Human brain detects short-time nonlinear predictability in the temporal fine structure of deterministic chaotic sounds

NASA Astrophysics Data System (ADS)

Itoh, Kosuke; Nakada, Tsutomu

2013-04-01

Deterministic nonlinear dynamical processes are ubiquitous in nature. Chaotic sounds generated by such processes may appear irregular and random in waveform, but these sounds are mathematically distinguished from random stochastic sounds in that they contain deterministic short-time predictability in their temporal fine structures. We show that the human brain distinguishes deterministic chaotic sounds from spectrally matched stochastic sounds in neural processing and perception. Deterministic chaotic sounds, even without being attended to, elicited greater cerebral cortical responses than the surrogate control sounds after about 150 ms in latency after sound onset. Listeners also clearly discriminated these sounds in perception. The results support the hypothesis that the human auditory system is sensitive to the subtle short-time predictability embedded in the temporal fine structure of sounds.

Performance of protein-structure predictions with the physics-based UNRES force field in CASP11.

PubMed

Krupa, Paweł; Mozolewska, Magdalena A; Wiśniewska, Marta; Yin, Yanping; He, Yi; Sieradzan, Adam K; Ganzynkowicz, Robert; Lipska, Agnieszka G; Karczyńska, Agnieszka; Ślusarz, Magdalena; Ślusarz, Rafał; Giełdoń, Artur; Czaplewski, Cezary; Jagieła, Dawid; Zaborowski, Bartłomiej; Scheraga, Harold A; Liwo, Adam

2016-11-01

Participating as the Cornell-Gdansk group, we have used our physics-based coarse-grained UNited RESidue (UNRES) force field to predict protein structure in the 11th Community Wide Experiment on the Critical Assessment of Techniques for Protein Structure Prediction (CASP11). Our methodology involved extensive multiplexed replica exchange simulations of the target proteins with a recently improved UNRES force field to provide better reproductions of the local structures of polypeptide chains. All simulations were started from fully extended polypeptide chains, and no external information was included in the simulation process except for weak restraints on secondary structure to enable us to finish each prediction within the allowed 3-week time window. Because of simplified UNRES representation of polypeptide chains, use of enhanced sampling methods, code optimization and parallelization and sufficient computational resources, we were able to treat, for the first time, all 55 human prediction targets with sizes from 44 to 595 amino acid residues, the average size being 251 residues. Complete structures of six single-domain proteins were predicted accurately, with the highest accuracy being attained for the T0769, for which the CαRMSD was 3.8 Å for 97 residues of the experimental structure. Correct structures were also predicted for 13 domains of multi-domain proteins with accuracy comparable to that of the best template-based modeling methods. With further improvements of the UNRES force field that are now underway, our physics-based coarse-grained approach to protein-structure prediction will eventually reach global prediction capacity and, consequently, reliability in simulating protein structure and dynamics that are important in biochemical processes. Freely available on the web at http://www.unres.pl/ CONTACT: has5@cornell.edu. © The Author 2016. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com.

Toward a Time-Domain Fractal Lightning Simulation

NASA Astrophysics Data System (ADS)

Liang, C.; Carlson, B. E.; Lehtinen, N. G.; Cohen, M.; Lauben, D.; Inan, U. S.

2010-12-01

Electromagnetic simulations of lightning are useful for prediction of lightning properties and exploration of the underlying physical behavior. Fractal lightning models predict the spatial structure of the discharge, but thus far do not provide much information about discharge behavior in time and therefore cannot predict electromagnetic wave emissions or current characteristics. Here we develop a time-domain fractal lightning simulation from Maxwell's equations, the method of moments with the thin wire approximation, an adaptive time-stepping scheme, and a simplified electrical model of the lightning channel. The model predicts current pulse structure and electromagnetic wave emissions and can be used to simulate the entire duration of a lightning discharge. The model can be used to explore the electrical characteristics of the lightning channel, the temporal development of the discharge, and the effects of these characteristics on observable electromagnetic wave emissions.

Development of an engineering analysis of progressive damage in composites during low velocity impact

NASA Technical Reports Server (NTRS)

Humphreys, E. A.

1981-01-01

A computerized, analytical methodology was developed to study damage accumulation during low velocity lateral impact of layered composite plates. The impact event was modeled as perfectly plastic with complete momentum transfer to the plate structure. A transient dynamic finite element approach was selected to predict the displacement time response of the plate structure. Composite ply and interlaminar stresses were computed at selected time intervals and subsequently evaluated to predict layer and interlaminar damage. The effects of damage on elemental stiffness were then incorporated back into the analysis for subsequent time steps. Damage predicted included fiber failure, matrix ply failure and interlaminar delamination.

Linking melodic expectation to expressive performance timing and perceived musical tension.

PubMed

Gingras, Bruno; Pearce, Marcus T; Goodchild, Meghan; Dean, Roger T; Wiggins, Geraint; McAdams, Stephen

2016-04-01

This research explored the relations between the predictability of musical structure, expressive timing in performance, and listeners' perceived musical tension. Studies analyzing the influence of expressive timing on listeners' affective responses have been constrained by the fact that, in most pieces, the notated durations limit performers' interpretive freedom. To circumvent this issue, we focused on the unmeasured prelude, a semi-improvisatory genre without notated durations. In Experiment 1, 12 professional harpsichordists recorded an unmeasured prelude on a harpsichord equipped with a MIDI console. Melodic expectation was assessed using a probabilistic model (IDyOM [Information Dynamics of Music]) whose expectations have been previously shown to match closely those of human listeners. Performance timing information was extracted from the MIDI data using a score-performance matching algorithm. Time-series analyses showed that, in a piece with unspecified note durations, the predictability of melodic structure measurably influenced tempo fluctuations in performance. In Experiment 2, another 10 harpsichordists, 20 nonharpsichordist musicians, and 20 nonmusicians listened to the recordings from Experiment 1 and rated the perceived tension continuously. Granger causality analyses were conducted to investigate predictive relations among melodic expectation, expressive timing, and perceived tension. Although melodic expectation, as modeled by IDyOM, modestly predicted perceived tension for all participant groups, neither of its components, information content or entropy, was Granger causal. In contrast, expressive timing was a strong predictor and was Granger causal. However, because melodic expectation was also predictive of expressive timing, our results outline a complete chain of influence from predictability of melodic structure via expressive performance timing to perceived musical tension. (PsycINFO Database Record (c) 2016 APA, all rights reserved).

Correlation of MFOLD-predicted DNA secondary structures with separation patterns obtained by capillary electrophoresis single-strand conformation polymorphism (CE-SSCP) analysis.

PubMed

Glavac, Damjan; Potocnik, Uros; Podpecnik, Darja; Zizek, Teofil; Smerkolj, Sava; Ravnik-Glavac, Metka

2002-04-01

We have studied 57 different mutations within three beta-globin gene promoter fragments with sizes 52 bp, 77 bp, and 193 bp by fluorescent capillary electrophoresis CE-SSCP analysis. For each mutation and wild type, energetically most-favorable predicted secondary structures were calculated for sense and antisense strands using the MFOLD DNA-folding algorithm in order to investigate if any correlation exists between predicted DNA structures and actual CE migration time shifts. The overall CE-SSCP detection rate was 100% for all mutations in three studied DNA fragments. For shorter 52 bp and 77 bp DNA fragments we obtained a positive correlation between the migration time shifts and difference in free energy values of predicted secondary structures at all temperatures. For longer 193 bp beta-globin gene fragments with 46 mutations MFOLD predicted different secondary structures for 89% of mutated strands at 25 degrees C and 40 degrees C. However, the magnitude of the mobility shifts did not necessarily correlate with their secondary structures and free energy values except for the sense strand at 40 degrees C where this correlation was statistically significant (r = 0.312, p = 0.033). Results of this study provided more direct insight into the mechanism of CE-SSCP and showed that MFOLD prediction could be helpful in making decisions about the running temperatures and in prediction of CE-SSCP data patterns, especially for shorter (50-100 bp) DNA fragments. Copyright 2002 Wiley-Liss, Inc.

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

Sixty-five years of the long march in protein secondary structure prediction: the final stretch?

PubMed Central

Yang, Yuedong; Gao, Jianzhao; Wang, Jihua; Heffernan, Rhys; Hanson, Jack; Paliwal, Kuldip; Zhou, Yaoqi

2018-01-01

Abstract Protein secondary structure prediction began in 1951 when Pauling and Corey predicted helical and sheet conformations for protein polypeptide backbone even before the first protein structure was determined. Sixty-five years later, powerful new methods breathe new life into this field. The highest three-state accuracy without relying on structure templates is now at 82–84%, a number unthinkable just a few years ago. These improvements came from increasingly larger databases of protein sequences and structures for training, the use of template secondary structure information and more powerful deep learning techniques. As we are approaching to the theoretical limit of three-state prediction (88–90%), alternative to secondary structure prediction (prediction of backbone torsion angles and Cα-atom-based angles and torsion angles) not only has more room for further improvement but also allows direct prediction of three-dimensional fragment structures with constantly improved accuracy. About 20% of all 40-residue fragments in a database of 1199 non-redundant proteins have <6 Å root-mean-squared distance from the native conformations by SPIDER2. More powerful deep learning methods with improved capability of capturing long-range interactions begin to emerge as the next generation of techniques for secondary structure prediction. The time has come to finish off the final stretch of the long march towards protein secondary structure prediction. PMID:28040746

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.

Affective-cognitive meta-bases versus structural bases of attitudes predict processing interest versus efficiency.

PubMed

See, Ya Hui Michelle; Petty, Richard E; Fabrigar, Leandre R

2013-08-01

We proposed that (a) processing interest for affective over cognitive information is captured by meta-bases (i.e., the extent to which people subjectively perceive themselves to rely on affect or cognition in their attitudes) and (b) processing efficiency for affective over cognitive information is captured by structural bases (i.e., the extent to which attitudes are more evaluatively congruent with affect or cognition). Because processing speed can disentangle interest from efficiency by being manifest as longer or shorter reading times, we hypothesized and found that more affective meta-bases predicted longer affective than cognitive reading time when processing efficiency was held constant (Study 1). In contrast, more affective structural bases predicted shorter affective than cognitive reading time when participants were constrained in their ability to allocate resources deliberatively (Study 2). When deliberation was neither encouraged nor constrained, effects for meta-bases and structural bases emerged (Study 3). Implications for affective-cognitive processing and other attitudes-relevant constructs are discussed.

Optimal pollution mitigation in Monterey Bay based on coastal radar data and nonlinear dynamics.

PubMed

Coulliette, Chad; Lekien, Francois; Paduan, Jeffrey D; Haller, George; Marsden, Jerrold E

2007-09-15

High-frequency (HF) radar technology produces detailed velocity maps near the surface of estuaries and bays. The use of velocity data in environmental prediction, nonetheless, remains unexplored. In this paper, we uncover a striking flow structure in coastal radar observations of Monterey Bay, along the California coastline. This complex structure governs the spread of organic contaminants, such as agricultural runoff which is a typical source of pollution in the bay. We show that a HF radar-based pollution release scheme using this flow structure reduces the impact of pollution on the coastal environment in the bay. We predict the motion of the Lagrangian flow structures from finite-time Lyapunov exponents of the coastal HF velocity data. From this prediction, we obtain optimal release times, at which pollution leaves the bay most efficiently.

Quantitative structure-retention relationship models for the prediction of the reversed-phase HPLC gradient retention based on the heuristic method and support vector machine.

PubMed

Du, Hongying; Wang, Jie; Yao, Xiaojun; Hu, Zhide

2009-01-01

The heuristic method (HM) and support vector machine (SVM) were used to construct quantitative structure-retention relationship models by a series of compounds to predict the gradient retention times of reversed-phase high-performance liquid chromatography (HPLC) in three different columns. The aims of this investigation were to predict the retention times of multifarious compounds, to find the main properties of the three columns, and to indicate the theory of separation procedures. In our method, we correlated the retention times of many diverse structural analytes in three columns (Symmetry C18, Chromolith, and SG-MIX) with their representative molecular descriptors, calculated from the molecular structures alone. HM was used to select the most important molecular descriptors and build linear regression models. Furthermore, non-linear regression models were built using the SVM method; the performance of the SVM models were better than that of the HM models, and the prediction results were in good agreement with the experimental values. This paper could give some insights into the factors that were likely to govern the gradient retention process of the three investigated HPLC columns, which could theoretically supervise the practical experiment.

Operational flood control of a low-lying delta system using large time step Model Predictive Control

NASA Astrophysics Data System (ADS)

Tian, Xin; van Overloop, Peter-Jules; Negenborn, Rudy R.; van de Giesen, Nick

2015-01-01

The safety of low-lying deltas is threatened not only by riverine flooding but by storm-induced coastal flooding as well. For the purpose of flood control, these deltas are mostly protected in a man-made environment, where dikes, dams and other adjustable infrastructures, such as gates, barriers and pumps are widely constructed. Instead of always reinforcing and heightening these structures, it is worth considering making the most of the existing infrastructure to reduce the damage and manage the delta in an operational and overall way. In this study, an advanced real-time control approach, Model Predictive Control, is proposed to operate these structures in the Dutch delta system (the Rhine-Meuse delta). The application covers non-linearity in the dynamic behavior of the water system and the structures. To deal with the non-linearity, a linearization scheme is applied which directly uses the gate height instead of the structure flow as the control variable. Given the fact that MPC needs to compute control actions in real-time, we address issues regarding computational time. A new large time step scheme is proposed in order to save computation time, in which different control variables can have different control time steps. Simulation experiments demonstrate that Model Predictive Control with the large time step setting is able to control a delta system better and much more efficiently than the conventional operational schemes.

Sequential search leads to faster, more efficient fragment-based de novo protein structure prediction.

PubMed

de Oliveira, Saulo H P; Law, Eleanor C; Shi, Jiye; Deane, Charlotte M

2018-04-01

Most current de novo structure prediction methods randomly sample protein conformations and thus require large amounts of computational resource. Here, we consider a sequential sampling strategy, building on ideas from recent experimental work which shows that many proteins fold cotranslationally. We have investigated whether a pseudo-greedy search approach, which begins sequentially from one of the termini, can improve the performance and accuracy of de novo protein structure prediction. We observed that our sequential approach converges when fewer than 20 000 decoys have been produced, fewer than commonly expected. Using our software, SAINT2, we also compared the run time and quality of models produced in a sequential fashion against a standard, non-sequential approach. Sequential prediction produces an individual decoy 1.5-2.5 times faster than non-sequential prediction. When considering the quality of the best model, sequential prediction led to a better model being produced for 31 out of 41 soluble protein validation cases and for 18 out of 24 transmembrane protein cases. Correct models (TM-Score > 0.5) were produced for 29 of these cases by the sequential mode and for only 22 by the non-sequential mode. Our comparison reveals that a sequential search strategy can be used to drastically reduce computational time of de novo protein structure prediction and improve accuracy. Data are available for download from: http://opig.stats.ox.ac.uk/resources. SAINT2 is available for download from: https://github.com/sauloho/SAINT2. saulo.deoliveira@dtc.ox.ac.uk. Supplementary data are available at Bioinformatics online.

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.

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.

Corrosion Prediction with Parallel Finite Element Modeling for Coupled Hygro-Chemo Transport into Concrete under Chloride-Rich Environment

PubMed Central

Na, Okpin; Cai, Xiao-Chuan; Xi, Yunping

2017-01-01

The prediction of the chloride-induced corrosion is very important because of the durable life of concrete structure. To simulate more realistic durability performance of concrete structures, complex scientific methods and more accurate material models are needed. In order to predict the robust results of corrosion initiation time and to describe the thin layer from concrete surface to reinforcement, a large number of fine meshes are also used. The purpose of this study is to suggest more realistic physical model regarding coupled hygro-chemo transport and to implement the model with parallel finite element algorithm. Furthermore, microclimate model with environmental humidity and seasonal temperature is adopted. As a result, the prediction model of chloride diffusion under unsaturated condition was developed with parallel algorithms and was applied to the existing bridge to validate the model with multi-boundary condition. As the number of processors increased, the computational time decreased until the number of processors became optimized. Then, the computational time increased because the communication time between the processors increased. The framework of present model can be extended to simulate the multi-species de-icing salts ingress into non-saturated concrete structures in future work. PMID:28772714

Knotty: Efficient and Accurate Prediction of Complex RNA Pseudoknot Structures.

PubMed

Jabbari, Hosna; Wark, Ian; Montemagno, Carlo; Will, Sebastian

2018-06-01

The computational prediction of RNA secondary structure by free energy minimization has become an important tool in RNA research. However in practice, energy minimization is mostly limited to pseudoknot-free structures or rather simple pseudoknots, not covering many biologically important structures such as kissing hairpins. Algorithms capable of predicting sufficiently complex pseudoknots (for sequences of length n) used to have extreme complexities, e.g. Pknots (Rivas and Eddy, 1999) has O(n6) time and O(n4) space complexity. The algorithm CCJ (Chen et al., 2009) dramatically improves the asymptotic run time for predicting complex pseudoknots (handling almost all relevant pseudoknots, while being slightly less general than Pknots), but this came at the cost of large constant factors in space and time, which strongly limited its practical application (∼200 bases already require 256GB space). We present a CCJ-type algorithm, Knotty, that handles the same comprehensive pseudoknot class of structures as CCJ with improved space complexity of Θ(n3 + Z)-due to the applied technique of sparsification, the number of "candidates", Z, appears to grow significantly slower than n4 on our benchmark set (which include pseudoknotted RNAs up to 400 nucleotides). In terms of run time over this benchmark, Knotty clearly outperforms Pknots and the original CCJ implementation, CCJ 1.0; Knotty's space consumption fundamentally improves over CCJ 1.0, being on a par with the space-economic Pknots. By comparing to CCJ 2.0, our unsparsified Knotty variant, we demonstrate the isolated effect of sparsification. Moreover, Knotty employs the state-of-the-art energy model of "HotKnots DP09", which results in superior prediction accuracy over Pknots. Our software is available at https://github.com/HosnaJabbari/Knotty. will@tbi.unvie.ac.at. Supplementary data are available at Bioinformatics online.

Ab initio RNA folding by discrete molecular dynamics: From structure prediction to folding mechanisms

PubMed Central

Ding, Feng; Sharma, Shantanu; Chalasani, Poornima; Demidov, Vadim V.; Broude, Natalia E.; Dokholyan, Nikolay V.

2008-01-01

RNA molecules with novel functions have revived interest in the accurate prediction of RNA three-dimensional (3D) structure and folding dynamics. However, existing methods are inefficient in automated 3D structure prediction. Here, we report a robust computational approach for rapid folding of RNA molecules. We develop a simplified RNA model for discrete molecular dynamics (DMD) simulations, incorporating base-pairing and base-stacking interactions. We demonstrate correct folding of 150 structurally diverse RNA sequences. The majority of DMD-predicted 3D structures have <4 Å deviations from experimental structures. The secondary structures corresponding to the predicted 3D structures consist of 94% native base-pair interactions. Folding thermodynamics and kinetics of tRNAPhe, pseudoknots, and mRNA fragments in DMD simulations are in agreement with previous experimental findings. Folding of RNA molecules features transient, non-native conformations, suggesting non-hierarchical RNA folding. Our method allows rapid conformational sampling of RNA folding, with computational time increasing linearly with RNA length. We envision this approach as a promising tool for RNA structural and functional analyses. PMID:18456842

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.

Real-time open-loop frequency response analysis of flight test data

NASA Technical Reports Server (NTRS)

Bosworth, J. T.; West, J. C.

1986-01-01

A technique has been developed to compare the open-loop frequency response of a flight test aircraft real time with linear analysis predictions. The result is direct feedback to the flight control systems engineer on the validity of predictions and adds confidence for proceeding with envelope expansion. Further, gain and phase margins can be tracked for trends in a manner similar to the techniques used by structural dynamics engineers in tracking structural modal damping.

A computer program for cyclic plasticity and structural fatigue analysis

NASA Technical Reports Server (NTRS)

Kalev, I.

1980-01-01

A computerized tool for the analysis of time independent cyclic plasticity structural response, life to crack initiation prediction, and crack growth rate prediction for metallic materials is described. Three analytical items are combined: the finite element method with its associated numerical techniques for idealization of the structural component, cyclic plasticity models for idealization of the material behavior, and damage accumulation criteria for the fatigue failure.

Parallel protein secondary structure prediction based on neural networks.

PubMed

Zhong, Wei; Altun, Gulsah; Tian, Xinmin; Harrison, Robert; Tai, Phang C; Pan, Yi

2004-01-01

Protein secondary structure prediction has a fundamental influence on today's bioinformatics research. In this work, binary and tertiary classifiers of protein secondary structure prediction are implemented on Denoeux belief neural network (DBNN) architecture. Hydrophobicity matrix, orthogonal matrix, BLOSUM62 and PSSM (position specific scoring matrix) are experimented separately as the encoding schemes for DBNN. The experimental results contribute to the design of new encoding schemes. New binary classifier for Helix versus not Helix ( approximately H) for DBNN produces prediction accuracy of 87% when PSSM is used for the input profile. The performance of DBNN binary classifier is comparable to other best prediction methods. The good test results for binary classifiers open a new approach for protein structure prediction with neural networks. Due to the time consuming task of training the neural networks, Pthread and OpenMP are employed to parallelize DBNN in the hyperthreading enabled Intel architecture. Speedup for 16 Pthreads is 4.9 and speedup for 16 OpenMP threads is 4 in the 4 processors shared memory architecture. Both speedup performance of OpenMP and Pthread is superior to that of other research. With the new parallel training algorithm, thousands of amino acids can be processed in reasonable amount of time. Our research also shows that hyperthreading technology for Intel architecture is efficient for parallel biological algorithms.

Predictive modeling of multicellular structure formation by using Cellular Particle Dynamics simulations

NASA Astrophysics Data System (ADS)

McCune, Matthew; Shafiee, Ashkan; Forgacs, Gabor; Kosztin, Ioan

2014-03-01

Cellular Particle Dynamics (CPD) is an effective computational method for describing and predicting the time evolution of biomechanical relaxation processes of multicellular systems. A typical example is the fusion of spheroidal bioink particles during post bioprinting structure formation. In CPD cells are modeled as an ensemble of cellular particles (CPs) that interact via short-range contact interactions, characterized by an attractive (adhesive interaction) and a repulsive (excluded volume interaction) component. The time evolution of the spatial conformation of the multicellular system is determined by following the trajectories of all CPs through integration of their equations of motion. CPD was successfully applied to describe and predict the fusion of 3D tissue construct involving identical spherical aggregates. Here, we demonstrate that CPD can also predict tissue formation involving uneven spherical aggregates whose volumes decrease during the fusion process. Work supported by NSF [PHY-0957914]. Computer time provided by the University of Missouri Bioinformatics Consortium.

Assessment of concrete damage and strength degradation caused by reinforcement corrosion

NASA Astrophysics Data System (ADS)

Nepal, Jaya; Chen, Hua-Peng

2015-07-01

Structural performance deterioration of reinforced concrete structures has been extensively investigated, but very limited studies have been carried out to investigate the effect of reinforcement corrosion on time-dependent reliability with consideration of the influence of mechanical characteristics of the bond interface due to corrosion. This paper deals with how corrosion in reinforcement creates different types of defects in concrete structure and how they are responsible for the structural capacity deterioration of corrosion affected reinforced concrete structures during their service life. Cracking in cover concrete due to reinforcement corrosion is investigated by using rebar-concrete model and realistic concrete properties. The flexural strength deterioration is analytically predicted on the basis of bond strength evolution due to reinforcement corrosion, which is examined by the experimental data available. The time-dependent reliability analysis is undertaken to calculate the life time structural reliability of corrosion damaged concrete structures by stochastic deterioration modelling of reinforced concrete. The results from the numerical example show that the proposed approach is capable of evaluating the damage caused by reinforcement corrosion and also predicting the structural reliability of concrete structures during their lifecycle.

Temperature dependence of the hydrated electron's excited-state relaxation. I. Simulation predictions of resonance Raman and pump-probe transient absorption spectra of cavity and non-cavity models

NASA Astrophysics Data System (ADS)

Zho, Chen-Chen; Farr, Erik P.; Glover, William J.; Schwartz, Benjamin J.

2017-08-01

We use one-electron non-adiabatic mixed quantum/classical simulations to explore the temperature dependence of both the ground-state structure and the excited-state relaxation dynamics of the hydrated electron. We compare the results for both the traditional cavity picture and a more recent non-cavity model of the hydrated electron and make definite predictions for distinguishing between the different possible structural models in future experiments. We find that the traditional cavity model shows no temperature-dependent change in structure at constant density, leading to a predicted resonance Raman spectrum that is essentially temperature-independent. In contrast, the non-cavity model predicts a blue-shift in the hydrated electron's resonance Raman O-H stretch with increasing temperature. The lack of a temperature-dependent ground-state structural change of the cavity model also leads to a prediction of little change with temperature of both the excited-state lifetime and hot ground-state cooling time of the hydrated electron following photoexcitation. This is in sharp contrast to the predictions of the non-cavity model, where both the excited-state lifetime and hot ground-state cooling time are expected to decrease significantly with increasing temperature. These simulation-based predictions should be directly testable by the results of future time-resolved photoelectron spectroscopy experiments. Finally, the temperature-dependent differences in predicted excited-state lifetime and hot ground-state cooling time of the two models also lead to different predicted pump-probe transient absorption spectroscopy of the hydrated electron as a function of temperature. We perform such experiments and describe them in Paper II [E. P. Farr et al., J. Chem. Phys. 147, 074504 (2017)], and find changes in the excited-state lifetime and hot ground-state cooling time with temperature that match well with the predictions of the non-cavity model. In particular, the experiments reveal stimulated emission from the excited state with an amplitude and lifetime that decreases with increasing temperature, a result in contrast to the lack of stimulated emission predicted by the cavity model but in good agreement with the non-cavity model. Overall, until ab initio calculations describing the non-adiabatic excited-state dynamics of an excess electron with hundreds of water molecules at a variety of temperatures become computationally feasible, the simulations presented here provide a definitive route for connecting the predictions of cavity and non-cavity models of the hydrated electron with future experiments.

Fractal structure enables temporal prediction in music.

PubMed

Rankin, Summer K; Fink, Philip W; Large, Edward W

2014-10-01

1/f serial correlations and statistical self-similarity (fractal structure) have been measured in various dimensions of musical compositions. Musical performances also display 1/f properties in expressive tempo fluctuations, and listeners predict tempo changes when synchronizing. Here the authors show that the 1/f structure is sufficient for listeners to predict the onset times of upcoming musical events. These results reveal what information listeners use to anticipate events in complex, non-isochronous acoustic rhythms, and this will entail innovative models of temporal synchronization. This finding could improve therapies for Parkinson's and related disorders and inform deeper understanding of how endogenous neural rhythms anticipate events in complex, temporally structured communication signals.

Strength of figure-ground activity in monkey primary visual cortex predicts saccadic reaction time in a delayed detection task.

PubMed

Supèr, Hans; Lamme, Victor A F

2007-06-01

When and where are decisions made? In the visual system a saccade, which is a fast shift of gaze toward a target in the visual scene, is the behavioral outcome of a decision. Current neurophysiological data and reaction time models show that saccadic reaction times are determined by a build-up of activity in motor-related structures, such as the frontal eye fields. These structures depend on the sensory evidence of the stimulus. Here we use a delayed figure-ground detection task to show that late modulated activity in the visual cortex (V1) predicts saccadic reaction time. This predictive activity is part of the process of figure-ground segregation and is specific for the saccade target location. These observations indicate that sensory signals are directly involved in the decision of when and where to look.

Prediction and verification of creep behavior in metallic materials and components for the space shuttle thermal protection system. Volume 2: Phase 2 subsize panel cyclic creep predictions

NASA Technical Reports Server (NTRS)

Cramer, B. A.; Davis, J. W.

1975-01-01

A method for predicting permanent cyclic creep deflections in stiffened panel structures was developed. The resulting computer program may be applied to either the time-hardening or strain-hardening theories of creep accumulation. Iterative techniques were used to determine structural rotations, creep strains, and stresses as a function of time. Deflections were determined by numerical integration of structural rotations along the panel length. The analytical approach was developed for analyzing thin-gage entry vehicle metallic-thermal-protection system panels subjected to cyclic bending loads at high temperatures, but may be applied to any panel subjected to bending loads. Predicted panel creep deflections were compared with results from cyclic tests of subsize corrugation and rib-stiffened panels. Empirical equations were developed for each material based on correlation with tensile cyclic creep data and both the subsize panels and tensile specimens were fabricated from the same sheet material. For Vol. 1, see N75-21431.

Managing uncertainty in metabolic network structure and improving predictions using EnsembleFBA

PubMed Central

2017-01-01

Genome-scale metabolic network reconstructions (GENREs) are repositories of knowledge about the metabolic processes that occur in an organism. GENREs have been used to discover and interpret metabolic functions, and to engineer novel network structures. A major barrier preventing more widespread use of GENREs, particularly to study non-model organisms, is the extensive time required to produce a high-quality GENRE. Many automated approaches have been developed which reduce this time requirement, but automatically-reconstructed draft GENREs still require curation before useful predictions can be made. We present a novel approach to the analysis of GENREs which improves the predictive capabilities of draft GENREs by representing many alternative network structures, all equally consistent with available data, and generating predictions from this ensemble. This ensemble approach is compatible with many reconstruction methods. We refer to this new approach as Ensemble Flux Balance Analysis (EnsembleFBA). We validate EnsembleFBA by predicting growth and gene essentiality in the model organism Pseudomonas aeruginosa UCBPP-PA14. We demonstrate how EnsembleFBA can be included in a systems biology workflow by predicting essential genes in six Streptococcus species and mapping the essential genes to small molecule ligands from DrugBank. We found that some metabolic subsystems contributed disproportionately to the set of predicted essential reactions in a way that was unique to each Streptococcus species, leading to species-specific outcomes from small molecule interactions. Through our analyses of P. aeruginosa and six Streptococci, we show that ensembles increase the quality of predictions without drastically increasing reconstruction time, thus making GENRE approaches more practical for applications which require predictions for many non-model organisms. All of our functions and accompanying example code are available in an open online repository. PMID:28263984

Managing uncertainty in metabolic network structure and improving predictions using EnsembleFBA.

PubMed

Biggs, Matthew B; Papin, Jason A

2017-03-01

Genome-scale metabolic network reconstructions (GENREs) are repositories of knowledge about the metabolic processes that occur in an organism. GENREs have been used to discover and interpret metabolic functions, and to engineer novel network structures. A major barrier preventing more widespread use of GENREs, particularly to study non-model organisms, is the extensive time required to produce a high-quality GENRE. Many automated approaches have been developed which reduce this time requirement, but automatically-reconstructed draft GENREs still require curation before useful predictions can be made. We present a novel approach to the analysis of GENREs which improves the predictive capabilities of draft GENREs by representing many alternative network structures, all equally consistent with available data, and generating predictions from this ensemble. This ensemble approach is compatible with many reconstruction methods. We refer to this new approach as Ensemble Flux Balance Analysis (EnsembleFBA). We validate EnsembleFBA by predicting growth and gene essentiality in the model organism Pseudomonas aeruginosa UCBPP-PA14. We demonstrate how EnsembleFBA can be included in a systems biology workflow by predicting essential genes in six Streptococcus species and mapping the essential genes to small molecule ligands from DrugBank. We found that some metabolic subsystems contributed disproportionately to the set of predicted essential reactions in a way that was unique to each Streptococcus species, leading to species-specific outcomes from small molecule interactions. Through our analyses of P. aeruginosa and six Streptococci, we show that ensembles increase the quality of predictions without drastically increasing reconstruction time, thus making GENRE approaches more practical for applications which require predictions for many non-model organisms. All of our functions and accompanying example code are available in an open online repository.

The combination of circle topology and leaky integrator neurons remarkably improves the performance of echo state network on time series prediction.

PubMed

Xue, Fangzheng; Li, Qian; Li, Xiumin

2017-01-01

Recently, echo state network (ESN) has attracted a great deal of attention due to its high accuracy and efficient learning performance. Compared with the traditional random structure and classical sigmoid units, simple circle topology and leaky integrator neurons have more advantages on reservoir computing of ESN. In this paper, we propose a new model of ESN with both circle reservoir structure and leaky integrator units. By comparing the prediction capability on Mackey-Glass chaotic time series of four ESN models: classical ESN, circle ESN, traditional leaky integrator ESN, circle leaky integrator ESN, we find that our circle leaky integrator ESN shows significantly better performance than other ESNs with roughly 2 orders of magnitude reduction of the predictive error. Moreover, this model has stronger ability to approximate nonlinear dynamics and resist noise than conventional ESN and ESN with only simple circle structure or leaky integrator neurons. Our results show that the combination of circle topology and leaky integrator neurons can remarkably increase dynamical diversity and meanwhile decrease the correlation of reservoir states, which contribute to the significant improvement of computational performance of Echo state network on time series prediction.

Sensitivity study on durability variables of marine concrete structures

NASA Astrophysics Data System (ADS)

Zhou, Xin'gang; Li, Kefei

2013-06-01

In order to study the influence of parameters on durability of marine concrete structures, the parameter's sensitivity analysis was studied in this paper. With the Fick's 2nd law of diffusion and the deterministic sensitivity analysis method (DSA), the sensitivity factors of apparent surface chloride content, apparent chloride diffusion coefficient and its time dependent attenuation factor were analyzed. The results of the analysis show that the impact of design variables on concrete durability was different. The values of sensitivity factor of chloride diffusion coefficient and its time dependent attenuation factor were higher than others. Relative less error in chloride diffusion coefficient and its time dependent attenuation coefficient induces a bigger error in concrete durability design and life prediction. According to probability sensitivity analysis (PSA), the influence of mean value and variance of concrete durability design variables on the durability failure probability was studied. The results of the study provide quantitative measures of the importance of concrete durability design and life prediction variables. It was concluded that the chloride diffusion coefficient and its time dependent attenuation factor have more influence on the reliability of marine concrete structural durability. In durability design and life prediction of marine concrete structures, it was very important to reduce the measure and statistic error of durability design variables.

Neural Network of Predictive Motor Timing in the Context of Gender Differences

PubMed Central

Lošák, Jan; Kašpárek, Tomáš; Vaníček, Jiří; Bareš, Martin

2016-01-01

Time perception is an essential part of our everyday lives, in both the prospective and the retrospective domains. However, our knowledge of temporal processing is mainly limited to the networks responsible for comparing or maintaining specific intervals or frequencies. In the presented fMRI study, we sought to characterize the neural nodes engaged specifically in predictive temporal analysis, the estimation of the future position of an object with varying movement parameters, and the contingent neuroanatomical signature of differences in behavioral performance between genders. The established dominant cerebellar engagement offers novel evidence in favor of a pivotal role of this structure in predictive short-term timing, overshadowing the basal ganglia reported together with the frontal cortex as dominant in retrospective temporal processing in the subsecond spectrum. Furthermore, we discovered lower performance in this task and massively increased cerebellar activity in women compared to men, indicative of strategy differences between the genders. This promotes the view that predictive temporal computing utilizes comparable structures in the retrospective timing processes, but with a definite dominance of the cerebellum. PMID:27019753

Time series analysis of temporal networks

NASA Astrophysics Data System (ADS)

Sikdar, Sandipan; Ganguly, Niloy; Mukherjee, Animesh

2016-01-01

A common but an important feature of all real-world networks is that they are temporal in nature, i.e., the network structure changes over time. Due to this dynamic nature, it becomes difficult to propose suitable growth models that can explain the various important characteristic properties of these networks. In fact, in many application oriented studies only knowing these properties is sufficient. For instance, if one wishes to launch a targeted attack on a network, this can be done even without the knowledge of the full network structure; rather an estimate of some of the properties is sufficient enough to launch the attack. We, in this paper show that even if the network structure at a future time point is not available one can still manage to estimate its properties. We propose a novel method to map a temporal network to a set of time series instances, analyze them and using a standard forecast model of time series, try to predict the properties of a temporal network at a later time instance. To our aim, we consider eight properties such as number of active nodes, average degree, clustering coefficient etc. and apply our prediction framework on them. We mainly focus on the temporal network of human face-to-face contacts and observe that it represents a stochastic process with memory that can be modeled as Auto-Regressive-Integrated-Moving-Average (ARIMA). We use cross validation techniques to find the percentage accuracy of our predictions. An important observation is that the frequency domain properties of the time series obtained from spectrogram analysis could be used to refine the prediction framework by identifying beforehand the cases where the error in prediction is likely to be high. This leads to an improvement of 7.96% (for error level ≤20%) in prediction accuracy on an average across all datasets. As an application we show how such prediction scheme can be used to launch targeted attacks on temporal networks. Contribution to the Topical Issue "Temporal Network Theory and Applications", edited by Petter Holme.

Ongoing behavior predicts perceptual report of interval duration

PubMed Central

Gouvêa, Thiago S.; Monteiro, Tiago; Soares, Sofia; Atallah, Bassam V.; Paton, Joseph J.

2014-01-01

The ability to estimate the passage of time is essential for adaptive behavior in complex environments. Yet, it is not known how the brain encodes time over the durations necessary to explain animal behavior. Under temporally structured reinforcement schedules, animals tend to develop temporally structured behavior, and interval timing has been suggested to be accomplished by learning sequences of behavioral states. If this is true, trial to trial fluctuations in behavioral sequences should be predictive of fluctuations in time estimation. We trained rodents in an duration categorization task while continuously monitoring their behavior with a high speed camera. Animals developed highly reproducible behavioral sequences during the interval being timed. Moreover, those sequences were often predictive of perceptual report from early in the trial, providing support to the idea that animals may use learned behavioral patterns to estimate the duration of time intervals. To better resolve the issue, we propose that continuous and simultaneous behavioral and neural monitoring will enable identification of neural activity related to time perception that is not explained by ongoing behavior. PMID:24672473

Aromatic claw: A new fold with high aromatic content that evades structural prediction: Aromatic Claw

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

Sachleben, Joseph R.; Adhikari, Aashish N.; Gawlak, Grzegorz

2016-11-10

We determined the NMR structure of a highly aromatic (13%) protein of unknown function, Aq1974 from Aquifex aeolicus (PDB ID: 5SYQ). The unusual sequence of this protein has a tryptophan content five times the normal (six tryptophan residues of 114 or 5.2% while the average tryptophan content is 1.0%) with the tryptophans occurring in a WXW motif. It has no detectable sequence homology with known protein structures. Although its NMR spectrum suggested that the protein was rich in β-sheet, upon resonance assignment and solution structure determination, the protein was found to be primarily α-helical with a small two-stranded β-sheet withmore » a novel fold that we have termed an Aromatic Claw. As this fold was previously unknown and the sequence unique, we submitted the sequence to CASP10 as a target for blind structural prediction. At the end of the competition, the sequence was classified a hard template based model; the structural relationship between the template and the experimental structure was small and the predictions all failed to predict the structure. CSRosetta was found to predict the secondary structure and its packing; however, it was found that there was little correlation between CSRosetta score and the RMSD between the CSRosetta structure and the NMR determined one. This work demonstrates that even in relatively small proteins, we do not yet have the capacity to accurately predict the fold for all primary sequences. The experimental discovery of new folds helps guide the improvement of structural prediction methods.« less

Stochastic simulation of predictive space–time scenarios of wind speed using observations and physical model outputs

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

Bessac, Julie; Constantinescu, Emil; Anitescu, Mihai

We propose a statistical space-time model for predicting atmospheric wind speed based on deterministic numerical weather predictions and historical measurements. We consider a Gaussian multivariate space-time framework that combines multiple sources of past physical model outputs and measurements in order to produce a probabilistic wind speed forecast within the prediction window. We illustrate this strategy on wind speed forecasts during several months in 2012 for a region near the Great Lakes in the United States. The results show that the prediction is improved in the mean-squared sense relative to the numerical forecasts as well as in probabilistic scores. Moreover, themore » samples are shown to produce realistic wind scenarios based on sample spectra and space-time correlation structure.« less

Stochastic simulation of predictive space–time scenarios of wind speed using observations and physical model outputs

DOE PAGES

Bessac, Julie; Constantinescu, Emil; Anitescu, Mihai

2018-03-01

We propose a statistical space-time model for predicting atmospheric wind speed based on deterministic numerical weather predictions and historical measurements. We consider a Gaussian multivariate space-time framework that combines multiple sources of past physical model outputs and measurements in order to produce a probabilistic wind speed forecast within the prediction window. We illustrate this strategy on wind speed forecasts during several months in 2012 for a region near the Great Lakes in the United States. The results show that the prediction is improved in the mean-squared sense relative to the numerical forecasts as well as in probabilistic scores. Moreover, themore » samples are shown to produce realistic wind scenarios based on sample spectra and space-time correlation structure.« less

High Cycle Fatigue Prediction for Mistuned Bladed Disks with Fully Coupled Fluid-Structural Interaction

DTIC Science & Technology

2006-06-01

response (time domain) structural vibration model for mistuned rotor bladed disk based on the efficient SNM model has been developed. The vi- bration...airfoil and 3D wing, unsteady vortex shedding of a stationary cylinder, induced vibration of a cylinder, forced vibration of a pitching airfoil, induced... vibration and flutter boundary of 2D NACA 64A010 transonic airfoil, 3D plate wing structural response. The predicted results agree well with benchmark

Ages and transit times as important diagnostics of model performance for predicting C allocation in ecosystem models

NASA Astrophysics Data System (ADS)

Ceballos-Núñez, Verónika; Richardson, Andrew; Sierra, Carlos

2017-04-01

The global carbon cycle is strongly controlled by the source/sink strength of vegetation as well as the capacity of terrestrial ecosystems to retain this carbon. However, it is uncertain how some vegetation dynamics such as the allocation of carbon to different ecosystem compartments should be represented in models. The assumptions behind model structures may result in highly divergent model predictions. Here, we asses model performance by calculating the age of the carbon in the system and in each compartment, and the overall transit time of C in the system. We used these diagnostics to assess the influence of three different carbon allocation schemes on the rates of C cycling in vegetation. First, we used published measurements of ecosystem C compartments from the Harvard Forest Environmental Measurement Site to find the best set of parameters for the different model structures. Second, we calculated C stocks, respiration fluxes, radiocarbon values, ages, and transit times. We found a good fit of the three model structures to the available data, but the time series of C in foliage and wood need to be complemented with other ecosystem compartments in order to reduce the high parameter collinearity that we observed and reduce model equifinality. Differences in model structures had a small impact on predicting ecosystem C compartments, but overall they resulted in very different predictions of age and transit time distributions. In particular, the inclusion of a storage compartment had an important impact on predicting system ages and transit times. In the case of the models with 1 or 2 storage compartments, the age of carbon in the system and in each of the compartments was distributed more towards younger ages than in the model that had no storage; the mean system age of these two models with storage was 80 years younger than in the model without storage. As expected from these age distributions, the mean transit time for the two models with storage compartments was 50 years faster than for the model without storage. These results suggest that ages and transit times, which can be indirectly measured using isotope tracers, serve as important diagnostics of model structure and could largely help to reduce uncertainties in model predictions. Furthermore, by considering age and transit times of C in vegetation compartments as distributions, not only their mean values, we obtain additional insights on the temporal dynamics of carbon use, storage, and allocation to plant parts, which not only depends on the rate at which this C is transferred in and out of the compartments, but also on the stochastic nature of the process itself.

