jewsbury; Williams, Shannon Tierney; Little, Wendy M; Masyn, Katherine E; Shebloski, Barbara

2009-03-01

A sense of mastery is an important component of psychological health and wellbeing across the life-span; however relatively little is known about the development of mastery during childhood and adolescence. Utilizing prospective, longitudinal data from 444 adolescent sibling pairs and their parents, our conceptual model proposes that family socioeconomic status (SES) in the form of parental education promotes effective family problem-solving, which, in turn, fosters adolescent mastery. Results show: (1) a significant increase in mastery for younger and older siblings, (2) parental education promoted effective problem-solving between parents and adolescents and between siblings but not between the parents themselves, and (3) all forms of effective family problem-solving predicted greater adolescent mastery. Parental education had a direct effect on adolescent mastery as well as the hypothesized indirect effect through problem-solving effectiveness, suggesting both a social structural and social process influence on the development of mastery during adolescence.
Facial expressions of emotion and psychopathology in adolescent boys.

PubMed

Keltner, D; Moffitt, T E; Stouthamer-Loeber, M

1995-11-01

On the basis of the widespread belief that emotions underpin psychological adjustment, the authors tested 3 predicted relations between externalizing problems and anger, internalizing problems and fear and sadness, and the absence of externalizing problems and social-moral emotion (embarrassment). Seventy adolescent boys were classified into 1 of 4 comparison groups on the basis of teacher reports using a behavior problem checklist: internalizers, externalizers, mixed (both internalizers and externalizers), and nondisordered boys. The authors coded the facial expressions of emotion shown by the boys during a structured social interaction. Results supported the 3 hypotheses: (a) Externalizing adolescents showed increased facial expressions of anger, (b) on 1 measure internalizing adolescents showed increased facial expressions of fear, and (c) the absence of externalizing problems (or nondisordered classification) was related to increased displays of embarrassment. Discussion focused on the relations of these findings to hypotheses concerning the role of impulse control in antisocial behavior.

Simulation of physiological systems in order to evaluate and predict the human condition in a space flight

NASA Technical Reports Server (NTRS)

Verigo, V. V.

1979-01-01

Simulation models were used to study theoretical problems of space biology and medicine. The reaction and adaptation of the main physiological systems to the complex effects of space flight were investigated. Mathematical models were discussed in terms of their significance in the selection of the structure and design of biological life support systems.
Modeling of polymer networks for application to solid propellant formulating

NASA Technical Reports Server (NTRS)

Marsh, H. E.

1979-01-01

Methods for predicting the network structural characteristics formed by the curing of pourable elastomers were presented; as well as the logic which was applied in the development of mathematical models. A universal approach for modeling was developed and verified by comparison with other methods in application to a complex system. Several applications of network models to practical problems are described.
Trajectories of Family Management Practices and Early Adolescent Behavioral Outcomes

PubMed Central

Wang, Ming-Te; Dishion, Thomas J.; Stormshak, Elizabeth A.; Willett, John B.

2013-01-01

Stage– environment fit theory was used to examine the reciprocal lagged relations between family management practices and early adolescent problem behavior during the middle school years. In addition, the potential moderating roles of family structure and of gender were explored. Hierarchical linear modeling was used to describe patterns of growth in family management practices and adolescents’ behavioral outcomes and to detect predictors of interindividual differences in initial status and rate of change. The sample comprised approximately 1,000 adolescents between ages 11 years and 15 years. The results indicated that adolescents’ antisocial behaviors and substance use increased and their positive behavioral engagement decreased over time. As adolescent age increased, parental knowledge of their adolescent’s activities decreased, as did parental rule making and support. The level and rate of change in family management and adolescent behavioral outcomes varied by family structure and by gender. Reciprocal longitudinal associations between parenting practices and adolescent problem behavior were found. Specifically, parenting practices predicted subsequent adolescent behavior, and adolescent behavior predicted subsequent parenting practices. In addition, parental warmth moderated the effects of parental knowledge and rule making on adolescent antisocial behavior and substance use over time. PMID:21688899
Network-constrained group lasso for high-dimensional multinomial classification with application to cancer subtype prediction.