TRANSAT-- method for detecting the conserved helices of functional RNA structures, including transient, pseudo-knotted and alternative structures.

PubMed

Wiebe, Nicholas J P; Meyer, Irmtraud M

2010-06-24

The prediction of functional RNA structures has attracted increased interest, as it allows us to study the potential functional roles of many genes. RNA structure prediction methods, however, assume that there is a unique functional RNA structure and also do not predict functional features required for in vivo folding. In order to understand how functional RNA structures form in vivo, we require sophisticated experiments or reliable prediction methods. So far, there exist only a few, experimentally validated transient RNA structures. On the computational side, there exist several computer programs which aim to predict the co-transcriptional folding pathway in vivo, but these make a range of simplifying assumptions and do not capture all features known to influence RNA folding in vivo. We want to investigate if evolutionarily related RNA genes fold in a similar way in vivo. To this end, we have developed a new computational method, Transat, which detects conserved helices of high statistical significance. We introduce the method, present a comprehensive performance evaluation and show that Transat is able to predict the structural features of known reference structures including pseudo-knotted ones as well as those of known alternative structural configurations. Transat can also identify unstructured sub-sequences bound by other molecules and provides evidence for new helices which may define folding pathways, supporting the notion that homologous RNA sequence not only assume a similar reference RNA structure, but also fold similarly. Finally, we show that the structural features predicted by Transat differ from those assuming thermodynamic equilibrium. Unlike the existing methods for predicting folding pathways, our method works in a comparative way. This has the disadvantage of not being able to predict features as function of time, but has the considerable advantage of highlighting conserved features and of not requiring a detailed knowledge of the cellular environment.

Evaluating the effect of disturbed ensemble distributions on SCFG based statistical sampling of RNA secondary structures.

PubMed

Scheid, Anika; Nebel, Markus E

2012-07-09

Over the past years, statistical and Bayesian approaches have become increasingly appreciated to address the long-standing problem of computational RNA structure prediction. Recently, a novel probabilistic method for the prediction of RNA secondary structures from a single sequence has been studied which is based on generating statistically representative and reproducible samples of the entire ensemble of feasible structures for a particular input sequence. This method samples the possible foldings from a distribution implied by a sophisticated (traditional or length-dependent) stochastic context-free grammar (SCFG) that mirrors the standard thermodynamic model applied in modern physics-based prediction algorithms. Specifically, that grammar represents an exact probabilistic counterpart to the energy model underlying the Sfold software, which employs a sampling extension of the partition function (PF) approach to produce statistically representative subsets of the Boltzmann-weighted ensemble. Although both sampling approaches have the same worst-case time and space complexities, it has been indicated that they differ in performance (both with respect to prediction accuracy and quality of generated samples), where neither of these two competing approaches generally outperforms the other. In this work, we will consider the SCFG based approach in order to perform an analysis on how the quality of generated sample sets and the corresponding prediction accuracy changes when different degrees of disturbances are incorporated into the needed sampling probabilities. This is motivated by the fact that if the results prove to be resistant to large errors on the distinct sampling probabilities (compared to the exact ones), then it will be an indication that these probabilities do not need to be computed exactly, but it may be sufficient and more efficient to approximate them. Thus, it might then be possible to decrease the worst-case time requirements of such an SCFG based sampling method without significant accuracy losses. If, on the other hand, the quality of sampled structures can be observed to strongly react to slight disturbances, there is little hope for improving the complexity by heuristic procedures. We hence provide a reliable test for the hypothesis that a heuristic method could be implemented to improve the time scaling of RNA secondary structure prediction in the worst-case - without sacrificing much of the accuracy of the results. Our experiments indicate that absolute errors generally lead to the generation of useless sample sets, whereas relative errors seem to have only small negative impact on both the predictive accuracy and the overall quality of resulting structure samples. Based on these observations, we present some useful ideas for developing a time-reduced sampling method guaranteeing an acceptable predictive accuracy. We also discuss some inherent drawbacks that arise in the context of approximation. The key results of this paper are crucial for the design of an efficient and competitive heuristic prediction method based on the increasingly accepted and attractive statistical sampling approach. This has indeed been indicated by the construction of prototype algorithms.

Evaluating the effect of disturbed ensemble distributions on SCFG based statistical sampling of RNA secondary structures

PubMed Central

2012-01-01

Background Over the past years, statistical and Bayesian approaches have become increasingly appreciated to address the long-standing problem of computational RNA structure prediction. Recently, a novel probabilistic method for the prediction of RNA secondary structures from a single sequence has been studied which is based on generating statistically representative and reproducible samples of the entire ensemble of feasible structures for a particular input sequence. This method samples the possible foldings from a distribution implied by a sophisticated (traditional or length-dependent) stochastic context-free grammar (SCFG) that mirrors the standard thermodynamic model applied in modern physics-based prediction algorithms. Specifically, that grammar represents an exact probabilistic counterpart to the energy model underlying the Sfold software, which employs a sampling extension of the partition function (PF) approach to produce statistically representative subsets of the Boltzmann-weighted ensemble. Although both sampling approaches have the same worst-case time and space complexities, it has been indicated that they differ in performance (both with respect to prediction accuracy and quality of generated samples), where neither of these two competing approaches generally outperforms the other. Results In this work, we will consider the SCFG based approach in order to perform an analysis on how the quality of generated sample sets and the corresponding prediction accuracy changes when different degrees of disturbances are incorporated into the needed sampling probabilities. This is motivated by the fact that if the results prove to be resistant to large errors on the distinct sampling probabilities (compared to the exact ones), then it will be an indication that these probabilities do not need to be computed exactly, but it may be sufficient and more efficient to approximate them. Thus, it might then be possible to decrease the worst-case time requirements of such an SCFG based sampling method without significant accuracy losses. If, on the other hand, the quality of sampled structures can be observed to strongly react to slight disturbances, there is little hope for improving the complexity by heuristic procedures. We hence provide a reliable test for the hypothesis that a heuristic method could be implemented to improve the time scaling of RNA secondary structure prediction in the worst-case – without sacrificing much of the accuracy of the results. Conclusions Our experiments indicate that absolute errors generally lead to the generation of useless sample sets, whereas relative errors seem to have only small negative impact on both the predictive accuracy and the overall quality of resulting structure samples. Based on these observations, we present some useful ideas for developing a time-reduced sampling method guaranteeing an acceptable predictive accuracy. We also discuss some inherent drawbacks that arise in the context of approximation. The key results of this paper are crucial for the design of an efficient and competitive heuristic prediction method based on the increasingly accepted and attractive statistical sampling approach. This has indeed been indicated by the construction of prototype algorithms. PMID:22776037

Accelerated Test Method for Corrosion Protective Coatings Project

NASA Technical Reports Server (NTRS)

Falker, John; Zeitlin, Nancy; Calle, Luz

2015-01-01

This project seeks to develop a new accelerated corrosion test method that predicts the long-term corrosion protection performance of spaceport structure coatings as accurately and reliably as current long-term atmospheric exposure tests. This new accelerated test method will shorten the time needed to evaluate the corrosion protection performance of coatings for NASA's critical ground support structures. Lifetime prediction for spaceport structure coatings has a 5-year qualification cycle using atmospheric exposure. Current accelerated corrosion tests often provide false positives and negatives for coating performance, do not correlate to atmospheric corrosion exposure results, and do not correlate with atmospheric exposure timescales for lifetime prediction.

Computational prediction of kink properties of helices in membrane proteins

NASA Astrophysics Data System (ADS)

Mai, T.-L.; Chen, C.-M.

2014-02-01

We have combined molecular dynamics simulations and fold identification procedures to investigate the structure of 696 kinked and 120 unkinked transmembrane (TM) helices in the PDBTM database. Our main aim of this study is to understand the formation of helical kinks by simulating their quasi-equilibrium heating processes, which might be relevant to the prediction of their structural features. The simulated structural features of these TM helices, including the position and the angle of helical kinks, were analyzed and compared with statistical data from PDBTM. From quasi-equilibrium heating processes of TM helices with four very different relaxation time constants, we found that these processes gave comparable predictions of the structural features of TM helices. Overall, 95 % of our best kink position predictions have an error of no more than two residues and 75 % of our best angle predictions have an error of less than 15°. Various structure assessments have been carried out to assess our predicted models of TM helices in PDBTM. Our results show that, in 696 predicted kinked helices, 70 % have a RMSD less than 2 Å, 71 % have a TM-score greater than 0.5, 69 % have a MaxSub score greater than 0.8, 60 % have a GDT-TS score greater than 85, and 58 % have a GDT-HA score greater than 70. For unkinked helices, our predicted models are also highly consistent with their crystal structure. These results provide strong supports for our assumption that kink formation of TM helices in quasi-equilibrium heating processes is relevant to predicting the structure of TM helices.

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.

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

Prediction of protein secondary structure content for the twilight zone sequences.

PubMed

Homaeian, Leila; Kurgan, Lukasz A; Ruan, Jishou; Cios, Krzysztof J; Chen, Ke

2007-11-15

Secondary protein structure carries information about local structural arrangements, which include three major conformations: alpha-helices, beta-strands, and coils. Significant majority of successful methods for prediction of the secondary structure is based on multiple sequence alignment. However, multiple alignment fails to provide accurate results when a sequence comes from the twilight zone, that is, it is characterized by low (<30%) homology. To this end, we propose a novel method for prediction of secondary structure content through comprehensive sequence representation, called PSSC-core. The method uses a multiple linear regression model and introduces a comprehensive feature-based sequence representation to predict amount of helices and strands for sequences from the twilight zone. The PSSC-core method was tested and compared with two other state-of-the-art prediction methods on a set of 2187 twilight zone sequences. The results indicate that our method provides better predictions for both helix and strand content. The PSSC-core is shown to provide statistically significantly better results when compared with the competing methods, reducing the prediction error by 5-7% for helix and 7-9% for strand content predictions. The proposed feature-based sequence representation uses a comprehensive set of physicochemical properties that are custom-designed for each of the helix and strand content predictions. It includes composition and composition moment vectors, frequency of tetra-peptides associated with helical and strand conformations, various property-based groups like exchange groups, chemical groups of the side chains and hydrophobic group, auto-correlations based on hydrophobicity, side-chain masses, hydropathy, and conformational patterns for beta-sheets. The PSSC-core method provides an alternative for predicting the secondary structure content that can be used to validate and constrain results of other structure prediction methods. At the same time, it also provides useful insight into design of successful protein sequence representations that can be used in developing new methods related to prediction of different aspects of the secondary protein structure. (c) 2007 Wiley-Liss, Inc.

Conformational Transitions upon Ligand Binding: Holo-Structure Prediction from Apo Conformations

PubMed Central

Seeliger, Daniel; de Groot, Bert L.

2010-01-01

Biological function of proteins is frequently associated with the formation of complexes with small-molecule ligands. Experimental structure determination of such complexes at atomic resolution, however, can be time-consuming and costly. Computational methods for structure prediction of protein/ligand complexes, particularly docking, are as yet restricted by their limited consideration of receptor flexibility, rendering them not applicable for predicting protein/ligand complexes if large conformational changes of the receptor upon ligand binding are involved. Accurate receptor models in the ligand-bound state (holo structures), however, are a prerequisite for successful structure-based drug design. Hence, if only an unbound (apo) structure is available distinct from the ligand-bound conformation, structure-based drug design is severely limited. We present a method to predict the structure of protein/ligand complexes based solely on the apo structure, the ligand and the radius of gyration of the holo structure. The method is applied to ten cases in which proteins undergo structural rearrangements of up to 7.1 Å backbone RMSD upon ligand binding. In all cases, receptor models within 1.6 Å backbone RMSD to the target were predicted and close-to-native ligand binding poses were obtained for 8 of 10 cases in the top-ranked complex models. A protocol is presented that is expected to enable structure modeling of protein/ligand complexes and structure-based drug design for cases where crystal structures of ligand-bound conformations are not available. PMID:20066034

Changing work, changing health: can real work-time flexibility promote health behaviors and well-being?

PubMed

Moen, Phyllis; Kelly, Erin L; Tranby, Eric; Huang, Qinlei

2011-12-01

This article investigates a change in the structuring of work time, using a natural experiment to test whether participation in a corporate initiative (Results Only Work Environment; ROWE) predicts corresponding changes in health-related outcomes. Drawing on job strain and stress process models, we theorize greater schedule control and reduced work-family conflict as key mechanisms linking this initiative with health outcomes. Longitudinal survey data from 659 employees at a corporate headquarters shows that ROWE predicts changes in health-related behaviors, including almost an extra hour of sleep on work nights. Increasing employees' schedule control and reducing their work-family conflict are key mechanisms linking the ROWE innovation with changes in employees' health behaviors; they also predict changes in well-being measures, providing indirect links between ROWE and well-being. This study demonstrates that organizational changes in the structuring of time can promote employee wellness, particularly in terms of prevention behaviors.

Numerical Modeling of Internal Flow Aerodynamics. Part 2: Unsteady Flows

DTIC Science & Technology

2004-01-01

fluid- structure coupling, ...). • • • • • Prediction: in this simulation, we want to assess the effect of a change in SRM geometry, propellant...surface reaches the structure ). The third characteristic time describes the slow evolution of the internal geometry. The last characteristic time...incorporates fluid- structure coupling facility, and is parallel. MOPTI® manages exchanges between two principal computational modules: • • A varying

Bridge Structure Deformation Prediction Based on GNSS Data Using Kalman-ARIMA-GARCH Model

PubMed Central

Li, Xiaoqing; Wang, Yu

2018-01-01

Bridges are an essential part of the ground transportation system. Health monitoring is fundamentally important for the safety and service life of bridges. A large amount of structural information is obtained from various sensors using sensing technology, and the data processing has become a challenging issue. To improve the prediction accuracy of bridge structure deformation based on data mining and to accurately evaluate the time-varying characteristics of bridge structure performance evolution, this paper proposes a new method for bridge structure deformation prediction, which integrates the Kalman filter, autoregressive integrated moving average model (ARIMA), and generalized autoregressive conditional heteroskedasticity (GARCH). Firstly, the raw deformation data is directly pre-processed using the Kalman filter to reduce the noise. After that, the linear recursive ARIMA model is established to analyze and predict the structure deformation. Finally, the nonlinear recursive GARCH model is introduced to further improve the accuracy of the prediction. Simulation results based on measured sensor data from the Global Navigation Satellite System (GNSS) deformation monitoring system demonstrated that: (1) the Kalman filter is capable of denoising the bridge deformation monitoring data; (2) the prediction accuracy of the proposed Kalman-ARIMA-GARCH model is satisfactory, where the mean absolute error increases only from 3.402 mm to 5.847 mm with the increment of the prediction step; and (3) in comparision to the Kalman-ARIMA model, the Kalman-ARIMA-GARCH model results in superior prediction accuracy as it includes partial nonlinear characteristics (heteroscedasticity); the mean absolute error of five-step prediction using the proposed model is improved by 10.12%. This paper provides a new way for structural behavior prediction based on data processing, which can lay a foundation for the early warning of bridge health monitoring system based on sensor data using sensing technology. PMID:29351254

Bridge Structure Deformation Prediction Based on GNSS Data Using Kalman-ARIMA-GARCH Model.

PubMed

Xin, Jingzhou; Zhou, Jianting; Yang, Simon X; Li, Xiaoqing; Wang, Yu

2018-01-19

Bridges are an essential part of the ground transportation system. Health monitoring is fundamentally important for the safety and service life of bridges. A large amount of structural information is obtained from various sensors using sensing technology, and the data processing has become a challenging issue. To improve the prediction accuracy of bridge structure deformation based on data mining and to accurately evaluate the time-varying characteristics of bridge structure performance evolution, this paper proposes a new method for bridge structure deformation prediction, which integrates the Kalman filter, autoregressive integrated moving average model (ARIMA), and generalized autoregressive conditional heteroskedasticity (GARCH). Firstly, the raw deformation data is directly pre-processed using the Kalman filter to reduce the noise. After that, the linear recursive ARIMA model is established to analyze and predict the structure deformation. Finally, the nonlinear recursive GARCH model is introduced to further improve the accuracy of the prediction. Simulation results based on measured sensor data from the Global Navigation Satellite System (GNSS) deformation monitoring system demonstrated that: (1) the Kalman filter is capable of denoising the bridge deformation monitoring data; (2) the prediction accuracy of the proposed Kalman-ARIMA-GARCH model is satisfactory, where the mean absolute error increases only from 3.402 mm to 5.847 mm with the increment of the prediction step; and (3) in comparision to the Kalman-ARIMA model, the Kalman-ARIMA-GARCH model results in superior prediction accuracy as it includes partial nonlinear characteristics (heteroscedasticity); the mean absolute error of five-step prediction using the proposed model is improved by 10.12%. This paper provides a new way for structural behavior prediction based on data processing, which can lay a foundation for the early warning of bridge health monitoring system based on sensor data using sensing technology.

Reward Dependent Invigoration Relates to Theta Oscillations and Is Predicted by Dopaminergic Midbrain Integrity in Healthy Elderly.

PubMed

Steiger, Tineke K; Bunzeck, Nico

2017-01-01

Motivation can have invigorating effects on behavior via dopaminergic neuromodulation. While this relationship has mainly been established in theoretical models and studies in younger subjects, the impact of structural declines of the dopaminergic system during healthy aging remains unclear. To investigate this issue, we used electroencephalography (EEG) in healthy young and elderly humans in a reward-learning paradigm. Specifically, scene images were initially encoded by combining them with cues predicting monetary reward (high vs. low reward). Subsequently, recognition memory for the scenes was tested. As a main finding, we can show that response times (RTs) during encoding were faster for high reward predicting images in the young but not elderly participants. This pattern was resembled in power changes in the theta-band (4-7 Hz). Importantly, analyses of structural MRI data revealed that individual reward-related differences in the elderlies' response time could be predicted by the structural integrity of the dopaminergic substantia nigra (SN; as measured by magnetization transfer (MT)). These findings suggest a close relationship between reward-based invigoration, theta oscillations and age-dependent changes of the dopaminergic system.

Long Duration Exposure Facility (LDEF) structural verification test report

NASA Technical Reports Server (NTRS)

Jones, T. C.; Lucy, M. H.; Shearer, R. L.

1983-01-01

Structural load tests on the Long Duration Exposure Facility's (LDEF) primary structure were conducted. These tests had three purposes: (1) demonstrate structural adequacy of the assembled LDEF primary structure when subjected to anticipated flight loads; (2) verify analytical models and methods used in loads and stress analysis; and (3) perform tests to comply with the Space Transportation System (STS) requirements. Test loads were based on predicted limit loads which consider all flight events. Good agreement is shown between predicted and observed load, strain, and deflection data. Test data show that the LDEF structure was subjected to 1.2 times limit load to meet the STS requirements. The structural adequacy of the LDEF is demonstrated.

Analysis of simple 2-D and 3-D metal structures subjected to fragment impact

NASA Technical Reports Server (NTRS)

Witmer, E. A.; Stagliano, T. R.; Spilker, R. L.; Rodal, J. J. A.

1977-01-01

Theoretical methods were developed for predicting the large-deflection elastic-plastic transient structural responses of metal containment or deflector (C/D) structures to cope with rotor burst fragment impact attack. For two-dimensional C/D structures both, finite element and finite difference analysis methods were employed to analyze structural response produced by either prescribed transient loads or fragment impact. For the latter category, two time-wise step-by-step analysis procedures were devised to predict the structural responses resulting from a succession of fragment impacts: the collision force method (CFM) which utilizes an approximate prediction of the force applied to the attacked structure during fragment impact, and the collision imparted velocity method (CIVM) in which the impact-induced velocity increment acquired by a region of the impacted structure near the impact point is computed. The merits and limitations of these approaches are discussed. For the analysis of 3-d responses of C/D structures, only the CIVM approach was investigated.

Visual anticipation biases conscious decision making but not bottom-up visual processing.

PubMed

Mathews, Zenon; Cetnarski, Ryszard; Verschure, Paul F M J

2014-01-01

Prediction plays a key role in control of attention but it is not clear which aspects of prediction are most prominent in conscious experience. An evolving view on the brain is that it can be seen as a prediction machine that optimizes its ability to predict states of the world and the self through the top-down propagation of predictions and the bottom-up presentation of prediction errors. There are competing views though on whether prediction or prediction errors dominate the formation of conscious experience. Yet, the dynamic effects of prediction on perception, decision making and consciousness have been difficult to assess and to model. We propose a novel mathematical framework and a psychophysical paradigm that allows us to assess both the hierarchical structuring of perceptual consciousness, its content and the impact of predictions and/or errors on conscious experience, attention and decision-making. Using a displacement detection task combined with reverse correlation, we reveal signatures of the usage of prediction at three different levels of perceptual processing: bottom-up fast saccades, top-down driven slow saccades and consciousnes decisions. Our results suggest that the brain employs multiple parallel mechanism at different levels of perceptual processing in order to shape effective sensory consciousness within a predicted perceptual scene. We further observe that bottom-up sensory and top-down predictive processes can be dissociated through cognitive load. We propose a probabilistic data association model from dynamical systems theory to model the predictive multi-scale bias in perceptual processing that we observe and its role in the formation of conscious experience. We propose that these results support the hypothesis that consciousness provides a time-delayed description of a task that is used to prospectively optimize real time control structures, rather than being engaged in the real-time control of behavior itself.

Predicting Retention Times of Naturally Occurring Phenolic Compounds in Reversed-Phase Liquid Chromatography: A Quantitative Structure-Retention Relationship (QSRR) Approach

PubMed Central

Akbar, Jamshed; Iqbal, Shahid; Batool, Fozia; Karim, Abdul; Chan, Kim Wei

2012-01-01

Quantitative structure-retention relationships (QSRRs) have successfully been developed for naturally occurring phenolic compounds in a reversed-phase liquid chromatographic (RPLC) system. A total of 1519 descriptors were calculated from the optimized structures of the molecules using MOPAC2009 and DRAGON softwares. The data set of 39 molecules was divided into training and external validation sets. For feature selection and mapping we used step-wise multiple linear regression (SMLR), unsupervised forward selection followed by step-wise multiple linear regression (UFS-SMLR) and artificial neural networks (ANN). Stable and robust models with significant predictive abilities in terms of validation statistics were obtained with negation of any chance correlation. ANN models were found better than remaining two approaches. HNar, IDM, Mp, GATS2v, DISP and 3D-MoRSE (signals 22, 28 and 32) descriptors based on van der Waals volume, electronegativity, mass and polarizability, at atomic level, were found to have significant effects on the retention times. The possible implications of these descriptors in RPLC have been discussed. All the models are proven to be quite able to predict the retention times of phenolic compounds and have shown remarkable validation, robustness, stability and predictive performance. PMID:23203132

Knowledge-based fragment binding prediction.

PubMed

Tang, Grace W; Altman, Russ B

2014-04-01

Target-based drug discovery must assess many drug-like compounds for potential activity. Focusing on low-molecular-weight compounds (fragments) can dramatically reduce the chemical search space. However, approaches for determining protein-fragment interactions have limitations. Experimental assays are time-consuming, expensive, and not always applicable. At the same time, computational approaches using physics-based methods have limited accuracy. With increasing high-resolution structural data for protein-ligand complexes, there is now an opportunity for data-driven approaches to fragment binding prediction. We present FragFEATURE, a machine learning approach to predict small molecule fragments preferred by a target protein structure. We first create a knowledge base of protein structural environments annotated with the small molecule substructures they bind. These substructures have low-molecular weight and serve as a proxy for fragments. FragFEATURE then compares the structural environments within a target protein to those in the knowledge base to retrieve statistically preferred fragments. It merges information across diverse ligands with shared substructures to generate predictions. Our results demonstrate FragFEATURE's ability to rediscover fragments corresponding to the ligand bound with 74% precision and 82% recall on average. For many protein targets, it identifies high scoring fragments that are substructures of known inhibitors. FragFEATURE thus predicts fragments that can serve as inputs to fragment-based drug design or serve as refinement criteria for creating target-specific compound libraries for experimental or computational screening.

Knowledge-based Fragment Binding Prediction

PubMed Central

Tang, Grace W.; Altman, Russ B.

2014-01-01

Target-based drug discovery must assess many drug-like compounds for potential activity. Focusing on low-molecular-weight compounds (fragments) can dramatically reduce the chemical search space. However, approaches for determining protein-fragment interactions have limitations. Experimental assays are time-consuming, expensive, and not always applicable. At the same time, computational approaches using physics-based methods have limited accuracy. With increasing high-resolution structural data for protein-ligand complexes, there is now an opportunity for data-driven approaches to fragment binding prediction. We present FragFEATURE, a machine learning approach to predict small molecule fragments preferred by a target protein structure. We first create a knowledge base of protein structural environments annotated with the small molecule substructures they bind. These substructures have low-molecular weight and serve as a proxy for fragments. FragFEATURE then compares the structural environments within a target protein to those in the knowledge base to retrieve statistically preferred fragments. It merges information across diverse ligands with shared substructures to generate predictions. Our results demonstrate FragFEATURE's ability to rediscover fragments corresponding to the ligand bound with 74% precision and 82% recall on average. For many protein targets, it identifies high scoring fragments that are substructures of known inhibitors. FragFEATURE thus predicts fragments that can serve as inputs to fragment-based drug design or serve as refinement criteria for creating target-specific compound libraries for experimental or computational screening. PMID:24762971

A Note on the Problem of Proper Time in Weyl Space-Time

NASA Astrophysics Data System (ADS)

Avalos, R.; Dahia, F.; Romero, C.

2018-02-01

We discuss the question of whether or not a general Weyl structure is a suitable mathematical model of space-time. This is an issue that has been in debate since Weyl formulated his unified field theory for the first time. We do not present the discussion from the point of view of a particular unification theory, but instead from a more general standpoint, in which the viability of such a structure as a model of space-time is investigated. Our starting point is the well known axiomatic approach to space-time given by Elhers, Pirani and Schild (EPS). In this framework, we carry out an exhaustive analysis of what is required for a consistent definition for proper time and show that such a definition leads to the prediction of the so-called "second clock effect". We take the view that if, based on experience, we were to reject space-time models predicting this effect, this could be incorporated as the last axiom in the EPS approach. Finally, we provide a proof that, in this case, we are led to a Weyl integrable space-time as the most general structure that would be suitable to model space-time.

Critical Song Features for Auditory Pattern Recognition in Crickets

PubMed Central

Meckenhäuser, Gundula; Hennig, R. Matthias; Nawrot, Martin P.

2013-01-01

Many different invertebrate and vertebrate species use acoustic communication for pair formation. In the cricket Gryllus bimaculatus, females recognize their species-specific calling song and localize singing males by positive phonotaxis. The song pattern of males has a clear structure consisting of brief and regular pulses that are grouped into repetitive chirps. Information is thus present on a short and a long time scale. Here, we ask which structural features of the song critically determine the phonotactic performance. To this end we employed artificial neural networks to analyze a large body of behavioral data that measured females’ phonotactic behavior under systematic variation of artificially generated song patterns. In a first step we used four non-redundant descriptive temporal features to predict the female response. The model prediction showed a high correlation with the experimental results. We used this behavioral model to explore the integration of the two different time scales. Our result suggested that only an attractive pulse structure in combination with an attractive chirp structure reliably induced phonotactic behavior to signals. In a further step we investigated all feature sets, each one consisting of a different combination of eight proposed temporal features. We identified feature sets of size two, three, and four that achieve highest prediction power by using the pulse period from the short time scale plus additional information from the long time scale. PMID:23437054

Wave models for turbulent free shear flows

NASA Technical Reports Server (NTRS)

Liou, W. W.; Morris, P. J.

1991-01-01

New predictive closure models for turbulent free shear flows are presented. They are based on an instability wave description of the dominant large scale structures in these flows using a quasi-linear theory. Three model were developed to study the structural dynamics of turbulent motions of different scales in free shear flows. The local characteristics of the large scale motions are described using linear theory. Their amplitude is determined from an energy integral analysis. The models were applied to the study of an incompressible free mixing layer. In all cases, predictions are made for the development of the mean flow field. In the last model, predictions of the time dependent motion of the large scale structure of the mixing region are made. The predictions show good agreement with experimental observations.

Text Mining Improves Prediction of Protein Functional Sites

PubMed Central

Cohn, Judith D.; Ravikumar, Komandur E.

2012-01-01

We present an approach that integrates protein structure analysis and text mining for protein functional site prediction, called LEAP-FS (Literature Enhanced Automated Prediction of Functional Sites). The structure analysis was carried out using Dynamics Perturbation Analysis (DPA), which predicts functional sites at control points where interactions greatly perturb protein vibrations. The text mining extracts mentions of residues in the literature, and predicts that residues mentioned are functionally important. We assessed the significance of each of these methods by analyzing their performance in finding known functional sites (specifically, small-molecule binding sites and catalytic sites) in about 100,000 publicly available protein structures. The DPA predictions recapitulated many of the functional site annotations and preferentially recovered binding sites annotated as biologically relevant vs. those annotated as potentially spurious. The text-based predictions were also substantially supported by the functional site annotations: compared to other residues, residues mentioned in text were roughly six times more likely to be found in a functional site. The overlap of predictions with annotations improved when the text-based and structure-based methods agreed. Our analysis also yielded new high-quality predictions of many functional site residues that were not catalogued in the curated data sources we inspected. We conclude that both DPA and text mining independently provide valuable high-throughput protein functional site predictions, and that integrating the two methods using LEAP-FS further improves the quality of these predictions. PMID:22393388

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

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

Chemical and protein structural basis for biological crosstalk between PPAR α and COX enzymes

NASA Astrophysics Data System (ADS)

Cleves, Ann E.; Jain, Ajay N.

2015-02-01

We have previously validated a probabilistic framework that combined computational approaches for predicting the biological activities of small molecule drugs. Molecule comparison methods included molecular structural similarity metrics and similarity computed from lexical analysis of text in drug package inserts. Here we present an analysis of novel drug/target predictions, focusing on those that were not obvious based on known pharmacological crosstalk. Considering those cases where the predicted target was an enzyme with known 3D structure allowed incorporation of information from molecular docking and protein binding pocket similarity in addition to ligand-based comparisons. Taken together, the combination of orthogonal information sources led to investigation of a surprising predicted relationship between a transcription factor and an enzyme, specifically, PPAR α and the cyclooxygenase enzymes. These predictions were confirmed by direct biochemical experiments which validate the approach and show for the first time that PPAR α agonists are cyclooxygenase inhibitors.

Age structure is critical to the population dynamics and survival of honeybee colonies.

PubMed

Betti, M I; Wahl, L M; Zamir, M

2016-11-01

Age structure is an important feature of the division of labour within honeybee colonies, but its effects on colony dynamics have rarely been explored. We present a model of a honeybee colony that incorporates this key feature, and use this model to explore the effects of both winter and disease on the fate of the colony. The model offers a novel explanation for the frequently observed phenomenon of 'spring dwindle', which emerges as a natural consequence of the age-structured dynamics. Furthermore, the results indicate that a model taking age structure into account markedly affects the predicted timing and severity of disease within a bee colony. The timing of the onset of disease with respect to the changing seasons may also have a substantial impact on the fate of a honeybee colony. Finally, simulations predict that an infection may persist in a honeybee colony over several years, with effects that compound over time. Thus, the ultimate collapse of the colony may be the result of events several years past.

A fast and robust iterative algorithm for prediction of RNA pseudoknotted secondary structures

PubMed Central

2014-01-01

Background Improving accuracy and efficiency of computational methods that predict pseudoknotted RNA secondary structures is an ongoing challenge. Existing methods based on free energy minimization tend to be very slow and are limited in the types of pseudoknots that they can predict. Incorporating known structural information can improve prediction accuracy; however, there are not many methods for prediction of pseudoknotted structures that can incorporate structural information as input. There is even less understanding of the relative robustness of these methods with respect to partial information. Results We present a new method, Iterative HFold, for pseudoknotted RNA secondary structure prediction. Iterative HFold takes as input a pseudoknot-free structure, and produces a possibly pseudoknotted structure whose energy is at least as low as that of any (density-2) pseudoknotted structure containing the input structure. Iterative HFold leverages strengths of earlier methods, namely the fast running time of HFold, a method that is based on the hierarchical folding hypothesis, and the energy parameters of HotKnots V2.0. Our experimental evaluation on a large data set shows that Iterative HFold is robust with respect to partial information, with average accuracy on pseudoknotted structures steadily increasing from roughly 54% to 79% as the user provides up to 40% of the input structure. Iterative HFold is much faster than HotKnots V2.0, while having comparable accuracy. Iterative HFold also has significantly better accuracy than IPknot on our HK-PK and IP-pk168 data sets. Conclusions Iterative HFold is a robust method for prediction of pseudoknotted RNA secondary structures, whose accuracy with more than 5% information about true pseudoknot-free structures is better than that of IPknot, and with about 35% information about true pseudoknot-free structures compares well with that of HotKnots V2.0 while being significantly faster. Iterative HFold and all data used in this work are freely available at http://www.cs.ubc.ca/~hjabbari/software.php. PMID:24884954

MEGADOCK-Web: an integrated database of high-throughput structure-based protein-protein interaction predictions.

PubMed

Hayashi, Takanori; Matsuzaki, Yuri; Yanagisawa, Keisuke; Ohue, Masahito; Akiyama, Yutaka

2018-05-08

Protein-protein interactions (PPIs) play several roles in living cells, and computational PPI prediction is a major focus of many researchers. The three-dimensional (3D) structure and binding surface are important for the design of PPI inhibitors. Therefore, rigid body protein-protein docking calculations for two protein structures are expected to allow elucidation of PPIs different from known complexes in terms of 3D structures because known PPI information is not explicitly required. We have developed rapid PPI prediction software based on protein-protein docking, called MEGADOCK. In order to fully utilize the benefits of computational PPI predictions, it is necessary to construct a comprehensive database to gather prediction results and their predicted 3D complex structures and to make them easily accessible. Although several databases exist that provide predicted PPIs, the previous databases do not contain a sufficient number of entries for the purpose of discovering novel PPIs. In this study, we constructed an integrated database of MEGADOCK PPI predictions, named MEGADOCK-Web. MEGADOCK-Web provides more than 10 times the number of PPI predictions than previous databases and enables users to conduct PPI predictions that cannot be found in conventional PPI prediction databases. In MEGADOCK-Web, there are 7528 protein chains and 28,331,628 predicted PPIs from all possible combinations of those proteins. Each protein structure is annotated with PDB ID, chain ID, UniProt AC, related KEGG pathway IDs, and known PPI pairs. Additionally, MEGADOCK-Web provides four powerful functions: 1) searching precalculated PPI predictions, 2) providing annotations for each predicted protein pair with an experimentally known PPI, 3) visualizing candidates that may interact with the query protein on biochemical pathways, and 4) visualizing predicted complex structures through a 3D molecular viewer. MEGADOCK-Web provides a huge amount of comprehensive PPI predictions based on docking calculations with biochemical pathways and enables users to easily and quickly assess PPI feasibilities by archiving PPI predictions. MEGADOCK-Web also promotes the discovery of new PPIs and protein functions and is freely available for use at http://www.bi.cs.titech.ac.jp/megadock-web/ .

Guaranteeing robustness of structural condition monitoring to environmental variability

NASA Astrophysics Data System (ADS)

Van Buren, Kendra; Reilly, Jack; Neal, Kyle; Edwards, Harry; Hemez, François

2017-01-01

Advances in sensor deployment and computational modeling have allowed significant strides to be recently made in the field of Structural Health Monitoring (SHM). One widely used SHM strategy is to perform a vibration analysis where a model of the structure's pristine (undamaged) condition is compared with vibration response data collected from the physical structure. Discrepancies between model predictions and monitoring data can be interpreted as structural damage. Unfortunately, multiple sources of uncertainty must also be considered in the analysis, including environmental variability, unknown model functional forms, and unknown values of model parameters. Not accounting for these sources of uncertainty can lead to false-positives or false-negatives in the structural condition assessment. To manage the uncertainty, we propose a robust SHM methodology that combines three technologies. A time series algorithm is trained using "baseline" data to predict the vibration response, compare predictions to actual measurements collected on a potentially damaged structure, and calculate a user-defined damage indicator. The second technology handles the uncertainty present in the problem. An analysis of robustness is performed to propagate this uncertainty through the time series algorithm and obtain the corresponding bounds of variation of the damage indicator. The uncertainty description and robustness analysis are both inspired by the theory of info-gap decision-making. Lastly, an appropriate "size" of the uncertainty space is determined through physical experiments performed in laboratory conditions. Our hypothesis is that examining how the uncertainty space changes throughout time might lead to superior diagnostics of structural damage as compared to only monitoring the damage indicator. This methodology is applied to a portal frame structure to assess if the strategy holds promise for robust SHM. (Publication approved for unlimited, public release on October-28-2015, LA-UR-15-28442, unclassified.)

Real-Time Impact Visualization Inspection of Aerospace Composite Structures with Distributed Sensors.

PubMed

Si, Liang; Baier, Horst

2015-07-08

For the future design of smart aerospace structures, the development and application of a reliable, real-time and automatic monitoring and diagnostic technique is essential. Thus, with distributed sensor networks, a real-time automatic structural health monitoring (SHM) technique is designed and investigated to monitor and predict the locations and force magnitudes of unforeseen foreign impacts on composite structures and to estimate in real time mode the structural state when impacts occur. The proposed smart impact visualization inspection (IVI) technique mainly consists of five functional modules, which are the signal data preprocessing (SDP), the forward model generator (FMG), the impact positioning calculator (IPC), the inverse model operator (IMO) and structural state estimator (SSE). With regard to the verification of the practicality of the proposed IVI technique, various structure configurations are considered, which are a normal CFRP panel and another CFRP panel with "orange peel" surfaces and a cutout hole. Additionally, since robustness against several background disturbances is also an essential criterion for practical engineering demands, investigations and experimental tests are carried out under random vibration interfering noise (RVIN) conditions. The accuracy of the predictions for unknown impact events on composite structures using the IVI technique is validated under various structure configurations and under changing environmental conditions. The evaluated errors all fall well within a satisfactory limit range. Furthermore, it is concluded that the IVI technique is applicable for impact monitoring, diagnosis and assessment of aerospace composite structures in complex practical engineering environments.

Real-Time Impact Visualization Inspection of Aerospace Composite Structures with Distributed Sensors

PubMed Central

Si, Liang; Baier, Horst

2015-01-01

For the future design of smart aerospace structures, the development and application of a reliable, real-time and automatic monitoring and diagnostic technique is essential. Thus, with distributed sensor networks, a real-time automatic structural health monitoring (SHM) technique is designed and investigated to monitor and predict the locations and force magnitudes of unforeseen foreign impacts on composite structures and to estimate in real time mode the structural state when impacts occur. The proposed smart impact visualization inspection (IVI) technique mainly consists of five functional modules, which are the signal data preprocessing (SDP), the forward model generator (FMG), the impact positioning calculator (IPC), the inverse model operator (IMO) and structural state estimator (SSE). With regard to the verification of the practicality of the proposed IVI technique, various structure configurations are considered, which are a normal CFRP panel and another CFRP panel with “orange peel” surfaces and a cutout hole. Additionally, since robustness against several background disturbances is also an essential criterion for practical engineering demands, investigations and experimental tests are carried out under random vibration interfering noise (RVIN) conditions. The accuracy of the predictions for unknown impact events on composite structures using the IVI technique is validated under various structure configurations and under changing environmental conditions. The evaluated errors all fall well within a satisfactory limit range. Furthermore, it is concluded that the IVI technique is applicable for impact monitoring, diagnosis and assessment of aerospace composite structures in complex practical engineering environments. PMID:26184196

Predicting β-turns and their types using predicted backbone dihedral angles and secondary structures

PubMed Central

2010-01-01

Background β-turns are secondary structure elements usually classified as coil. Their prediction is important, because of their role in protein folding and their frequent occurrence in protein chains. Results We have developed a novel method that predicts β-turns and their types using information from multiple sequence alignments, predicted secondary structures and, for the first time, predicted dihedral angles. Our method uses support vector machines, a supervised classification technique, and is trained and tested on three established datasets of 426, 547 and 823 protein chains. We achieve a Matthews correlation coefficient of up to 0.49, when predicting the location of β-turns, the highest reported value to date. Moreover, the additional dihedral information improves the prediction of β-turn types I, II, IV, VIII and "non-specific", achieving correlation coefficients up to 0.39, 0.33, 0.27, 0.14 and 0.38, respectively. Our results are more accurate than other methods. Conclusions We have created an accurate predictor of β-turns and their types. Our method, called DEBT, is available online at http://comp.chem.nottingham.ac.uk/debt/. PMID:20673368

Predicting beta-turns and their types using predicted backbone dihedral angles and secondary structures.

PubMed

Kountouris, Petros; Hirst, Jonathan D

2010-07-31

Beta-turns are secondary structure elements usually classified as coil. Their prediction is important, because of their role in protein folding and their frequent occurrence in protein chains. We have developed a novel method that predicts beta-turns and their types using information from multiple sequence alignments, predicted secondary structures and, for the first time, predicted dihedral angles. Our method uses support vector machines, a supervised classification technique, and is trained and tested on three established datasets of 426, 547 and 823 protein chains. We achieve a Matthews correlation coefficient of up to 0.49, when predicting the location of beta-turns, the highest reported value to date. Moreover, the additional dihedral information improves the prediction of beta-turn types I, II, IV, VIII and "non-specific", achieving correlation coefficients up to 0.39, 0.33, 0.27, 0.14 and 0.38, respectively. Our results are more accurate than other methods. We have created an accurate predictor of beta-turns and their types. Our method, called DEBT, is available online at http://comp.chem.nottingham.ac.uk/debt/.