PubMed

Tian, Xinyu; Wang, Xuefeng; Chen, Jun

2014-01-01

Classic multinomial logit model, commonly used in multiclass regression problem, is restricted to few predictors and does not take into account the relationship among variables. It has limited use for genomic data, where the number of genomic features far exceeds the sample size. Genomic features such as gene expressions are usually related by an underlying biological network. Efficient use of the network information is important to improve classification performance as well as the biological interpretability. We proposed a multinomial logit model that is capable of addressing both the high dimensionality of predictors and the underlying network information. Group lasso was used to induce model sparsity, and a network-constraint was imposed to induce the smoothness of the coefficients with respect to the underlying network structure. To deal with the non-smoothness of the objective function in optimization, we developed a proximal gradient algorithm for efficient computation. The proposed model was compared to models with no prior structure information in both simulations and a problem of cancer subtype prediction with real TCGA (the cancer genome atlas) gene expression data. The network-constrained mode outperformed the traditional ones in both cases.
A combinatorial approach to protein docking with flexible side chains.

PubMed

Althaus, Ernst; Kohlbacher, Oliver; Lenhof, Hans-Peter; Müller, Peter

2002-01-01

Rigid-body docking approaches are not sufficient to predict the structure of a protein complex from the unbound (native) structures of the two proteins. Accounting for side chain flexibility is an important step towards fully flexible protein docking. This work describes an approach that allows conformational flexibility for the side chains while keeping the protein backbone rigid. Starting from candidates created by a rigid-docking algorithm, we demangle the side chains of the docking site, thus creating reasonable approximations of the true complex structure. These structures are ranked with respect to the binding free energy. We present two new techniques for side chain demangling. Both approaches are based on a discrete representation of the side chain conformational space by the use of a rotamer library. This leads to a combinatorial optimization problem. For the solution of this problem, we propose a fast heuristic approach and an exact, albeit slower, method that uses branch-and-cut techniques. As a test set, we use the unbound structures of three proteases and the corresponding protein inhibitors. For each of the examples, the highest-ranking conformation produced was a good approximation of the true complex structure.
A method and knowledge base for automated inference of patient problems from structured data in an electronic medical record.

PubMed

Wright, Adam; Pang, Justine; Feblowitz, Joshua C; Maloney, Francine L; Wilcox, Allison R; Ramelson, Harley Z; Schneider, Louise I; Bates, David W

2011-01-01

Accurate knowledge of a patient's medical problems is critical for clinical decision making, quality measurement, research, billing and clinical decision support. Common structured sources of problem information include the patient problem list and billing data; however, these sources are often inaccurate or incomplete. To develop and validate methods of automatically inferring patient problems from clinical and billing data, and to provide a knowledge base for inferring problems. We identified 17 target conditions and designed and validated a set of rules for identifying patient problems based on medications, laboratory results, billing codes, and vital signs. A panel of physicians provided input on a preliminary set of rules. Based on this input, we tested candidate rules on a sample of 100,000 patient records to assess their performance compared to gold standard manual chart review. The physician panel selected a final rule for each condition, which was validated on an independent sample of 100,000 records to assess its accuracy. Seventeen rules were developed for inferring patient problems. Analysis using a validation set of 100,000 randomly selected patients showed high sensitivity (range: 62.8-100.0%) and positive predictive value (range: 79.8-99.6%) for most rules. Overall, the inference rules performed better than using either the problem list or billing data alone. We developed and validated a set of rules for inferring patient problems. These rules have a variety of applications, including clinical decision support, care improvement, augmentation of the problem list, and identification of patients for research cohorts.
Efficient first-principles prediction of solid stability: Towards chemical accuracy

DOE PAGES

Zhang, Yubo; Kitchaev, Daniil A.; Yang, Julia; ...

2018-03-09

The question of material stability is of fundamental importance to any analysis of system properties in condensed matter physics and materials science. The ability to evaluate chemical stability, i.e., whether a stoichiometry will persist in some chemical environment, and structure selection, i.e. what crystal structure a stoichiometry will adopt, is critical to the prediction of materials synthesis, reactivity and properties. In this paper, we demonstrate that density functional theory, with the recently developed strongly constrained and appropriately normed (SCAN) functional, has advanced to a point where both facets of the stability problem can be reliably and efficiently predicted for mainmore » group compounds, while transition metal compounds are improved but remain a challenge. SCAN therefore offers a robust model for a significant portion of the periodic table, presenting an opportunity for the development of novel materials and the study of fine phase transformations even in largely unexplored systems with little to no experimental data.« less
Efficient first-principles prediction of solid stability: Towards chemical accuracy