Predicting Protein Structure Using Parallel Genetic Algorithms.

DTIC Science & Technology

1994-12-01

Molecular dynamics attempts to simulate the protein folding process. However, the time steps required for this simulation are on the order of one...harmonics. These two factors have limited molecular dynamics simulations to less than a few nanoseconds (10-9 sec), even on today’s fastest supercomputers...By " Predicting rotein Structure D istribticfiar.. ................ Using Parallel Genetic Algorithms ,Avaiu " ’ •"... Dist THESIS I IGeorge H

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

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.

bpRNA: large-scale automated annotation and analysis of RNA secondary structure.

PubMed

Danaee, Padideh; Rouches, Mason; Wiley, Michelle; Deng, Dezhong; Huang, Liang; Hendrix, David

2018-05-09

While RNA secondary structure prediction from sequence data has made remarkable progress, there is a need for improved strategies for annotating the features of RNA secondary structures. Here, we present bpRNA, a novel annotation tool capable of parsing RNA structures, including complex pseudoknot-containing RNAs, to yield an objective, precise, compact, unambiguous, easily-interpretable description of all loops, stems, and pseudoknots, along with the positions, sequence, and flanking base pairs of each such structural feature. We also introduce several new informative representations of RNA structure types to improve structure visualization and interpretation. We have further used bpRNA to generate a web-accessible meta-database, 'bpRNA-1m', of over 100 000 single-molecule, known secondary structures; this is both more fully and accurately annotated and over 20-times larger than existing databases. We use a subset of the database with highly similar (≥90% identical) sequences filtered out to report on statistical trends in sequence, flanking base pairs, and length. Both the bpRNA method and the bpRNA-1m database will be valuable resources both for specific analysis of individual RNA molecules and large-scale analyses such as are useful for updating RNA energy parameters for computational thermodynamic predictions, improving machine learning models for structure prediction, and for benchmarking structure-prediction algorithms.

A computational study of coherent structures in the wakes of two-dimensional bluff bodies

NASA Astrophysics Data System (ADS)

Pearce, Jeffrey Alan

1988-08-01

The periodic shedding of vortices from bluff bodies was first recognized in the late 1800's. Currently, there is great interest concerning the effect of vortex shedding on structures and on vehicle stability. In the design of bluff structures which will be exposed to a flow, knowledge of the shedding frequency and the amplitude of the aerodynamic forces is critical. The ability to computationally predict parameters associated with periodic vortex shedding is thus a valuable tool. In this study, the periodic shedding of vortices from several bluff body geometries is predicted. The study is conducted with a two-dimensional finite-difference code employed on various grid sizes. The effects of the grid size and time step on the accuracy of the solution are addressed. Strouhal numbers and aerodynamic force coefficients are computed for all of the bodies considered and compared with previous experimental results. Results indicate that the finite-difference code is capable of predicting periodic vortex shedding for all of the geometries tested. Refinement of the finite-difference grid was found to give little improvement in the prediction; however, the choice of time step size was shown to be critical. Predictions of Strouhal numbers were generally accurate, and the calculated aerodynamic forces generally exhibited behavior consistent with previous studies.

Hybrid neural network for density limit disruption prediction and avoidance on J-TEXT tokamak

NASA Astrophysics Data System (ADS)

Zheng, W.; Hu, F. R.; Zhang, M.; Chen, Z. Y.; Zhao, X. Q.; Wang, X. L.; Shi, P.; Zhang, X. L.; Zhang, X. Q.; Zhou, Y. N.; Wei, Y. N.; Pan, Y.; J-TEXT team

2018-05-01

Increasing the plasma density is one of the key methods in achieving an efficient fusion reaction. High-density operation is one of the hot topics in tokamak plasmas. Density limit disruptions remain an important issue for safe operation. An effective density limit disruption prediction and avoidance system is the key to avoid density limit disruptions for long pulse steady state operations. An artificial neural network has been developed for the prediction of density limit disruptions on the J-TEXT tokamak. The neural network has been improved from a simple multi-layer design to a hybrid two-stage structure. The first stage is a custom network which uses time series diagnostics as inputs to predict plasma density, and the second stage is a three-layer feedforward neural network to predict the probability of density limit disruptions. It is found that hybrid neural network structure, combined with radiation profile information as an input can significantly improve the prediction performance, especially the average warning time ({{T}warn} ). In particular, the {{T}warn} is eight times better than that in previous work (Wang et al 2016 Plasma Phys. Control. Fusion 58 055014) (from 5 ms to 40 ms). The success rate for density limit disruptive shots is above 90%, while, the false alarm rate for other shots is below 10%. Based on the density limit disruption prediction system and the real-time density feedback control system, the on-line density limit disruption avoidance system has been implemented on the J-TEXT tokamak.

Structure and Stability of Molecular Crystals with Many-Body Dispersion-Inclusive Density Functional Tight Binding.

PubMed

Mortazavi, Majid; Brandenburg, Jan Gerit; Maurer, Reinhard J; Tkatchenko, Alexandre

2018-01-18

Accurate prediction of structure and stability of molecular crystals is crucial in materials science and requires reliable modeling of long-range dispersion interactions. Semiempirical electronic structure methods are computationally more efficient than their ab initio counterparts, allowing structure sampling with significant speedups. We combine the Tkatchenko-Scheffler van der Waals method (TS) and the many-body dispersion method (MBD) with third-order density functional tight-binding (DFTB3) via a charge population-based method. We find an overall good performance for the X23 benchmark database of molecular crystals, despite an underestimation of crystal volume that can be traced to the DFTB parametrization. We achieve accurate lattice energy predictions with DFT+MBD energetics on top of vdW-inclusive DFTB3 structures, resulting in a speedup of up to 3000 times compared with a full DFT treatment. This suggests that vdW-inclusive DFTB3 can serve as a viable structural prescreening tool in crystal structure prediction.

Ab initio structure prediction of silicon and germanium sulfides for lithium-ion battery materials

NASA Astrophysics Data System (ADS)

Hsueh, Connie; Mayo, Martin; Morris, Andrew J.

Conventional experimental-based approaches to materials discovery, which can rely heavily on trial and error, are time-intensive and costly. We discuss approaches to coupling experimental and computational techniques in order to systematize, automate, and accelerate the process of materials discovery, which is of particular relevance to developing new battery materials. We use the ab initio random structure searching (AIRSS) method to conduct a systematic investigation of Si-S and Ge-S binary compounds in order to search for novel materials for lithium-ion battery (LIB) anodes. AIRSS is a high-throughput, density functional theory-based approach to structure prediction which has been successful at predicting the structures of LIBs containing sulfur and silicon and germanium. We propose a lithiation mechanism for Li-GeS2 anodes as well as report new, theoretically stable, layered and porous structures in the Si-S and Ge-S systems that pique experimental interest.

A deep learning framework for modeling structural features of RNA-binding protein targets

PubMed Central

Zhang, Sai; Zhou, Jingtian; Hu, Hailin; Gong, Haipeng; Chen, Ligong; Cheng, Chao; Zeng, Jianyang

2016-01-01

RNA-binding proteins (RBPs) play important roles in the post-transcriptional control of RNAs. Identifying RBP binding sites and characterizing RBP binding preferences are key steps toward understanding the basic mechanisms of the post-transcriptional gene regulation. Though numerous computational methods have been developed for modeling RBP binding preferences, discovering a complete structural representation of the RBP targets by integrating their available structural features in all three dimensions is still a challenging task. In this paper, we develop a general and flexible deep learning framework for modeling structural binding preferences and predicting binding sites of RBPs, which takes (predicted) RNA tertiary structural information into account for the first time. Our framework constructs a unified representation that characterizes the structural specificities of RBP targets in all three dimensions, which can be further used to predict novel candidate binding sites and discover potential binding motifs. Through testing on the real CLIP-seq datasets, we have demonstrated that our deep learning framework can automatically extract effective hidden structural features from the encoded raw sequence and structural profiles, and predict accurate RBP binding sites. In addition, we have conducted the first study to show that integrating the additional RNA tertiary structural features can improve the model performance in predicting RBP binding sites, especially for the polypyrimidine tract-binding protein (PTB), which also provides a new evidence to support the view that RBPs may own specific tertiary structural binding preferences. In particular, the tests on the internal ribosome entry site (IRES) segments yield satisfiable results with experimental support from the literature and further demonstrate the necessity of incorporating RNA tertiary structural information into the prediction model. The source code of our approach can be found in https://github.com/thucombio/deepnet-rbp. PMID:26467480

A High Performance Cloud-Based Protein-Ligand Docking Prediction Algorithm

PubMed Central

Chen, Jui-Le; Yang, Chu-Sing

2013-01-01

The potential of predicting druggability for a particular disease by integrating biological and computer science technologies has witnessed success in recent years. Although the computer science technologies can be used to reduce the costs of the pharmaceutical research, the computation time of the structure-based protein-ligand docking prediction is still unsatisfied until now. Hence, in this paper, a novel docking prediction algorithm, named fast cloud-based protein-ligand docking prediction algorithm (FCPLDPA), is presented to accelerate the docking prediction algorithm. The proposed algorithm works by leveraging two high-performance operators: (1) the novel migration (information exchange) operator is designed specially for cloud-based environments to reduce the computation time; (2) the efficient operator is aimed at filtering out the worst search directions. Our simulation results illustrate that the proposed method outperforms the other docking algorithms compared in this paper in terms of both the computation time and the quality of the end result. PMID:23762864

Ages and transit times as important diagnostics of model performance for predicting carbon dynamics in terrestrial vegetation models

NASA Astrophysics Data System (ADS)

Ceballos-Núñez, Verónika; Richardson, Andrew D.; Sierra, Carlos A.

2018-03-01

The global carbon cycle is strongly controlled by the source/sink strength of vegetation as well as the capacity of terrestrial ecosystems to retain this carbon. These dynamics, as well as processes such as the mixing of old and newly fixed carbon, have been studied using ecosystem models, but different assumptions regarding the carbon allocation strategies and other model structures may result in highly divergent model predictions. We assessed the influence of three different carbon allocation schemes on the C cycling in vegetation. First, we described each model with a set of ordinary differential equations. Second, we used published measurements of ecosystem C compartments from the Harvard Forest Environmental Measurement Site to find suitable parameters for the different model structures. And third, we calculated C stocks, release fluxes, radiocarbon values (based on the bomb spike), ages, and transit times. We obtained model simulations in accordance with the available data, but the time series of C in foliage and wood need to be complemented with other ecosystem compartments in order to reduce the high parameter collinearity that we observed, and reduce model equifinality. Although the simulated C stocks in ecosystem compartments were similar, the different model structures resulted in very different predictions of age and transit time distributions. In particular, the inclusion of two storage compartments resulted in the prediction of a system mean age that was 12-20 years older than in the models with one or no storage compartments. The age of carbon in the wood compartment of this model was also distributed towards older ages, whereas fast cycling compartments had an age distribution that did not exceed 5 years. As expected, models with C distributed towards older ages also had longer transit times. These results suggest that ages and transit times, which can be indirectly measured using isotope tracers, serve as important diagnostics of model structure and could largely help to reduce uncertainties in model predictions. Furthermore, by considering age and transit times of C in vegetation compartments as distributions, not only their mean values, we obtain additional insights into the temporal dynamics of carbon use, storage, and allocation to plant parts, which not only depends on the rate at which this C is transferred in and out of the compartments but also on the stochastic nature of the process itself.

Frame prediction using recurrent convolutional encoder with residual learning

NASA Astrophysics Data System (ADS)

Yue, Boxuan; Liang, Jun

2018-05-01

The prediction for the frame of a video is difficult but in urgent need in auto-driving. Conventional methods can only predict some abstract trends of the region of interest. The boom of deep learning makes the prediction for frames possible. In this paper, we propose a novel recurrent convolutional encoder and DE convolutional decoder structure to predict frames. We introduce the residual learning in the convolution encoder structure to solve the gradient issues. The residual learning can transform the gradient back propagation to an identity mapping. It can reserve the whole gradient information and overcome the gradient issues in Recurrent Neural Networks (RNN) and Convolutional Neural Networks (CNN). Besides, compared with the branches in CNNs and the gated structures in RNNs, the residual learning can save the training time significantly. In the experiments, we use UCF101 dataset to train our networks, the predictions are compared with some state-of-the-art methods. The results show that our networks can predict frames fast and efficiently. Furthermore, our networks are used for the driving video to verify the practicability.

Statistical time-dependent model for the interstellar gas

NASA Technical Reports Server (NTRS)

Gerola, H.; Kafatos, M.; Mccray, R.

1974-01-01

We present models for temperature and ionization structure of low, uniform-density (approximately 0.3 per cu cm) interstellar gas in a galactic disk which is exposed to soft X rays from supernova outbursts occurring randomly in space and time. The structure was calculated by computing the time record of temperature and ionization at a given point by Monte Carlo simulation. The calculation yields probability distribution functions for ionized fraction, temperature, and their various observable moments. These time-dependent models predict a bimodal temperature distribution of the gas that agrees with various observations. Cold regions in the low-density gas may have the appearance of clouds in 21-cm absorption. The time-dependent model, in contrast to the steady-state model, predicts large fluctuations in ionization rate and the existence of cold (approximately 30 K), ionized (ionized fraction equal to about 0.1) regions.

Link Prediction in Evolving Networks Based on Popularity of Nodes.

PubMed

Wang, Tong; He, Xing-Sheng; Zhou, Ming-Yang; Fu, Zhong-Qian

2017-08-02

Link prediction aims to uncover the underlying relationship behind networks, which could be utilized to predict missing edges or identify the spurious edges. The key issue of link prediction is to estimate the likelihood of potential links in networks. Most classical static-structure based methods ignore the temporal aspects of networks, limited by the time-varying features, such approaches perform poorly in evolving networks. In this paper, we propose a hypothesis that the ability of each node to attract links depends not only on its structural importance, but also on its current popularity (activeness), since active nodes have much more probability to attract future links. Then a novel approach named popularity based structural perturbation method (PBSPM) and its fast algorithm are proposed to characterize the likelihood of an edge from both existing connectivity structure and current popularity of its two endpoints. Experiments on six evolving networks show that the proposed methods outperform state-of-the-art methods in accuracy and robustness. Besides, visual results and statistical analysis reveal that the proposed methods are inclined to predict future edges between active nodes, rather than edges between inactive nodes.

Structure and magnetic properties of mechanically alloyed Co and Co-Ni

NASA Astrophysics Data System (ADS)

Guessasma, S.; Fenineche, N.

The influence of milling process on magnetic properties of Co and Co-Ni materials is studied. Coercivity, squareness ratio and crystallite size of mechanically alloyed Co-Ni material were related to milling time. For Co material, coercivity, cubic phase ratio and crystallite size were related to milling energy considering the vial and plateau rotation velocities. An artificial neural network (ANN) combining the parameters for both materials is used to predict magnetic and structure results versus milling conditions. Predicted results showed that milling energy is mostly dependent on the ratio vial to plateau rotation velocities and that milling times larger than 40 h do not add significant change to both structure and magnetic responses. Magnetic parameters were correlated to crystallite size and the D 6 law was only valid for small sizes.

Are there ergodic limits to evolution? Ergodic exploration of genome space and convergence

PubMed Central

McLeish, Tom C. B.

2015-01-01

We examine the analogy between evolutionary dynamics and statistical mechanics to include the fundamental question of ergodicity—the representative exploration of the space of possible states (in the case of evolution this is genome space). Several properties of evolutionary dynamics are identified that allow a generalization of the ergodic dynamics, familiar in dynamical systems theory, to evolution. Two classes of evolved biological structure then arise, differentiated by the qualitative duration of their evolutionary time scales. The first class has an ergodicity time scale (the time required for representative genome exploration) longer than available evolutionary time, and has incompletely explored the genotypic and phenotypic space of its possibilities. This case generates no expectation of convergence to an optimal phenotype or possibility of its prediction. The second, more interesting, class exhibits an evolutionary form of ergodicity—essentially all of the structural space within the constraints of slower evolutionary variables have been sampled; the ergodicity time scale for the system evolution is less than the evolutionary time. In this case, some convergence towards similar optima may be expected for equivalent systems in different species where both possess ergodic evolutionary dynamics. When the fitness maximum is set by physical, rather than co-evolved, constraints, it is additionally possible to make predictions of some properties of the evolved structures and systems. We propose four structures that emerge from evolution within genotypes whose fitness is induced from their phenotypes. Together, these result in an exponential speeding up of evolution, when compared with complete exploration of genomic space. We illustrate a possible case of application and a prediction of convergence together with attaining a physical fitness optimum in the case of invertebrate compound eye resolution. PMID:26640648

Are there ergodic limits to evolution? Ergodic exploration of genome space and convergence.

PubMed

McLeish, Tom C B

2015-12-06

We examine the analogy between evolutionary dynamics and statistical mechanics to include the fundamental question of ergodicity-the representative exploration of the space of possible states (in the case of evolution this is genome space). Several properties of evolutionary dynamics are identified that allow a generalization of the ergodic dynamics, familiar in dynamical systems theory, to evolution. Two classes of evolved biological structure then arise, differentiated by the qualitative duration of their evolutionary time scales. The first class has an ergodicity time scale (the time required for representative genome exploration) longer than available evolutionary time, and has incompletely explored the genotypic and phenotypic space of its possibilities. This case generates no expectation of convergence to an optimal phenotype or possibility of its prediction. The second, more interesting, class exhibits an evolutionary form of ergodicity-essentially all of the structural space within the constraints of slower evolutionary variables have been sampled; the ergodicity time scale for the system evolution is less than the evolutionary time. In this case, some convergence towards similar optima may be expected for equivalent systems in different species where both possess ergodic evolutionary dynamics. When the fitness maximum is set by physical, rather than co-evolved, constraints, it is additionally possible to make predictions of some properties of the evolved structures and systems. We propose four structures that emerge from evolution within genotypes whose fitness is induced from their phenotypes. Together, these result in an exponential speeding up of evolution, when compared with complete exploration of genomic space. We illustrate a possible case of application and a prediction of convergence together with attaining a physical fitness optimum in the case of invertebrate compound eye resolution.

Prediction of light aircraft interior noise

NASA Technical Reports Server (NTRS)

Howlett, J. T.; Morales, D. A.

1976-01-01

At the present time, predictions of aircraft interior noise depend heavily on empirical correction factors derived from previous flight measurements. However, to design for acceptable interior noise levels and to optimize acoustic treatments, analytical techniques which do not depend on empirical data are needed. This paper describes a computerized interior noise prediction method for light aircraft. An existing analytical program (developed for commercial jets by Cockburn and Jolly in 1968) forms the basis of some modal analysis work which is described. The accuracy of this modal analysis technique for predicting low-frequency coupled acoustic-structural natural frequencies is discussed along with trends indicating the effects of varying parameters such as fuselage length and diameter, structural stiffness, and interior acoustic absorption.

Combining the Finite Element Method with Structural Connectome-based Analysis for Modeling Neurotrauma: Connectome Neurotrauma Mechanics

PubMed Central

Kraft, Reuben H.; Mckee, Phillip Justin; Dagro, Amy M.; Grafton, Scott T.

2012-01-01

This article presents the integration of brain injury biomechanics and graph theoretical analysis of neuronal connections, or connectomics, to form a neurocomputational model that captures spatiotemporal characteristics of trauma. We relate localized mechanical brain damage predicted from biofidelic finite element simulations of the human head subjected to impact with degradation in the structural connectome for a single individual. The finite element model incorporates various length scales into the full head simulations by including anisotropic constitutive laws informed by diffusion tensor imaging. Coupling between the finite element analysis and network-based tools is established through experimentally-based cellular injury thresholds for white matter regions. Once edges are degraded, graph theoretical measures are computed on the “damaged” network. For a frontal impact, the simulations predict that the temporal and occipital regions undergo the most axonal strain and strain rate at short times (less than 24 hrs), which leads to cellular death initiation, which results in damage that shows dependence on angle of impact and underlying microstructure of brain tissue. The monotonic cellular death relationships predict a spatiotemporal change of structural damage. Interestingly, at 96 hrs post-impact, computations predict no network nodes were completely disconnected from the network, despite significant damage to network edges. At early times () network measures of global and local efficiency were degraded little; however, as time increased to 96 hrs the network properties were significantly reduced. In the future, this computational framework could help inform functional networks from physics-based structural brain biomechanics to obtain not only a biomechanics-based understanding of injury, but also neurophysiological insight. PMID:22915997

A New Hybrid-Multiscale SSA Prediction of Non-Stationary Time Series

NASA Astrophysics Data System (ADS)

Ghanbarzadeh, Mitra; Aminghafari, Mina

2016-02-01

Singular spectral analysis (SSA) is a non-parametric method used in the prediction of non-stationary time series. It has two parameters, which are difficult to determine and very sensitive to their values. Since, SSA is a deterministic-based method, it does not give good results when the time series is contaminated with a high noise level and correlated noise. Therefore, we introduce a novel method to handle these problems. It is based on the prediction of non-decimated wavelet (NDW) signals by SSA and then, prediction of residuals by wavelet regression. The advantages of our method are the automatic determination of parameters and taking account of the stochastic structure of time series. As shown through the simulated and real data, we obtain better results than SSA, a non-parametric wavelet regression method and Holt-Winters method.

Limit of Predictability in Mantle Convection

NASA Astrophysics Data System (ADS)

Bello, L.; Coltice, N.; Rolf, T.; Tackley, P. J.

2013-12-01

Linking mantle convection models with Earth's tectonic history has received considerable attention in recent years: modeling the evolution of supercontinent cycles, predicting present-day mantle structure or improving plate reconstructions. Predictions of future supercontinents are currently being made based on seismic tomography images, plate motion history and mantle convection models, and methods of data assimilation for mantle flow are developing. However, so far there are no studies of the limit of predictability these models are facing. Indeed, given the chaotic nature of mantle convection, we can expect forecasts and hindcasts to have a limited range of predictability. We propose here to use an approach similar to those used in dynamic meteorology, and more recently for the geodynamo, to evaluate the predictability limit of mantle dynamics forecasts. Following the pioneering works in weather forecast (Lorenz 1965), we study the time evolution of twin experiments, started from two very close initial temperature fields and monitor the error growth. We extract a characteristic time of the system, known as the e-folding timescale, which will be used to estimate the predictability limit. The final predictability time will depend on the imposed initial error and the error tolerance in our model. We compute 3D spherical convection solutions using StagYY (Tackley, 2008). We first evaluate the influence of the Rayleigh number on the limit of predictability of isoviscous convection. Then, we investigate the effects of various rheologies, from the simplest (isoviscous mantle) to more complex ones (plate-like behavior and floating continents). We show that the e-folding time increases with the wavelength of the flow and reaches 10Myrs with plate-like behavior and continents. Such an e-folding time together with the uncertainties in mantle temperature distribution suggests prediction of mantle structure from an initial given state is limited to <50 Myrs. References: 1. Lorenz, B. E. N., Norake, D. & Meteorologiake, I. A study of the predictability of a 28-variable atmospheric model. Tellus XXVII, 322-333 (1965). 2. Tackley, P. J. Modelling compressible mantle convection with large viscosity contrasts in a three-dimensional spherical shell using the yin-yang grid. Physics of the Earth and Planetary Interiors 171, 7-18 (2008).

Identification of structural damage using wavelet-based data classification

NASA Astrophysics Data System (ADS)

Koh, Bong-Hwan; Jeong, Min-Joong; Jung, Uk

2008-03-01

Predicted time-history responses from a finite-element (FE) model provide a baseline map where damage locations are clustered and classified by extracted damage-sensitive wavelet coefficients such as vertical energy threshold (VET) positions having large silhouette statistics. Likewise, the measured data from damaged structure are also decomposed and rearranged according to the most dominant positions of wavelet coefficients. Having projected the coefficients to the baseline map, the true localization of damage can be identified by investigating the level of closeness between the measurement and predictions. The statistical confidence of baseline map improves as the number of prediction cases increases. The simulation results of damage detection in a truss structure show that the approach proposed in this study can be successfully applied for locating structural damage even in the presence of a considerable amount of process and measurement noise.

Application of the Collision-Imparted Velocity Method for Analyzing the Responses of Containment and Deflector Structures to Engine Rotor Fragment Impact

NASA Technical Reports Server (NTRS)

Collins, T. P.; Witmer, E. A.

1973-01-01

An approximate analysis, termed the Collision Imparted Velocity Method (CIVM), was employed for predicting the transient structural responses of containment rings or deflector rings which are subjected to impact from turbojet-engine rotor burst fragments. These 2-d structural rings may be initially circular or arbitrarily curved and may have either uniform or variable thickness; elastic, strain hardening, and strain rate material properties are accommodated. This approximate analysis utilizes kinetic energy and momentum conservation relations in order to predict the after-impact velocities of the fragment and the impacted ring segment. This information is then used in conjunction with a finite element structural response computation code to predict the transient, large deflection responses of the ring. Similarly, the equations of motion for each fragment are solved in small steps in time. Also, some comparisons of predictions with experimental data for fragment-impacted free containment rings are presented.

Changing Work, Changing Health: Can Real Work-Time Flexibility Promote Health Behaviors and Well-Being?

PubMed Central

Moen, Phyllis; Kelly, Erin L.; Tranby, Eric; Huang, Qinlei

2012-01-01

This article investigates a change in the structuring of work time, using a natural experiment to test whether participation in a corporate initiative (Results Only Work Environment; ROWE) predicts corresponding changes in health-related outcomes. Drawing on job strain and stress process models, we theorize greater schedule control and reduced work-family conflict as key mechanisms linking this initiative with health outcomes. Longitudinal survey data from 659 employees at a corporate headquarters shows that ROWE predicts changes in health-related behaviors, including almost an extra hour of sleep on work nights. Increasing employees’ schedule control and reducing their work-family conflict are key mechanisms linking the ROWE innovation with changes in employees’ health behaviors; they also predict changes in well-being measures, providing indirect links between ROWE and well-being. This study demonstrates that organizational changes in the structuring of time can promote employee wellness, particularly in terms of prevention behaviors. PMID:22144731

The effect of char structure on burnout during pulverized coal combustion at pressure

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

Liu, G.; Wu, H.; Benfell, K.E.

An Australian bituminous coal sample was burnt in a drop tube furnace (DTF) at 1 atm and a pressurized drop tube furnace (PDTF) at 15 atm. The char samples were collected at different burnout levels, and a scanning electron microscope was used to examine the structures of chars. A model was developed to predict the burnout of char particles with different structures. The model accounts for combustion of the thin-walled structure of cenospheric char and its fragmentation during burnout. The effect of pressure on reaction rate was also considered in the model. As a result, approximately 40% and 70% cenosphericmore » char particles were observed in the char samples collected after coal pyrolysis in the DTF and PDTF respectively. A large number of fine particles (< 30 mm) were observed in the 1 atm char samples at burnout levels between 30% and 50%, which suggests that significant fragmentation occurred during early combustion. Ash particle size distributions show that a large number of small ash particles formed during burnout at high pressure. The time needed for 70% char burnout at 15 atm is approximately 1.6 times that at 1 atm under the same temperature and gas environment conditions, which is attributed to the different pressures as well as char structures. The overall reaction rate for cenospheric char was predicted to be approximately 2 times that of the dense chars, which is consistent with previous experimental results. The predicted char burnout including char structures agrees reasonably well with the experimental measurements that were obtained at 1 atm and 15 atm pressures.« less

NNvPDB: Neural Network based Protein Secondary Structure Prediction with PDB Validation.

PubMed

Sakthivel, Seethalakshmi; S K M, Habeeb

2015-01-01

The predicted secondary structural states are not cross validated by any of the existing servers. Hence, information on the level of accuracy for every sequence is not reported by the existing servers. This was overcome by NNvPDB, which not only reported greater Q3 but also validates every prediction with the homologous PDB entries. NNvPDB is based on the concept of Neural Network, with a new and different approach of training the network every time with five PDB structures that are similar to query sequence. The average accuracy for helix is 76%, beta sheet is 71% and overall (helix, sheet and coil) is 66%. http://bit.srmuniv.ac.in/cgi-bin/bit/cfpdb/nnsecstruct.pl.

Prediction of subsurface fracture in mining zone of Papua using passive seismic tomography based on Fresnel zone

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

Setiadi, Herlan; Nurhandoko, Bagus Endar B.; Wely, Woen

Fracture prediction in a block cave of underground mine is very important to monitor the structure of the fracture that can be harmful to the mining activities. Many methods can be used to obtain such information, such as TDR (Time Domain Relectometry) and open hole. Both of them have limitations in range measurement. Passive seismic tomography is one of the subsurface imaging method. It has advantage in terms of measurements, cost, and rich of rock physical information. This passive seismic tomography studies using Fresnel zone to model the wavepath by using frequency parameter. Fresnel zone was developed by Nurhandoko inmore » 2000. The result of this study is tomography of P and S wave velocity which can predict position of fracture. The study also attempted to use sum of the wavefronts to obtain position and time of seismic event occurence. Fresnel zone tomography and the summation wavefront can predict location of geological structure of mine area as well.« less

Multi-scale predictive modeling of nano-material and realistic electron devices

NASA Astrophysics Data System (ADS)

Palaria, Amritanshu

Among the challenges faced in further miniaturization of electronic devices, heavy influence of the detailed atomic configuration of the material(s) involved, which often differs significantly from that of the bulk material(s), is prominent. Device design has therefore become highly interrelated with material engineering at the atomic level. This thesis aims at outlining, with examples, a multi-scale simulation procedure that allows one to integrate material and device aspects of nano-electronic design to predict behavior of novel devices with novel material. This is followed in four parts: (1) An approach that combines a higher time scale reactive force field analysis with density functional theory to predict structure of new material is demonstrated for the first time for nanowires. Novel stable structures for very small diameter silicon nanowires are predicted. (2) Density functional theory is used to show that the new nanowire structures derived in 1 above have properties different from diamond core wires even though the surface bonds in some may be similar to the surface of bulk silicon. (3) Electronic structure of relatively large-scale germanium sections of realistically strained Si/strained Ge/ strained Si nanowire heterostructures is computed using empirical tight binding and it is shown that the average non-homogeneous strain in these structures drives their interesting non-conventional electronic characteristics such as hole effective masses which decrease as the wire cross-section is reduced. (4) It is shown that tight binding, though empirical in nature, is not necessarily limited to the material and atomic structure for which the parameters have been empirically derived, but that simple changes may adapt the derived parameters to new bond environments. Si (100) surface electronic structure is obtained from bulk Si parameters.

Spanish version of the Time Management Behavior Questionnaire for university students.

PubMed

García-Ros, Rafael; Pérez-González, Francisco

2012-11-01

The main objective of the study is to analyze the psychometric properties and predictive capacity on academic performance in university contexts of a Spanish adaptation of the Time Management Behavior Questionnaire. The scale was applied to 462 students newly admitted at the Universitat de València in the 2006-2007 school year. The analyses performed made it possible to reproduce the factorial structure of the original version of the questionnaire with slight modifications in the ascription of various items. The underlying factorial structure includes four interrelated dimensions (Establishing objectives and priorities, Time management tools, Perception of time control and Preference for disorganization), which present satisfactory levels of reliability and an adequate convergent validity with the Time management subscale of the Motivated Strategies for Learning Questionnaire. The scores on the dimensions of time management show significant levels of association with academic performance in the first year of university studies, especially highlighting the predictive capacity of the subscale dealing with the Establishment of objectives and priorities. These results show the reliability and validity of this adaptation of the scale for evaluating how the students manage their academic time, and predicting their performance in the year they initiate the degree program, thus aiding in the development of intervention proposals directed towards improving these skills.

Space-Time Earthquake Prediction: The Error Diagrams

NASA Astrophysics Data System (ADS)

Molchan, G.

2010-08-01

The quality of earthquake prediction is usually characterized by a two-dimensional diagram n versus τ, where n is the rate of failures-to-predict and τ is a characteristic of space-time alarm. Unlike the time prediction case, the quantity τ is not defined uniquely. We start from the case in which τ is a vector with components related to the local alarm times and find a simple structure of the space-time diagram in terms of local time diagrams. This key result is used to analyze the usual 2-d error sets { n, τ w } in which τ w is a weighted mean of the τ components and w is the weight vector. We suggest a simple algorithm to find the ( n, τ w ) representation of all random guess strategies, the set D, and prove that there exists the unique case of w when D degenerates to the diagonal n + τ w = 1. We find also a confidence zone of D on the ( n, τ w ) plane when the local target rates are known roughly. These facts are important for correct interpretation of ( n, τ w ) diagrams when we discuss the prediction capability of the data or prediction methods.

Mutual connectivity analysis (MCA) using generalized radial basis function neural networks for nonlinear functional connectivity network recovery in resting-state functional MRI

NASA Astrophysics Data System (ADS)

D'Souza, Adora M.; Abidin, Anas Zainul; Nagarajan, Mahesh B.; Wismüller, Axel

2016-03-01

We investigate the applicability of a computational framework, called mutual connectivity analysis (MCA), for directed functional connectivity analysis in both synthetic and resting-state functional MRI data. This framework comprises of first evaluating non-linear cross-predictability between every pair of time series prior to recovering the underlying network structure using community detection algorithms. We obtain the non-linear cross-prediction score between time series using Generalized Radial Basis Functions (GRBF) neural networks. These cross-prediction scores characterize the underlying functionally connected networks within the resting brain, which can be extracted using non-metric clustering approaches, such as the Louvain method. We first test our approach on synthetic models with known directional influence and network structure. Our method is able to capture the directional relationships between time series (with an area under the ROC curve = 0.92 +/- 0.037) as well as the underlying network structure (Rand index = 0.87 +/- 0.063) with high accuracy. Furthermore, we test this method for network recovery on resting-state fMRI data, where results are compared to the motor cortex network recovered from a motor stimulation sequence, resulting in a strong agreement between the two (Dice coefficient = 0.45). We conclude that our MCA approach is effective in analyzing non-linear directed functional connectivity and in revealing underlying functional network structure in complex systems.

Mutual Connectivity Analysis (MCA) Using Generalized Radial Basis Function Neural Networks for Nonlinear Functional Connectivity Network Recovery in Resting-State Functional MRI.

PubMed

DSouza, Adora M; Abidin, Anas Zainul; Nagarajan, Mahesh B; Wismüller, Axel

2016-03-29

We investigate the applicability of a computational framework, called mutual connectivity analysis (MCA), for directed functional connectivity analysis in both synthetic and resting-state functional MRI data. This framework comprises of first evaluating non-linear cross-predictability between every pair of time series prior to recovering the underlying network structure using community detection algorithms. We obtain the non-linear cross-prediction score between time series using Generalized Radial Basis Functions (GRBF) neural networks. These cross-prediction scores characterize the underlying functionally connected networks within the resting brain, which can be extracted using non-metric clustering approaches, such as the Louvain method. We first test our approach on synthetic models with known directional influence and network structure. Our method is able to capture the directional relationships between time series (with an area under the ROC curve = 0.92 ± 0.037) as well as the underlying network structure (Rand index = 0.87 ± 0.063) with high accuracy. Furthermore, we test this method for network recovery on resting-state fMRI data, where results are compared to the motor cortex network recovered from a motor stimulation sequence, resulting in a strong agreement between the two (Dice coefficient = 0.45). We conclude that our MCA approach is effective in analyzing non-linear directed functional connectivity and in revealing underlying functional network structure in complex systems.

Analysis/test correlation using VAWT-SDS on a step-relaxation test for the rotating Sandia 34 m test bed

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

Argueello, J.G.; Dohrmann, C.R.; Carne, T.G.

The combined analysis/test effort described in this paper compares predictions with measured data from a step-relaxation test in the absence of significant wind-driven aerodynamic loading. The process described here is intended to illustrate a method for validation of time domain codes for structural analysis of wind turbine structures. Preliminary analyses were performed to investigate the transient dynamic response that the rotating Sandia 34 m Vertical Axis Wind Turbine (VAWT) would undergo when one of the two blades was excited by step-relaxation. The calculations served two purposes. The first was for pretest planning to evaluate the relative importance of the variousmore » forces that would be acting on the structure during the test and to determine if the applied force in the step-relaxation would be sufficient to produce an excitation that was distinguishable from that produced by the aerodynamic loads. The second was to provide predictions that could subsequently be compared to the data from the test. The test was carried out specifically to help in the validation of the time-domain structural dynamics code, VAWT-SDS, which predicts the dynamic response of VAWTs subject to transient events. Post-test comparisons with the data were performed and showed a qualitative agreement between pretest predictions and measured response. However, they also showed that there was significantly more damping in the measurements than included in the predictions. Efforts to resolve this difference, including post-test analyses, were undertaken and are reported herein. The overall effort described in this paper represents a major step in the process of arriving at a validated structural dynamics code.« less

Cyclic coordinate descent: A robotics algorithm for protein loop closure.

PubMed

Canutescu, Adrian A; Dunbrack, Roland L

2003-05-01

In protein structure prediction, it is often the case that a protein segment must be adjusted to connect two fixed segments. This occurs during loop structure prediction in homology modeling as well as in ab initio structure prediction. Several algorithms for this purpose are based on the inverse Jacobian of the distance constraints with respect to dihedral angle degrees of freedom. These algorithms are sometimes unstable and fail to converge. We present an algorithm developed originally for inverse kinematics applications in robotics. In robotics, an end effector in the form of a robot hand must reach for an object in space by altering adjustable joint angles and arm lengths. In loop prediction, dihedral angles must be adjusted to move the C-terminal residue of a segment to superimpose on a fixed anchor residue in the protein structure. The algorithm, referred to as cyclic coordinate descent or CCD, involves adjusting one dihedral angle at a time to minimize the sum of the squared distances between three backbone atoms of the moving C-terminal anchor and the corresponding atoms in the fixed C-terminal anchor. The result is an equation in one variable for the proposed change in each dihedral. The algorithm proceeds iteratively through all of the adjustable dihedral angles from the N-terminal to the C-terminal end of the loop. CCD is suitable as a component of loop prediction methods that generate large numbers of trial structures. It succeeds in closing loops in a large test set 99.79% of the time, and fails occasionally only for short, highly extended loops. It is very fast, closing loops of length 8 in 0.037 sec on average.

Visual anticipation biases conscious decision making but not bottom-up visual processing

PubMed Central

Mathews, Zenon; Cetnarski, Ryszard; Verschure, Paul F. M. J.

2015-01-01

Prediction plays a key role in control of attention but it is not clear which aspects of prediction are most prominent in conscious experience. An evolving view on the brain is that it can be seen as a prediction machine that optimizes its ability to predict states of the world and the self through the top-down propagation of predictions and the bottom-up presentation of prediction errors. There are competing views though on whether prediction or prediction errors dominate the formation of conscious experience. Yet, the dynamic effects of prediction on perception, decision making and consciousness have been difficult to assess and to model. We propose a novel mathematical framework and a psychophysical paradigm that allows us to assess both the hierarchical structuring of perceptual consciousness, its content and the impact of predictions and/or errors on conscious experience, attention and decision-making. Using a displacement detection task combined with reverse correlation, we reveal signatures of the usage of prediction at three different levels of perceptual processing: bottom-up fast saccades, top-down driven slow saccades and consciousnes decisions. Our results suggest that the brain employs multiple parallel mechanism at different levels of perceptual processing in order to shape effective sensory consciousness within a predicted perceptual scene. We further observe that bottom-up sensory and top-down predictive processes can be dissociated through cognitive load. We propose a probabilistic data association model from dynamical systems theory to model the predictive multi-scale bias in perceptual processing that we observe and its role in the formation of conscious experience. We propose that these results support the hypothesis that consciousness provides a time-delayed description of a task that is used to prospectively optimize real time control structures, rather than being engaged in the real-time control of behavior itself. PMID:25741290

Creep prediction of a layered fiberglass plastic

NASA Astrophysics Data System (ADS)

Aniskevich, K.; Korsgaard, J.; Mālmeisters, A.; Jansons, J.

1998-05-01

The results of short-term creep tests of a layered glass fiber/polyester resin plastic in tension at angles of 90, 70, and 45° to the direction of the principal fiber orientation are presented. The applicability of the principle of time-temperature analogy for the prediction of long-term creep of the composite and its structural components is revealed. The possibility of evaluating the viscoelastic properties of the composite from the properties of structural components is shown.

Age structure is critical to the population dynamics and survival of honeybee colonies

PubMed Central

Betti, M. I.; Wahl, L. M.

2016-01-01

Age structure is an important feature of the division of labour within honeybee colonies, but its effects on colony dynamics have rarely been explored. We present a model of a honeybee colony that incorporates this key feature, and use this model to explore the effects of both winter and disease on the fate of the colony. The model offers a novel explanation for the frequently observed phenomenon of ‘spring dwindle’, which emerges as a natural consequence of the age-structured dynamics. Furthermore, the results indicate that a model taking age structure into account markedly affects the predicted timing and severity of disease within a bee colony. The timing of the onset of disease with respect to the changing seasons may also have a substantial impact on the fate of a honeybee colony. Finally, simulations predict that an infection may persist in a honeybee colony over several years, with effects that compound over time. Thus, the ultimate collapse of the colony may be the result of events several years past. PMID:28018627

A Step Made Toward Designing Microelectromechanical System (MEMS) Structures With High Reliability

NASA Technical Reports Server (NTRS)

Nemeth, Noel N.

2003-01-01

The mechanical design of microelectromechanical systems-particularly for micropower generation applications-requires the ability to predict the strength capacity of load-carrying components over the service life of the device. These microdevices, which typically are made of brittle materials such as polysilicon, show wide scatter (stochastic behavior) in strength as well as a different average strength for different sized structures (size effect). These behaviors necessitate either costly and time-consuming trial-and-error designs or, more efficiently, the development of a probabilistic design methodology for MEMS. Over the years, the NASA Glenn Research Center s Life Prediction Branch has developed the CARES/Life probabilistic design methodology to predict the reliability of advanced ceramic components. In this study, done in collaboration with Johns Hopkins University, the ability of the CARES/Life code to predict the reliability of polysilicon microsized structures with stress concentrations is successfully demonstrated.

Prediction of Launch Vehicle Ignition Overpressure and Liftoff Acoustics

NASA Technical Reports Server (NTRS)

Casiano, Matthew

2009-01-01

The LAIOP (Launch Vehicle Ignition Overpressure and Liftoff Acoustic Environments) program predicts the external pressure environment generated during liftoff for a large variety of rocket types. These environments include ignition overpressure, produced by the rapid acceleration of exhaust gases during rocket-engine start transient, and launch acoustics, produced by turbulence in the rocket plume. The ignition overpressure predictions are time-based, and the launch acoustic predictions are frequency-based. Additionally, the software can predict ignition overpressure mitigation, using water-spray injection into the rocket exhaust stream, for a limited number of configurations. The framework developed for these predictions is extensive, though some options require additional relevant data and development time. Once these options are enabled, the already extensively capable code will be further enhanced. The rockets, or launch vehicles, can either be elliptically or cylindrically shaped, and up to eight strap-on structures (boosters or tanks) are allowed. Up to four engines are allowed for the core launch vehicle, which can be of two different types. Also, two different sizes of strap-on structures can be used, and two different types of booster engines are allowed. Both tabular and graphical presentations of the predicted environments at the selected locations can be reviewed by the user. The output includes summaries of rocket-engine operation, ignition overpressure time histories, and one-third octave sound pressure spectra of the predicted launch acoustics. Also, documentation is available to the user to help him or her understand the various aspects of the graphical user interface and the required input parameters.

Intelligent seismic risk mitigation system on structure building

NASA Astrophysics Data System (ADS)

Suryanita, R.; Maizir, H.; Yuniorto, E.; Jingga, H.

2018-01-01

Indonesia located on the Pacific Ring of Fire, is one of the highest-risk seismic zone in the world. The strong ground motion might cause catastrophic collapse of the building which leads to casualties and property damages. Therefore, it is imperative to properly design the structural response of building against seismic hazard. Seismic-resistant building design process requires structural analysis to be performed to obtain the necessary building responses. However, the structural analysis could be very difficult and time consuming. This study aims to predict the structural response includes displacement, velocity, and acceleration of multi-storey building with the fixed floor plan using Artificial Neural Network (ANN) method based on the 2010 Indonesian seismic hazard map. By varying the building height, soil condition, and seismic location in 47 cities in Indonesia, 6345 data sets were obtained and fed into the ANN model for the learning process. The trained ANN can predict the displacement, velocity, and acceleration responses with up to 96% of predicted rate. The trained ANN architecture and weight factors were later used to build a simple tool in Visual Basic program which possesses the features for prediction of structural response as mentioned previously.

Prediction of protein tertiary structure from sequences using a very large back-propagation neural network

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

Liu, X.; Wilcox, G.L.

1993-12-31

We have implemented large scale back-propagation neural networks on a 544 node Connection Machine, CM-5, using the C language in MIMD mode. The program running on 512 processors performs backpropagation learning at 0.53 Gflops, which provides 76 million connection updates per second. We have applied the network to the prediction of protein tertiary structure from sequence information alone. A neural network with one hidden layer and 40 million connections is trained to learn the relationship between sequence and tertiary structure. The trained network yields predicted structures of some proteins on which it has not been trained given only their sequences.more » Presentation of the Fourier transform of the sequences accentuates periodicity in the sequence and yields good generalization with greatly increased training efficiency. Training simulations with a large, heterologous set of protein structures (111 proteins from CM-5 time) to solutions with under 2% RMS residual error within the training set (random responses give an RMS error of about 20%). Presentation of 15 sequences of related proteins in a testing set of 24 proteins yields predicted structures with less than 8% RMS residual error, indicating good apparent generalization.« less

Hydrologic filtering of fish life history strategies across the United States: implications for stream flow alteration

DOE PAGES

McManamay, Ryan A.; Frimpong, Emmanuel A.

2015-01-01

Lotic fish have developed life history strategies adapted to the natural variation in stream flow regimes. The natural timing, duration, and magnitude of flow events has contributed to the diversity, production, and composition of fish assemblages over time. Studies evaluating the role of hydrology in structuring fish assemblages have been more common at the local or regional scale with very few studies conducted at the continental scale. Furthermore, quantitative linkages between natural hydrologic patterns and fish assemblages are rarely used to make predictions of ecological consequences of hydrologic alterations. We ask two questions: (1) what is the relative role ofmore » hydrology in structuring fish assemblages at large scales? and (2) can relationships between fish assemblages and natural hydrology be utilized to predict fish assemblage responses to hydrologic disturbance? We developed models to relate fish life histories and reproductive strategies to landscape and hydrologic variables separately and then combined. Models were then used to predict the ecological consequences of altered hydrology due to dam regulation. Although hydrology plays a considerable role in structuring fish assemblages, the performance of models using only hydrologic variables was lower than that of models constructed using landscape variables. Isolating the relative importance of hydrology in structuring fish assemblages at the continental scale is difficult since hydrology is interrelated to many landscape factors. By applying models to dam-regulated hydrologic data, we observed some consistent predicted responses in fish life history strategies and modes of reproduction. In agreement with existing literature, equilibrium strategists are predicted to increase following dam regulation, whereas opportunistic and periodic species are predicted to decrease. In addition, dam regulation favors the selection of reproductive strategies with extended spawning seasons and preference for stable conditions.« less

Hydrologic filtering of fish life history strategies across the United States: implications for stream flow alteration

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

McManamay, Ryan A.; Frimpong, Emmanuel A.

Lotic fish have developed life history strategies adapted to the natural variation in stream flow regimes. The natural timing, duration, and magnitude of flow events has contributed to the diversity, production, and composition of fish assemblages over time. Studies evaluating the role of hydrology in structuring fish assemblages have been more common at the local or regional scale with very few studies conducted at the continental scale. Furthermore, quantitative linkages between natural hydrologic patterns and fish assemblages are rarely used to make predictions of ecological consequences of hydrologic alterations. We ask two questions: (1) what is the relative role ofmore » hydrology in structuring fish assemblages at large scales? and (2) can relationships between fish assemblages and natural hydrology be utilized to predict fish assemblage responses to hydrologic disturbance? We developed models to relate fish life histories and reproductive strategies to landscape and hydrologic variables separately and then combined. Models were then used to predict the ecological consequences of altered hydrology due to dam regulation. Although hydrology plays a considerable role in structuring fish assemblages, the performance of models using only hydrologic variables was lower than that of models constructed using landscape variables. Isolating the relative importance of hydrology in structuring fish assemblages at the continental scale is difficult since hydrology is interrelated to many landscape factors. By applying models to dam-regulated hydrologic data, we observed some consistent predicted responses in fish life history strategies and modes of reproduction. In agreement with existing literature, equilibrium strategists are predicted to increase following dam regulation, whereas opportunistic and periodic species are predicted to decrease. In addition, dam regulation favors the selection of reproductive strategies with extended spawning seasons and preference for stable conditions.« less

Mixed time integration methods for transient thermal analysis of structures

NASA Technical Reports Server (NTRS)

Liu, W. K.

1982-01-01

The computational methods used to predict and optimize the thermal structural behavior of aerospace vehicle structures are reviewed. In general, two classes of algorithms, implicit and explicit, are used in transient thermal analysis of structures. Each of these two methods has its own merits. Due to the different time scales of the mechanical and thermal responses, the selection of a time integration method can be a different yet critical factor in the efficient solution of such problems. Therefore mixed time integration methods for transient thermal analysis of structures are being developed. The computer implementation aspects and numerical evaluation of these mixed time implicit-explicit algorithms in thermal analysis of structures are presented. A computationally useful method of estimating the critical time step for linear quadrilateral element is also given. Numerical tests confirm the stability criterion and accuracy characteristics of the methods. The superiority of these mixed time methods to the fully implicit method or the fully explicit method is also demonstrated.

Mixed time integration methods for transient thermal analysis of structures

NASA Technical Reports Server (NTRS)

Liu, W. K.

1983-01-01

The computational methods used to predict and optimize the thermal-structural behavior of aerospace vehicle structures are reviewed. In general, two classes of algorithms, implicit and explicit, are used in transient thermal analysis of structures. Each of these two methods has its own merits. Due to the different time scales of the mechanical and thermal responses, the selection of a time integration method can be a difficult yet critical factor in the efficient solution of such problems. Therefore mixed time integration methods for transient thermal analysis of structures are being developed. The computer implementation aspects and numerical evaluation of these mixed time implicit-explicit algorithms in thermal analysis of structures are presented. A computationally-useful method of estimating the critical time step for linear quadrilateral element is also given. Numerical tests confirm the stability criterion and accuracy characteristics of the methods. The superiority of these mixed time methods to the fully implicit method or the fully explicit method is also demonstrated.

The general alcoholics anonymous tools of recovery: the adoption of 12-step practices and beliefs.

PubMed

Greenfield, Brenna L; Tonigan, J Scott

2013-09-01

Working the 12 steps is widely prescribed for Alcoholics Anonymous (AA) members although the relative merits of different methods for measuring step work have received minimal attention and even less is known about how step work predicts later substance use. The current study (1) compared endorsements of step work on an face-valid or direct measure, the Alcoholics Anonymous Inventory (AAI), with an indirect measure of step work, the General Alcoholics Anonymous Tools of Recovery (GAATOR); (2) evaluated the underlying factor structure of the GAATOR and changes in step work over time; (3) examined changes in the endorsement of step work over time; and (4) investigated how, if at all, 12-step work predicted later substance use. New AA affiliates (N = 130) completed assessments at intake, 3, 6, and 9 months. Significantly more participants endorsed step work on the GAATOR than on the AAI for nine of the 12 steps. An exploratory factor analysis revealed a two-factor structure for the GAATOR comprising behavioral step work and spiritual step work. Behavioral step work did not change over time, but was predicted by having a sponsor, while Spiritual step work decreased over time and increases were predicted by attending 12-step meetings or treatment. Behavioral step work did not prospectively predict substance use. In contrast, spiritual step work predicted percent days abstinent. Behavioral step work and spiritual step work appear to be conceptually distinct components of step work that have distinct predictors and unique impacts on outcomes. PsycINFO Database Record (c) 2013 APA, all rights reserved.

Probabilistic Fatigue Damage Prognosis Using a Surrogate Model Trained Via 3D Finite Element Analysis

NASA Technical Reports Server (NTRS)

Leser, Patrick E.; Hochhalter, Jacob D.; Newman, John A.; Leser, William P.; Warner, James E.; Wawrzynek, Paul A.; Yuan, Fuh-Gwo

2015-01-01

Utilizing inverse uncertainty quantification techniques, structural health monitoring can be integrated with damage progression models to form probabilistic predictions of a structure's remaining useful life. However, damage evolution in realistic structures is physically complex. Accurately representing this behavior requires high-fidelity models which are typically computationally prohibitive. In the present work, a high-fidelity finite element model is represented by a surrogate model, reducing computation times. The new approach is used with damage diagnosis data to form a probabilistic prediction of remaining useful life for a test specimen under mixed-mode conditions.

An evaluation of the real-time tropical cyclone forecast skill of the Navy Operational Global Atmospheric Prediction System in the western North Pacific

NASA Technical Reports Server (NTRS)

Fiorino, Michael; Goerss, James S.; Jensen, Jack J.; Harrison, Edward J., Jr.

1993-01-01

The paper evaluates the meteorological quality and operational utility of the Navy Operational Global Atmospheric Prediction System (NOGAPS) in forecasting tropical cyclones. It is shown that the model can provide useful predictions of motion and formation on a real-time basis in the western North Pacific. The meterological characteristics of the NOGAPS tropical cyclone predictions are evaluated by examining the formation of low-level cyclone systems in the tropics and vortex structure in the NOGAPS analysis and verifying 72-h forecasts. The adjusted NOGAPS track forecasts showed equitable skill to the baseline aid and the dynamical model. NOGAPS successfully predicted unusual equatorward turns for several straight-running cyclones.

Temporal Structure of Support Surface Translations Drive the Temporal Structure of Postural Control During Standing

PubMed Central

Rand, Troy J.; Myers, Sara A.; Kyvelidou, Anastasia; Mukherjee, Mukul

2015-01-01

A healthy biological system is characterized by a temporal structure that exhibits fractal properties and is highly complex. Unhealthy systems demonstrate lowered complexity and either greater or less predictability in the temporal structure of a time series. The purpose of this research was to determine if support surface translations with different temporal structures would affect the temporal structure of the center of pressure (COP) signal. Eight healthy young participants stood on a force platform that was translated in the anteroposterior direction for input conditions of varying complexity: white noise, pink noise, brown noise, and sine wave. Detrended fluctuation analysis was used to characterize the long-range correlations of the COP time series in the AP direction. Repeated measures ANOVA revealed differences among conditions (P < .001). The less complex support surface translations resulted in a less complex COP compared to normal standing. A quadratic trend analysis demonstrated an inverted-u shape across an increasing order of predictability of the conditions (P < .001). The ability to influence the complexity of postural control through support surface translations can have important implications for rehabilitation. PMID:25994281

The Ordered Network Structure and Prediction Summary for M≥7 Earthquakes in Xinjiang Region of China

NASA Astrophysics Data System (ADS)

Men, Ke-Pei; Zhao, Kai

2014-12-01

M ≥7 earthquakes have showed an obvious commensurability and orderliness in Xinjiang of China and its adjacent region since 1800. The main orderly values are 30 a × k (k = 1,2,3), 11 12 a, 41 43 a, 18 19 a, and 5 6 a. In the guidance of the information forecasting theory of Wen-Bo Weng, based on previous research results, combining ordered network structure analysis with complex network technology, we focus on the prediction summary of M ≥ 7 earthquakes by using the ordered network structure, and add new information to further optimize network, hence construct the 2D- and 3D-ordered network structure of M ≥ 7 earthquakes. In this paper, the network structure revealed fully the regularity of seismic activity of M ≥ 7 earthquakes in the study region during the past 210 years. Based on this, the Karakorum M7.1 earthquake in 1996, the M7.9 earthquake on the frontier of Russia, Mongol, and China in 2003, and two Yutian M7.3 earthquakes in 2008 and 2014 were predicted successfully. At the same time, a new prediction opinion is presented that the future two M ≥ 7 earthquakes will probably occur around 2019 - 2020 and 2025 - 2026 in this region. The results show that large earthquake occurred in defined region can be predicted. The method of ordered network structure analysis produces satisfactory results for the mid-and-long term prediction of M ≥ 7 earthquakes.

Causal discovery and inference: concepts and recent methodological advances.

PubMed

Spirtes, Peter; Zhang, Kun

This paper aims to give a broad coverage of central concepts and principles involved in automated causal inference and emerging approaches to causal discovery from i.i.d data and from time series. After reviewing concepts including manipulations, causal models, sample predictive modeling, causal predictive modeling, and structural equation models, we present the constraint-based approach to causal discovery, which relies on the conditional independence relationships in the data, and discuss the assumptions underlying its validity. We then focus on causal discovery based on structural equations models, in which a key issue is the identifiability of the causal structure implied by appropriately defined structural equation models: in the two-variable case, under what conditions (and why) is the causal direction between the two variables identifiable? We show that the independence between the error term and causes, together with appropriate structural constraints on the structural equation, makes it possible. Next, we report some recent advances in causal discovery from time series. Assuming that the causal relations are linear with nonGaussian noise, we mention two problems which are traditionally difficult to solve, namely causal discovery from subsampled data and that in the presence of confounding time series. Finally, we list a number of open questions in the field of causal discovery and inference.

A robust damage-detection technique with environmental variability combining time-series models with principal components

NASA Astrophysics Data System (ADS)

Lakshmi, K.; Rama Mohan Rao, A.

2014-10-01

In this paper, a novel output-only damage-detection technique based on time-series models for structural health monitoring in the presence of environmental variability and measurement noise is presented. The large amount of data obtained in the form of time-history response is transformed using principal component analysis, in order to reduce the data size and thereby improve the computational efficiency of the proposed algorithm. The time instant of damage is obtained by fitting the acceleration time-history data from the structure using autoregressive (AR) and AR with exogenous inputs time-series prediction models. The probability density functions (PDFs) of damage features obtained from the variances of prediction errors corresponding to references and healthy current data are found to be shifting from each other due to the presence of various uncertainties such as environmental variability and measurement noise. Control limits using novelty index are obtained using the distances of the peaks of the PDF curves in healthy condition and used later for determining the current condition of the structure. Numerical simulation studies have been carried out using a simply supported beam and also validated using an experimental benchmark data corresponding to a three-storey-framed bookshelf structure proposed by Los Alamos National Laboratory. Studies carried out in this paper clearly indicate the efficiency of the proposed algorithm for damage detection in the presence of measurement noise and environmental variability.

Utilization of Model Predictive Control to Balance Power Absorption Against Load Accumulation

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

Abbas, Nikhar; Tom, Nathan M

2017-06-03

Wave energy converter (WEC) control strategies have been primarily focused on maximizing power absorption. The use of model predictive control strategies allows for a finite-horizon, multiterm objective function to be solved. This work utilizes a multiterm objective function to maximize power absorption while minimizing the structural loads on the WEC system. Furthermore, a Kalman filter and autoregressive model were used to estimate and forecast the wave exciting force and predict the future dynamics of the WEC. The WEC's power-take-off time-averaged power and structural loads under a perfect forecast assumption in irregular waves were compared against results obtained from the Kalmanmore » filter and autoregressive model to evaluate model predictive control performance.« less

Utilization of Model Predictive Control to Balance Power Absorption Against Load Accumulation: Preprint

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

Abbas, Nikhar; Tom, Nathan

Wave energy converter (WEC) control strategies have been primarily focused on maximizing power absorption. The use of model predictive control strategies allows for a finite-horizon, multiterm objective function to be solved. This work utilizes a multiterm objective function to maximize power absorption while minimizing the structural loads on the WEC system. Furthermore, a Kalman filter and autoregressive model were used to estimate and forecast the wave exciting force and predict the future dynamics of the WEC. The WEC's power-take-off time-averaged power and structural loads under a perfect forecast assumption in irregular waves were compared against results obtained from the Kalmanmore » filter and autoregressive model to evaluate model predictive control performance.« less

Surrogate Modeling of High-Fidelity Fracture Simulations for Real-Time Residual Strength Predictions

NASA Technical Reports Server (NTRS)

Spear, Ashley D.; Priest, Amanda R.; Veilleux, Michael G.; Ingraffea, Anthony R.; Hochhalter, Jacob D.

2011-01-01

A surrogate model methodology is described for predicting in real time the residual strength of flight structures with discrete-source damage. Starting with design of experiment, an artificial neural network is developed that takes as input discrete-source damage parameters and outputs a prediction of the structural residual strength. Target residual strength values used to train the artificial neural network are derived from 3D finite element-based fracture simulations. A residual strength test of a metallic, integrally-stiffened panel is simulated to show that crack growth and residual strength are determined more accurately in discrete-source damage cases by using an elastic-plastic fracture framework rather than a linear-elastic fracture mechanics-based method. Improving accuracy of the residual strength training data would, in turn, improve accuracy of the surrogate model. When combined, the surrogate model methodology and high-fidelity fracture simulation framework provide useful tools for adaptive flight technology.

Comparative Study of Shrinkage and Non-Shrinkage Model of Food Drying

NASA Astrophysics Data System (ADS)

Shahari, N.; Jamil, N.; Rasmani, KA.

2016-08-01

A single phase heat and mass model has always been used to represent the moisture and temperature distribution during the drying of food. Several effects of the drying process, such as physical and structural changes, have been considered in order to increase understanding of the movement of water and temperature. However, the comparison between the heat and mass equation with and without structural change (in terms of shrinkage), which can affect the accuracy of the prediction model, has been little investigated. In this paper, two mathematical models to describe the heat and mass transfer in food, with and without the assumption of structural change, were analysed. The equations were solved using the finite difference method. The converted coordinate system was introduced within the numerical computations for the shrinkage model. The result shows that the temperature with shrinkage predicts a higher temperature at a specific time compared to that of the non-shrinkage model. Furthermore, the predicted moisture content decreased faster at a specific time when the shrinkage effect was included in the model.

Optimism as a predictor of the effects of laboratory-induced stress on fears and hope.

PubMed

Kimhi, Shaul; Eshel, Yohanan; Shahar, Eldad

2013-01-01

The objective of the current study is to explore optimism as a predictor of personal and collective fear, as well as hope, following laboratory-induced stress. Students (N = 107; 74 female, 33 male) were assigned randomly to either the experimental (stress--political violence video clip) or the control group (no-stress--nature video clip). Questionnaires of fear and hope were administered immediately after the experiment (Time 1) and 3 weeks later (Time 2). Structural equation modeling indicated the following: (a) Optimism significantly predicted both fear and hope in the stress group at Time 1, but not in the no-stress group. (b) Optimism predicted hope but not fear at Time 2 in the stress group. (c) Hope at Time 1 significantly predicted hope at Time 2, in both the stress and the no-stress groups. (d) Gender did not predict significantly fear at Time 1 in the stress group, despite a significant difference between genders. This study supports previous studies indicating that optimism plays an important role in people's coping with stress. However, based on our research the data raise the question of whether optimism, by itself, or environmental stress, by itself, may accurately predict stress response.

Fast large-scale clustering of protein structures using Gauss integrals.

PubMed

Harder, Tim; Borg, Mikael; Boomsma, Wouter; Røgen, Peter; Hamelryck, Thomas

2012-02-15

Clustering protein structures is an important task in structural bioinformatics. De novo structure prediction, for example, often involves a clustering step for finding the best prediction. Other applications include assigning proteins to fold families and analyzing molecular dynamics trajectories. We present Pleiades, a novel approach to clustering protein structures with a rigorous mathematical underpinning. The method approximates clustering based on the root mean square deviation by first mapping structures to Gauss integral vectors--which were introduced by Røgen and co-workers--and subsequently performing K-means clustering. Compared to current methods, Pleiades dramatically improves on the time needed to perform clustering, and can cluster a significantly larger number of structures, while providing state-of-the-art results. The number of low energy structures generated in a typical folding study, which is in the order of 50,000 structures, can be clustered within seconds to minutes.

Use long short-term memory to enhance Internet of Things for combined sewer overflow monitoring

NASA Astrophysics Data System (ADS)

Zhang, Duo; Lindholm, Geir; Ratnaweera, Harsha

2018-01-01

Combined sewer overflow causes severe water pollution, urban flooding and reduced treatment plant efficiency. Understanding the behavior of CSO structures is vital for urban flooding prevention and overflow control. Neural networks have been extensively applied in water resource related fields. In this study, we collect data from an Internet of Things monitoring CSO structure and build different neural network models for simulating and predicting the water level of the CSO structure. Through a comparison of four different neural networks, namely multilayer perceptron (MLP), wavelet neural network (WNN), long short-term memory (LSTM) and gated recurrent unit (GRU), the LSTM and GRU present superior capabilities for multi-step-ahead time series prediction. Furthermore, GRU achieves prediction performances similar to LSTM with a quicker learning curve.

Motional timescale predictions by molecular dynamics simulations: case study using proline and hydroxyproline sidechain dynamics.

PubMed

Aliev, Abil E; Kulke, Martin; Khaneja, Harmeet S; Chudasama, Vijay; Sheppard, Tom D; Lanigan, Rachel M

2014-02-01

We propose a new approach for force field optimizations which aims at reproducing dynamics characteristics using biomolecular MD simulations, in addition to improved prediction of motionally averaged structural properties available from experiment. As the source of experimental data for dynamics fittings, we use (13) C NMR spin-lattice relaxation times T1 of backbone and sidechain carbons, which allow to determine correlation times of both overall molecular and intramolecular motions. For structural fittings, we use motionally averaged experimental values of NMR J couplings. The proline residue and its derivative 4-hydroxyproline with relatively simple cyclic structure and sidechain dynamics were chosen for the assessment of the new approach in this work. Initially, grid search and simplexed MD simulations identified large number of parameter sets which fit equally well experimental J couplings. Using the Arrhenius-type relationship between the force constant and the correlation time, the available MD data for a series of parameter sets were analyzed to predict the value of the force constant that best reproduces experimental timescale of the sidechain dynamics. Verification of the new force-field (termed as AMBER99SB-ILDNP) against NMR J couplings and correlation times showed consistent and significant improvements compared to the original force field in reproducing both structural and dynamics properties. The results suggest that matching experimental timescales of motions together with motionally averaged characteristics is the valid approach for force field parameter optimization. Such a comprehensive approach is not restricted to cyclic residues and can be extended to other amino acid residues, as well as to the backbone. Copyright © 2013 Wiley Periodicals, Inc.

Fatigue-Crack-Growth Structural Analysis

NASA Technical Reports Server (NTRS)

Newman, J. C., Jr.

1986-01-01

Elastic and plastic deformations calculated under variety of loading conditions. Prediction of fatigue-crack-growth lives made with FatigueCrack-Growth Structural Analysis (FASTRAN) computer program. As cyclic loads are applied to initial crack configuration, FASTRAN predicts crack length and other parameters until complete break occurs. Loads are tensile or compressive and of variable or constant amplitude. FASTRAN incorporates linear-elastic fracture mechanics with modifications of load-interaction effects caused by crack closure. FASTRAN considered research tool, because of lengthy calculation times. FASTRAN written in FORTRAN IV for batch execution.

A novel parallel pipeline structure of VP9 decoder

NASA Astrophysics Data System (ADS)

Qin, Huabiao; Chen, Wu; Yi, Sijun; Tan, Yunfei; Yi, Huan

2018-04-01

To improve the efficiency of VP9 decoder, a novel parallel pipeline structure of VP9 decoder is presented in this paper. According to the decoding workflow, VP9 decoder can be divided into sub-modules which include entropy decoding, inverse quantization, inverse transform, intra prediction, inter prediction, deblocking and pixel adaptive compensation. By analyzing the computing time of each module, hotspot modules are located and the causes of low efficiency of VP9 decoder can be found. Then, a novel pipeline decoder structure is designed by using mixed parallel decoding methods of data division and function division. The experimental results show that this structure can greatly improve the decoding efficiency of VP9.

Target Highlights in CASP9: Experimental Target Structures for the Critical Assessment of Techniques for Protein Structure Prediction

PubMed Central

Kryshtafovych, Andriy; Moult, John; Bartual, Sergio G.; Bazan, J. Fernando; Berman, Helen; Casteel, Darren E.; Christodoulou, Evangelos; Everett, John K.; Hausmann, Jens; Heidebrecht, Tatjana; Hills, Tanya; Hui, Raymond; Hunt, John F.; Jayaraman, Seetharaman; Joachimiak, Andrzej; Kennedy, Michael A.; Kim, Choel; Lingel, Andreas; Michalska, Karolina; Montelione, Gaetano T.; Otero, José M.; Perrakis, Anastassis; Pizarro, Juan C.; van Raaij, Mark J.; Ramelot, Theresa A.; Rousseau, Francois; Tong, Liang; Wernimont, Amy K.; Young, Jasmine; Schwede, Torsten

2011-01-01

One goal of the CASP Community Wide Experiment on the Critical Assessment of Techniques for Protein Structure Prediction is to identify the current state of the art in protein structure prediction and modeling. A fundamental principle of CASP is blind prediction on a set of relevant protein targets, i.e. the participating computational methods are tested on a common set of experimental target proteins, for which the experimental structures are not known at the time of modeling. Therefore, the CASP experiment would not have been possible without broad support of the experimental protein structural biology community. In this manuscript, several experimental groups discuss the structures of the proteins which they provided as prediction targets for CASP9, highlighting structural and functional peculiarities of these structures: the long tail fibre protein gp37 from bacteriophage T4, the cyclic GMP-dependent protein kinase Iβ (PKGIβ) dimerization/docking domain, the ectodomain of the JTB (Jumping Translocation Breakpoint) transmembrane receptor, Autotaxin (ATX) in complex with an inhibitor, the DNA-Binding J-Binding Protein 1 (JBP1) domain essential for biosynthesis and maintenance of DNA base-J (β-D-glucosyl-hydroxymethyluracil) in Trypanosoma and Leishmania, an so far uncharacterized 73 residue domain from Ruminococcus gnavus with a fold typical for PDZ-like domains, a domain from the Phycobilisome (PBS) core-membrane linker (LCM) phycobiliprotein ApcE from Synechocystis, the Heat shock protein 90 (Hsp90) activators PFC0360w and PFC0270w from Plasmodium falciparum, and 2-oxo-3-deoxygalactonate kinase from Klebsiella pneumoniae. PMID:22020785

Implementation and Verification of the Chen Prediction Technique for Forecasting Large Nonrecurrent Storms*

NASA Astrophysics Data System (ADS)

Arge, C. N.; Chen, J.; Slinker, S.; Pizzo, V. J.

2000-05-01

The method of Chen et al. [1997, JGR, 101, 27499] is designed to accurately identify and predict the occurrence, duration, and strength of largegeomagnetic storms using real-time solar wind data. The method estimates the IMF and the geoeffectiveness of the solar wind upstream of a monitor and can provide warning times that range from a few hours to more than 10 hours. The model uses physical features of solar wind structures that cause large storms: long durations of southward interplanetary magnetic field. It is currently undergoing testing, improvement, and validation at NOAA/SEC in effort to transition it into a real-time space weather forecasting tool. The original version of the model has modified so that it now makes hourly (as opposed to daily) predictions and has been improved in effort to enhance both its predictive capability and reliability. In this paper, we report on the results of a 2-year historical verification study of the model using ACE real-time data. The prediction performances of the original and improved versions of the model are then compared. A real-time prediction web page has been developed and is on line at NOAA/SEC. *Work supported by ONR.

Application of a baseflow filter for evaluating model structure suitability of the IHACRES CMD

NASA Astrophysics Data System (ADS)

Kim, H. S.

2015-02-01

The main objective of this study was to assess the predictive uncertainty from the rainfall-runoff model structure coupling a conceptual module (non-linear module) with a metric transfer function module (linear module). The methodology was primarily based on the comparison between the outputs of the rainfall-runoff model and those from an alternative model approach. An alternative model approach was used to minimise uncertainties arising from data and the model structure. A baseflow filter was adopted to better understand deficiencies in the forms of the rainfall-runoff model by avoiding the uncertainties related to data and the model structure. The predictive uncertainty from the model structure was investigated for representative groups of catchments having similar hydrological response characteristics in the upper Murrumbidgee Catchment. In the assessment of model structure suitability, the consistency (or variability) of catchment response over time and space in model performance and parameter values has been investigated to detect problems related to the temporal and spatial variability of the model accuracy. The predictive error caused by model uncertainty was evaluated through analysis of the variability of the model performance and parameters. A graphical comparison of model residuals, effective rainfall estimates and hydrographs was used to determine a model's ability related to systematic model deviation between simulated and observed behaviours and general behavioural differences in the timing and magnitude of peak flows. The model's predictability was very sensitive to catchment response characteristics. The linear module performs reasonably well in the wetter catchments but has considerable difficulties when applied to the drier catchments where a hydrologic response is dominated by quick flow. The non-linear module has a potential limitation in its capacity to capture non-linear processes for converting observed rainfall into effective rainfall in both the wetter and drier catchments. The comparative study based on a better quantification of the accuracy and precision of hydrological modelling predictions yields a better understanding for the potential improvement of model deficiencies.

Interactions of ice sheet evolution, sea level and GIA in a region of complex Earth structure

NASA Astrophysics Data System (ADS)

Gomez, N. A.; Chan, N. H.; Latychev, K.; Pollard, D.; Powell, E. M.

2017-12-01

Constraining glacial isostatic adjustment (GIA) is challenging in Antarctica, where the solid Earth deformation, sea level changes and ice dynamics are strongly linked on all timescales. Furthermore, Earth structure beneath the Antarctic Ice Sheet is characterized by significant lateral variability. A stable, thick craton exists in the east, while the west is underlain by a large continental rift system, with a relatively thin lithosphere and hot, low viscosity asthenosphere, as indicated by high resolution seismic tomography. This implies that in parts of the West Antarctic, the Earth's mantle may respond to surface loading on shorter than average (centennial, or even decadal) timescales. Accounting for lateral variations in viscoelastic Earth structure alters the timing and geometry of load-induced Earth deformation, which in turn impacts the timing and extent of the ice-sheet retreat via a sea-level feedback, as well as predictions of relative sea-level change and GIA. We explore the impact of laterally varying Earth structure on ice-sheet evolution, sea level change and Earth deformation in the Antarctic region since the Last Glacial Maximum using a newly developed coupled ice sheet - sea level model that incorporates 3-D variations in lithospheric thickness and mantle viscosity derived from recent seismic tomographic datasets. Our results focus on identifying the regions and time periods in which the incorporation of 3-D Earth structure is critical for accurate predictions of ice sheet evolution and interpretation of geological and geodetic observations. We also investigate the sensitivity to the regional Earth structure of the relative contributions to modern GIA predictions of Last Deglacial and more recent Holocene ice cover changes.

Predicting complex syntactic structure in real time: Processing of negative sentences in Russian.

PubMed

Kazanina, Nina

2017-11-01

In Russian negative sentences the verb's direct object may appear either in the accusative case, which is licensed by the verb (as is common cross-linguistically), or in the genitive case, which is licensed by the negation (Russian-specific "genitive-of-negation" phenomenon). Such sentences were used to investigate whether case marking is employed for anticipating syntactic structure, and whether lexical heads other than the verb can be predicted on the basis of a case-marked noun phrase. Experiment 1, a completion task, confirmed that genitive-of-negation is part of Russian speakers' active grammatical repertoire. In Experiments 2 and 3, the genitive/accusative case manipulation on the preverbal object led to shorter reading times at the negation and verb in the genitive versus accusative condition. Furthermore, Experiment 3 manipulated linear order of the direct object and the negated verb in order to distinguish whether the abovementioned facilitatory effect was predictive or integrative in nature, and concluded that the parser actively predicts a verb and (otherwise optional) negation on the basis of a preceding genitive-marked object. Similarly to a head-final language, case-marking information on preverbal noun phrases (NPs) is used by the parser to enable incremental structure building in a free-word-order language such as Russian.

Surrogate data--a secure way to share corporate data.

PubMed

Tetko, Igor V; Abagyan, Ruben; Oprea, Tudor I

2005-01-01

The privacy of chemical structure is of paramount importance for the industrial sector, in particular for the pharmaceutical industry. At the same time, companies handle large amounts of physico-chemical and biological data that could be shared in order to improve our molecular understanding of pharmacokinetic and toxicological properties, which could lead to improved predictivity and shorten the development time for drugs, in particular in the early phases of drug discovery. The current study provides some theoretical limits on the information required to produce reverse engineering of molecules from generated descriptors and demonstrates that the information content of molecules can be as low as less than one bit per atom. Thus theoretically just one descriptor can be used to completely disclose the molecular structure. Instead of sharing descriptors, we propose to share surrogate data. The sharing of surrogate data is nothing else but sharing of reliably predicted molecules. The use of surrogate data can provide the same information as the original set. We consider the practical application of this idea to predict lipophilicity of chemical compounds and we demonstrate that surrogate and real (original) data provides similar prediction ability. Thus, our proposed strategy makes it possible not only to share descriptors, but also complete collections of surrogate molecules without the danger of disclosing the underlying molecular structures.

Estimating epidemic arrival times using linear spreading theory

NASA Astrophysics Data System (ADS)

Chen, Lawrence M.; Holzer, Matt; Shapiro, Anne

2018-01-01

We study the dynamics of a spatially structured model of worldwide epidemics and formulate predictions for arrival times of the disease at any city in the network. The model is composed of a system of ordinary differential equations describing a meta-population susceptible-infected-recovered compartmental model defined on a network where each node represents a city and the edges represent the flight paths connecting cities. Making use of the linear determinacy of the system, we consider spreading speeds and arrival times in the system linearized about the unstable disease free state and compare these to arrival times in the nonlinear system. Two predictions are presented. The first is based upon expansion of the heat kernel for the linearized system. The second assumes that the dominant transmission pathway between any two cities can be approximated by a one dimensional lattice or a homogeneous tree and gives a uniform prediction for arrival times independent of the specific network features. We test these predictions on a real network describing worldwide airline traffic.

The flow of plasma in the solar terrestrial environment

NASA Technical Reports Server (NTRS)

Schunk, R. W.

1992-01-01

The overall goal of our NASA Theory Program is to study the coupling, time delays, and feedback mechanisms between the various regions of the solar-terrestrial system in a self-consistent, quantitative manner. To accomplish this goal, it will eventually be necessary to have time-dependent macroscopic models of the different regions of the solar-terrestrial system and we are continually working toward this goal. However, our immediate emphasis is on the near-earth plasma environment, including the ionosphere, the plasmasphere, and the polar wind. In this area, we have developed unique global models that allow us to study the coupling between the different regions. Another important aspect of our NASA Theory Program concerns the effect that localized structure has on the macroscopic flow in the ionosphere, plasmasphere, thermosphere, and polar wind. The localized structure can be created by structured magnetospheric inputs (i.e., structured plasma convection, particle precipitation or Birkeland current patterns) or time variations in these inputs due to storms and substorms. Also, some of the plasma flows that we predict with our macroscopic models may be unstable, and another one of our goals is to examine the stability of our predicted flows. Because time-dependent, three-dimensional numerical models of the solar-terrestrial environment generally require extensive computer resources, they are usually based on relatively simple mathematical formulations (i.e., simple MHD or hydrodynamic formulation). Therefore, another long-range goal of our NASA Theory Program is to study the conditions under which various mathematical formulations can be applied to specific solar-terrestrial regions. This may involve a detailed comparison of kinetic, semikinetic, and hydrodynamic predictions for a given polar wind scenario or it may involve the comparison of a small-scale particle-in-cell (PIC) simulation of a plasma expansion event with a similar macroscopic expansion event. The different mathematical formulations have different strengths and weaknesses and a careful comparison of model predictions for similar geophysical situations will provide insight into when the various models can be used with confidence.

A Reinvestigation of the Dimer of para-Benzoquinone with Pyrimidine with MP2, CCSD(T) and DFT using Functionals including those Designed to Describe Dispersion

PubMed Central

Marianski, Mateusz; Oliva, Antoni

2012-01-01

We reevaluate the interaction of pyridine and p-benzoquinone using functionals designed to treat dispersion. We compare the relative energies of four different structures: stacked, T-shaped (identified for the first time) and two planar H-bonded geometries using these functionals (B97-D, ωB97x-D, M05, M05-2X, M06, M06L, M06-2X), other functionals (PBE1PBE, B3LYP, X3LYP), MP2 and CCSD(T) using basis sets as large as cc-pVTZ. The functionals designed to treat dispersion behave erratically as the predictions of the most stable structure vary considerably. MP2 predicts the experimentally observed structure (H-bonded) to be the least stable, while single point CCSD(T) at the MP2 optimized geometry correctly predicts the observed structure to be most stable. We have confirmed the assignment of the experimental structure using new calculations of the vibrational frequency shifts previously used to identify the structure. The MP2/cc-pVTZ vibrational calculations are in excellent agreement with the observations. All methods used to calculate the energies provide vibrational shifts that agree with the observed structure even though most do not predict this structure to be most stable. The implications for evaluating possible π-stacking in biologically important systems are discussed. PMID:22765283

A reinvestigation of the dimer of para-benzoquinone and pyrimidine with MP2, CCSD(T), and DFT using functionals including those designed to describe dispersion.

PubMed

Marianski, Mateusz; Oliva, Antoni; Dannenberg, J J

2012-08-02

We reevaluate the interaction of pyridine and p-benzoquinone using functionals designed to treat dispersion. We compare the relative energies of four different structures: stacked, T-shaped (identified for the first time), and two planar H-bonded geometries using these functionals (B97-D, ωB97x-D, M05, M05-2X, M06, M06L, and M06-2X), other functionals (PBE1PBE, B3LYP, X3LYP), MP2, and CCSD(T) using basis sets as large as cc-pVTZ. The functionals designed to treat dispersion behave erratically as the predictions of the most stable structure vary considerably. MP2 predicts the experimentally observed structure (H-bonded) to be the least stable, while single-point CCSD(T) at the MP2 optimized geometry correctly predicts the observed structure to be the most stable. We have confirmed the assignment of the experimental structure using new calculations of the vibrational frequency shifts previously used to identify the structure. The MP2/cc-pVTZ vibrational calculations are in excellent agreement with the observations. All methods used to calculate the energies provide vibrational shifts that agree with the observed structure even though most do not predict this structure to be most stable. The implications for evaluating possible π-stacking in biologically important systems are discussed.

Rapid Method Development in Hydrophilic Interaction Liquid Chromatography for Pharmaceutical Analysis Using a Combination of Quantitative Structure-Retention Relationships and Design of Experiments.

PubMed

Taraji, Maryam; Haddad, Paul R; Amos, Ruth I J; Talebi, Mohammad; Szucs, Roman; Dolan, John W; Pohl, Chris A

2017-02-07

A design-of-experiment (DoE) model was developed, able to describe the retention times of a mixture of pharmaceutical compounds in hydrophilic interaction liquid chromatography (HILIC) under all possible combinations of acetonitrile content, salt concentration, and mobile-phase pH with R 2 > 0.95. Further, a quantitative structure-retention relationship (QSRR) model was developed to predict retention times for new analytes, based only on their chemical structures, with a root-mean-square error of prediction (RMSEP) as low as 0.81%. A compound classification based on the concept of similarity was applied prior to QSRR modeling. Finally, we utilized a combined QSRR-DoE approach to propose an optimal design space in a quality-by-design (QbD) workflow to facilitate the HILIC method development. The mathematical QSRR-DoE model was shown to be highly predictive when applied to an independent test set of unseen compounds in unseen conditions with a RMSEP value of 5.83%. The QSRR-DoE computed retention time of pharmaceutical test analytes and subsequently calculated separation selectivity was used to optimize the chromatographic conditions for efficient separation of targets. A Monte Carlo simulation was performed to evaluate the risk of uncertainty in the model's prediction, and to define the design space where the desired quality criterion was met. Experimental realization of peak selectivity between targets under the selected optimal working conditions confirmed the theoretical predictions. These results demonstrate how discovery of optimal conditions for the separation of new analytes can be accelerated by the use of appropriate theoretical tools.

Slow Crack Growth and Fatigue Life Prediction of Ceramic Components Subjected to Variable Load History

NASA Technical Reports Server (NTRS)

Jadaan, Osama

2001-01-01

Present capabilities of the NASA CARES/Life (Ceramic Analysis and Reliability Evaluation of Structures/Life) code include probabilistic life prediction of ceramic components subjected to fast fracture, slow crack growth (stress corrosion), and cyclic fatigue failure modes. Currently, this code has the capability to compute the time-dependent reliability of ceramic structures subjected to simple time-dependent loading. For example, in slow crack growth (SCG) type failure conditions CARES/Life can handle the cases of sustained and linearly increasing time-dependent loads, while for cyclic fatigue applications various types of repetitive constant amplitude loads can be accounted for. In real applications applied loads are rarely that simple, but rather vary with time in more complex ways such as, for example, engine start up, shut down, and dynamic and vibrational loads. In addition, when a given component is subjected to transient environmental and or thermal conditions, the material properties also vary with time. The objective of this paper is to demonstrate a methodology capable of predicting the time-dependent reliability of components subjected to transient thermomechanical loads that takes into account the change in material response with time. In this paper, the dominant delayed failure mechanism is assumed to be SCG. This capability has been added to the NASA CARES/Life (Ceramic Analysis and Reliability Evaluation of Structures/Life) code, which has also been modified to have the ability of interfacing with commercially available FEA codes executed for transient load histories. An example involving a ceramic exhaust valve subjected to combustion cycle loads is presented to demonstrate the viability of this methodology and the CARES/Life program.