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

Zhang, Yubo; Kitchaev, Daniil A.; Yang, Julia

The question of material stability is of fundamental importance to any analysis of system properties in condensed matter physics and materials science. The ability to evaluate chemical stability, i.e., whether a stoichiometry will persist in some chemical environment, and structure selection, i.e. what crystal structure a stoichiometry will adopt, is critical to the prediction of materials synthesis, reactivity and properties. In this paper, we demonstrate that density functional theory, with the recently developed strongly constrained and appropriately normed (SCAN) functional, has advanced to a point where both facets of the stability problem can be reliably and efficiently predicted for mainmore » group compounds, while transition metal compounds are improved but remain a challenge. SCAN therefore offers a robust model for a significant portion of the periodic table, presenting an opportunity for the development of novel materials and the study of fine phase transformations even in largely unexplored systems with little to no experimental data.« less
Statistical Issues in Galaxy Cluster Cosmology

NASA Technical Reports Server (NTRS)

Mantz, Adam

2013-01-01

The number and growth of massive galaxy clusters are sensitive probes of cosmological structure formation. Surveys at various wavelengths can detect clusters to high redshift, but the fact that cluster mass is not directly observable complicates matters, requiring us to simultaneously constrain scaling relations of observable signals with mass. The problem can be cast as one of regression, in which the data set is truncated, the (cosmology-dependent) underlying population must be modeled, and strong, complex correlations between measurements often exist. Simulations of cosmological structure formation provide a robust prediction for the number of clusters in the Universe as a function of mass and redshift (the mass function), but they cannot reliably predict the observables used to detect clusters in sky surveys (e.g. X-ray luminosity). Consequently, observers must constrain observable-mass scaling relations using additional data, and use the scaling relation model in conjunction with the mass function to predict the number of clusters as a function of redshift and luminosity.
4D Origami by Smart Embroidery.

PubMed

Stoychev, Georgi; Razavi, Mir Jalil; Wang, Xianqiao; Ionov, Leonid

2017-09-01

There exist many methods for processing of materials: extrusion, injection molding, fibers spinning, 3D printing, to name a few. In most cases, materials with a static, fixed shape are produced. However, numerous advanced applications require customized elements with reconfigurable shape. The few available techniques capable of overcoming this problem are expensive and/or time-consuming. Here, the use of one of the most ancient technologies for structuring, embroidering, is proposed to generate sophisticated patterns of active materials, and, in this way, to achieve complex actuation. By combining experiments and computational modeling, the fundamental rules that can predict the folding behavior of sheets with a variety of stitch-patterns are elucidated. It is demonstrated that theoretical mechanics analysis is only suitable to predict the behavior of the simplest experimental setups, whereas computer modeling gives better predictions for more complex cases. Finally, the applicability of the rules by designing basic origami structures and wrinkling substrates with controlled thermal insulation properties is shown. © 2017 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.
Developing a novel hierarchical approach for multiscale structural reliability predictions for ultra-high consequence applications

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

Emery, John M.; Coffin, Peter; Robbins, Brian A.

Microstructural variabilities are among the predominant sources of uncertainty in structural performance and reliability. We seek to develop efficient algorithms for multiscale calcu- lations for polycrystalline alloys such as aluminum alloy 6061-T6 in environments where ductile fracture is the dominant failure mode. Our approach employs concurrent multiscale methods, but does not focus on their development. They are a necessary but not sufficient ingredient to multiscale reliability predictions. We have focused on how to efficiently use concurrent models for forward propagation because practical applications cannot include fine-scale details throughout the problem domain due to exorbitant computational demand. Our approach begins withmore » a low-fidelity prediction at the engineering scale that is sub- sequently refined with multiscale simulation. The results presented in this report focus on plasticity and damage at the meso-scale, efforts to expedite Monte Carlo simulation with mi- crostructural considerations, modeling aspects regarding geometric representation of grains and second-phase particles, and contrasting algorithms for scale coupling.« less
Flavor structure in F-theory compactifications

NASA Astrophysics Data System (ADS)