Composite Stress Rupture: A New Reliability Model Based on Strength Decay

NASA Technical Reports Server (NTRS)

Reeder, James R.

2012-01-01

A model is proposed to estimate reliability for stress rupture of composite overwrap pressure vessels (COPVs) and similar composite structures. This new reliability model is generated by assuming a strength degradation (or decay) over time. The model suggests that most of the strength decay occurs late in life. The strength decay model will be shown to predict a response similar to that predicted by a traditional reliability model for stress rupture based on tests at a single stress level. In addition, the model predicts that even though there is strength decay due to proof loading, a significant overall increase in reliability is gained by eliminating any weak vessels, which would fail early. The model predicts that there should be significant periods of safe life following proof loading, because time is required for the strength to decay from the proof stress level to the subsequent loading level. Suggestions for testing the strength decay reliability model have been made. If the strength decay reliability model predictions are shown through testing to be accurate, COPVs may be designed to carry a higher level of stress than is currently allowed, which will enable the production of lighter structures

Mining the key predictors for event outbreaks in social networks

NASA Astrophysics Data System (ADS)

Yi, Chengqi; Bao, Yuanyuan; Xue, Yibo

2016-04-01

It will be beneficial to devise a method to predict a so-called event outbreak. Existing works mainly focus on exploring effective methods for improving the accuracy of predictions, while ignoring the underlying causes: What makes event go viral? What factors that significantly influence the prediction of an event outbreak in social networks? In this paper, we proposed a novel definition for an event outbreak, taking into account the structural changes to a network during the propagation of content. In addition, we investigated features that were sensitive to predicting an event outbreak. In order to investigate the universality of these features at different stages of an event, we split the entire lifecycle of an event into 20 equal segments according to the proportion of the propagation time. We extracted 44 features, including features related to content, users, structure, and time, from each segment of the event. Based on these features, we proposed a prediction method using supervised classification algorithms to predict event outbreaks. Experimental results indicate that, as time goes by, our method is highly accurate, with a precision rate ranging from 79% to 97% and a recall rate ranging from 74% to 97%. In addition, after applying a feature-selection algorithm, the top five selected features can considerably improve the accuracy of the prediction. Data-driven experimental results show that the entropy of the eigenvector centrality, the entropy of the PageRank, the standard deviation of the betweenness centrality, the proportion of re-shares without content, and the average path length are the key predictors for an event outbreak. Our findings are especially useful for further exploring the intrinsic characteristics of outbreak prediction.

Impulsive Personality and Alcohol Use: Bidirectional Relations Over One Year

PubMed Central

Kaiser, Alison; Bonsu, Jacqueline A.; Charnigo, Richard J.; Milich, Richard; Lynam, Donald R.

2016-01-01

Objective: Impulsive personality traits have been found to be robust predictors of substance use and problems in both cross-sectional and longitudinal research. Studies examining the relations of substance use and impulsive personality over time indicate a bidirectional relation, where substance use is also predictive of increases in later impulsive personality. The present study sought to build on these findings by examining the bidirectional relations among the different impulsive personality traits assessed by the UPPS-P Impulsive Behavior Scale, with an interest in urgency (the tendency to act rashly when experiencing strong affect). Method: Participants were 525 first-year college students (48.0% male, 81.1% White), who completed self-report measures assessing personality traits and a structured interview assessing past and current substance use. Data collection took place at two different time points: the first occurred during the participants’ first year of college, and the second occurred approximately 1 year later. Bidirectional relations were examined using structural equation modeling. Results: Time 1 (T1) positive urgency predicted higher levels of alcohol use at Time 2 (T2), whereas T1 lack of perseverance predicted lower levels of alcohol use at T2. T1 alcohol use predicted higher levels of positive urgency, negative urgency, sensation seeking, and lack of premeditation at T2. Conclusions: Findings provide greater resolution in characterizing the bidirectional relation between impulsive personality traits and substance use. PMID:27172580

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.

Real-time sensing of fatigue crack damage for information-based decision and control

NASA Astrophysics Data System (ADS)

Keller, Eric Evans

Information-based decision and control for structures that are subject to failure by fatigue cracking is based on the following notion: Maintenance, usage scheduling, and control parameter tuning can be optimized through real time knowledge of the current state of fatigue crack damage. Additionally, if the material properties of a mechanical structure can be identified within a smaller range, then the remaining life prediction of that structure will be substantially more accurate. Information-based decision systems can rely one physical models, estimation of material properties, exact knowledge of usage history, and sensor data to synthesize an accurate snapshot of the current state of damage and the likely remaining life of a structure under given assumed loading. The work outlined in this thesis is structured to enhance the development of information-based decision and control systems. This is achieved by constructing a test facility for laboratory experiments on real-time damage sensing. This test facility makes use of a methodology that has been formulated for fatigue crack model parameter estimation and significantly improves the quality of predictions of remaining life. Specifically, the thesis focuses on development of an on-line fatigue crack damage sensing and life prediction system that is built upon the disciplines of Systems Sciences and Mechanics of Materials. A major part of the research effort has been expended to design and fabricate a test apparatus which allows: (i) measurement and recording of statistical data for fatigue crack growth in metallic materials via different sensing techniques; and (ii) identification of stochastic model parameters for prediction of fatigue crack damage. To this end, this thesis describes the test apparatus and the associated instrumentation based on four different sensing techniques, namely, traveling optical microscopy, ultrasonic flaw detection, Alternating Current Potential Drop (ACPD), and fiber-optic extensometry-based compliance, for crack length measurements.

Extending the validity of the Feeding Practices and Structure Questionnaire.

PubMed

Jansen, Elena; Mallan, Kimberley M; Daniels, Lynne A

2015-06-30

Feeding practices are commonly examined as potentially modifiable determinants of children's eating behaviours and weight status. Although a variety of questionnaires exist to assess different feeding aspects, many lack thorough reliability and validity testing. The Feeding Practices and Structure Questionnaire (FPSQ) is a tool designed to measure early feeding practices related to non-responsive feeding and structure of the meal environment. Face validity, factorial validity, internal reliability and cross-sectional correlations with children's eating behaviours have been established in mothers with 2-year-old children. The aim of the present study was to further extend the validity of the FPSQ by examining factorial, construct and predictive validity, and stability. Participants were from the NOURISH randomised controlled trial which evaluated an intervention with first-time mothers designed to promote protective feeding practices. Maternal feeding practices (FP) and child eating behaviours were assessed when children were aged 2 years and 3.7 years (n = 388). Confirmatory Factor analysis, group differences, predictive relationships, and stability were tested. The original 9-factor structure was confirmed when children were aged 3.7 ± 0.3 years. Cronbach's alpha was above the recommended 0.70 cut-off for all factors except Structured Meal Timing, Over Restriction and Distrust in Appetite which were 0.58, 0.67 and 0.66 respectively. Allocated group differences reflected behaviour consistent with intervention content and all feeding practices were stable across both time points (range of r = 0.45-0.70). There was some evidence for the predictive validity of factors with 2 FP showing expected relationships, 2 FP showing expected and unexpected relationships and 5 FP showing no relationship. Reliability and validity was demonstrated for most subscales of the FPSQ. Future validation is warranted with culturally diverse samples and with fathers and other caregivers. The use of additional outcomes to further explore predictive validity is recommended as well as testing test-retest reliability of the questionnaire.

Estimation of Uncertainties in the Global Distance Test (GDT_TS) for CASP Models.

PubMed

Li, Wenlin; Schaeffer, R Dustin; Otwinowski, Zbyszek; Grishin, Nick V

2016-01-01

The Critical Assessment of techniques for protein Structure Prediction (or CASP) is a community-wide blind test experiment to reveal the best accomplishments of structure modeling. Assessors have been using the Global Distance Test (GDT_TS) measure to quantify prediction performance since CASP3 in 1998. However, identifying significant score differences between close models is difficult because of the lack of uncertainty estimations for this measure. Here, we utilized the atomic fluctuations caused by structure flexibility to estimate the uncertainty of GDT_TS scores. Structures determined by nuclear magnetic resonance are deposited as ensembles of alternative conformers that reflect the structural flexibility, whereas standard X-ray refinement produces the static structure averaged over time and space for the dynamic ensembles. To recapitulate the structural heterogeneous ensemble in the crystal lattice, we performed time-averaged refinement for X-ray datasets to generate structural ensembles for our GDT_TS uncertainty analysis. Using those generated ensembles, our study demonstrates that the time-averaged refinements produced structure ensembles with better agreement with the experimental datasets than the averaged X-ray structures with B-factors. The uncertainty of the GDT_TS scores, quantified by their standard deviations (SDs), increases for scores lower than 50 and 70, with maximum SDs of 0.3 and 1.23 for X-ray and NMR structures, respectively. We also applied our procedure to the high accuracy version of GDT-based score and produced similar results with slightly higher SDs. To facilitate score comparisons by the community, we developed a user-friendly web server that produces structure ensembles for NMR and X-ray structures and is accessible at http://prodata.swmed.edu/SEnCS. Our work helps to identify the significance of GDT_TS score differences, as well as to provide structure ensembles for estimating SDs of any scores.

The effect of data structures on INGRES performance

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

Creighton, J.R.

1987-01-01

Computer experiments were conducted to determine the effect of using Heap, ISAM, Hash and B-tree data structures for INGRES relations. Average times for retrieve, append and update were determined for searches by unique key and non-key data. The experiments were conducted on relations of approximately 1000 tuples of 332 byte width. Multiple operations were performed, where appropriate, to obtain average times. Simple models of the data structures are presented and shown to be consistent with experimental results. The models can be used to predict performance, and to select the appropriate data structure for various applications.

Prediction of protein structural classes by recurrence quantification analysis based on chaos game representation.

PubMed

Yang, Jian-Yi; Peng, Zhen-Ling; Yu, Zu-Guo; Zhang, Rui-Jie; Anh, Vo; Wang, Desheng

2009-04-21

In this paper, we intend to predict protein structural classes (alpha, beta, alpha+beta, or alpha/beta) for low-homology data sets. Two data sets were used widely, 1189 (containing 1092 proteins) and 25PDB (containing 1673 proteins) with sequence homology being 40% and 25%, respectively. We propose to decompose the chaos game representation of proteins into two kinds of time series. Then, a novel and powerful nonlinear analysis technique, recurrence quantification analysis (RQA), is applied to analyze these time series. For a given protein sequence, a total of 16 characteristic parameters can be calculated with RQA, which are treated as feature representation of protein sequences. Based on such feature representation, the structural class for each protein is predicted with Fisher's linear discriminant algorithm. The jackknife test is used to test and compare our method with other existing methods. The overall accuracies with step-by-step procedure are 65.8% and 64.2% for 1189 and 25PDB data sets, respectively. With one-against-others procedure used widely, we compare our method with five other existing methods. Especially, the overall accuracies of our method are 6.3% and 4.1% higher for the two data sets, respectively. Furthermore, only 16 parameters are used in our method, which is less than that used by other methods. This suggests that the current method may play a complementary role to the existing methods and is promising to perform the prediction of protein structural classes.

The General Alcoholics Anonymous Tools of Recovery: The Adoption of 12-Step Practices and Beliefs

PubMed Central

Greenfield, Brenna L.; Tonigan, J. Scott

2013-01-01

Working the 12 steps is widely prescribed for Alcoholics Anonymous (AA) members although the relative merits of different methods for measuring step-work have received minimal attention and even less is known about how step-work predicts later substance use. The current study (1) compared endorsements of step-work on an face-valid or direct measure, the Alcoholics Anonymous Inventory (AAI), with an indirect measure of step-work, the General Alcoholics Anonymous Tools of Recovery (GAATOR), (2) evaluated the underlying factor structure of the GAATOR and changes in step-work over time, (3) examined changes in the endorsement of step-work over time, and (4) investigated how, if at all, 12-step-work predicted later substance use. New AA affiliates (N = 130) completed assessments at intake, 3, 6, and 9 months. Significantly more participants endorsed step-work on the GAATOR than on the AAI for nine of the 12 steps. An exploratory factor analysis revealed a two-factor structure for the GAATOR comprising Behavioral Step-Work and Spiritual Step-Work. Behavioral Step-Work did not change over time, but was predicted by having a sponsor, while Spiritual Step-Work decreased over time and increases were predicted by attending 12-step meetings or treatment. Behavioral Step-Work did not prospectively predict substance use. In contrast, Spiritual Step-Work predicted percent days abstinent, an effect that is consistent with recent work on the mediating effects of spiritual growth, AA, and increased abstinence. Behavioral and Spiritual Step-Work appear to be conceptually distinct components of step-work that have distinct predictors and unique impacts on outcomes. PMID:22867293

Structure and non-structure of centrosomal proteins.

PubMed

Dos Santos, Helena G; Abia, David; Janowski, Robert; Mortuza, Gulnahar; Bertero, Michela G; Boutin, Maïlys; Guarín, Nayibe; Méndez-Giraldez, Raúl; Nuñez, Alfonso; Pedrero, Juan G; Redondo, Pilar; Sanz, María; Speroni, Silvia; Teichert, Florian; Bruix, Marta; Carazo, José M; Gonzalez, Cayetano; Reina, José; Valpuesta, José M; Vernos, Isabelle; Zabala, Juan C; Montoya, Guillermo; Coll, Miquel; Bastolla, Ugo; Serrano, Luis

2013-01-01

Here we perform a large-scale study of the structural properties and the expression of proteins that constitute the human Centrosome. Centrosomal proteins tend to be larger than generic human proteins (control set), since their genes contain in average more exons (20.3 versus 14.6). They are rich in predicted disordered regions, which cover 57% of their length, compared to 39% in the general human proteome. They also contain several regions that are dually predicted to be disordered and coiled-coil at the same time: 55 proteins (15%) contain disordered and coiled-coil fragments that cover more than 20% of their length. Helices prevail over strands in regions homologous to known structures (47% predicted helical residues against 17% predicted as strands), and even more in the whole centrosomal proteome (52% against 7%), while for control human proteins 34.5% of the residues are predicted as helical and 12.8% are predicted as strands. This difference is mainly due to residues predicted as disordered and helical (30% in centrosomal and 9.4% in control proteins), which may correspond to alpha-helix forming molecular recognition features (α-MoRFs). We performed expression assays for 120 full-length centrosomal proteins and 72 domain constructs that we have predicted to be globular. These full-length proteins are often insoluble: Only 39 out of 120 expressed proteins (32%) and 19 out of 72 domains (26%) were soluble. We built or retrieved structural models for 277 out of 361 human proteins whose centrosomal localization has been experimentally verified. We could not find any suitable structural template with more than 20% sequence identity for 84 centrosomal proteins (23%), for which around 74% of the residues are predicted to be disordered or coiled-coils. The three-dimensional models that we built are available at http://ub.cbm.uam.es/centrosome/models/index.php.

QSRR using evolved artificial neural network for 52 common pharmaceuticals and drugs of abuse in hair from UPLC-TOF-MS.

PubMed

Noorizadeh, Hadi; Farmany, Abbas; Narimani, Hojat; Noorizadeh, Mehrab

2013-05-01

A quantitative structure-retention relationship (QSRR) study based on an artificial neural network (ANN) was carried out for the prediction of the ultra-performance liquid chromatography-Time-of-Flight mass spectrometry (UPLC-TOF-MS) retention time (RT) of a set of 52 pharmaceuticals and drugs of abuse in hair. The genetic algorithm was used as a variable selection tool. A partial least squares (PLS) method was used to select the best descriptors which were used as input neurons in neural network model. For choosing the best predictive model from among comparable models, square correlation coefficient R(2) for the whole set calculated based on leave-group-out predicted values of the training set and model-derived predicted values for the test set compounds is suggested to be a good criterion. Finally, to improve the results, structure-retention relationships were followed by a non-linear approach using artificial neural networks and consequently better results were obtained. This also demonstrates the advantages of ANN. Copyright © 2011 John Wiley & Sons, Ltd.

Modeling and prediction of peptide drift times in ion mobility spectrometry using sequence-based and structure-based approaches.

PubMed

Zhang, Yiming; Jin, Quan; Wang, Shuting; Ren, Ren

2011-05-01

The mobile behavior of 1481 peptides in ion mobility spectrometry (IMS), which are generated by protease digestion of the Drosophila melanogaster proteome, is modeled and predicted based on two different types of characterization methods, i.e. sequence-based approach and structure-based approach. In this procedure, the sequence-based approach considers both the amino acid composition of a peptide and the local environment profile of each amino acid in the peptide; the structure-based approach is performed with the CODESSA protocol, which regards a peptide as a common organic compound and generates more than 200 statistically significant variables to characterize the whole structure profile of a peptide molecule. Subsequently, the nonlinear support vector machine (SVM) and Gaussian process (GP) as well as linear partial least squares (PLS) regression is employed to correlate the structural parameters of the characterizations with the IMS drift times of these peptides. The obtained quantitative structure-spectrum relationship (QSSR) models are evaluated rigorously and investigated systematically via both one-deep and two-deep cross-validations as well as the rigorous Monte Carlo cross-validation (MCCV). We also give a comprehensive comparison on the resulting statistics arising from the different combinations of variable types with modeling methods and find that the sequence-based approach can give the QSSR models with better fitting ability and predictive power but worse interpretability than the structure-based approach. In addition, though the QSSR modeling using sequence-based approach is not needed for the preparation of the minimization structures of peptides before the modeling, it would be considerably efficient as compared to that using structure-based approach. Copyright © 2011 Elsevier Ltd. All rights reserved.

Pressure And Thermal Modeling Of Rocket Launches

NASA Technical Reports Server (NTRS)

Smith, Sheldon D.; Myruski, Brian L.; Farmer, Richard C.; Freeman, Jon A.

1995-01-01

Report presents mathematical model for use in designing rocket-launching stand. Predicts pressure and thermal environment, as well as thermal responses of structures to impinging rocket-exhaust plumes. Enables relatively inexperienced analyst to determine time-varying distributions and absolute levels of pressure and heat loads on structures.

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.

Performance of combined fragmentation and retention prediction for the identification of organic micropollutants by LC-HRMS.

PubMed

Hu, Meng; Müller, Erik; Schymanski, Emma L; Ruttkies, Christoph; Schulze, Tobias; Brack, Werner; Krauss, Martin

2018-03-01

In nontarget screening, structure elucidation of small molecules from high resolution mass spectrometry (HRMS) data is challenging, particularly the selection of the most likely candidate structure among the many retrieved from compound databases. Several fragmentation and retention prediction methods have been developed to improve this candidate selection. In order to evaluate their performance, we compared two in silico fragmenters (MetFrag and CFM-ID) and two retention time prediction models (based on the chromatographic hydrophobicity index (CHI) and on log D). A set of 78 known organic micropollutants was analyzed by liquid chromatography coupled to a LTQ Orbitrap HRMS with electrospray ionization (ESI) in positive and negative mode using two fragmentation techniques with different collision energies. Both fragmenters (MetFrag and CFM-ID) performed well for most compounds, with average ranking the correct candidate structure within the top 25% and 22 to 37% for ESI+ and ESI- mode, respectively. The rank of the correct candidate structure slightly improved when MetFrag and CFM-ID were combined. For unknown compounds detected in both ESI+ and ESI-, generally positive mode mass spectra were better for further structure elucidation. Both retention prediction models performed reasonably well for more hydrophobic compounds but not for early eluting hydrophilic substances. The log D prediction showed a better accuracy than the CHI model. Although the two fragmentation prediction methods are more diagnostic and sensitive for candidate selection, the inclusion of retention prediction by calculating a consensus score with optimized weighting can improve the ranking of correct candidates as compared to the individual methods. Graphical abstract Consensus workflow for combining fragmentation and retention prediction in LC-HRMS-based micropollutant identification.

Atomic Oxygen Erosion Yield Predictive Tool for Spacecraft Polymers in Low Earth Orbit

NASA Technical Reports Server (NTRS)

Bank, Bruce A.; de Groh, Kim K.; Backus, Jane A.

2008-01-01

A predictive tool was developed to estimate the low Earth orbit (LEO) atomic oxygen erosion yield of polymers based on the results of the Polymer Erosion and Contamination Experiment (PEACE) Polymers experiment flown as part of the Materials International Space Station Experiment 2 (MISSE 2). The MISSE 2 PEACE experiment accurately measured the erosion yield of a wide variety of polymers and pyrolytic graphite. The 40 different materials tested were selected specifically to represent a variety of polymers used in space as well as a wide variety of polymer chemical structures. The resulting erosion yield data was used to develop a predictive tool which utilizes chemical structure and physical properties of polymers that can be measured in ground laboratory testing to predict the in-space atomic oxygen erosion yield of a polymer. The properties include chemical structure, bonding information, density and ash content. The resulting predictive tool has a correlation coefficient of 0.914 when compared with actual MISSE 2 space data for 38 polymers and pyrolytic graphite. The intent of the predictive tool is to be able to make estimates of atomic oxygen erosion yields for new polymers without requiring expensive and time consumptive in-space testing.

Experimental evaluation of a flat wake theory for predicting rotor inflow-wake velocities

NASA Technical Reports Server (NTRS)

Wilson, John C.

1992-01-01

The theory for predicting helicopter inflow-wake velocities called flat wake theory was correlated with several sets of experimental data. The theory was developed by V. E. Baskin of the USSR, and a computer code known as DOWN was developed at Princeton University to implement the theory. The theory treats the wake geometry as rigid without interaction between induced velocities and wake structure. The wake structure is assumed to be a flat sheet of vorticity composed of trailing elements whose strength depends on the azimuthal and radial distributions of circulation on a rotor blade. The code predicts the three orthogonal components of flow velocity in the field surrounding the rotor. The predictions can be utilized in rotor performance and helicopter real-time flight-path simulation. The predictive capability of the coded version of flat wake theory provides vertical inflow patterns similar to experimental patterns.

Multiple Simulated Annealing-Molecular Dynamics (MSA-MD) for Conformational Space Search of Peptide and Miniprotein

PubMed Central

Hao, Ge-Fei; Xu, Wei-Fang; Yang, Sheng-Gang; Yang, Guang-Fu

2015-01-01

Protein and peptide structure predictions are of paramount importance for understanding their functions, as well as the interactions with other molecules. However, the use of molecular simulation techniques to directly predict the peptide structure from the primary amino acid sequence is always hindered by the rough topology of the conformational space and the limited simulation time scale. We developed here a new strategy, named Multiple Simulated Annealing-Molecular Dynamics (MSA-MD) to identify the native states of a peptide and miniprotein. A cluster of near native structures could be obtained by using the MSA-MD method, which turned out to be significantly more efficient in reaching the native structure compared to continuous MD and conventional SA-MD simulation. PMID:26492886

Protein secondary structure and stability determined by combining exoproteolysis and matrix-assisted laser desorption/ionization time-of-flight mass spectrometry.

PubMed

Villanueva, Josep; Villegas, Virtudes; Querol, Enrique; Avilés, Francesc X; Serrano, Luis

2002-09-01

In the post-genomic era, several projects focused on the massive experimental resolution of the three-dimensional structures of all the proteins of different organisms have been initiated. Simultaneously, significant progress has been made in the ab initio prediction of protein three-dimensional structure. One of the keys to the success of such a prediction is the use of local information (i.e. secondary structure). Here we describe a new limited proteolysis methodology, based on the use of unspecific exoproteases coupled with matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF MS), to map quickly secondary structure elements of a protein from both ends, the N- and C-termini. We show that the proteolytic patterns (mass spectra series) obtained can be interpreted in the light of the conformation and local stability of the analyzed proteins, a direct correlation being observed between the predicted and the experimentally derived protein secondary structure. Further, this methodology can be easily applied to check rapidly the folding state of a protein and characterize mutational effects on protein conformation and stability. Moreover, given global stability information, this methodology allows one to locate the protein regions of increased or decreased conformational stability. All of this can be done with a small fraction of the amount of protein required by most of the other methods for conformational analysis. Thus limited exoproteolysis, together with MALDI-TOF MS, can be a useful tool to achieve quickly the elucidation of protein structure and stability. Copyright 2002 John Wiley & Sons, Ltd.

Predict or classify: The deceptive role of time-locking in brain signal classification

NASA Astrophysics Data System (ADS)

Rusconi, Marco; Valleriani, Angelo

2016-06-01

Several experimental studies claim to be able to predict the outcome of simple decisions from brain signals measured before subjects are aware of their decision. Often, these studies use multivariate pattern recognition methods with the underlying assumption that the ability to classify the brain signal is equivalent to predict the decision itself. Here we show instead that it is possible to correctly classify a signal even if it does not contain any predictive information about the decision. We first define a simple stochastic model that mimics the random decision process between two equivalent alternatives, and generate a large number of independent trials that contain no choice-predictive information. The trials are first time-locked to the time point of the final event and then classified using standard machine-learning techniques. The resulting classification accuracy is above chance level long before the time point of time-locking. We then analyze the same trials using information theory. We demonstrate that the high classification accuracy is a consequence of time-locking and that its time behavior is simply related to the large relaxation time of the process. We conclude that when time-locking is a crucial step in the analysis of neural activity patterns, both the emergence and the timing of the classification accuracy are affected by structural properties of the network that generates the signal.

Evaluating Predictive Uncertainty of Hyporheic Exchange Modelling

NASA Astrophysics Data System (ADS)

Chow, R.; Bennett, J.; Dugge, J.; Wöhling, T.; Nowak, W.

2017-12-01

Hyporheic exchange is the interaction of water between rivers and groundwater, and is difficult to predict. One of the largest contributions to predictive uncertainty for hyporheic fluxes have been attributed to the representation of heterogeneous subsurface properties. This research aims to evaluate which aspect of the subsurface representation - the spatial distribution of hydrofacies or the model for local-scale (within-facies) heterogeneity - most influences the predictive uncertainty. Also, we seek to identify data types that help reduce this uncertainty best. For this investigation, we conduct a modelling study of the Steinlach River meander, in Southwest Germany. The Steinlach River meander is an experimental site established in 2010 to monitor hyporheic exchange at the meander scale. We use HydroGeoSphere, a fully integrated surface water-groundwater model, to model hyporheic exchange and to assess the predictive uncertainty of hyporheic exchange transit times (HETT). A highly parameterized complex model is built and treated as `virtual reality', which is in turn modelled with simpler subsurface parameterization schemes (Figure). Then, we conduct Monte-Carlo simulations with these models to estimate the predictive uncertainty. Results indicate that: Uncertainty in HETT is relatively small for early times and increases with transit times. Uncertainty from local-scale heterogeneity is negligible compared to uncertainty in the hydrofacies distribution. Introducing more data to a poor model structure may reduce predictive variance, but does not reduce predictive bias. Hydraulic head observations alone cannot constrain the uncertainty of HETT, however an estimate of hyporheic exchange flux proves to be more effective at reducing this uncertainty. Figure: Approach for evaluating predictive model uncertainty. A conceptual model is first developed from the field investigations. A complex model (`virtual reality') is then developed based on that conceptual model. This complex model then serves as the basis to compare simpler model structures. Through this approach, predictive uncertainty can be quantified relative to a known reference solution.

Motional timescale predictions by molecular dynamics simulations: Case study using proline and hydroxyproline sidechain dynamics

PubMed Central

Aliev, Abil E; Kulke, Martin; Khaneja, Harmeet S; Chudasama, Vijay; Sheppard, Tom D; Lanigan, Rachel M

2014-01-01

We propose a new approach for force field optimizations which aims at reproducing dynamics characteristics using biomolecular MD simulations, in addition to improved prediction of motionally averaged structural properties available from experiment. As the source of experimental data for dynamics fittings, we use 13C NMR spin-lattice relaxation times T1 of backbone and sidechain carbons, which allow to determine correlation times of both overall molecular and intramolecular motions. For structural fittings, we use motionally averaged experimental values of NMR J couplings. The proline residue and its derivative 4-hydroxyproline with relatively simple cyclic structure and sidechain dynamics were chosen for the assessment of the new approach in this work. Initially, grid search and simplexed MD simulations identified large number of parameter sets which fit equally well experimental J couplings. Using the Arrhenius-type relationship between the force constant and the correlation time, the available MD data for a series of parameter sets were analyzed to predict the value of the force constant that best reproduces experimental timescale of the sidechain dynamics. Verification of the new force-field (termed as AMBER99SB-ILDNP) against NMR J couplings and correlation times showed consistent and significant improvements compared to the original force field in reproducing both structural and dynamics properties. The results suggest that matching experimental timescales of motions together with motionally averaged characteristics is the valid approach for force field parameter optimization. Such a comprehensive approach is not restricted to cyclic residues and can be extended to other amino acid residues, as well as to the backbone. Proteins 2014; 82:195–215. © 2013 Wiley Periodicals, Inc. PMID:23818175

Concurrent and longitudinal associations of peers' acceptance with emotion and effortful control in kindergarten

PubMed Central

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

2015-01-01

The purpose of the study was to evaluate bidirectional associations between peer acceptance and both emotion and effortful control during kindergarten (N = 301). In both the fall and spring semesters, we obtained peer nominations of acceptance, measures of positive and negative emotion based on naturalistic observations in school (i.e., classroom, lunch/recess), and observers’ reports of effortful control (i.e., inhibitory control, attention focusing) and emotions (i.e., positive, negative). In structural equation panel models, peer acceptance in fall predicted higher effortful control in spring. Effortful control in fall did not predict peer acceptance in spring. Negative emotion predicted lower peer acceptance across time for girls but not for boys. Peer acceptance did not predict negative or positive emotion over time. In addition, we tested interactions between positive or negative emotion and effortful control predicting peer acceptance. Positive emotion predicted higher peer acceptance for children low in effortful control. PMID:28348445

Reducing process delays for real-time earthquake parameter estimation - An application of KD tree to large databases for Earthquake Early Warning

NASA Astrophysics Data System (ADS)

Yin, Lucy; Andrews, Jennifer; Heaton, Thomas

2018-05-01

Earthquake parameter estimations using nearest neighbor searching among a large database of observations can lead to reliable prediction results. However, in the real-time application of Earthquake Early Warning (EEW) systems, the accurate prediction using a large database is penalized by a significant delay in the processing time. We propose to use a multidimensional binary search tree (KD tree) data structure to organize large seismic databases to reduce the processing time in nearest neighbor search for predictions. We evaluated the performance of KD tree on the Gutenberg Algorithm, a database-searching algorithm for EEW. We constructed an offline test to predict peak ground motions using a database with feature sets of waveform filter-bank characteristics, and compare the results with the observed seismic parameters. We concluded that large database provides more accurate predictions of the ground motion information, such as peak ground acceleration, velocity, and displacement (PGA, PGV, PGD), than source parameters, such as hypocenter distance. Application of the KD tree search to organize the database reduced the average searching process by 85% time cost of the exhaustive method, allowing the method to be feasible for real-time implementation. The algorithm is straightforward and the results will reduce the overall time of warning delivery for EEW.

Integrated Modeling Activities for the James Webb Space Telescope (JWST): Structural-Thermal-Optical Analysis

NASA Technical Reports Server (NTRS)

Johnston, John D.; Parrish, Keith; Howard, Joseph M.; Mosier, Gary E.; McGinnis, Mark; Bluth, Marcel; Kim, Kevin; Ha, Hong Q.

2004-01-01

This is a continuation of a series of papers on modeling activities for JWST. The structural-thermal- optical, often referred to as "STOP", analysis process is used to predict the effect of thermal distortion on optical performance. The benchmark STOP analysis for JWST assesses the effect of an observatory slew on wavefront error. The paper begins an overview of multi-disciplinary engineering analysis, or integrated modeling, which is a critical element of the JWST mission. The STOP analysis process is then described. This process consists of the following steps: thermal analysis, structural analysis, and optical analysis. Temperatures predicted using geometric and thermal math models are mapped to the structural finite element model in order to predict thermally-induced deformations. Motions and deformations at optical surfaces are input to optical models and optical performance is predicted using either an optical ray trace or WFE estimation techniques based on prior ray traces or first order optics. Following the discussion of the analysis process, results based on models representing the design at the time of the System Requirements Review. In addition to baseline performance predictions, sensitivity studies are performed to assess modeling uncertainties. Of particular interest is the sensitivity of optical performance to uncertainties in temperature predictions and variations in metal properties. The paper concludes with a discussion of modeling uncertainty as it pertains to STOP analysis.

Combined electrochemical, heat generation, and thermal model for large prismatic lithium-ion batteries in real-time applications

NASA Astrophysics Data System (ADS)

Farag, Mohammed; Sweity, Haitham; Fleckenstein, Matthias; Habibi, Saeid

2017-08-01

Real-time prediction of the battery's core temperature and terminal voltage is very crucial for an accurate battery management system. In this paper, a combined electrochemical, heat generation, and thermal model is developed for large prismatic cells. The proposed model consists of three sub-models, an electrochemical model, heat generation model, and thermal model which are coupled together in an iterative fashion through physicochemical temperature dependent parameters. The proposed parameterization cycles identify the sub-models' parameters separately by exciting the battery under isothermal and non-isothermal operating conditions. The proposed combined model structure shows accurate terminal voltage and core temperature prediction at various operating conditions while maintaining a simple mathematical structure, making it ideal for real-time BMS applications. Finally, the model is validated against both isothermal and non-isothermal drive cycles, covering a broad range of C-rates, and temperature ranges [-25 °C to 45 °C].

Modeling the time--varying subjective quality of HTTP video streams with rate adaptations.

PubMed

Chen, Chao; Choi, Lark Kwon; de Veciana, Gustavo; Caramanis, Constantine; Heath, Robert W; Bovik, Alan C

2014-05-01

Newly developed hypertext transfer protocol (HTTP)-based video streaming technologies enable flexible rate-adaptation under varying channel conditions. Accurately predicting the users' quality of experience (QoE) for rate-adaptive HTTP video streams is thus critical to achieve efficiency. An important aspect of understanding and modeling QoE is predicting the up-to-the-moment subjective quality of a video as it is played, which is difficult due to hysteresis effects and nonlinearities in human behavioral responses. This paper presents a Hammerstein-Wiener model for predicting the time-varying subjective quality (TVSQ) of rate-adaptive videos. To collect data for model parameterization and validation, a database of longer duration videos with time-varying distortions was built and the TVSQs of the videos were measured in a large-scale subjective study. The proposed method is able to reliably predict the TVSQ of rate adaptive videos. Since the Hammerstein-Wiener model has a very simple structure, the proposed method is suitable for online TVSQ prediction in HTTP-based streaming.

Plant traits determine forest flammability

NASA Astrophysics Data System (ADS)

Zylstra, Philip; Bradstock, Ross

2016-04-01

Carbon and nutrient cycles in forest ecosystems are influenced by their inherent flammability - a property determined by the traits of the component plant species that form the fuel and influence the micro climate of a fire. In the absence of a model capable of explaining the complexity of such a system however, flammability is frequently represented by simple metrics such as surface fuel load. The implications of modelling fire - flammability feedbacks using surface fuel load were examined and compared to a biophysical, mechanistic model (Forest Flammability Model) that incorporates the influence of structural plant traits (e.g. crown shape and spacing) and leaf traits (e.g. thickness, dimensions and moisture). Fuels burn with values of combustibility modelled from leaf traits, transferring convective heat along vectors defined by flame angle and with plume temperatures that decrease with distance from the flame. Flames are re-calculated in one-second time-steps, with new leaves within the plant, neighbouring plants or higher strata ignited when the modelled time to ignition is reached, and other leaves extinguishing when their modelled flame duration is exceeded. The relative influence of surface fuels, vegetation structure and plant leaf traits were examined by comparing flame heights modelled using three treatments that successively added these components within the FFM. Validation was performed across a diverse range of eucalypt forests burnt under widely varying conditions during a forest fire in the Brindabella Ranges west of Canberra (ACT) in 2003. Flame heights ranged from 10 cm to more than 20 m, with an average of 4 m. When modelled from surface fuels alone, flame heights were on average 1.5m smaller than observed values, and were predicted within the error range 28% of the time. The addition of plant structure produced predicted flame heights that were on average 1.5m larger than observed, but were correct 53% of the time. The over-prediction in this case was the result of a small number of large errors, where higher strata such as forest canopy were modelled to ignite but did not. The addition of leaf traits largely addressed this error, so that the mean flame height over-prediction was reduced to 0.3m and the fully parameterised FFM gave correct predictions 62% of the time. When small (<1m) flames were excluded, the fully parameterised model correctly predicted flame heights 12 times more often than could be predicted using surface fuels alone, and the Mean Absolute Error was 4 times smaller. The inadequate consideration of plant traits within a mechanistic framework introduces significant error to forest fire behaviour modelling. The FFM provides a solution to this, and an avenue by which plant trait information can be used to better inform Global Vegetation Models and decision-making tools used to mitigate the impacts of fire.

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.

Predicting vibratory stresses from aero-acoustic loads

NASA Astrophysics Data System (ADS)

Shaw, Matthew D.

Sonic fatigue has been a concern of jet aircraft engineers for many years. As engines become more powerful, structures become more lightly damped and complex, and materials become lighter, stiffer, and more complicated, the need to understand and predict structural response to aeroacoustic loads becomes more important. Despite decades of research, vibration in panels caused by random pressure loads, such as those found in a supersonic jet, is still difficult to predict. The work in this research improves on current prediction methods in several ways, in particular for the structural response due to wall pressures induced by supersonic turbulent flows. First, solutions are calculated using time-domain input pressure loads that include shock cells and their interaction with turbulent flow. The solutions include both mean (static) and oscillatory components. Second, the time series of stresses are required for many fatigue assessment counting algorithms. To do this, a method is developed to compute time-dependent solutions in the frequency domain. The method is first applied to a single-degree-of-freedom system. The equations of motion are derived and solved in both the frequency domain and the time domain. The pressure input is a random (broadband) signal representative of jet flow. The method is then applied to a simply-supported beam vibrating in flexure using a line of pressure inputs computed with computational fluid dynamics (CFD). A modal summation approach is used to compute structural response. The coupling between the pressure field and the structure, through the joint acceptance, is reviewed and discussed for its application to more complicated structures. Results from the new method and from a direct time domain method are compared for method verification. Because the match is good and the new frequency domain method is faster computationally, it is chosen for use in a more complicated structure. The vibration of a two-dimensional panel loaded by jet nozzle discharge flow is addressed. The surface pressures calculated at Pratt and Whitney using viscous and compressible CFD are analyzed and compared to surface pressure measurements made at the United Technologies Research Center (UTRC). A structural finite element model is constructed to represent a flexible panel also used in the UTRC setup. The mode shapes, resonance frequencies, modal loss factors, and surface pressures are input into the solution method. Displacement time series and power spectral densities are computed and compared to measurement and show good agreement. The concept of joint acceptance is further addressed for two-dimensional plates excited by supersonic jet flow. Static and alternating stresses in the panel are also computed, and the most highly stressed modes are identified. The surface pressures are further analyzed in the wavenumber domain for insight into the physics of sonic fatigue. Most of the energy in the wall pressure wavenumber-frequency spectrum at subsonic speeds is in turbulent structures near the convective wavenumber. In supersonic flow, however, the shock region dominates the spectrum at low frequencies, but convective behavior is still dominant at higher frequencies. When the forcing function wavenumber energy overlaps the modal wavenumbers, the acceptance of energy by the structure from the flow field is greatest. The wavenumber analysis suggests a means of designing structures to minimize overlap of excitation and structural wavenumber peaks to minimize vibration and sonic fatigue.

Real-time flutter analysis

NASA Technical Reports Server (NTRS)

Walker, R.; Gupta, N.

1984-01-01

The important algorithm issues necessary to achieve a real time flutter monitoring system; namely, the guidelines for choosing appropriate model forms, reduction of the parameter convergence transient, handling multiple modes, the effect of over parameterization, and estimate accuracy predictions, both online and for experiment design are addressed. An approach for efficiently computing continuous-time flutter parameter Cramer-Rao estimate error bounds were developed. This enables a convincing comparison of theoretical and simulation results, as well as offline studies in preparation for a flight test. Theoretical predictions, simulation and flight test results from the NASA Drones for Aerodynamic and Structural Test (DAST) Program are compared.

A Time Dependent Model of HD209458b

NASA Astrophysics Data System (ADS)

Iro, N.; Bézard, B.; Guillot, T.

2004-12-01

We developed a time-dependent radiative model for the atmosphere of HD209458b to investigate its thermal structure and chemical composition. Time-dependent temperature profiles were calculated, using a uniform zonal wind modelled as a solid body rotation. We predict day/night temperature variations of 600K around 0.1 bar, for a 1 km/s wind velocity, in good agreement with the predictions by Showman & Guillot (2002). On the night side, the low temperature allows the sodium to condense. Depletion of sodium in the morning limb may explain the lower than expected abundance found by Charbonneau et al. (2002).

Structural constraints on the three-dimensional geometry of simple viruses: case studies of a new predictive tool

PubMed Central

Keef, Thomas; Wardman, Jessica P.; Ranson, Neil A.; Stockley, Peter G.; Twarock, Reidun

2013-01-01

Understanding the fundamental principles of virus architecture is one of the most important challenges in biology and medicine. Crick and Watson were the first to propose that viruses exhibit symmetry in the organization of their protein containers for reasons of genetic economy. Based on this, Caspar and Klug introduced quasi-equivalence theory to predict the relative locations of the coat proteins within these containers and classified virus structure in terms of T-numbers. Here it is shown that quasi-equivalence is part of a wider set of structural constraints on virus structure. These constraints can be formulated using an extension of the underlying symmetry group and this is demonstrated with a number of case studies. This new concept in virus biology provides for the first time predictive information on the structural constraints on coat protein and genome topography, and reveals a previously unrecognized structural interdependence of the shapes and sizes of different viral components. It opens up the possibility of distinguishing the structures of different viruses with the same T-number, suggesting a refined viral structure classification scheme. It can moreover be used as a basis for models of virus function, e.g. to characterize the start and end configurations of a structural transition important for infection. PMID:23403965

Structural constraints on the three-dimensional geometry of simple viruses: case studies of a new predictive tool.

PubMed

Keef, Thomas; Wardman, Jessica P; Ranson, Neil A; Stockley, Peter G; Twarock, Reidun

2013-03-01

Understanding the fundamental principles of virus architecture is one of the most important challenges in biology and medicine. Crick and Watson were the first to propose that viruses exhibit symmetry in the organization of their protein containers for reasons of genetic economy. Based on this, Caspar and Klug introduced quasi-equivalence theory to predict the relative locations of the coat proteins within these containers and classified virus structure in terms of T-numbers. Here it is shown that quasi-equivalence is part of a wider set of structural constraints on virus structure. These constraints can be formulated using an extension of the underlying symmetry group and this is demonstrated with a number of case studies. This new concept in virus biology provides for the first time predictive information on the structural constraints on coat protein and genome topography, and reveals a previously unrecognized structural interdependence of the shapes and sizes of different viral components. It opens up the possibility of distinguishing the structures of different viruses with the same T-number, suggesting a refined viral structure classification scheme. It can moreover be used as a basis for models of virus function, e.g. to characterize the start and end configurations of a structural transition important for infection.

Predicting the process of extinction in experimental microcosms and accounting for interspecific interactions in single-species time series

PubMed Central

Ferguson, Jake M; Ponciano, José M

2014-01-01

Predicting population extinction risk is a fundamental application of ecological theory to the practice of conservation biology. Here, we compared the prediction performance of a wide array of stochastic, population dynamics models against direct observations of the extinction process from an extensive experimental data set. By varying a series of biological and statistical assumptions in the proposed models, we were able to identify the assumptions that affected predictions about population extinction. We also show how certain autocorrelation structures can emerge due to interspecific interactions, and that accounting for the stochastic effect of these interactions can improve predictions of the extinction process. We conclude that it is possible to account for the stochastic effects of community interactions on extinction when using single-species time series. PMID:24304946

The accuracy of matrix population model projections for coniferous trees in the Sierra Nevada, California

USGS Publications Warehouse

van Mantgem, P.J.; Stephenson, N.L.

2005-01-01

1 We assess the use of simple, size-based matrix population models for projecting population trends for six coniferous tree species in the Sierra Nevada, California. We used demographic data from 16 673 trees in 15 permanent plots to create 17 separate time-invariant, density-independent population projection models, and determined differences between trends projected from initial surveys with a 5-year interval and observed data during two subsequent 5-year time steps. 2 We detected departures from the assumptions of the matrix modelling approach in terms of strong growth autocorrelations. We also found evidence of observation errors for measurements of tree growth and, to a more limited degree, recruitment. Loglinear analysis provided evidence of significant temporal variation in demographic rates for only two of the 17 populations. 3 Total population sizes were strongly predicted by model projections, although population dynamics were dominated by carryover from the previous 5-year time step (i.e. there were few cases of recruitment or death). Fractional changes to overall population sizes were less well predicted. Compared with a null model and a simple demographic model lacking size structure, matrix model projections were better able to predict total population sizes, although the differences were not statistically significant. Matrix model projections were also able to predict short-term rates of survival, growth and recruitment. Mortality frequencies were not well predicted. 4 Our results suggest that simple size-structured models can accurately project future short-term changes for some tree populations. However, not all populations were well predicted and these simple models would probably become more inaccurate over longer projection intervals. The predictive ability of these models would also be limited by disturbance or other events that destabilize demographic rates. ?? 2005 British Ecological Society.

The relationship between manuscript title structure and success: editorial decisions and citation performance for an ecological journal

PubMed Central

Fox, Charles W; Burns, C Sean

2015-01-01

A poorly chosen article title may make a paper difficult to discover or discourage readership when discovered, reducing an article's impact. Yet, it is unclear how the structure of a manuscript's title influences readership and impact. We used manuscript tracking data for all manuscripts submitted to the journal Functional Ecology from 2004 to 2013 and citation data for papers published in this journal from 1987 to 2011 to examine how title features changed and whether a manuscript's title structure was predictive of success during the manuscript review process and/or impact (citation) after publication. Titles of manuscripts submitted to Functional Ecology became marginally longer (after controlling for other variables), broader in focus (less frequent inclusion of genus and species names), and included more humor and subtitles over the period of the study. Papers with subtitles were less likely to be rejected by editors both pre- and post-peer review, although both effects were small and the presence of subtitles in published papers was not predictive of citations. Papers with specific names of study organisms in their titles fared poorly during editorial (but not peer) review and, if published, were less well cited than papers whose titles did not include specific names. Papers with intermediate length titles were more successful during editorial review, although the effect was small and title word count was not predictive of citations. No features of titles were predictive of reviewer willingness to review papers or the length of time a paper was in peer review. We conclude that titles have changed in structure over time, but features of title structure have only small or no relationship with success during editorial review and post-publication impact. The title feature that was most predictive of manuscript success: papers whose titles emphasize broader conceptual or comparative issues fare better both pre- and post-publication than do papers with organism-specific titles. PMID:26045949

The relationship between manuscript title structure and success: editorial decisions and citation performance for an ecological journal.

PubMed

Fox, Charles W; Burns, C Sean

2015-05-01

A poorly chosen article title may make a paper difficult to discover or discourage readership when discovered, reducing an article's impact. Yet, it is unclear how the structure of a manuscript's title influences readership and impact. We used manuscript tracking data for all manuscripts submitted to the journal Functional Ecology from 2004 to 2013 and citation data for papers published in this journal from 1987 to 2011 to examine how title features changed and whether a manuscript's title structure was predictive of success during the manuscript review process and/or impact (citation) after publication. Titles of manuscripts submitted to Functional Ecology became marginally longer (after controlling for other variables), broader in focus (less frequent inclusion of genus and species names), and included more humor and subtitles over the period of the study. Papers with subtitles were less likely to be rejected by editors both pre- and post-peer review, although both effects were small and the presence of subtitles in published papers was not predictive of citations. Papers with specific names of study organisms in their titles fared poorly during editorial (but not peer) review and, if published, were less well cited than papers whose titles did not include specific names. Papers with intermediate length titles were more successful during editorial review, although the effect was small and title word count was not predictive of citations. No features of titles were predictive of reviewer willingness to review papers or the length of time a paper was in peer review. We conclude that titles have changed in structure over time, but features of title structure have only small or no relationship with success during editorial review and post-publication impact. The title feature that was most predictive of manuscript success: papers whose titles emphasize broader conceptual or comparative issues fare better both pre- and post-publication than do papers with organism-specific titles.

Detection of free nickel monocarbonyl, NiCO: rotational spectrum and structure.

PubMed

Yamazaki, Emi; Okabayashi, Toshiaki; Tanimoto, Mitsutoshi

2004-02-04

Unsaturated transition metal carbonyls are important in processes such as organometallic synthesis, homogeneous catalysis, and photochemical decomposition of organometallics. In particular, a metal monocarbonyl offers a zeroth-order model for interpreting the chemisorption of a CO molecule on a metal surface in catalytic activation processes. Quite large numbers of theoretical papers have appeared which predict spectroscopic and structural properties of transition metal carbonyls. The nickel monocarbonyl NiCO has been one of the metal carbonyls most extensively studied by the theoretical calculations. At least 50 theoretical studies have been published on this simplest transition metal carbonyl up to the present time. However, experimental evidence of NiCO is much more sparse than theoretical predictions, and the actual structure of NiCO has never been determined by any experimental methods. This Communication reports the first preparation of free nickel monocarbonyl and observation of its rotational transitions. The NiCO molecule was generated by the sputtering reaction of a Ni cathode in the presence of CO. The accurate bond lengths of Ni-C and C-O were experimentally determined from isotopic data and were compared with the theoretical predictions for the first time.

Rate-based structural health monitoring using permanently installed sensors

PubMed Central

2017-01-01

Permanently installed sensors are becoming increasingly ubiquitous, facilitating very frequent in situ measurements and consequently improved monitoring of ‘trends’ in the observed system behaviour. It is proposed that this newly available data may be used to provide prior warning and forecasting of critical events, particularly system failure. Numerous damage mechanisms are examples of positive feedback; they are ‘self-accelerating’ with an increasing rate of damage towards failure. The positive feedback leads to a common time-response behaviour which may be described by an empirical relation allowing prediction of the time to criticality. This study focuses on Structural Health Monitoring of engineering components; failure times are projected well in advance of failure for fatigue, creep crack growth and volumetric creep damage experiments. The proposed methodology provides a widely applicable framework for using newly available near-continuous data from permanently installed sensors to predict time until failure in a range of application areas including engineering, geophysics and medicine. PMID:28989308

Assessment of CAPRI predictions in rounds 3-5 shows progress in docking procedures.

PubMed

Méndez, Raúl; Leplae, Raphaël; Lensink, Marc F; Wodak, Shoshana J

2005-08-01

The current status of docking procedures for predicting protein-protein interactions starting from their three-dimensional (3D) structure is reassessed by evaluating blind predictions, performed during 2003-2004 as part of Rounds 3-5 of the community-wide experiment on Critical Assessment of PRedicted Interactions (CAPRI). Ten newly determined structures of protein-protein complexes were used as targets for these rounds. They comprised 2 enzyme-inhibitor complexes, 2 antigen-antibody complexes, 2 complexes involved in cellular signaling, 2 homo-oligomers, and a complex between 2 components of the bacterial cellulosome. For most targets, the predictors were given the experimental structures of 1 unbound and 1 bound component, with the latter in a random orientation. For some, the structure of the free component was derived from that of a related protein, requiring the use of homology modeling. In some of the targets, significant differences in conformation were displayed between the bound and unbound components, representing a major challenge for the docking procedures. For 1 target, predictions could not go to completion. In total, 1866 predictions submitted by 30 groups were evaluated. Over one-third of these groups applied completely novel docking algorithms and scoring functions, with several of them specifically addressing the challenge of dealing with side-chain and backbone flexibility. The quality of the predicted interactions was evaluated by comparison to the experimental structures of the targets, made available for the evaluation, using the well-agreed-upon criteria used previously. Twenty-four groups, which for the first time included an automatic Web server, produced predictions ranking from acceptable to highly accurate for all targets, including those where the structures of the bound and unbound forms differed substantially. These results and a brief survey of the methods used by participants of CAPRI Rounds 3-5 suggest that genuine progress in the performance of docking methods is being achieved, with CAPRI acting as the catalyst.

Cognitive Ability and Personality Variables as Predictors of School Grades and Test Scores in Adolescents

ERIC Educational Resources Information Center

Hofer, Manfred; Kuhnle, Claudia; Kilian, Britta; Fries, Stefan

2012-01-01

The predictive power of cognitive ability and self-control strength for self-reported grades and an achievement test were studied. It was expected that the variables use of time structure, academic procrastination, and motivational interference during learning further aid in predicting students' achievement because they are operative in situations…

Predicting Graduation Rates at 4-Year Broad Access Institutions Using a Bayesian Modeling Approach

ERIC Educational Resources Information Center

Crisp, Gloria; Doran, Erin; Salis Reyes, Nicole A.

2018-01-01

This study models graduation rates at 4-year broad access institutions (BAIs). We examine the student body, structural-demographic, and financial characteristics that best predict 6-year graduation rates across two time periods (2008-2009 and 2014-2015). A Bayesian model averaging approach is utilized to account for uncertainty in variable…

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.

TIMES-SS--recent refinements resulting from an industrial skin sensitisation consortium.

PubMed

Patlewicz, G; Kuseva, C; Mehmed, A; Popova, Y; Dimitrova, G; Ellis, G; Hunziker, R; Kern, P; Low, L; Ringeissen, S; Roberts, D W; Mekenyan, O

2014-01-01

The TImes MEtabolism Simulator platform for predicting Skin Sensitisation (TIMES-SS) is a hybrid expert system, first developed at Bourgas University using funding and data from a consortium of industry and regulators. TIMES-SS encodes structure-toxicity and structure-skin metabolism relationships through a number of transformations, some of which are underpinned by mechanistic 3D QSARs. The model estimates semi-quantitative skin sensitisation potency classes and has been developed with the aim of minimising animal testing, and also to be scientifically valid in accordance with the OECD principles for (Q)SAR validation. In 2007 an external validation exercise was undertaken to fully address these principles. In 2010, a new industry consortium was established to coordinate research efforts in three specific areas: refinement of abiotic reactions in the skin (namely autoxidation) in the skin, refinement of the manner in which chemical reactivity was captured in terms of structure-toxicity rules (inclusion of alert reliability parameters) and defining the domain based on the underlying experimental data (study of discrepancies between local lymph node assay Local Lymph Node Assay (LLNA) and Guinea Pig Maximisation Test (GPMT)). The present paper summarises the progress of these activities and explains how the insights derived have been translated into refinements, resulting in increased confidence and transparency in the robustness of the TIMES-SS predictions.

A coupled aero-structural model of a HAWT blade for dynamic load and response prediction in time-domain for health monitoring applications

NASA Astrophysics Data System (ADS)

Sauder, Heather Scot

To reach the high standards set for renewable energy production in the US and around the globe, wind turbines with taller towers and longer blades are being designed for onshore and offshore wind developments to capture more energy from higher winds aloft and a larger rotor diameter. However, amongst all the wind turbine components wind turbine blades are still the most prone to damage. Given that wind turbine blades experience dynamic loads from multiple sources, there is a need to be able to predict the real-time load, stress distribution and response of the blade in a given wind environment for damage, flutter and fatigue life predictions. Current methods of wind-induced response analysis for wind turbine blades use approximations that are not suitable for wind turbine blade airfoils which are thick, and therefore lead to inaccurate life predictions. Additionally, a time-domain formulation can prove to be especially advantageous for predicting aerodynamic loads on wind turbine blades since they operate in a turbulent atmospheric boundary layer. This will help to analyze the blades on wind turbines that operate individually or in a farm setting where they experience high turbulence in the wake of another wind turbine. A time-domain formulation is also useful for examining the effects of gusty winds that are transient in nature like in gust fronts, thunderstorms or extreme events such as hurricanes, microbursts, and tornadoes. Time-domain methods present the opportunity for real-time health monitoring strategies that can easily be used with finite element methods for prediction of fatigue life or onset of flutter instability. The purpose of the proposed work is to develop a robust computational model to predict the loads, stresses and response of a wind turbine blade in operating and extreme wind conditions. The model can be used to inform health monitoring strategies for preventative maintenance and provide a realistic number of stress cycles that the blade will experience for fatigue life prediction procedures. To fill in the gaps in the existing knowledge and meet the overall goal of the proposed research, the following objectives were accomplished: (a) improve the existing aeroelastic (motion- and turbulence-induced) load models to predict the response of wind turbine blade airfoils to understand its behavior in turbulent wind, (b) understand, model and predict the response of wind turbine blades in transient or gusty wind, boundary-layer wind and incoherent wind over the span of the blade, (c) understand the effects of aero-structural coupling between the along-wind, cross-wind and torsional vibrations, and finally (d) develop a computational tool using the improved time-domain load model to predict the real-time load, stress distribution and response of a given wind turbine blade during operating and parked conditions subject to a specific wind environment both in a short and long term for damage, flutter and fatigue life predictions.

Small-angle X-Ray analysis of macromolecular structure: the structure of protein NS2 (NEP) in solution

NASA Astrophysics Data System (ADS)

Shtykova, E. V.; Bogacheva, E. N.; Dadinova, L. A.; Jeffries, C. M.; Fedorova, N. V.; Golovko, A. O.; Baratova, L. A.; Batishchev, O. V.

2017-11-01

A complex structural analysis of nuclear export protein NS2 (NEP) of influenza virus A has been performed using bioinformatics predictive methods and small-angle X-ray scattering data. The behavior of NEP molecules in a solution (their aggregation, oligomerization, and dissociation, depending on the buffer composition) has been investigated. It was shown that stable associates are formed even in a conventional aqueous salt solution at physiological pH value. For the first time we have managed to get NEP dimers in solution, to analyze their structure, and to compare the models obtained using the method of the molecular tectonics with the spatial protein structure predicted by us using the bioinformatics methods. The results of the study provide a new insight into the structural features of nuclear export protein NS2 (NEP) of the influenza virus A, which is very important for viral infection development.

Automatic measurement of voice onset time using discriminative structured prediction.

PubMed

Sonderegger, Morgan; Keshet, Joseph

2012-12-01

A discriminative large-margin algorithm for automatic measurement of voice onset time (VOT) is described, considered as a case of predicting structured output from speech. Manually labeled data are used to train a function that takes as input a speech segment of an arbitrary length containing a voiceless stop, and outputs its VOT. The function is explicitly trained to minimize the difference between predicted and manually measured VOT; it operates on a set of acoustic feature functions designed based on spectral and temporal cues used by human VOT annotators. The algorithm is applied to initial voiceless stops from four corpora, representing different types of speech. Using several evaluation methods, the algorithm's performance is near human intertranscriber reliability, and compares favorably with previous work. Furthermore, the algorithm's performance is minimally affected by training and testing on different corpora, and remains essentially constant as the amount of training data is reduced to 50-250 manually labeled examples, demonstrating the method's practical applicability to new datasets.

A Method for Predicting Protein Complexes from Dynamic Weighted Protein-Protein Interaction Networks.

PubMed

Liu, Lizhen; Sun, Xiaowu; Song, Wei; Du, Chao

2018-06-01

Predicting protein complexes from protein-protein interaction (PPI) network is of great significance to recognize the structure and function of cells. A protein may interact with different proteins under different time or conditions. Existing approaches only utilize static PPI network data that may lose much temporal biological information. First, this article proposed a novel method that combines gene expression data at different time points with traditional static PPI network to construct different dynamic subnetworks. Second, to further filter out the data noise, the semantic similarity based on gene ontology is regarded as the network weight together with the principal component analysis, which is introduced to deal with the weight computing by three traditional methods. Third, after building a dynamic PPI network, a predicting protein complexes algorithm based on "core-attachment" structural feature is applied to detect complexes from each dynamic subnetworks. Finally, it is revealed from the experimental results that our method proposed in this article performs well on detecting protein complexes from dynamic weighted PPI networks.

First-harmonic nonlinearities can predict unseen third-harmonics in medium-amplitude oscillatory shear (MAOS)

NASA Astrophysics Data System (ADS)

Carey-De La Torre, Olivia; Ewoldt, Randy H.

2018-02-01

We use first-harmonic MAOS nonlinearities from G 1' and G 1″ to test a predictive structure-rheology model for a transient polymer network. Using experiments with PVA-Borax (polyvinyl alcohol cross-linked by sodium tetraborate (borax)) at 11 different compositions, the model is calibrated to first-harmonic MAOS data on a torque-controlled rheometer at a fixed frequency, and used to predict third-harmonic MAOS on a displacement controlled rheometer at a different frequency three times larger. The prediction matches experiments for decomposed MAOS measures [ e 3] and [ v 3] with median disagreement of 13% and 25%, respectively, across all 11 compositions tested. This supports the validity of this model, and demonstrates the value of using all four MAOS signatures to understand and test structure-rheology relations for complex fluids.

Finite-strain large-deflection elastic-viscoplastic finite-element transient response analysis of structures

NASA Technical Reports Server (NTRS)

Rodal, J. J. A.; Witmer, E. A.

1979-01-01

A method of analysis for thin structures that incorporates finite strain, elastic-plastic, strain hardening, time dependent material behavior implemented with respect to a fixed configuration and is consistently valid for finite strains and finite rotations is developed. The theory is formulated systematically in a body fixed system of convected coordinates with materially embedded vectors that deform in common with continuum. Tensors are considered as linear vector functions and use is made of the dyadic representation. The kinematics of a deformable continuum is treated in detail, carefully defining precisely all quantities necessary for the analysis. The finite strain theory developed gives much better predictions and agreement with experiment than does the traditional small strain theory, and at practically no additional cost. This represents a very significant advance in the capability for the reliable prediction of nonlinear transient structural responses, including the reliable prediction of strains large enough to produce ductile metal rupture.

Real-time prediction of atmospheric Lagrangian coherent structures based on forecast data: An application and error analysis

NASA Astrophysics Data System (ADS)

BozorgMagham, Amir E.; Ross, Shane D.; Schmale, David G.

2013-09-01

The language of Lagrangian coherent structures (LCSs) provides a new means for studying transport and mixing of passive particles advected by an atmospheric flow field. Recent observations suggest that LCSs govern the large-scale atmospheric motion of airborne microorganisms, paving the way for more efficient models and management strategies for the spread of infectious diseases affecting plants, domestic animals, and humans. In addition, having reliable predictions of the timing of hyperbolic LCSs may contribute to improved aerobiological sampling of microorganisms with unmanned aerial vehicles and LCS-based early warning systems. Chaotic atmospheric dynamics lead to unavoidable forecasting errors in the wind velocity field, which compounds errors in LCS forecasting. In this study, we reveal the cumulative effects of errors of (short-term) wind field forecasts on the finite-time Lyapunov exponent (FTLE) fields and the associated LCSs when realistic forecast plans impose certain limits on the forecasting parameters. Objectives of this paper are to (a) quantify the accuracy of prediction of FTLE-LCS features and (b) determine the sensitivity of such predictions to forecasting parameters. Results indicate that forecasts of attracting LCSs exhibit less divergence from the archive-based LCSs than the repelling features. This result is important since attracting LCSs are the backbone of long-lived features in moving fluids. We also show under what circumstances one can trust the forecast results if one merely wants to know if an LCS passed over a region and does not need to precisely know the passage time.

Real time wave forecasting using wind time history and numerical model

NASA Astrophysics Data System (ADS)

Jain, Pooja; Deo, M. C.; Latha, G.; Rajendran, V.

Operational activities in the ocean like planning for structural repairs or fishing expeditions require real time prediction of waves over typical time duration of say a few hours. Such predictions can be made by using a numerical model or a time series model employing continuously recorded waves. This paper presents another option to do so and it is based on a different time series approach in which the input is in the form of preceding wind speed and wind direction observations. This would be useful for those stations where the costly wave buoys are not deployed and instead only meteorological buoys measuring wind are moored. The technique employs alternative artificial intelligence approaches of an artificial neural network (ANN), genetic programming (GP) and model tree (MT) to carry out the time series modeling of wind to obtain waves. Wind observations at four offshore sites along the east coast of India were used. For calibration purpose the wave data was generated using a numerical model. The predicted waves obtained using the proposed time series models when compared with the numerically generated waves showed good resemblance in terms of the selected error criteria. Large differences across the chosen techniques of ANN, GP, MT were not noticed. Wave hindcasting at the same time step and the predictions over shorter lead times were better than the predictions over longer lead times. The proposed method is a cost effective and convenient option when a site-specific information is desired.

A Prediction Model for ROS1-Rearranged Lung Adenocarcinomas based on Histologic Features.

PubMed

Zhou, Jianya; Zhao, Jing; Zheng, Jing; Kong, Mei; Sun, Ke; Wang, Bo; Chen, Xi; Ding, Wei; Zhou, Jianying

2016-01-01

To identify the clinical and histological characteristics of ROS1-rearranged non-small-cell lung carcinomas (NSCLCs) and build a prediction model to prescreen suitable patients for molecular testing. We identified 27 cases of ROS1-rearranged lung adenocarcinomas in 1165 patients with NSCLCs confirmed by real-time PCR and FISH and performed univariate and multivariate analyses to identify predictive factors associated with ROS1 rearrangement and finally developed prediction model. Detected with ROS1 immunochemistry, 59 cases of 1165 patients had a certain degree of ROS1 expression. Among these cases, 19 cases (68%, 19/28) with 3+ and 8 cases (47%, 8/17) with 2+ staining were ROS1 rearrangement verified by real-time PCR and FISH. In the resected group, the acinar-predominant growth pattern was the most commonly observed (57%, 8/14), while in the biopsy group, solid patterns were the most frequently observed (78%, 7/13). Based on multiple logistic regression analysis, we determined that female sex, cribriform structure and the presence of psammoma body were the three most powerful indicators of ROS1 rearrangement, and we have developed a predictive model for the presence of ROS1 rearrangements in lung adenocarcinomas. Female, cribriform structure and presence of psammoma body were the three most powerful indicator of ROS1 rearrangement status, and predictive formula was helpful in screening ROS1-rearranged NSCLC, especially for ROS1 immunochemistry equivocal cases.

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.

Modeling laser-induced periodic surface structures: Finite-difference time-domain feedback simulations

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

Skolski, J. Z. P., E-mail: j.z.p.skolski@utwente.nl; Vincenc Obona, J.; Römer, G. R. B. E.

2014-03-14

A model predicting the formation of laser-induced periodic surface structures (LIPSSs) is presented. That is, the finite-difference time domain method is used to study the interaction of electromagnetic fields with rough surfaces. In this approach, the rough surface is modified by “ablation after each laser pulse,” according to the absorbed energy profile, in order to account for inter-pulse feedback mechanisms. LIPSSs with a periodicity significantly smaller than the laser wavelength are found to “grow” either parallel or orthogonal to the laser polarization. The change in orientation and periodicity follow from the model. LIPSSs with a periodicity larger than the wavelengthmore » of the laser radiation and complex superimposed LIPSS patterns are also predicted by the model.« less

Shock wave structure in rarefied polyatomic gases with large relaxation time for the dynamic pressure

NASA Astrophysics Data System (ADS)

Taniguchi, Shigeru; Arima, Takashi; Ruggeri, Tommaso; Sugiyama, Masaru

2018-05-01

The shock wave structure in rarefied polyatomic gases is analyzed based on extended thermodynamics (ET). In particular, the case with large relaxation time for the dynamic pressure, which corresponds to large bulk viscosity, is considered by adopting the simplest version of extended thermodynamics with only 6 independent fields (ET6); the mass density, the velocity, the temperature and the dynamic pressure. Recently, the validity of the theoretical predictions by ET was confirmed by the numerical analysis based on the kinetic theory in [S Kosuge and K Aoki: Phys. Rev. Fluids, Vol. 3, 023401 (2018)]. It was shown that numerical results using the polyatomic version of ellipsoidal statistical model agree with the theoretical predictions by ET for small or moderately large Mach numbers. In the present paper, first, we compare the theoretical predictions by ET6 with the ones by kinetic theory for large Mach number under the same assumptions, that is, the gas is polytropic and the bulk viscosity is proportional to the temperature. Second, the shock wave structure for large Mach number in a non-polytropic gas is analyzed with the particular interest in the effect of the temperature dependence of specific heat and the bulk viscosity on the shock wave structure. Through the analysis of the case of a rarefied carbon dioxide (CO2) gas, it is shown that these temperature dependences play important roles in the precise analysis of the structure for strong shock waves.

Structure and dynamics of stock market in times of crisis

NASA Astrophysics Data System (ADS)

Zhao, Longfeng; Li, Wei; Cai, Xu

2016-02-01

Daily correlations among 322 S&P 500 constituent stocks are investigated by means of correlation-based (CB) network. By using the heterogeneous time scales, we identify global expansion and local clustering market behaviors during crises, which are mainly caused by community splits and inter-sector edge number decreases. The CB networks display distinctive community and sector structures. Graph edit distance is applied to capturing the dynamics of CB networks in which drastic structure reconfigurations can be observed during crisis periods. Edge statistics reveal the power-law nature of edges' duration time distribution. Despite the networks' strong structural changes during crises, we still find some long-duration edges that serve as the backbone of the stock market. Finally the dynamical change of network structure has shown its capability in predicting the implied volatility index (VIX).

Displacement Theories for In-Flight Deformed Shape Predictions of Aerospace Structures

NASA Technical Reports Server (NTRS)

Ko, William L.; Richards, W. L.; Tran, Van t.

2007-01-01

Displacement theories are developed for a variety of structures with the goal of providing real-time shape predictions for aerospace vehicles during flight. These theories are initially developed for a cantilever beam to predict the deformed shapes of the Helios flying wing. The main structural configuration of the Helios wing is a cantilever wing tubular spar subjected to bending, torsion, and combined bending and torsion loading. The displacement equations that are formulated are expressed in terms of strains measured at multiple sensing stations equally spaced on the surface of the wing spar. Displacement theories for other structures, such as tapered cantilever beams, two-point supported beams, wing boxes, and plates also are developed. The accuracy of the displacement theories is successfully validated by finite-element analysis and classical beam theory using input-strains generated by finite-element analysis. The displacement equations and associated strain-sensing system (such as fiber optic sensors) create a powerful means for in-flight deformation monitoring of aerospace structures. This method serves multiple purposes for structural shape sensing, loads monitoring, and structural health monitoring. Ultimately, the calculated displacement data can be visually displayed to the ground-based pilot or used as input to the control system to actively control the shape of structures during flight.

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.

A feature-based developmental model of the infant brain in structural MRI.

PubMed

Toews, Matthew; Wells, William M; Zöllei, Lilla

2012-01-01

In this paper, anatomical development is modeled as a collection of distinctive image patterns localized in space and time. A Bayesian posterior probability is defined over a random variable of subject age, conditioned on data in the form of scale-invariant image features. The model is automatically learned from a large set of images exhibiting significant variation, used to discover anatomical structure related to age and development, and fit to new images to predict age. The model is applied to a set of 230 infant structural MRIs of 92 subjects acquired at multiple sites over an age range of 8-590 days. Experiments demonstrate that the model can be used to identify age-related anatomical structure, and to predict the age of new subjects with an average error of 72 days.

Detecting determinism with improved sensitivity in time series: rank-based nonlinear predictability score.

PubMed

Naro, Daniel; Rummel, Christian; Schindler, Kaspar; Andrzejak, Ralph G

2014-09-01

The rank-based nonlinear predictability score was recently introduced as a test for determinism in point processes. We here adapt this measure to time series sampled from time-continuous flows. We use noisy Lorenz signals to compare this approach against a classical amplitude-based nonlinear prediction error. Both measures show an almost identical robustness against Gaussian white noise. In contrast, when the amplitude distribution of the noise has a narrower central peak and heavier tails than the normal distribution, the rank-based nonlinear predictability score outperforms the amplitude-based nonlinear prediction error. For this type of noise, the nonlinear predictability score has a higher sensitivity for deterministic structure in noisy signals. It also yields a higher statistical power in a surrogate test of the null hypothesis of linear stochastic correlated signals. We show the high relevance of this improved performance in an application to electroencephalographic (EEG) recordings from epilepsy patients. Here the nonlinear predictability score again appears of higher sensitivity to nonrandomness. Importantly, it yields an improved contrast between signals recorded from brain areas where the first ictal EEG signal changes were detected (focal EEG signals) versus signals recorded from brain areas that were not involved at seizure onset (nonfocal EEG signals).

Detecting determinism with improved sensitivity in time series: Rank-based nonlinear predictability score

NASA Astrophysics Data System (ADS)

Naro, Daniel; Rummel, Christian; Schindler, Kaspar; Andrzejak, Ralph G.

2014-09-01

The rank-based nonlinear predictability score was recently introduced as a test for determinism in point processes. We here adapt this measure to time series sampled from time-continuous flows. We use noisy Lorenz signals to compare this approach against a classical amplitude-based nonlinear prediction error. Both measures show an almost identical robustness against Gaussian white noise. In contrast, when the amplitude distribution of the noise has a narrower central peak and heavier tails than the normal distribution, the rank-based nonlinear predictability score outperforms the amplitude-based nonlinear prediction error. For this type of noise, the nonlinear predictability score has a higher sensitivity for deterministic structure in noisy signals. It also yields a higher statistical power in a surrogate test of the null hypothesis of linear stochastic correlated signals. We show the high relevance of this improved performance in an application to electroencephalographic (EEG) recordings from epilepsy patients. Here the nonlinear predictability score again appears of higher sensitivity to nonrandomness. Importantly, it yields an improved contrast between signals recorded from brain areas where the first ictal EEG signal changes were detected (focal EEG signals) versus signals recorded from brain areas that were not involved at seizure onset (nonfocal EEG signals).

Time Span of Discretion and Administrative Work in School Systems: Results of a Pilot Study.

ERIC Educational Resources Information Center

Allison, Derek J.; Morfitt, Grace

This paper presents findings of a study that utilized Elliott Jaques' theories of organizational depth structure and time span of discretion in administrative work to examine administrators' responsibilities in two Ontario (Canada) school systems. The theory predicts that the time-span of discretion associated with the administrative tasks will…

Predicting students' physical activity and health-related well-being: a prospective cross-domain investigation of motivation across school physical education and exercise settings.

PubMed

Standage, Martyn; Gillison, Fiona B; Ntoumanis, Nikos; Treasure, Darren C

2012-02-01

A three-wave prospective design was used to assess a model of motivation guided by self-determination theory (Ryan & Deci, 2008) spanning the contexts of school physical education (PE) and exercise. The outcome variables examined were health-related quality of life (HRQoL), physical self-concept (PSC), and 4 days of objectively assessed estimates of activity. Secondary school students (n = 494) completed questionnaires at three separate time points and were familiarized with how to use a sealed pedometer. Results of structural equation modeling supported a model in which perceptions of autonomy support from a PE teacher positively predicted PE-related need satisfaction (autonomy, competence, and relatedness). Competence predicted PSC, whereas relatedness predicted HRQoL. Autonomy and competence positively predicted autonomous motivation toward PE, which in turn positively predicted autonomous motivation toward exercise (i.e., 4-day pedometer step count). Autonomous motivation toward exercise positively predicted step count, HRQoL, and PSC. Results of multisample structural equation modeling supported gender invariance. Suggestions for future work are discussed.

Paternal and maternal influences on the psychological well-being of Chinese adolescents.

PubMed

Shek, D T

1999-08-01

Adolescents' (N = 378) perceptions of and satisfaction with parenting styles, perceived parent-adolescent conflict, perceived frequency of parent-adolescent communication and related feelings, perceived parent-adolescent relationship, and mental health were assessed with rating scales and structured interviews on 2 occasions separated by 1 year. Results showed that the questionnaire and interview measures at each time could be grouped into 2 stable factors: Paternal Parenthood Qualities (PPQ) and Maternal Parenthood Qualities (MPQ). Although both factors generally had significant concurrent and longitudinal correlations with adolescents' mental health, PPQ at Time 1-predicted changes in adolescent life satisfaction, hopelessness, self-esteem, purpose in life, and general psychiatric morbidity at Time 2, whereas MPQ at Time 1 did not predict those changes. Adolescents' mental health at Time 1 was found to predict changes in MPQ but not PPQ at Time 2. Relative to maternal qualities, paternal qualities were generally found to exert a stronger impact on adolescent psychological well-being.

Timed picture naming in seven languages

PubMed Central

BATES, ELIZABETH; D’AMICO, SIMONA; JACOBSEN, THOMAS; SZÉKELY, ANNA; ANDONOVA, ELENA; DEVESCOVI, ANTONELLA; HERRON, DAN; LU, CHING CHING; PECHMANN, THOMAS; PLÉH, CSABA; WICHA, NICOLE; FEDERMEIER, KARA; GERDJIKOVA, IRINI; GUTIERREZ, GABRIEL; HUNG, DAISY; HSU, JEANNE; IYER, GOWRI; KOHNERT, KATHERINE; MEHOTCHEVA, TEODORA; OROZCO-FIGUEROA, ARACELI; TZENG, ANGELA; TZENG, OVID

2012-01-01

Timed picture naming was compared in seven languages that vary along dimensions known to affect lexical access. Analyses over items focused on factors that determine cross-language universals and cross-language disparities. With regard to universals, number of alternative names had large effects on reaction time within and across languages after target–name agreement was controlled, suggesting inhibitory effects from lexical competitors. For all the languages, word frequency and goodness of depiction had large effects, but objective picture complexity did not. Effects of word structure variables (length, syllable structure, compounding, and initial frication) varied markedly over languages. Strong cross-language correlations were found in naming latencies, frequency, and length. Other-language frequency effects were observed (e.g., Chinese frequencies predicting Spanish reaction times) even after within-language effects were controlled (e.g., Spanish frequencies predicting Spanish reaction times). These surprising cross-language correlations challenge widely held assumptions about the lexical locus of length and frequency effects, suggesting instead that they may (at least in part) reflect familiarity and accessibility at a conceptual level that is shared over languages. PMID:12921412

Why is Past Depression the Best Predictor of Future Depression? Stress Generation as a Mechanism of Depression Continuity in Girls

PubMed Central

Rudolph, Karen D.; Flynn, Megan; Abaied, Jamie; Groot, Alison; Thompson, Renee

2009-01-01

This study examined whether a transactional interpersonal life stress model helps to explain the continuity in depression over time in girls. Youth (86 girls, 81 boys; M age = 12.41, SD = 1.19) and their caregivers participated in a three-wave longitudinal study. Depression and episodic life stress were assessed with semi-structured interviews. Path analysis provided support for a transactional interpersonal life stress model in girls but not in boys, wherein depression predicted the generation of interpersonal stress, which predicted subsequent depression. Moreover, self-generated interpersonal stress partially accounted for the continuity of depression over time. Although depression predicted noninterpersonal stress generation in girls (but not in boys), noninterpersonal stress did not predict subsequent depression. PMID:20183635

Structural evolution of the methane cation in subfemtosecond photodynamics

NASA Astrophysics Data System (ADS)

Mondal, T.; Varandas, A. J. C.

2015-07-01

An ab initio quantum dynamics study has been performed to explore the structural rearrangement of ground state CH 4+ in subfemtosecond resolved photodynamics. The method utilizes time-dependent wave-packet propagation on the X ˜ 2 T 2 electronic manifold of the title cation in full dimensionality, including nonadiabatic coupling of the three electronic sheets. Good agreement is obtained with recent experiments [Baker et al., Science 312, 424 (2006)] which use high-order harmonic generation to probe the attosecond proton dynamics. The novel results provide direct theoretical support of the observations while unravelling the underlying details. With the geometrical changes obtained by calculating the expectation values of the nuclear coordinates as a function of time, the structural evolution is predicted to begin through activation of the totally symmetric a1 and doubly degenerate e modes. While the former retains the original Td symmetry of the cation, the Jahn-Teller active e mode conducts it to a D2d structure. At ˜1.85 fs, the intermediate D2d structure is further predicted to rearrange to local C2v minimum geometry via Jahn-Teller active bending vibrations of t2 symmetry.

Geometrically Nonlinear Static Analysis of 3D Trusses Using the Arc-Length Method

NASA Technical Reports Server (NTRS)

Hrinda, Glenn A.

2006-01-01

Rigorous analysis of geometrically nonlinear structures demands creating mathematical models that accurately include loading and support conditions and, more importantly, model the stiffness and response of the structure. Nonlinear geometric structures often contain critical points with snap-through behavior during the response to large loads. Studying the post buckling behavior during a portion of a structure's unstable load history may be necessary. Primary structures made from ductile materials will stretch enough prior to failure for loads to redistribute producing sudden and often catastrophic collapses that are difficult to predict. The responses and redistribution of the internal loads during collapses and possible sharp snap-back of structures have frequently caused numerical difficulties in analysis procedures. The presence of critical stability points and unstable equilibrium paths are major difficulties that numerical solutions must pass to fully capture the nonlinear response. Some hurdles still exist in finding nonlinear responses of structures under large geometric changes. Predicting snap-through and snap-back of certain structures has been difficult and time consuming. Also difficult is finding how much load a structure may still carry safely. Highly geometrically nonlinear responses of structures exhibiting complex snap-back behavior are presented and analyzed with a finite element approach. The arc-length method will be reviewed and shown to predict the proper response and follow the nonlinear equilibrium path through limit points.

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

Prediction of delayed retention of antibodies in hydrophobic interaction chromatography from sequence using machine learning.

PubMed

Jain, Tushar; Boland, Todd; Lilov, Asparouh; Burnina, Irina; Brown, Michael; Xu, Yingda; Vásquez, Maximiliano

2017-12-01

The hydrophobicity of a monoclonal antibody is an important biophysical property relevant for its developability into a therapeutic. In addition to characterizing heterogeneity, Hydrophobic Interaction Chromatography (HIC) is an assay that is often used to quantify the hydrophobicity of an antibody to assess downstream risks. Earlier studies have shown that retention times in this assay can be correlated to amino-acid or atomic propensities weighted by the surface areas obtained from protein 3-dimensional structures. The goal of this study is to develop models to enable prediction of delayed HIC retention times directly from sequence. We utilize the randomforest machine learning approach to estimate the surface exposure of amino-acid side-chains in the variable region directly from the antibody sequence. We obtain mean-absolute errors of 4.6% for the prediction of surface exposure. Using experimental HIC data along with the estimated surface areas, we derive an amino-acid propensity scale that enables prediction of antibodies likely to have delayed retention times in the assay. We achieve a cross-validation Area Under Curve of 0.85 for the Receiver Operating Characteristic curve of our model. The low computational expense and high accuracy of this approach enables real-time assessment of hydrophobic character to enable prioritization of antibodies during the discovery process and rational engineering to reduce hydrophobic liabilities. Structure data, aligned sequences, experimental data and prediction scores for test-cases, and R scripts used in this work are provided as part of the Supplementary Material. tushar.jain@adimab.com. Supplementary data are available at Bioinformatics online. © The Author (2017). Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oup.com

Development of a 3D finite element acoustic model to predict the sound reduction index of stud based double-leaf walls

NASA Astrophysics Data System (ADS)

Arjunan, A.; Wang, C. J.; Yahiaoui, K.; Mynors, D. J.; Morgan, T.; Nguyen, V. B.; English, M.

2014-11-01

Building standards incorporating quantitative acoustical criteria to ensure adequate sound insulation are now being implemented. Engineers are making great efforts to design acoustically efficient double-wall structures. Accordingly, efficient simulation models to predict the acoustic insulation of double-leaf wall structures are needed. This paper presents the development of a numerical tool that can predict the frequency dependent sound reduction index R of stud based double-leaf walls at one-third-octave band frequency range. A fully vibro-acoustic 3D model consisting of two rooms partitioned using a double-leaf wall, considering the structure and acoustic fluid coupling incorporating the existing fluid and structural solvers are presented. The validity of the finite element (FE) model is assessed by comparison with experimental test results carried out in a certified laboratory. Accurate representation of the structural damping matrix to effectively predict the R values are studied. The possibilities of minimising the simulation time using a frequency dependent mesh model was also investigated. The FEA model presented in this work is capable of predicting the weighted sound reduction index Rw along with A-weighted pink noise C and A-weighted urban noise Ctr within an error of 1 dB. The model developed can also be used to analyse the acoustically induced frequency dependent geometrical behaviour of the double-leaf wall components to optimise them for best acoustic performance. The FE modelling procedure reported in this paper can be extended to other building components undergoing fluid-structure interaction (FSI) to evaluate their acoustic insulation.