Hayashi, Hirotaka; Kawano, Teruhiko; Tsuchiya, Yoichi; Watari, Taizan

2010-08-01

F-theory is one of frameworks in string theory where supersymmetric grand unification is accommodated, and all the Yukawa couplings and Majorana masses of righthanded neutrinos are generated. Yukawa couplings of charged fermions are generated at codimension-3 singularities, and a contribution from a given singularity point is known to be approximately rank 1. Thus, the approximate rank of Yukawa matrices in low-energy effective theory of generic F-theory compactifications are minimum of either the number of generations N gen = 3 or the number of singularity points of certain types. If there is a geometry with only one E 6 type point and one D 6 type point over the entire 7-brane for SU(5) gauge fields, F-theory compactified on such a geometry would reproduce approximately rank-1 Yukawa matrices in the real world. We found, however, that there is no such geometry. Thus, it is a problem how to generate hierarchical Yukawa eigenvalues in F-theory compactifications. A solution in the literature so far is to take an appropriate factorization limit. In this article, we propose an alternative solution to the hierarchical structure problem (which requires to tune some parameters) by studying how zero mode wavefunctions depend on complex structure moduli. In this solution, the N gen × N gen CKM matrix is predicted to have only N gen entries of order unity without an extra tuning of parameters, and the lepton flavor anarchy is predicted for the lepton mixing matrix. The hierarchy among the Yukawa eigenvalues of the down-type and charged lepton sector is predicted to be smaller than that of the up-type sector, and the Majorana masses of left-handed neutrinos generated through the see-saw mechanism have small hierarchy. All of these predictions agree with what we observe in the real world. We also obtained a precise description of zero mode wavefunctions near the E 6 type singularity points, where the up-type Yukawa couplings are generated.
Discovery of optimal zeolites for challenging separations and chemical conversions through predictive materials modeling

NASA Astrophysics Data System (ADS)

Siepmann, J. Ilja; Bai, Peng; Tsapatsis, Michael; Knight, Chris; Deem, Michael W.

2015-03-01

Zeolites play numerous important roles in modern petroleum refineries and have the potential to advance the production of fuels and chemical feedstocks from renewable resources. The performance of a zeolite as separation medium and catalyst depends on its framework structure and the type or location of active sites. To date, 213 framework types have been synthesized and >330000 thermodynamically accessible zeolite structures have been predicted. Hence, identification of optimal zeolites for a given application from the large pool of candidate structures is attractive for accelerating the pace of materials discovery. Here we identify, through a large-scale, multi-step computational screening process, promising zeolite structures for two energy-related applications: the purification of ethanol beyond the ethanol/water azeotropic concentration in a single separation step from fermentation broths and the hydroisomerization of alkanes with 18-30 carbon atoms encountered in petroleum refining. These results demonstrate that predictive modeling and data-driven science can now be applied to solve some of the most challenging separation problems involving highly non-ideal mixtures and highly articulated compounds. Financial support from the Department of Energy Office of Basic Energy Sciences, Division of Chemical Sciences, Geosciences and Biosciences under Award DE-FG02-12ER16362 is gratefully acknowledged.
A comparison of experimental and calculated thin-shell leading-edge buckling due to thermal stresses

NASA Technical Reports Server (NTRS)

Jenkins, Jerald M.

1988-01-01

High-temperature thin-shell leading-edge buckling test data are analyzed using NASA structural analysis (NASTRAN) as a finite element tool for predicting thermal buckling characteristics. Buckling points are predicted for several combinations of edge boundary conditions. The problem of relating the appropriate plate area to the edge stress distribution and the stress gradient is addressed in terms of analysis assumptions. Local plasticity was found to occur on the specimen analyzed, and this tended to simplify the basic problem since it effectively equalized the stress gradient from loaded edge to loaded edge. The initial loading was found to be difficult to select for the buckling analysis because of the transient nature of thermal stress. Multiple initial model loadings are likely required for complicated thermal stress time histories before a pertinent finite element buckling analysis can be achieved. The basic mode shapes determined from experimentation were correctly identified from computation.
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.
Protein Loop Structure Prediction Using Conformational Space Annealing.