Electrical test prediction using hybrid metrology and machine learning

NASA Astrophysics Data System (ADS)

Breton, Mary; Chao, Robin; Muthinti, Gangadhara Raja; de la Peña, Abraham A.; Simon, Jacques; Cepler, Aron J.; Sendelbach, Matthew; Gaudiello, John; Emans, Susan; Shifrin, Michael; Etzioni, Yoav; Urenski, Ronen; Lee, Wei Ti

2017-03-01

Electrical test measurement in the back-end of line (BEOL) is crucial for wafer and die sorting as well as comparing intended process splits. Any in-line, nondestructive technique in the process flow to accurately predict these measurements can significantly improve mean-time-to-detect (MTTD) of defects and improve cycle times for yield and process learning. Measuring after BEOL metallization is commonly done for process control and learning, particularly with scatterometry (also called OCD (Optical Critical Dimension)), which can solve for multiple profile parameters such as metal line height or sidewall angle and does so within patterned regions. This gives scatterometry an advantage over inline microscopy-based techniques, which provide top-down information, since such techniques can be insensitive to sidewall variations hidden under the metal fill of the trench. But when faced with correlation to electrical test measurements that are specific to the BEOL processing, both techniques face the additional challenge of sampling. Microscopy-based techniques are sampling-limited by their small probe size, while scatterometry is traditionally limited (for microprocessors) to scribe targets that mimic device ground rules but are not necessarily designed to be electrically testable. A solution to this sampling challenge lies in a fast reference-based machine learning capability that allows for OCD measurement directly of the electrically-testable structures, even when they are not OCD-compatible. By incorporating such direct OCD measurements, correlation to, and therefore prediction of, resistance of BEOL electrical test structures is significantly improved. Improvements in prediction capability for multiple types of in-die electrically-testable device structures is demonstrated. To further improve the quality of the prediction of the electrical resistance measurements, hybrid metrology using the OCD measurements as well as X-ray metrology (XRF) is used. Hybrid metrology is the practice of combining information from multiple sources in order to enable or improve the measurement of one or more critical parameters. Here, the XRF measurements are used to detect subtle changes in barrier layer composition and thickness that can have second-order effects on the electrical resistance of the test structures. By accounting for such effects with the aid of the X-ray-based measurements, further improvement in the OCD correlation to electrical test measurements is achieved. Using both types of solution incorporation of fast reference-based machine learning on nonOCD-compatible test structures, and hybrid metrology combining OCD with XRF technology improvement in BEOL cycle time learning could be accomplished through improved prediction capability.

Mesoscale Ionospheric Prediction

DTIC Science & Technology

2006-09-30

Mesoscale Ionospheric Prediction Gary S. Bust 10000 Burnet Austin Texas, 78758 phone: (512) 835-3623 fax: (512) 835-3808 email: gbust...time-evolving non-linear numerical model of the mesoscale ionosphere , second to couple the mesoscale model to a mesoscale data assimilative analysis...third to use the new data-assimilative mesoscale model to investigate ionospheric structure and plasma instabilities, and fourth to apply the data

Predicting the process of extinction in experimental microcosms and accounting for interspecific interactions in single-species time series.

PubMed

Ferguson, Jake M; Ponciano, José M

2014-02-01

Predicting population extinction risk is a fundamental application of ecological theory to the practice of conservation biology. Here, we compared the prediction performance of a wide array of stochastic, population dynamics models against direct observations of the extinction process from an extensive experimental data set. By varying a series of biological and statistical assumptions in the proposed models, we were able to identify the assumptions that affected predictions about population extinction. We also show how certain autocorrelation structures can emerge due to interspecific interactions, and that accounting for the stochastic effect of these interactions can improve predictions of the extinction process. We conclude that it is possible to account for the stochastic effects of community interactions on extinction when using single-species time series. © 2013 The Authors. Ecology Letters published by John Wiley & Sons Ltd and CNRS.

[Application of an artificial neural network in the design of sustained-release dosage forms].

PubMed

Wei, X H; Wu, J J; Liang, W Q

2001-09-01

To use the artificial neural network (ANN) in Matlab 5.1 tool-boxes to predict the formulations of sustained-release tablets. The solubilities of nine drugs and various ratios of HPMC: Dextrin for 63 tablet formulations were used as the ANN model input, and in vitro accumulation released at 6 sampling times were used as output. The ANN model was constructed by selecting the optimal number of iterations (25) and model structure in which there are one hidden layer and five hidden layer nodes. The optimized ANN model was used for prediction of formulation based on desired target in vitro dissolution-time profiles. ANN predicted profiles based on ANN predicted formulations were closely similar to the target profiles. The ANN could be used for predicting the dissolution profiles of sustained release dosage form and for the design of optimal formulation.

An efficient method for the prediction of deleterious multiple-point mutations in the secondary structure of RNAs using suboptimal folding solutions

PubMed Central

Churkin, Alexander; Barash, Danny

2008-01-01

Background RNAmute is an interactive Java application which, given an RNA sequence, calculates the secondary structure of all single point mutations and organizes them into categories according to their similarity to the predicted structure of the wild type. The secondary structure predictions are performed using the Vienna RNA package. A more efficient implementation of RNAmute is needed, however, to extend from the case of single point mutations to the general case of multiple point mutations, which may often be desired for computational predictions alongside mutagenesis experiments. But analyzing multiple point mutations, a process that requires traversing all possible mutations, becomes highly expensive since the running time is O(nm) for a sequence of length n with m-point mutations. Using Vienna's RNAsubopt, we present a method that selects only those mutations, based on stability considerations, which are likely to be conformational rearranging. The approach is best examined using the dot plot representation for RNA secondary structure. Results Using RNAsubopt, the suboptimal solutions for a given wild-type sequence are calculated once. Then, specific mutations are selected that are most likely to cause a conformational rearrangement. For an RNA sequence of about 100 nts and 3-point mutations (n = 100, m = 3), for example, the proposed method reduces the running time from several hours or even days to several minutes, thus enabling the practical application of RNAmute to the analysis of multiple-point mutations. Conclusion A highly efficient addition to RNAmute that is as user friendly as the original application but that facilitates the practical analysis of multiple-point mutations is presented. Such an extension can now be exploited prior to site-directed mutagenesis experiments by virologists, for example, who investigate the change of function in an RNA virus via mutations that disrupt important motifs in its secondary structure. A complete explanation of the application, called MultiRNAmute, is available at [1]. PMID:18445289

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

NASA Astrophysics Data System (ADS)

Hansen, S. K.; Haslauer, C. P.; Cirpka, O. A.; Vesselinov, V. V.

2016-12-01

It is desirable to predict the shape of breakthrough curves downgradient of a solute source from subsurface structural parameters (as in the small-perturbation macrodispersion theory) both for realistically heterogeneous fields, and at early time, before any sort of Fickian model is applicable. Using a combination of a priori knowledge, large-scale Monte Carlo simulation, and regression techniques, we have developed closed-form predictive expressions for pre- and post-Fickian flux-weighted solute breakthrough curves as a function of distance from the source (in integral scales) and variance of the log hydraulic conductivity field. Using the ensemble of Monte Carlo realizations, we have simultaneously computed error envelopes for the estimated flux-weighted breakthrough, and for the divergence of point breakthrough curves from the flux-weighted average, as functions of the predictive parameters. We have also obtained implied late-time macrodispersion coefficients for highly heterogeneous environments from the breakthrough statistics. This analysis is relevant for the modelling of reactive as well as conservative transport, since for many kinetic sorption and decay reactions, Laplace-domain modification of the breakthrough curve for conservative solute produces the correct curve for the reactive system.

Application of cross-sectional time series modeling for the prediction of energy expenditure from heart rate and accelerometry

USDA-ARS?s Scientific Manuscript database

Accurate estimation of energy expenditure (EE) in children and adolescents is required for a better understanding of physiological, behavioral, and environmental factors affecting energy balance. Cross-sectional time series (CSTS) models, which account for correlation structure of repeated observati...

Coverage of whole proteome by structural genomics observed through protein homology modeling database

PubMed Central

Yamaguchi, Akihiro; Go, Mitiko

2006-01-01

We have been developing FAMSBASE, a protein homology-modeling database of whole ORFs predicted from genome sequences. The latest update of FAMSBASE (http://daisy.nagahama-i-bio.ac.jp/Famsbase/), which is based on the protein three-dimensional (3D) structures released by November 2003, contains modeled 3D structures for 368,724 open reading frames (ORFs) derived from genomes of 276 species, namely 17 archaebacterial, 130 eubacterial, 18 eukaryotic and 111 phage genomes. Those 276 genomes are predicted to have 734,193 ORFs in total and the current FAMSBASE contains protein 3D structure of approximately 50% of the ORF products. However, cases that a modeled 3D structure covers the whole part of an ORF product are rare. When portion of an ORF with 3D structure is compared in three kingdoms of life, in archaebacteria and eubacteria, approximately 60% of the ORFs have modeled 3D structures covering almost the entire amino acid sequences, however, the percentage falls to about 30% in eukaryotes. When annual differences in the number of ORFs with modeled 3D structure are calculated, the fraction of modeled 3D structures of soluble protein for archaebacteria is increased by 5%, and that for eubacteria by 7% in the last 3 years. Assuming that this rate would be maintained and that determination of 3D structures for predicted disordered regions is unattainable, whole soluble protein model structures of prokaryotes without the putative disordered regions will be in hand within 15 years. For eukaryotic proteins, they will be in hand within 25 years. The 3D structures we will have at those times are not the 3D structure of the entire proteins encoded in single ORFs, but the 3D structures of separate structural domains. Measuring or predicting spatial arrangements of structural domains in an ORF will then be a coming issue of structural genomics. PMID:17146617

The flow of plasma in the solar terrestrial environment

NASA Technical Reports Server (NTRS)

Schunk, Robert W.; Banks, P.; Barakat, A. R.; Crain, D. J.; Demars, H. G.; Lemaire, J.; Ma, T.-Z.; Rasmussen, C. E.; Richards, P.; Sica, R.

1990-01-01

The overall goal of our NASA Theory Program was to study the coupling, time delays, and feedback mechanisms between the various regions of the solar-terrestrial system in a self-consistent, quantitative manner. To accomplish this goal, it will eventually be necessary to have time-dependent macroscopic models of the different regions of the solar-terrestrial system and we are continually working toward this goal. However, with the funding from this NASA program, we concentrated on the near-earth plasma environment, including the ionosphere, the plasmasphere, and the polar wind. In this area, we developed unique global models that allowed us to study the coupling between the different regions. These results are highlighted in the next section. Another important aspect of our NASA Theory Program concerned the effect that localized 'structure' had on the macroscopic flow in the ionosphere, plasmasphere, thermosphere, and polar wind. The localized structure can be created by structured magnetospheric inputs (i.e., structured plasma convection, particle precipitation or Birkland current patterns) or time variations in these input due to storms and substorms. Also, some of the plasma flows that we predicted with our macroscopic models could be unstable, and another one of our goals was to examine the stability of our predicted flows. Because time-dependent, three-dimensional numerical models of the solar-terrestrial environment generally require extensive computer resources, they are usually based on relatively simple mathematical formulations (i.e., simple MHD or hydrodynamic formulations). Therefore, another goal of our NASA Theory Program was to study the conditions under which various mathematical formulations can be applied to specific solar-terrestrial regions. This could involve a detailed comparison of kinetic, semi-kinetic, and hydrodynamic predictions for a given polar wind scenario or it could involve the comparison of a small-scale particle-in-cell (PIC) simulation of a plasma expansion event with a similar macroscopic expansion event. The different mathematical formulations have different strengths and weaknesses and a careful comparison of model predictions for similar geophysical situations provides insight into when the various models can be used with confidence.

The flow of plasma in the solar terrestrial environment

NASA Technical Reports Server (NTRS)

Schunk, Robert W.

1991-01-01

The overall goal of our NASA Theory Program is to study the coupling, time delays, and feedback mechanisms between the various regions of the solar-terrestrial system in a self-consistent, quantitative, manner. To accomplish this goal, it will eventually be necessary to have time-dependent macroscopic models of the different regions of the solar-terrestrial system and we are continually working toward this goal. However, our immediate emphasis is on the near-earth plasma environment, including the ionosphere, the plasmasphere, and the polar wind. In this area, we have developed unique global models that allow us to study the coupling between the different regions. These results are highlighted. Another important aspect of our NASA Theory Program concerns the effect that localized structure has on the macroscopic flow in the ionosphere, plasmasphere, thermosphere and polar wind. The localized structure can be created by structured magnetospheric inputs (i.e., structured plasma convection, particle precipitation or Birkeland current patterns) or time variations in these inputs due to storms and substorms. Also, some of the plasma flows that we predict with our macroscopic models may be unstable. Another one of our goals is to examine the stability of our predicted flows. Because time-dependent three-dimensional numerical models of the solar-terrestrial environment generally require extensive computer resources, they are usually based on relatively simple mathematical formulations (i.e., simple MHD or hydrodynamic formulations). Therefore, another long-range goal of our NASA Theory Program is to study the conditions under which various mathematical formulations can be applied to specific solar-terrestrial regions. This may involve a detailed comparison of kinetic, semikinetic, and hydrodynamic predictions for a given polar wind scenario or it may involve the comparison of a small-scale particle-in-cell (PIC) simulation of a plasma expansion event with a similar macroscopic expansion event. The different mathematical formulations have different strengths and weaknesses and a careful comparison of model predictions for similar geophysical situations will provide insight into when the various models can be used with confidence.

Predicting the conformations of peptides and proteins in early evolution. A review article submitted to Biology Direct

PubMed Central

Milner-White, E James; Russell, Michael J

2008-01-01

Considering that short, mainly heterochiral, polypeptides with a high glycine content are expected to have played a prominent role in evolution at the earliest stage of life before nucleic acids were available, we review recent knowledge about polypeptide three-dimensional structure to predict the types of conformations they would have adopted. The possible existence of such structures at this time leads to a consideration of their functional significance, and the consequences for the course of evolution. This article was reviewed by Bill Martin, Eugene Koonin and Nick Grishin. PMID:18226248

Viscoelastic behavior and life-time predictions

NASA Technical Reports Server (NTRS)

Dillard, D. A.; Brinson, H. F.

1985-01-01

Fiber reinforced plastics were considered for many structural applications in automotive, aerospace and other industries. A major concern was and remains the failure modes associated with the polymer matrix which serves to bind the fibers together and transfer the load through connections, from fiber to fiber and ply to ply. An accelerated characterization procedure for prediction of delayed failures was developed. This method utilizes time-temperature-stress-moisture superposition principles in conjunction with laminated plate theory. Because failures are inherently nonlinear, the testing and analytic modeling for both moduli and strength is based upon nonlinear viscoelastic concepts.

System identification of an unmanned quadcopter system using MRAN neural

NASA Astrophysics Data System (ADS)

Pairan, M. F.; Shamsudin, S. S.

2017-12-01

This project presents the performance analysis of the radial basis function neural network (RBF) trained with Minimal Resource Allocating Network (MRAN) algorithm for real-time identification of quadcopter. MRAN’s performance is compared with the RBF with Constant Trace algorithm for 2500 input-output pair data sampling. MRAN utilizes adding and pruning hidden neuron strategy to obtain optimum RBF structure, increase prediction accuracy and reduce training time. The results indicate that MRAN algorithm produces fast training time and more accurate prediction compared with standard RBF. The model proposed in this paper is capable of identifying and modelling a nonlinear representation of the quadcopter flight dynamics.

Loss Aversion and Time-Differentiated Electricity Pricing

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

Spurlock, C. Anna

2015-06-01

I develop a model of loss aversion over electricity expenditure, from which I derive testable predictions for household electricity consumption while on combination time-of-use (TOU) and critical peak pricing (CPP) plans. Testing these predictions results in evidence consistent with loss aversion: (1) spillover effects - positive expenditure shocks resulted in significantly more peak consumption reduction for several weeks thereafter; and (2) clustering - disproportionate probability of consuming such that expenditure would be equal between the TOUCPP or standard flat-rate pricing structures. This behavior is inconsistent with a purely neoclassical utility model, and has important implications for application of time-differentiated electricitymore » pricing.« less

Structure of Soot-Containing Laminar Jet Diffusion Flames

NASA Technical Reports Server (NTRS)

Mortazavi, S.; Sunderland, P. B.; Jurng, J.; Koylu, U. O.; Faeth, G. M.

1993-01-01

The structure and soot properties of nonbuoyant and weakly-buoyant round jet diffusion flames were studied, considering ethylene, propane and acetylene burning in air at pressures of 0.125-2.0 atm. Measurements of flame structure included radiative heat loss fractions, flame shape and temperature distributions in the fuel-lean (overfire) region. These measurements were used to evaluate flame structure predictions based on the conserved-scalar formalism in conjunction with the laminar flamelet concept, finding good agreement betweem predictions and measurements. Soot property measurements included laminar smoke points, soot volume function distributions using laser extinction, and soot structure using thermophoretic sampling and analysis by transmission electron microscopy. Nonbuoyant flames were found to exhibit laminar smoke points like buoyant flames but their properties are very different; in particular, nonbuoyant flames have laminar smoke point flame lengths and residence times that are shorter and longer, respectively, than buoyant flames.

Three-dimensional localized coherent structures of surface turbulence: Model validation with experiments and further computations.

PubMed

Demekhin, E A; Kalaidin, E N; Kalliadasis, S; Vlaskin, S Yu

2010-09-01

We validate experimentally the Kapitsa-Shkadov model utilized in the theoretical studies by Demekhin [Phys. Fluids 19, 114103 (2007)10.1063/1.2793148; Phys. Fluids 19, 114104 (2007)]10.1063/1.2793149 of surface turbulence on a thin liquid film flowing down a vertical planar wall. For water at 15° , surface turbulence typically occurs at an inlet Reynolds number of ≃40 . Of particular interest is to assess experimentally the predictions of the model for three-dimensional nonlinear localized coherent structures, which represent elementary processes of surface turbulence. For this purpose we devise simple experiments to investigate the instabilities and transitions leading to such structures. Our experimental results are in good agreement with the theoretical predictions of the model. We also perform time-dependent computations for the formation of coherent structures and their interaction with localized structures of smaller amplitude on the surface of the film.

Novel composites for wing and fuselage applications

NASA Technical Reports Server (NTRS)

Sobel, L. H.; Buttitta, C.; Suarez, J. A.

1995-01-01

Probabilistic predictions based on the IPACS code are presented for the material and structural response of unnotched and notched, IM6/3501-6 Gr/Ep laminates. Comparisons of predicted and measured modulus and strength distributions are given for unnotched unidirectional, cross-ply and quasi-isotropic laminates. The predicted modulus distributions were found to correlate well with the test results for all three unnotched laminates. Correlations of strength distributions for the unnotched laminates are judged good for the unidirectional laminate and fair for the cross-ply laminate, whereas the strength correlation for the quasi-isotropic laminate is judged poor because IPACS did not have a progressive failure capability at the time this work was performed. The report also presents probabilistic and structural reliability analysis predictions for the strain concentration factor (SCF) for an open-hole, quasi-isotropic laminate subjected to longitudinal tension. A special procedure was developed to adapt IPACS for the structural reliability analysis. The reliability results show the importance of identifying the most significant random variables upon which the SCF depends, and of having accurate scatter values for these variables.

Simulation and experimental comparison of the thermo-mechanical history and 3D microstructure evolution of 304L stainless steel tubes manufactured using LENS

NASA Astrophysics Data System (ADS)

Johnson, Kyle L.; Rodgers, Theron M.; Underwood, Olivia D.; Madison, Jonathan D.; Ford, Kurtis R.; Whetten, Shaun R.; Dagel, Daryl J.; Bishop, Joseph E.

2018-05-01

Additive manufacturing enables the production of previously unachievable designs in conjunction with time and cost savings. However, spatially and temporally fluctuating thermal histories can lead to residual stress states and microstructural variations that challenge conventional assumptions used to predict part performance. Numerical simulations offer a viable way to explore the root causes of these characteristics, and can provide insight into methods of controlling them. Here, the thermal history of a 304L stainless steel cylinder produced using the Laser Engineered Net Shape process is simulated using finite element analysis (FEA). The resultant thermal history is coupled to both a solid mechanics FEA simulation to predict residual stress and a kinetic Monte Carlo model to predict the three-dimensional grain structure evolution. Experimental EBSD measurements of grain structure and in-process infrared thermal data are compared to the predictions.

Simulation and experimental comparison of the thermo-mechanical history and 3D microstructure evolution of 304L stainless steel tubes manufactured using LENS

NASA Astrophysics Data System (ADS)

Johnson, Kyle L.; Rodgers, Theron M.; Underwood, Olivia D.; Madison, Jonathan D.; Ford, Kurtis R.; Whetten, Shaun R.; Dagel, Daryl J.; Bishop, Joseph E.

2017-12-01

Additive manufacturing enables the production of previously unachievable designs in conjunction with time and cost savings. However, spatially and temporally fluctuating thermal histories can lead to residual stress states and microstructural variations that challenge conventional assumptions used to predict part performance. Numerical simulations offer a viable way to explore the root causes of these characteristics, and can provide insight into methods of controlling them. Here, the thermal history of a 304L stainless steel cylinder produced using the Laser Engineered Net Shape process is simulated using finite element analysis (FEA). The resultant thermal history is coupled to both a solid mechanics FEA simulation to predict residual stress and a kinetic Monte Carlo model to predict the three-dimensional grain structure evolution. Experimental EBSD measurements of grain structure and in-process infrared thermal data are compared to the predictions.

SMARTIV: combined sequence and structure de-novo motif discovery for in-vivo RNA binding data.

PubMed

Polishchuk, Maya; Paz, Inbal; Yakhini, Zohar; Mandel-Gutfreund, Yael

2018-05-25

Gene expression regulation is highly dependent on binding of RNA-binding proteins (RBPs) to their RNA targets. Growing evidence supports the notion that both RNA primary sequence and its local secondary structure play a role in specific Protein-RNA recognition and binding. Despite the great advance in high-throughput experimental methods for identifying sequence targets of RBPs, predicting the specific sequence and structure binding preferences of RBPs remains a major challenge. We present a novel webserver, SMARTIV, designed for discovering and visualizing combined RNA sequence and structure motifs from high-throughput RNA-binding data, generated from in-vivo experiments. The uniqueness of SMARTIV is that it predicts motifs from enriched k-mers that combine information from ranked RNA sequences and their predicted secondary structure, obtained using various folding methods. Consequently, SMARTIV generates Position Weight Matrices (PWMs) in a combined sequence and structure alphabet with assigned P-values. SMARTIV concisely represents the sequence and structure motif content as a single graphical logo, which is informative and easy for visual perception. SMARTIV was examined extensively on a variety of high-throughput binding experiments for RBPs from different families, generated from different technologies, showing consistent and accurate results. Finally, SMARTIV is a user-friendly webserver, highly efficient in run-time and freely accessible via http://smartiv.technion.ac.il/.

Advances in Homology Protein Structure Modeling

PubMed Central

Xiang, Zhexin

2007-01-01

Homology modeling plays a central role in determining protein structure in the structural genomics project. The importance of homology modeling has been steadily increasing because of the large gap that exists between the overwhelming number of available protein sequences and experimentally solved protein structures, and also, more importantly, because of the increasing reliability and accuracy of the method. In fact, a protein sequence with over 30% identity to a known structure can often be predicted with an accuracy equivalent to a low-resolution X-ray structure. The recent advances in homology modeling, especially in detecting distant homologues, aligning sequences with template structures, modeling of loops and side chains, as well as detecting errors in a model, have contributed to reliable prediction of protein structure, which was not possible even several years ago. The ongoing efforts in solving protein structures, which can be time-consuming and often difficult, will continue to spur the development of a host of new computational methods that can fill in the gap and further contribute to understanding the relationship between protein structure and function. PMID:16787261

Benchmarking hydrological model predictive capability for UK River flows and flood peaks.

NASA Astrophysics Data System (ADS)

Lane, Rosanna; Coxon, Gemma; Freer, Jim; Wagener, Thorsten

2017-04-01

Data and hydrological models are now available for national hydrological analyses. However, hydrological model performance varies between catchments, and lumped, conceptual models are not able to produce adequate simulations everywhere. This study aims to benchmark hydrological model performance for catchments across the United Kingdom within an uncertainty analysis framework. We have applied four hydrological models from the FUSE framework to 1128 catchments across the UK. These models are all lumped models and run at a daily timestep, but differ in the model structural architecture and process parameterisations, therefore producing different but equally plausible simulations. We apply FUSE over a 20 year period from 1988-2008, within a GLUE Monte Carlo uncertainty analyses framework. Model performance was evaluated for each catchment, model structure and parameter set using standard performance metrics. These were calculated both for the whole time series and to assess seasonal differences in model performance. The GLUE uncertainty analysis framework was then applied to produce simulated 5th and 95th percentile uncertainty bounds for the daily flow time-series and additionally the annual maximum prediction bounds for each catchment. The results show that the model performance varies significantly in space and time depending on catchment characteristics including climate, geology and human impact. We identify regions where models are systematically failing to produce good results, and present reasons why this could be the case. We also identify regions or catchment characteristics where one model performs better than others, and have explored what structural component or parameterisation enables certain models to produce better simulations in these catchments. Model predictive capability was assessed for each catchment, through looking at the ability of the models to produce discharge prediction bounds which successfully bound the observed discharge. These results improve our understanding of the predictive capability of simple conceptual hydrological models across the UK and help us to identify where further effort is needed to develop modelling approaches to better represent different catchment and climate typologies.

Model Predictive Control application for real time operation of controlled structures for the Water Authority Noorderzijlvest, The Netherlands

NASA Astrophysics Data System (ADS)

van Heeringen, Klaas-Jan; Gooijer, Jan; Knot, Floris; Talsma, Jan

2015-04-01

In the Netherlands, flood protection has always been a key issue to protect settlements against storm surges and riverine floods. Whereas flood protection traditionally focused on structural measures, nowadays the availability of meteorological and hydrological forecasts enable the application of more advanced real-time control techniques for operating the existing hydraulic infrastructure in an anticipatory and more efficient way. Model Predictive Control (MPC) is a powerful technique to derive optimal control variables with the help of model based predictions evaluated against a control objective. In a project for the regional water authority Noorderzijlvest in the north of the Netherlands, it has been shown that MPC can increase the safety level of the system during flood events by an anticipatory pre-release of water. Furthermore, energy costs of pumps can be reduced by making tactical use of the water storage and shifting pump activities during normal operating conditions to off-peak hours. In this way cheap energy is used in combination of gravity flow through gates during low tide periods. MPC has now been implemented for daily operational use of the whole water system of the water authority Noorderzijlvest. The system developed to a real time decision support system which not only supports the daily operation but is able to directly implement the optimal control settings at the structures. We explain how we set-up and calibrated a prediction model (RTC-Tools) that is accurate and fast enough for optimization purposes, and how we integrated it in the operational flood early warning system (Delft-FEWS). Beside the prediction model, the weights and the factors of the objective function are an important element of MPC, since they shape the control objective. We developed special features in Delft-FEWS to allow the operators to adjust the objective function in order to meet changing requirements and to evaluate different control strategies.

Predicting RNA 3D structure using a coarse-grain helix-centered model

PubMed Central

Kerpedjiev, Peter; Höner zu Siederdissen, Christian; Hofacker, Ivo L.

2015-01-01

A 3D model of RNA structure can provide information about its function and regulation that is not possible with just the sequence or secondary structure. Current models suffer from low accuracy and long running times and either neglect or presume knowledge of the long-range interactions which stabilize the tertiary structure. Our coarse-grained, helix-based, tertiary structure model operates with only a few degrees of freedom compared with all-atom models while preserving the ability to sample tertiary structures given a secondary structure. It strikes a balance between the precision of an all-atom tertiary structure model and the simplicity and effectiveness of a secondary structure representation. It provides a simplified tool for exploring global arrangements of helices and loops within RNA structures. We provide an example of a novel energy function relying only on the positions of stems and loops. We show that coupling our model to this energy function produces predictions as good as or better than the current state of the art tools. We propose that given the wide range of conformational space that needs to be explored, a coarse-grain approach can explore more conformations in less iterations than an all-atom model coupled to a fine-grain energy function. Finally, we emphasize the overarching theme of providing an ensemble of predicted structures, something which our tool excels at, rather than providing a handful of the lowest energy structures. PMID:25904133

Four-Phase Dendritic Model for the Prediction of Macrosegregation, Shrinkage Cavity, and Porosity in a 55-Ton Ingot

NASA Astrophysics Data System (ADS)

Ge, Honghao; Ren, Fengli; Li, Jun; Han, Xiujun; Xia, Mingxu; Li, Jianguo

2017-03-01

A four-phase dendritic model was developed to predict the macrosegregation, shrinkage cavity, and porosity during solidification. In this four-phase dendritic model, some important factors, including dendritic structure for equiaxed crystals, melt convection, crystals sedimentation, nucleation, growth, and shrinkage of solidified phases, were taken into consideration. Furthermore, in this four-phase dendritic model, a modified shrinkage criterion was established to predict shrinkage porosity (microporosity) of a 55-ton industrial Fe-3.3 wt pct C ingot. The predicted macrosegregation pattern and shrinkage cavity shape are in a good agreement with experimental results. The shrinkage cavity has a significant effect on the formation of positive segregation in hot top region, which generally forms during the last stage of ingot casting. The dendritic equiaxed grains also play an important role on the formation of A-segregation. A three-dimensional laminar structure of A-segregation in industrial ingot was, for the first time, predicted by using a 3D case simulation.

Compact structure and non-Gaussian dynamics of ring polymer melts.

PubMed

Brás, Ana R; Goossen, Sebastian; Krutyeva, Margarita; Radulescu, Aurel; Farago, Bela; Allgaier, Jürgen; Pyckhout-Hintzen, Wim; Wischnewski, Andreas; Richter, Dieter

2014-05-28

We present a neutron scattering analysis of the structure and dynamics of PEO polymer rings with a molecular weight 2.5 times higher than the entanglement mass. The melt structure was found to be more compact than a Gaussian model would suggest. With increasing time the center of mass (c.o.m.) diffusion undergoes a transition from sub-diffusive to diffusive behavior. The transition time agrees well with the decorrelation time predicted by a mode coupling approach. As a novel feature well pronounced non-Gaussian behavior of the c.o.m. diffusion was found that shows surprising analogies to the cage effect known from glassy systems. Finally, the longest wavelength Rouse modes are suppressed possibly as a consequence of an onset of lattice animal features as hypothesized in theoretical approaches.

Structure of supersonic jet flow and its radiated sound

NASA Technical Reports Server (NTRS)

Mankbadi, Reda R.; Hayer, M. Ehtesham; Povinelli, Louis A.

1994-01-01

The present paper explores the use of large-eddy simulations as a tool for predicting noise from first principles. A high-order numerical scheme is used to perform large-eddy simulations of a supersonic jet flow with emphasis on capturing the time-dependent flow structure representating the sound source. The wavelike nature of this structure under random inflow disturbances is demonstrated. This wavelike structure is then enhanced by taking the inflow disturbances to be purely harmonic. Application of Lighthill's theory to calculate the far-field noise, with the sound source obtained from the calculated time-dependent near field, is demonstrated. Alternative approaches to coupling the near-field sound source to the far-field sound are discussed.

Continuum Damage Mechanics Used to Predict the Creep Life of Monolithic Ceramics

NASA Technical Reports Server (NTRS)

Powers, Lynn M.; Jadaan, Osama M.

1998-01-01

Significant improvements in propulsion and power generation for the next century will require revolutionary advances in high-temperature materials and structural design. Advanced ceramics are candidate materials for these elevated temperature applications. High-temperature and long-duration applications of monolithic ceramics can place their failure mode in the creep rupture regime. An analytical methodology in the form of the integrated design program-Ceramics Analysis and Reliability Evaluation of Structures/Creep (CARES/Creep) has been developed by the NASA Lewis Research Center to predict the life of ceramic structural components subjected to creep rupture conditions. This program utilizes commercially available finite element packages and takes into account the transient state of stress and creep strain distributions (stress relaxation as well as the asymmetric response to tension and compression). The creep life of a component is discretized into short time steps, during which the stress distribution is assumed constant. Then, the damage is calculated for each time step on the basis of a modified Monkman-Grant (MMG) creep rupture criterion. The cumulative damage is subsequently calculated as time elapses in a manner similar to Miner's rule for cyclic fatigue loading. Failure is assumed to occur when the normalized cumulative damage at any point in the component reaches unity. The corresponding time is the creep rupture life for that component.

Knowing too little or too much: the effects of familiarity with a co-performer's part on interpersonal coordination in musical ensembles

PubMed Central

Ragert, Marie; Schroeder, Tim; Keller, Peter E.

2013-01-01

Expert ensemble musicians produce exquisitely coordinated sounds, but rehearsal is typically required to do so. Ensemble coordination may thus be influenced by the degree to which individuals are familiar with each other's parts. Such familiarity may affect the ability to predict and synchronize with co-performers' actions. Internal models related to action simulation and anticipatory musical imagery may be affected by knowledge of (1) the musical structure of a co-performer's part (e.g., in terms of its rhythm and phrase structure) and/or (2) the co-performer's idiosyncratic playing style (e.g., expressive micro-timing variations). The current study investigated the effects of familiarity on interpersonal coordination in piano duos. Skilled pianists were required to play several duets with different partners. One condition included duets for which co-performers had previously practiced both parts, while another condition included duets for which each performer had practiced only their own part. Each piece was recorded six times without joint rehearsal or visual contact to examine the effects of increasing familiarity. Interpersonal coordination was quantified by measuring asynchronies between pianists' keystroke timing and the correlation of their body (head and torso) movements, which were recorded with a motion capture system. The results suggest that familiarity with a co-performer's part, in the absence of familiarity with their playing style, engenders predictions about micro-timing variations that are based instead upon one's own playing style, leading to a mismatch between predictions and actual events at short timescales. Predictions at longer timescales—that is, those related to musical measures and phrases, and reflected in head movements and body sway—are, however, facilitated by familiarity with the structure of a co-performer's part. These findings point to a dissociation between interpersonal coordination at the level of keystrokes and body movements. PMID:23805116

Comparison of time series models for predicting campylobacteriosis risk in New Zealand.

PubMed

Al-Sakkaf, A; Jones, G

2014-05-01

Predicting campylobacteriosis cases is a matter of considerable concern in New Zealand, after the number of the notified cases was the highest among the developed countries in 2006. Thus, there is a need to develop a model or a tool to predict accurately the number of campylobacteriosis cases as the Microbial Risk Assessment Model used to predict the number of campylobacteriosis cases failed to predict accurately the number of actual cases. We explore the appropriateness of classical time series modelling approaches for predicting campylobacteriosis. Finding the most appropriate time series model for New Zealand data has additional practical considerations given a possible structural change, that is, a specific and sudden change in response to the implemented interventions. A univariate methodological approach was used to predict monthly disease cases using New Zealand surveillance data of campylobacteriosis incidence from 1998 to 2009. The data from the years 1998 to 2008 were used to model the time series with the year 2009 held out of the data set for model validation. The best two models were then fitted to the full 1998-2009 data and used to predict for each month of 2010. The Holt-Winters (multiplicative) and ARIMA (additive) intervention models were considered the best models for predicting campylobacteriosis in New Zealand. It was noticed that the prediction by an additive ARIMA with intervention was slightly better than the prediction by a Holt-Winter multiplicative method for the annual total in year 2010, the former predicting only 23 cases less than the actual reported cases. It is confirmed that classical time series techniques such as ARIMA with intervention and Holt-Winters can provide a good prediction performance for campylobacteriosis risk in New Zealand. The results reported by this study are useful to the New Zealand Health and Safety Authority's efforts in addressing the problem of the campylobacteriosis epidemic. © 2013 Blackwell Verlag GmbH.

Improved Helicopter Rotor Performance Prediction through Loose and Tight CFD/CSD Coupling

NASA Astrophysics Data System (ADS)

Ickes, Jacob C.

Helicopters and other Vertical Take-Off or Landing (VTOL) vehicles exhibit an interesting combination of structural dynamic and aerodynamic phenomena which together drive the rotor performance. The combination of factors involved make simulating the rotor a challenging and multidisciplinary effort, and one which is still an active area of interest in the industry because of the money and time it could save during design. Modern tools allow the prediction of rotorcraft physics from first principles. Analysis of the rotor system with this level of accuracy provides the understanding necessary to improve its performance. There has historically been a divide between the comprehensive codes which perform aeroelastic rotor simulations using simplified aerodynamic models, and the very computationally intensive Navier-Stokes Computational Fluid Dynamics (CFD) solvers. As computer resources become more available, efforts have been made to replace the simplified aerodynamics of the comprehensive codes with the more accurate results from a CFD code. The objective of this work is to perform aeroelastic rotorcraft analysis using first-principles simulations for both fluids and structural predictions using tools available at the University of Toledo. Two separate codes are coupled together in both loose coupling (data exchange on a periodic interval) and tight coupling (data exchange each time step) schemes. To allow the coupling to be carried out in a reliable and efficient way, a Fluid-Structure Interaction code was developed which automatically performs primary functions of loose and tight coupling procedures. Flow phenomena such as transonics, dynamic stall, locally reversed flow on a blade, and Blade-Vortex Interaction (BVI) were simulated in this work. Results of the analysis show aerodynamic load improvement due to the inclusion of the CFD-based airloads in the structural dynamics analysis of the Computational Structural Dynamics (CSD) code. Improvements came in the form of improved peak/trough magnitude prediction, better phase prediction of these locations, and a predicted signal with a frequency content more like the flight test data than the CSD code acting alone. Additionally, a tight coupling analysis was performed as a demonstration of the capability and unique aspects of such an analysis. This work shows that away from the center of the flight envelope, the aerodynamic modeling of the CSD code can be replaced with a more accurate set of predictions from a CFD code with an improvement in the aerodynamic results. The better predictions come at substantially increased computational costs between 1,000 and 10,000 processor-hours.

Preface: Special Topic Section on Advanced Electronic Structure Methods for Solids and Surfaces.

PubMed

Michaelides, Angelos; Martinez, Todd J; Alavi, Ali; Kresse, Georg; Manby, Frederick R

2015-09-14

This Special Topic section on Advanced Electronic Structure Methods for Solids and Surfaces contains a collection of research papers that showcase recent advances in the high accuracy prediction of materials and surface properties. It provides a timely snapshot of a growing field that is of broad importance to chemistry, physics, and materials science.

Exploring Methods for Developing Behaviorally Anchored Rating Scales for Evaluating Structured Interview Performance. Research Report. ETS RR-17-28

ERIC Educational Resources Information Center

Kell, Harrison J.; Martin-Raugh, Michelle P.; Carney, Lauren M.; Inglese, Patricia A.; Chen, Lei; Feng, Gary

2017-01-01

Behaviorally anchored rating scales (BARS) are an essential component of structured interviews. Use of BARS to evaluate interviewees' performance is associated with greater predictive validity and reliability and less bias. BARS are time-consuming and expensive to construct, however. This report explores the feasibility of gathering participants'…

A hierarchical linear model for tree height prediction.

Treesearch

Vicente J. Monleon

2003-01-01

Measuring tree height is a time-consuming process. Often, tree diameter is measured and height is estimated from a published regression model. Trees used to develop these models are clustered into stands, but this structure is ignored and independence is assumed. In this study, hierarchical linear models that account explicitly for the clustered structure of the data...

Composition, structure, and dynamics of the Illinois Ozark Hills Forest

Treesearch

Lisa M. Helmig; James S. Fralish

2011-01-01

In the mature oak-hickory ecosystem of the Illinois Ozark Hills, forest community composition, dynamics, and structure were studied to examine the extent of conversion to mesophytic species and eventually predict the broad threshold time of complete conversion. Tree, sapling, and seedling data were collected from 87 plots distributed throughout the region. Data for the...

Empowerment and performance of managers and subordinates in elderly care: A longitudinal and multilevel study.

PubMed

Hagerman, Heidi; Högberg, Hans; Skytt, Bernice; Wadensten, Barbro; Engström, Maria

2017-11-01

To investigate relationships between first-line managers' ratings of structural and psychological empowerment, and the subordinates' ratings of structural empowerment, as well as their ratings of the managers' leadership-management performance. Work situations in elderly care are complex. To date, few studies have used a longitudinal, correlational and multilevel design to study the working life of subordinates and managers. In five Swedish municipalities, questionnaires were answered twice during 2010-12 by 56 first-line managers and 769 subordinates working in nursing homes or home-help services. First-line managers' empowerment at Time 1 partially predicted subordinate's structural empowerment and ratings of their managers' leadership-management performance at Time 2. Changes over time partially revealed that the more access managers had to structural empowerment, i.e. increase over time, the higher the ratings were for structural empowerment and managerial leadership-management performance among subordinates. Findings strengthen research and theoretical suggestions linking first-line managers' structural empowerment to their subordinates' structural empowerment and ratings of their manager's leadership-management performance. Managers with high access to structural empowerment are more likely to provide subordinates access to structural empowerment. © 2017 The Authors. Journal of Nursing Management Published by John Wiley & Sons Ltd.

A Feature-based Developmental Model of the Infant Brain in Structural MRI

PubMed Central

Toews, Matthew; Wells, William M.; Zöllei, Lilla

2014-01-01

In this paper, anatomical development is modeled as a collection of distinctive image patterns localized in space and time. A Bayesian posterior probability is defined over a random variable of subject age, conditioned on data in the form of scale-invariant image features. The model is automatically learned from a large set of images exhibiting significant variation, used to discover anatomical structure related to age and development, and fit to new images to predict age. The model is applied to a set of 230 infant structural MRIs of 92 subjects acquired at multiple sites over an age range of 8-590 days. Experiments demonstrate that the model can be used to identify age-related anatomical structure, and to predict the age of new subjects with an average error of 72 days. PMID:23286050

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).

Advective transport in heterogeneous aquifers: Are proxy models predictive?

NASA Astrophysics Data System (ADS)

Fiori, A.; Zarlenga, A.; Gotovac, H.; Jankovic, I.; Volpi, E.; Cvetkovic, V.; Dagan, G.