PubMed

Heo, Seungryong; Lee, Juyong; Joo, Keehyoung; Shin, Hang-Cheol; Lee, Jooyoung

2017-05-22

We have developed a protein loop structure prediction method by combining a new energy function, which we call E PLM (energy for protein loop modeling), with the conformational space annealing (CSA) global optimization algorithm. The energy function includes stereochemistry, dynamic fragment assembly, distance-scaled finite ideal gas reference (DFIRE), and generalized orientation- and distance-dependent terms. For the conformational search of loop structures, we used the CSA algorithm, which has been quite successful in dealing with various hard global optimization problems. We assessed the performance of E PLM with two widely used loop-decoy sets, Jacobson and RAPPER, and compared the results against the DFIRE potential. The accuracy of model selection from a pool of loop decoys as well as de novo loop modeling starting from randomly generated structures was examined separately. For the selection of a nativelike structure from a decoy set, E PLM was more accurate than DFIRE in the case of the Jacobson set and had similar accuracy in the case of the RAPPER set. In terms of sampling more nativelike loop structures, E PLM outperformed E DFIRE for both decoy sets. This new approach equipped with E PLM and CSA can serve as the state-of-the-art de novo loop modeling method.
Correcting pervasive errors in RNA crystallography through enumerative structure prediction.

PubMed

Chou, Fang-Chieh; Sripakdeevong, Parin; Dibrov, Sergey M; Hermann, Thomas; Das, Rhiju

2013-01-01

Three-dimensional RNA models fitted into crystallographic density maps exhibit pervasive conformational ambiguities, geometric errors and steric clashes. To address these problems, we present enumerative real-space refinement assisted by electron density under Rosetta (ERRASER), coupled to Python-based hierarchical environment for integrated 'xtallography' (PHENIX) diffraction-based refinement. On 24 data sets, ERRASER automatically corrects the majority of MolProbity-assessed errors, improves the average R(free) factor, resolves functionally important discrepancies in noncanonical structure and refines low-resolution models to better match higher-resolution models.
Animal Construction as a Free Boundary Problem: Evidence of Fractal Scaling Laws

NASA Astrophysics Data System (ADS)

Nicolis, S. C.

2014-12-01

We suggest that the main features of animal construction can be understood as the sum of locally independent actions of non-interacting individuals subjected to the global constraints imposed by the nascent structure. We first formulate an analytically tractable oscopic description of construction which predicts a 1/3 power law for how the length of the structure grows with time. We further show how the power law is modified when biases in random walk performed by the constructors as well as halting times between consecutive construction steps are included.
Paternal and maternal influences on problem behaviors among homeless and runaway youth.

PubMed

Stein, Judith A; Milburn, Norweeta G; Zane, Jazmin I; Rotheram-Borus, Mary Jane

2009-01-01

Using an Attachment Theory conceptual framework, associations were investigated among positive paternal and maternal relationships, and recent problem behaviors among 501 currently homeless and runaway adolescents (253 males, 248 females). Homeless and runaway youth commonly exhibit problem behaviors such as substance use, various forms of delinquency and risky sex behaviors, and report more emotional distress than typical adolescents. Furthermore, attachments to their families are often strained. In structural equation models, positive paternal relationships significantly predicted less substance use and less criminal behavior, whereas maternal relationships did not have a significant effect on or association with either behavior. Positive maternal relationships predicted less survival sex behavior. Separate gender analyses indicated that among the females, a longer time away from home was significantly associated with a poorer paternal relationship, and more substance use and criminal behavior. Paternal relations, a neglected area of research and often not addressed in attachment theory, should be investigated further. Attachments, particularly to fathers, were protective against many deleterious behaviors. Building on relatively positive relations and attachments may foster family reunifications and beneficial outcomes for at-risk youth.
Adaptive Finite Element Methods for Continuum Damage Modeling

NASA Technical Reports Server (NTRS)

Min, J. B.; Tworzydlo, W. W.; Xiques, K. E.

1995-01-01

The paper presents an application of adaptive finite element methods to the modeling of low-cycle continuum damage and life prediction of high-temperature components. The major objective is to provide automated and accurate modeling of damaged zones through adaptive mesh refinement and adaptive time-stepping methods. The damage modeling methodology is implemented in an usual way by embedding damage evolution in the transient nonlinear solution of elasto-viscoplastic deformation problems. This nonlinear boundary-value problem is discretized by adaptive finite element methods. The automated h-adaptive mesh refinements are driven by error indicators, based on selected principal variables in the problem (stresses, non-elastic strains, damage, etc.). In the time domain, adaptive time-stepping is used, combined with a predictor-corrector time marching algorithm. The time selection is controlled by required time accuracy. In order to take into account strong temperature dependency of material parameters, the nonlinear structural solution a coupled with thermal analyses (one-way coupling). Several test examples illustrate the importance and benefits of adaptive mesh refinements in accurate prediction of damage levels and failure time.

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