2015-12-01

We examine the prediction capability of two approximate models (Multi-Rate Mass Transfer (MRMT) and Continuous Time Random Walk (CTRW)) of non-Fickian transport, by comparison with accurate 2-D and 3-D numerical simulations. Both nonlocal in time approaches circumvent the need to solve the flow and transport equations by using proxy models to advection, providing the breakthrough curves (BTC) at control planes at any x, depending on a vector of five unknown parameters. Although underlain by different mechanisms, the two models have an identical structure in the Laplace Transform domain and have the Markovian property of independent transitions. We show that also the numerical BTCs enjoy the Markovian property. Following the procedure recommended in the literature, along a practitioner perspective, we first calibrate the parameters values by a best fit with the numerical BTC at a control plane at x1, close to the injection plane, and subsequently use it for prediction at further control planes for a few values of σY2≤8. Due to a similar structure and Markovian property, the two methods perform equally well in matching the numerical BTC. The identified parameters are generally not unique, making their identification somewhat arbitrary. The inverse Gaussian model and the recently developed Multi-Indicator Model (MIM), which does not require any fitting as it relates the BTC to the permeability structure, are also discussed. The application of the proxy models for prediction requires carrying out transport field tests of large plumes for a long duration.

Abstract shapes of RNA.

PubMed

Giegerich, Robert; Voss, Björn; Rehmsmeier, Marc

2004-01-01

The function of a non-protein-coding RNA is often determined by its structure. Since experimental determination of RNA structure is time-consuming and expensive, its computational prediction is of great interest, and efficient solutions based on thermodynamic parameters are known. Frequently, however, the predicted minimum free energy structures are not the native ones, leading to the necessity of generating suboptimal solutions. While this can be accomplished by a number of programs, the user is often confronted with large outputs of similar structures, although he or she is interested in structures with more fundamental differences, or, in other words, with different abstract shapes. Here, we formalize the concept of abstract shapes and introduce their efficient computation. Each shape of an RNA molecule comprises a class of similar structures and has a representative structure of minimal free energy within the class. Shape analysis is implemented in the program RNAshapes. We applied RNAshapes to the prediction of optimal and suboptimal abstract shapes of several RNAs. For a given energy range, the number of shapes is considerably smaller than the number of structures, and in all cases, the native structures were among the top shape representatives. This demonstrates that the researcher can quickly focus on the structures of interest, without processing up to thousands of near-optimal solutions. We complement this study with a large-scale analysis of the growth behaviour of structure and shape spaces. RNAshapes is available for download and as an online version on the Bielefeld Bioinformatics Server.

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.

Stalk model of membrane fusion: solution of energy crisis.

PubMed Central

Kozlovsky, Yonathan; Kozlov, Michael M

2002-01-01

Membrane fusion proceeds via formation of intermediate nonbilayer structures. The stalk model of fusion intermediate is commonly recognized to account for the major phenomenology of the fusion process. However, in its current form, the stalk model poses a challenge. On one hand, it is able to describe qualitatively the modulation of the fusion reaction by the lipid composition of the membranes. On the other, it predicts very large values of the stalk energy, so that the related energy barrier for fusion cannot be overcome by membranes within a biologically reasonable span of time. We suggest a new structure for the fusion stalk, which resolves the energy crisis of the model. Our approach is based on a combined deformation of the stalk membrane including bending of the membrane surface and tilt of the hydrocarbon chains of lipid molecules. We demonstrate that the energy of the fusion stalk is a few times smaller than those predicted previously and the stalks are feasible in real systems. We account quantitatively for the experimental results on dependence of the fusion reaction on the lipid composition of different membrane monolayers. We analyze the dependence of the stalk energy on the distance between the fusing membranes and provide the experimentally testable predictions for the structural features of the stalk intermediates. PMID:11806930

Bone volume fraction and structural parameters for estimation of mechanical stiffness and failure load of human cancellous bone samples; in-vitro comparison of ultrasound transit time spectroscopy and X-ray μCT.

PubMed

Alomari, Ali Hamed; Wille, Marie-Luise; Langton, Christian M

2018-02-01

Conventional mechanical testing is the 'gold standard' for assessing the stiffness (N mm -1 ) and strength (MPa) of bone, although it is not applicable in-vivo since it is inherently invasive and destructive. The mechanical integrity of a bone is determined by its quantity and quality; being related primarily to bone density and structure respectively. Several non-destructive, non-invasive, in-vivo techniques have been developed and clinically implemented to estimate bone density, both areal (dual-energy X-ray absorptiometry (DXA)) and volumetric (quantitative computed tomography (QCT)). Quantitative ultrasound (QUS) parameters of velocity and attenuation are dependent upon both bone quantity and bone quality, although it has not been possible to date to transpose one particular QUS parameter into separate estimates of quantity and quality. It has recently been shown that ultrasound transit time spectroscopy (UTTS) may provide an accurate estimate of bone density and hence quantity. We hypothesised that UTTS also has the potential to provide an estimate of bone structure and hence quality. In this in-vitro study, 16 human femoral bone samples were tested utilising three techniques; UTTS, micro computed tomography (μCT), and mechanical testing. UTTS was utilised to estimate bone volume fraction (BV/TV) and two novel structural parameters, inter-quartile range of the derived transit time (UTTS-IQR) and the transit time of maximum proportion of sonic-rays (TTMP). μCT was utilised to derive BV/TV along with several bone structure parameters. A destructive mechanical test was utilised to measure the stiffness and strength (failure load) of the bone samples. BV/TV was calculated from the derived transit time spectrum (TTS); the correlation coefficient (R 2 ) with μCT-BV/TV was 0.885. For predicting mechanical stiffness and strength, BV/TV derived by both μCT and UTTS provided the strongest correlation with mechanical stiffness (R 2 =0.567 and 0.618 respectively) and mechanical strength (R 2 =0.747 and 0.736 respectively). When respective structural parameters were incorporated to BV/TV, multiple regression analysis indicated that none of the μCT histomorphometric parameters could improve the prediction of mechanical stiffness and strength, while for UTTS, adding TTMP to BV/TV increased the prediction of mechanical stiffness to R 2 =0.711 and strength to R 2 =0.827. It is therefore envisaged that UTTS may have the ability to estimate BV/TV along with providing an improved prediction of osteoporotic fracture risk, within routine clinical practice in the future. Copyright © 2017 Elsevier Inc. All rights reserved.

Analytical modeling of fire growth on fire-resistive wood-based materials with changing conditions

Treesearch

Mark A. Dietenberger

2006-01-01

Our analytical model of fire growth for the ASTM E 84 tunnel, which simultaneously predicts heat release rate, flame-over area, and pyrolysis area as functions of time for constant conditions, was documented in the 2001 BCC Symposium for different treated wood materials. The model was extended to predict ignition and fire growth on exterior fire-resistive structures...

Assessing Time Management Skills as an Important Aspect of Student Learning: The Construction and Evaluation of a Time Management Scale with Spanish High School Students

ERIC Educational Resources Information Center

Garcia-Ros, Rafael; Perez-Gonzalez, Francisco; Hinojosa, Eugenia

2004-01-01

The main purpose of this study is to analyse the factorial structure, psychometric properties and predictive capacity for academic achievement of a scale designed to evaluate the time management skills of Spanish high school students. An adaptation of the Time Management Questionnaire was presented to two samples of 350 Spanish high school…

FRAGSION: ultra-fast protein fragment library generation by IOHMM sampling.

PubMed

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

2016-07-01

Speed, accuracy and robustness of building protein fragment library have important implications in de novo protein structure prediction since fragment-based methods are one of the most successful approaches in template-free modeling (FM). Majority of the existing fragment detection methods rely on database-driven search strategies to identify candidate fragments, which are inherently time-consuming and often hinder the possibility to locate longer fragments due to the limited sizes of databases. Also, it is difficult to alleviate the effect of noisy sequence-based predicted features such as secondary structures on the quality of fragment. Here, we present FRAGSION, a database-free method to efficiently generate protein fragment library by sampling from an Input-Output Hidden Markov Model. FRAGSION offers some unique features compared to existing approaches in that it (i) is lightning-fast, consuming only few seconds of CPU time to generate fragment library for a protein of typical length (300 residues); (ii) can generate dynamic-size fragments of any length (even for the whole protein sequence) and (iii) offers ways to handle noise in predicted secondary structure during fragment sampling. On a FM dataset from the most recent Critical Assessment of Structure Prediction, we demonstrate that FGRAGSION provides advantages over the state-of-the-art fragment picking protocol of ROSETTA suite by speeding up computation by several orders of magnitude while achieving comparable performance in fragment quality. Source code and executable versions of FRAGSION for Linux and MacOS is freely available to non-commercial users at http://sysbio.rnet.missouri.edu/FRAGSION/ It is bundled with a manual and example data. chengji@missouri.edu Supplementary data are available at Bioinformatics online. © The Author 2016. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com.

Effect of Helicopter Blade Dynamics on Blade Aerodynamic and Structural Loads

NASA Technical Reports Server (NTRS)

Heffernan, Ruth M.

1987-01-01

The effect of rotor blade dynamics on aerodynamic and structural loads is examined for a conventional, main- rotor helicopter using both a comprehensive rotorcraft analysis (CAMRAD) and night test data. The impact of blade dynamics on blade section lift-coefficient time histories is studied by comparing predictions from both a rigid blade analysis and an elastic blade analysis with helicopter flight test data. The elastic blade analysis better predicts high-frequency behavior of section lift. In addition, components of the blade angle of attack, such as elastic blade twist, blade nap rate, blade slope velocity, and inflow, are examined as a function of blade mode. Elastic blade motion affects the blade angle of attack by a few tenths of a degree, and up to the sixth rotor harmonic. A similar study of the influence of blade dynamics on bending and torsion moments was also conducted. The modal analysis of the predicted blade structural loads suggested that five elastic bending deg of freedom (four flap and one lag) and three elastic torsion deg of freedom contributed to calculations of the blade structural loads. However, when structural bending load predictions from several elastic blade analyses were compared with flight test data, an elastic blade model consisting of only three elastic bending modes (first and second flap, and first lag), and two elastic torsion modes was found to be sufficient for maximum correlation.

Pressure-induced structural transformations and polymerization in ThC2

PubMed Central

Guo, Yongliang; Yu, Cun; Lin, Jun; Wang, Changying; Ren, Cuilan; Sun, Baoxing; Huai, Ping; Xie, Ruobing; Ke, Xuezhi; Zhu, Zhiyuan; Xu, Hongjie

2017-01-01

Thorium-carbon systems have been thought as promising nuclear fuel for Generation IV reactors which require high-burnup and safe nuclear fuel. Existing knowledge on thorium carbides under extreme condition remains insufficient and some is controversial due to limited studies. Here we systematically predict all stable structures of thorium dicarbide (ThC2) under the pressure ranging from ambient to 300 GPa by merging ab initio total energy calculations and unbiased structure searching method, which are in sequence of C2/c, C2/m, Cmmm, Immm and P6/mmm phases. Among these phases, the C2/m is successfully observed for the first time via in situ synchrotron XRD measurements, which exhibits an excellent structural correspondence to our theoretical predictions. The transition sequence and the critical pressures are predicted. The calculated results also reveal the polymerization behaviors of the carbon atoms and the corresponding characteristic C-C bonding under various pressures. Our work provides key information on the fundamental material behavior and insights into the underlying mechanisms that lay the foundation for further exploration and application of ThC2. PMID:28383571

Pressure-induced structural transformations and polymerization in ThC2

NASA Astrophysics Data System (ADS)

Guo, Yongliang; Yu, Cun; Lin, Jun; Wang, Changying; Ren, Cuilan; Sun, Baoxing; Huai, Ping; Xie, Ruobing; Ke, Xuezhi; Zhu, Zhiyuan; Xu, Hongjie

2017-04-01

Thorium-carbon systems have been thought as promising nuclear fuel for Generation IV reactors which require high-burnup and safe nuclear fuel. Existing knowledge on thorium carbides under extreme condition remains insufficient and some is controversial due to limited studies. Here we systematically predict all stable structures of thorium dicarbide (ThC2) under the pressure ranging from ambient to 300 GPa by merging ab initio total energy calculations and unbiased structure searching method, which are in sequence of C2/c, C2/m, Cmmm, Immm and P6/mmm phases. Among these phases, the C2/m is successfully observed for the first time via in situ synchrotron XRD measurements, which exhibits an excellent structural correspondence to our theoretical predictions. The transition sequence and the critical pressures are predicted. The calculated results also reveal the polymerization behaviors of the carbon atoms and the corresponding characteristic C-C bonding under various pressures. Our work provides key information on the fundamental material behavior and insights into the underlying mechanisms that lay the foundation for further exploration and application of ThC2.

Pressure-induced structural transformations and polymerization in ThC2.

PubMed

Guo, Yongliang; Yu, Cun; Lin, Jun; Wang, Changying; Ren, Cuilan; Sun, Baoxing; Huai, Ping; Xie, Ruobing; Ke, Xuezhi; Zhu, Zhiyuan; Xu, Hongjie

2017-04-06

Thorium-carbon systems have been thought as promising nuclear fuel for Generation IV reactors which require high-burnup and safe nuclear fuel. Existing knowledge on thorium carbides under extreme condition remains insufficient and some is controversial due to limited studies. Here we systematically predict all stable structures of thorium dicarbide (ThC 2 ) under the pressure ranging from ambient to 300 GPa by merging ab initio total energy calculations and unbiased structure searching method, which are in sequence of C2/c, C2/m, Cmmm, Immm and P6/mmm phases. Among these phases, the C2/m is successfully observed for the first time via in situ synchrotron XRD measurements, which exhibits an excellent structural correspondence to our theoretical predictions. The transition sequence and the critical pressures are predicted. The calculated results also reveal the polymerization behaviors of the carbon atoms and the corresponding characteristic C-C bonding under various pressures. Our work provides key information on the fundamental material behavior and insights into the underlying mechanisms that lay the foundation for further exploration and application of ThC 2 .

DSSTOX WEBSITE LAUNCH: IMPROVING PUBLIC ACCESS ...

EPA Pesticide Factsheets

DSSTox Website Launch: Improving Public Access to Databases for Building Structure-Toxicity Prediction ModelsAnn M. RichardUS Environmental Protection Agency, Research Triangle Park, NC, USADistributed: Decentralized set of standardized, field-delimited databases, each separatelyauthored and maintained, that are able to accommodate diverse toxicity data content;Structure-Searchable: Standard format (SDF) structure-data files that can be readily imported into available chemical relational databases and structure-searched;Tox: Toxicity data as it exists in widely disparate forms in current public databases, spanning diverse toxicity endpoints, test systems, levels of biological content, degrees of summarization, and information content.INTRODUCTIONThe economic and social pressures to reduce the need for animal testing and to better anticipate the potential for human and eco-toxicity of environmental, industrial, or pharmaceutical chemicals are as pressing today as at any time prior. However, the goal of predicting chemical toxicity in its many manifestations, the `T' in 'ADMET' (adsorption, distribution, metabolism, elimination, toxicity), remains one of the most difficult and largely unmet challenges in a chemical screening paradigm [1]. It is widely acknowledged that the single greatest hurdle to improving structure-activity relationship (SAR) toxicity prediction capabilities, in both the pharmaceutical and environmental regulation arenas, is the lack of suffici

Conformational switching upon phosphorylation: a predictive framework based on energy landscape principles.

PubMed

Lätzer, Joachim; Shen, Tongye; Wolynes, Peter G

2008-02-19

We investigate how post-translational phosphorylation modifies the global conformation of a protein by changing its free energy landscape using two test proteins, cystatin and NtrC. We first examine the changes in a free energy landscape caused by phosphorylation using a model containing information about both structural forms. For cystatin the free energy cost is fairly large indicating a low probability of sampling the phosphorylated conformation in a perfectly funneled landscape. The predicted barrier for NtrC conformational transition is several times larger than the barrier for cystatin, indicating that the switch protein NtrC most probably follows a partial unfolding mechanism to move from one basin to the other. Principal component analysis and linear response theory show how the naturally occurring conformational changes in unmodified proteins are captured and stabilized by the change of interaction potential. We also develop a partially guided structure prediction Hamiltonian which is capable of predicting the global structure of a phosphorylated protein using only knowledge of the structure of the unphosphorylated protein or vice versa. This algorithm makes use of a generic transferable long-range residue contact potential along with details of structure short range in sequence. By comparing the results obtained with this guided transferable potential to those from the native-only, perfectly funneled Hamiltonians, we show that the transferable Hamiltonian correctly captures the nature of the global conformational changes induced by phosphorylation and can sample substantially correct structures for the modified protein with high probability.

A Prediction Model for ROS1-Rearranged Lung Adenocarcinomas based on Histologic Features

PubMed Central

Zheng, Jing; Kong, Mei; Sun, Ke; Wang, Bo; Chen, Xi; Ding, Wei; Zhou, Jianying

2016-01-01

Aims To identify the clinical and histological characteristics of ROS1-rearranged non-small-cell lung carcinomas (NSCLCs) and build a prediction model to prescreen suitable patients for molecular testing. Methods and Results We identified 27 cases of ROS1-rearranged lung adenocarcinomas in 1165 patients with NSCLCs confirmed by real-time PCR and FISH and performed univariate and multivariate analyses to identify predictive factors associated with ROS1 rearrangement and finally developed prediction model. Detected with ROS1 immunochemistry, 59 cases of 1165 patients had a certain degree of ROS1 expression. Among these cases, 19 cases (68%, 19/28) with 3+ and 8 cases (47%, 8/17) with 2+ staining were ROS1 rearrangement verified by real-time PCR and FISH. In the resected group, the acinar-predominant growth pattern was the most commonly observed (57%, 8/14), while in the biopsy group, solid patterns were the most frequently observed (78%, 7/13). Based on multiple logistic regression analysis, we determined that female sex, cribriform structure and the presence of psammoma body were the three most powerful indicators of ROS1 rearrangement, and we have developed a predictive model for the presence of ROS1 rearrangements in lung adenocarcinomas. Conclusions Female, cribriform structure and presence of psammoma body were the three most powerful indicator of ROS1 rearrangement status, and predictive formula was helpful in screening ROS1-rearranged NSCLC, especially for ROS1 immunochemistry equivocal cases. PMID:27648828

Structural models used in real-time biosurveillance outbreak detection and outbreak curve isolation from noisy background morbidity levels

PubMed Central

Cheng, Karen Elizabeth; Crary, David J; Ray, Jaideep; Safta, Cosmin

2013-01-01

Objective We discuss the use of structural models for the analysis of biosurveillance related data. Methods and results Using a combination of real and simulated data, we have constructed a data set that represents a plausible time series resulting from surveillance of a large scale bioterrorist anthrax attack in Miami. We discuss the performance of anomaly detection with structural models for these data using receiver operating characteristic (ROC) and activity monitoring operating characteristic (AMOC) analysis. In addition, we show that these techniques provide a method for predicting the level of the outbreak valid for approximately 2 weeks, post-alarm. Conclusions Structural models provide an effective tool for the analysis of biosurveillance data, in particular for time series with noisy, non-stationary background and missing data. PMID:23037798

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

Evaluation of Health Equity Impact of Structural Policies: Overview of Research Methods Used in the SOPHIE Project.

PubMed

Kunst, Anton E

2017-07-01

This article briefly assesses the research methods that were applied in the SOPHIE project to evaluate the impact of structural policies on population health and health inequalities. The evaluation of structural policies is one of the key methodological challenges in today's public health. The experience in the SOPHIE project was that mixed methods are essential to identify, understand, and predict the health impact of structural policies. On the one hand, quantitative studies that included spatial comparisons or time trend analyses, preferably in a quasi-experimental design, showed that some structural policies were associated with improved population health and smaller health inequalities. On the other hand, qualitative studies, often inspired by realist approaches, were important to understand how these policies could have achieved the observed impact and why they would succeed in some settings but fail in others. This review ends with five recommendations for future studies that aim to evaluate, understand, and predict how health inequalities can be reduced through structural policies.

How Sensitive Are Transdermal Transport Predictions by Microscopic Stratum Corneum Models to Geometric and Transport Parameter Input?

PubMed

Wen, Jessica; Koo, Soh Myoung; Lape, Nancy

2018-02-01

While predictive models of transdermal transport have the potential to reduce human and animal testing, microscopic stratum corneum (SC) model output is highly dependent on idealized SC geometry, transport pathway (transcellular vs. intercellular), and penetrant transport parameters (e.g., compound diffusivity in lipids). Most microscopic models are limited to a simple rectangular brick-and-mortar SC geometry and do not account for variability across delivery sites, hydration levels, and populations. In addition, these models rely on transport parameters obtained from pure theory, parameter fitting to match in vivo experiments, and time-intensive diffusion experiments for each compound. In this work, we develop a microscopic finite element model that allows us to probe model sensitivity to variations in geometry, transport pathway, and hydration level. Given the dearth of experimentally-validated transport data and the wide range in theoretically-predicted transport parameters, we examine the model's response to a variety of transport parameters reported in the literature. Results show that model predictions are strongly dependent on all aforementioned variations, resulting in order-of-magnitude differences in lag times and permeabilities for distinct structure, hydration, and parameter combinations. This work demonstrates that universally predictive models cannot fully succeed without employing experimentally verified transport parameters and individualized SC structures. Copyright © 2018 American Pharmacists Association®. Published by Elsevier Inc. All rights reserved.

Predicting strain using forward modelling of restored cross-sections: Application to rollover anticlines over listric normal faults

NASA Astrophysics Data System (ADS)

Poblet, Josep; Bulnes, Mayte

2007-12-01

A strategy to predict strain across geological structures, based on previous techniques, is modified and evaluated, and a practical application is shown. The technique, which employs cross-section restoration combined with kinematic forward modelling, consists of restoring a section, placing circular strain markers on different domains of the restoration, and forward modelling the restored section with strain markers until the present-day stage is reached. The restoration algorithm employed must be also used to forward model the structure. The ellipses in the forward modelled section allow determining the strain state of the structure and may indirectly predict orientation and distribution of minor structures such as small-scale fractures. The forward model may be frozen at different time steps (different growth stages) allowing prediction of both spatial and temporal variation of strain. The method is evaluated through its application to two stages of a clay experiment, that includes strain markers, and its geometry and deformation history are well documented, providing a strong control on the results. To demonstrate the method's potential, it is successfully applied to a depth-converted seismic profile in the Central Sumatra Basin, Indonesia. This allowed us to gain insight into the deformation undergone by rollover anticlines over listric normal faults.

Mass and stiffness estimation using mobile devices for structural health monitoring

NASA Astrophysics Data System (ADS)

Le, Viet; Yu, Tzuyang

2015-04-01

In the structural health monitoring (SHM) of civil infrastructure, dynamic methods using mass, damping, and stiffness for characterizing structural health have been a traditional and widely used approach. Changes in these system parameters over time indicate the progress of structural degradation or deterioration. In these methods, capability of predicting system parameters is essential to their success. In this paper, research work on the development of a dynamic SHM method based on perturbation analysis is reported. The concept is to use externally applied mass to perturb an unknown system and measure the natural frequency of the system. Derived theoretical expressions for mass and stiffness prediction are experimentally verified by a building model. Dynamic responses of the building model perturbed by various masses in free vibration were experimentally measured by a mobile device (cell phone) to extract the natural frequency of the building model. Single-degreeof- freedom (SDOF) modeling approach was adopted for the sake of using a cell phone. From the experimental result, it is shown that the percentage error of predicted mass increases when the mass ratio increases, while the percentage error of predicted stiffness decreases when the mass ratio increases. This work also demonstrated the potential use of mobile devices in the health monitoring of civil infrastructure.

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

High fidelity CFD-CSD aeroelastic analysis of slender bladed horizontal-axis wind turbine

NASA Astrophysics Data System (ADS)

Sayed, M.; Lutz, Th.; Krämer, E.; Shayegan, Sh.; Ghantasala, A.; Wüchner, R.; Bletzinger, K.-U.

2016-09-01

The aeroelastic response of large multi-megawatt slender horizontal-axis wind turbine blades is investigated by means of a time-accurate CFD-CSD coupling approach. A loose coupling approach is implemented and used to perform the simulations. The block- structured CFD solver FLOWer is utilized to obtain the aerodynamic blade loads based on the time-accurate solution of the unsteady Reynolds-averaged Navier-Stokes equations. The CSD solver Carat++ is applied to acquire the blade elastic deformations based on non-linear beam elements. In this contribution, the presented coupling approach is utilized to study the aeroelastic response of the generic DTU 10MW wind turbine. Moreover, the effect of the coupled results on the wind turbine performance is discussed. The results are compared to the aeroelastic response predicted by FLOWer coupled to the MBS tool SIMPACK as well as the response predicted by SIMPACK coupled to a Blade Element Momentum code for aerodynamic predictions. A comparative study among the different modelling approaches for this coupled problem is discussed to quantify the coupling effects of the structural models on the aeroelastic response.

Fast prediction of RNA-RNA interaction using heuristic algorithm.

PubMed

Montaseri, Soheila

2015-01-01

Interaction between two RNA molecules plays a crucial role in many medical and biological processes such as gene expression regulation. In this process, an RNA molecule prohibits the translation of another RNA molecule by establishing stable interactions with it. Some algorithms have been formed to predict the structure of the RNA-RNA interaction. High computational time is a common challenge in most of the presented algorithms. In this context, a heuristic method is introduced to accurately predict the interaction between two RNAs based on minimum free energy (MFE). This algorithm uses a few dot matrices for finding the secondary structure of each RNA and binding sites between two RNAs. Furthermore, a parallel version of this method is presented. We describe the algorithm's concurrency and parallelism for a multicore chip. The proposed algorithm has been performed on some datasets including CopA-CopT, R1inv-R2inv, Tar-Tar*, DIS-DIS, and IncRNA54-RepZ in Escherichia coli bacteria. The method has high validity and efficiency, and it is run in low computational time in comparison to other approaches.

Heterogeneous Structure of Stem Cells Dynamics: Statistical Models and Quantitative Predictions

PubMed Central

Bogdan, Paul; Deasy, Bridget M.; Gharaibeh, Burhan; Roehrs, Timo; Marculescu, Radu

2014-01-01

Understanding stem cell (SC) population dynamics is essential for developing models that can be used in basic science and medicine, to aid in predicting cells fate. These models can be used as tools e.g. in studying patho-physiological events at the cellular and tissue level, predicting (mal)functions along the developmental course, and personalized regenerative medicine. Using time-lapsed imaging and statistical tools, we show that the dynamics of SC populations involve a heterogeneous structure consisting of multiple sub-population behaviors. Using non-Gaussian statistical approaches, we identify the co-existence of fast and slow dividing subpopulations, and quiescent cells, in stem cells from three species. The mathematical analysis also shows that, instead of developing independently, SCs exhibit a time-dependent fractal behavior as they interact with each other through molecular and tactile signals. These findings suggest that more sophisticated models of SC dynamics should view SC populations as a collective and avoid the simplifying homogeneity assumption by accounting for the presence of more than one dividing sub-population, and their multi-fractal characteristics. PMID:24769917

Determining the end of a musical turn: Effects of tonal cues.

PubMed

Hadley, Lauren V; Sturt, Patrick; Moran, Nikki; Pickering, Martin J

2018-01-01

Successful duetting requires that musicians coordinate their performance with their partners. In the case of turn-taking in improvised performance they need to be able to predict their partner's turn-end in order to accurately time their own entries. Here we investigate the cues used for accurate turn-end prediction in musical improvisations, focusing on the role of tonal structure. In a response-time task, participants more accurately determined the endings of (tonal) jazz than (non-tonal) free improvisation turns. Moreover, for the jazz improvisations, removing low frequency information (<2100Hz) - and hence obscuring the pitch relationships conveying tonality - reduced response accuracy, but removing high frequency information (>2100Hz) had no effect. Neither form of filtering affected response accuracy in the free improvisation condition. We therefore argue that tonal cues aided prediction accuracy for the jazz improvisations compared to the free improvisations. We compare our results with those from related speech research (De Ruiter et al., 2006), to draw comparisons between the structural function of tonality and linguistic syntax. Copyright © 2017. Published by Elsevier B.V.

Predicting the Reliability of Brittle Material Structures Subjected to Transient Proof Test and Service Loading

NASA Astrophysics Data System (ADS)

Nemeth, Noel N.; Jadaan, Osama M.; Palfi, Tamas; Baker, Eric H.

Brittle materials today are being used, or considered, for a wide variety of high tech applications that operate in harsh environments, including static and rotating turbine parts, thermal protection systems, dental prosthetics, fuel cells, oxygen transport membranes, radomes, and MEMS. Designing brittle material components to sustain repeated load without fracturing while using the minimum amount of material requires the use of a probabilistic design methodology. The NASA CARES/Life 1 (Ceramic Analysis and Reliability Evaluation of Structure/Life) code provides a general-purpose analysis tool that predicts the probability of failure of a ceramic component as a function of its time in service. This capability includes predicting the time-dependent failure probability of ceramic components against catastrophic rupture when subjected to transient thermomechanical loads (including cyclic loads). The developed methodology allows for changes in material response that can occur with temperature or time (i.e. changing fatigue and Weibull parameters with temperature or time). For this article an overview of the transient reliability methodology and how this methodology is extended to account for proof testing is described. The CARES/Life code has been modified to have the ability to interface with commercially available finite element analysis (FEA) codes executed for transient load histories. Examples are provided to demonstrate the features of the methodology as implemented in the CARES/Life program.

Constitutive Theory Developed for Monolithic Ceramic Materials

NASA Technical Reports Server (NTRS)

Janosik, Lesley A.

1998-01-01

With the increasing use of advanced ceramic materials in high-temperature structural applications such as advanced heat engine components, the need arises to accurately predict thermomechanical behavior that is inherently time-dependent and that is hereditary in the sense that the current behavior depends not only on current conditions but also on the material's thermomechanical history. Most current analytical life prediction methods for both subcritical crack growth and creep models use elastic stress fields to predict the time-dependent reliability response of components subjected to elevated service temperatures. Inelastic response at high temperatures has been well documented in the materials science literature for these material systems, but this issue has been ignored by the engineering design community. From a design engineer's perspective, it is imperative to emphasize that accurate predictions of time-dependent reliability demand accurate stress field information. Ceramic materials exhibit different time-dependent behavior in tension and compression. Thus, inelastic deformation models for ceramics must be constructed in a fashion that admits both sensitivity to hydrostatic stress and differing behavior in tension and compression. A number of constitutive theories for materials that exhibit sensitivity to the hydrostatic component of stress have been proposed that characterize deformation using time-independent classical plasticity as a foundation. However, none of these theories allow different behavior in tension and compression. In addition, these theories are somewhat lacking in that they are unable to capture the creep, relaxation, and rate-sensitive phenomena exhibited by ceramic materials at high temperatures. The objective of this effort at the NASA Lewis Research Center has been to formulate a macroscopic continuum theory that captures these time-dependent phenomena. Specifically, the effort has focused on inelastic deformation behavior associated with these service conditions by developing a multiaxial viscoplastic constitutive model that accounts for time-dependent hereditary material deformation (such as creep and stress relaxation) in monolithic structural ceramics. Using continuum principles of engineering mechanics, we derived the complete viscoplastic theory from a scalar dissipative potential function.

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.

A Data Driven Model for Predicting RNA-Protein Interactions based on Gradient Boosting Machine.

PubMed

Jain, Dharm Skandh; Gupte, Sanket Rajan; Aduri, Raviprasad

2018-06-22

RNA protein interactions (RPI) play a pivotal role in the regulation of various biological processes. Experimental validation of RPI has been time-consuming, paving the way for computational prediction methods. The major limiting factor of these methods has been the accuracy and confidence of the predictions, and our in-house experiments show that they fail to accurately predict RPI involving short RNA sequences such as TERRA RNA. Here, we present a data-driven model for RPI prediction using a gradient boosting classifier. Amino acids and nucleotides are classified based on the high-resolution structural data of RNA protein complexes. The minimum structural unit consisting of five residues is used as the descriptor. Comparative analysis of existing methods shows the consistently higher performance of our method irrespective of the length of RNA present in the RPI. The method has been successfully applied to map RPI networks involving both long noncoding RNA as well as TERRA RNA. The method is also shown to successfully predict RNA and protein hubs present in RPI networks of four different organisms. The robustness of this method will provide a way for predicting RPI networks of yet unknown interactions for both long noncoding RNA and microRNA.

Utilizing knowledge base of amino acids structural neighborhoods to predict protein-protein interaction sites.

PubMed

Jelínek, Jan; Škoda, Petr; Hoksza, David

2017-12-06

Protein-protein interactions (PPI) play a key role in an investigation of various biochemical processes, and their identification is thus of great importance. Although computational prediction of which amino acids take part in a PPI has been an active field of research for some time, the quality of in-silico methods is still far from perfect. We have developed a novel prediction method called INSPiRE which benefits from a knowledge base built from data available in Protein Data Bank. All proteins involved in PPIs were converted into labeled graphs with nodes corresponding to amino acids and edges to pairs of neighboring amino acids. A structural neighborhood of each node was then encoded into a bit string and stored in the knowledge base. When predicting PPIs, INSPiRE labels amino acids of unknown proteins as interface or non-interface based on how often their structural neighborhood appears as interface or non-interface in the knowledge base. We evaluated INSPiRE's behavior with respect to different types and sizes of the structural neighborhood. Furthermore, we examined the suitability of several different features for labeling the nodes. Our evaluations showed that INSPiRE clearly outperforms existing methods with respect to Matthews correlation coefficient. In this paper we introduce a new knowledge-based method for identification of protein-protein interaction sites called INSPiRE. Its knowledge base utilizes structural patterns of known interaction sites in the Protein Data Bank which are then used for PPI prediction. Extensive experiments on several well-established datasets show that INSPiRE significantly surpasses existing PPI approaches.

Video game characteristics, happiness and flow as predictors of addiction among video game players: A pilot study.

PubMed

Hull, Damien C; Williams, Glenn A; Griffiths, Mark D

2013-09-01

Video games provide opportunities for positive psychological experiences such as flow-like phenomena during play and general happiness that could be associated with gaming achievements. However, research has shown that specific features of game play may be associated with problematic behaviour associated with addiction-like experiences. The study was aimed at analysing whether certain structural characteristics of video games, flow, and global happiness could be predictive of video game addiction. A total of 110 video game players were surveyed about a game they had recently played by using a 24-item checklist of structural characteristics, an adapted Flow State Scale, the Oxford Happiness Questionnaire, and the Game Addiction Scale. The study revealed decreases in general happiness had the strongest role in predicting increases in gaming addiction. One of the nine factors of the flow experience was a significant predictor of gaming addiction - perceptions of time being altered during play. The structural characteristic that significantly predicted addiction was its social element with increased sociability being associated with higher levels of addictive-like experiences. Overall, the structural characteristics of video games, elements of the flow experience, and general happiness accounted for 49.2% of the total variance in Game Addiction Scale levels. Implications for interventions are discussed, particularly with regard to making players more aware of time passing and in capitalising on benefits of social features of video game play to guard against addictive-like tendencies among video game players.

Video game characteristics, happiness and flow as predictors of addiction among video game players: A pilot study

PubMed Central

Hull, Damien C.; Williams, Glenn A.; Griffiths, Mark D.

2013-01-01

Aims: Video games provide opportunities for positive psychological experiences such as flow-like phenomena during play and general happiness that could be associated with gaming achievements. However, research has shown that specific features of game play may be associated with problematic behaviour associated with addiction-like experiences. The study was aimed at analysing whether certain structural characteristics of video games, flow, and global happiness could be predictive of video game addiction. Method: A total of 110 video game players were surveyed about a game they had recently played by using a 24-item checklist of structural characteristics, an adapted Flow State Scale, the Oxford Happiness Questionnaire, and the Game Addiction Scale. Results: The study revealed decreases in general happiness had the strongest role in predicting increases in gaming addiction. One of the nine factors of the flow experience was a significant predictor of gaming addiction – perceptions of time being altered during play. The structural characteristic that significantly predicted addiction was its social element with increased sociability being associated with higher levels of addictive-like experiences. Overall, the structural characteristics of video games, elements of the flow experience, and general happiness accounted for 49.2% of the total variance in Game Addiction Scale levels. Conclusions: Implications for interventions are discussed, particularly with regard to making players more aware of time passing and in capitalising on benefits of social features of video game play to guard against addictive-like tendencies among video game players. PMID:25215196

[Comparative analysis of clustered regularly interspaced short palindromic repeats (CRISPRs) loci in the genomes of halophilic archaea].

PubMed

Zhang, Fan; Zhang, Bing; Xiang, Hua; Hu, Songnian

2009-11-01

Clustered Regularly Interspaced Short Palindromic Repeats (CRISPR) is a widespread system that provides acquired resistance against phages in bacteria and archaea. Here we aim to genome-widely analyze the CRISPR in extreme halophilic archaea, of which the whole genome sequences are available at present time. We used bioinformatics methods including alignment, conservation analysis, GC content and RNA structure prediction to analyze the CRISPR structures of 7 haloarchaeal genomes. We identified the CRISPR structures in 5 halophilic archaea and revealed a conserved palindromic motif in the flanking regions of these CRISPR structures. In addition, we found that the repeat sequences of large CRISPR structures in halophilic archaea were greatly conserved, and two types of predicted RNA secondary structures derived from the repeat sequences were likely determined by the fourth base of the repeat sequence. Our results support the proposal that the leader sequence may function as recognition site by having palindromic structures in flanking regions, and the stem-loop secondary structure formed by repeat sequences may function in mediating the interaction between foreign genetic elements and CAS-encoded proteins.

Optimal design of structures for earthquake loads by a hybrid RBF-BPSO method

NASA Astrophysics Data System (ADS)

Salajegheh, Eysa; Gholizadeh, Saeed; Khatibinia, Mohsen

2008-03-01

The optimal seismic design of structures requires that time history analyses (THA) be carried out repeatedly. This makes the optimal design process inefficient, in particular, if an evolutionary algorithm is used. To reduce the overall time required for structural optimization, two artificial intelligence strategies are employed. In the first strategy, radial basis function (RBF) neural networks are used to predict the time history responses of structures in the optimization flow. In the second strategy, a binary particle swarm optimization (BPSO) is used to find the optimum design. Combining the RBF and BPSO, a hybrid RBF-BPSO optimization method is proposed in this paper, which achieves fast optimization with high computational performance. Two examples are presented and compared to determine the optimal weight of structures under earthquake loadings using both exact and approximate analyses. The numerical results demonstrate the computational advantages and effectiveness of the proposed hybrid RBF-BPSO optimization method for the seismic design of structures.

An operational global-scale ocean thermal analysis system

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

Clancy, R. M.; Pollak, K.D.; Phoebus, P.A.

1990-04-01

The Optimum Thermal Interpolation System (OTIS) is an ocean thermal analysis system designed for operational use at FNOC. It is based on the optimum interpolation of the assimilation technique and functions in an analysis-prediction-analysis data assimilation cycle with the TOPS mixed-layer model. OTIS provides a rigorous framework for combining real-time data, climatology, and predictions from numerical ocean prediction models to produce a large-scale synoptic representation of ocean thermal structure. The techniques and assumptions used in OTIS are documented and results of operational tests of global scale OTIS at FNOC are presented. The tests involved comparisons of OTIS against an existingmore » operational ocean thermal structure model and were conducted during February, March, and April 1988. Qualitative comparison of the two products suggests that OTIS gives a more realistic representation of subsurface anomalies and horizontal gradients and that it also gives a more accurate analysis of the thermal structure, with improvements largest below the mixed layer. 37 refs.« less

Life Predicted in a Probabilistic Design Space for Brittle Materials With Transient Loads

NASA Technical Reports Server (NTRS)

Nemeth, Noel N.; Palfi, Tamas; Reh, Stefan

2005-01-01

Analytical techniques have progressively become more sophisticated, and now we can consider the probabilistic nature of the entire space of random input variables on the lifetime reliability of brittle structures. This was demonstrated with NASA s CARES/Life (Ceramic Analysis and Reliability Evaluation of Structures/Life) code combined with the commercially available ANSYS/Probabilistic Design System (ANSYS/PDS), a probabilistic analysis tool that is an integral part of the ANSYS finite-element analysis program. ANSYS/PDS allows probabilistic loads, component geometry, and material properties to be considered in the finite-element analysis. CARES/Life predicts the time dependent probability of failure of brittle material structures under generalized thermomechanical loading--such as that found in a turbine engine hot-section. Glenn researchers coupled ANSYS/PDS with CARES/Life to assess the effects of the stochastic variables of component geometry, loading, and material properties on the predicted life of the component for fully transient thermomechanical loading and cyclic loading.

Piecewise multivariate modelling of sequential metabolic profiling data.

PubMed

Rantalainen, Mattias; Cloarec, Olivier; Ebbels, Timothy M D; Lundstedt, Torbjörn; Nicholson, Jeremy K; Holmes, Elaine; Trygg, Johan

2008-02-19

Modelling the time-related behaviour of biological systems is essential for understanding their dynamic responses to perturbations. In metabolic profiling studies, the sampling rate and number of sampling points are often restricted due to experimental and biological constraints. A supervised multivariate modelling approach with the objective to model the time-related variation in the data for short and sparsely sampled time-series is described. A set of piecewise Orthogonal Projections to Latent Structures (OPLS) models are estimated, describing changes between successive time points. The individual OPLS models are linear, but the piecewise combination of several models accommodates modelling and prediction of changes which are non-linear with respect to the time course. We demonstrate the method on both simulated and metabolic profiling data, illustrating how time related changes are successfully modelled and predicted. The proposed method is effective for modelling and prediction of short and multivariate time series data. A key advantage of the method is model transparency, allowing easy interpretation of time-related variation in the data. The method provides a competitive complement to commonly applied multivariate methods such as OPLS and Principal Component Analysis (PCA) for modelling and analysis of short time-series data.

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.

Multisource least-squares reverse-time migration with structure-oriented filtering

NASA Astrophysics Data System (ADS)

Fan, Jing-Wen; Li, Zhen-Chun; Zhang, Kai; Zhang, Min; Liu, Xue-Tong

2016-09-01

The technology of simultaneous-source acquisition of seismic data excited by several sources can significantly improve the data collection efficiency. However, direct imaging of simultaneous-source data or blended data may introduce crosstalk noise and affect the imaging quality. To address this problem, we introduce a structure-oriented filtering operator as preconditioner into the multisource least-squares reverse-time migration (LSRTM). The structure-oriented filtering operator is a nonstationary filter along structural trends that suppresses crosstalk noise while maintaining structural information. The proposed method uses the conjugate-gradient method to minimize the mismatch between predicted and observed data, while effectively attenuating the interference noise caused by exciting several sources simultaneously. Numerical experiments using synthetic data suggest that the proposed method can suppress the crosstalk noise and produce highly accurate images.

Elevated Temperature Crack Growth Behavior in HSCT Structural Materials

NASA Technical Reports Server (NTRS)

Saxena, Ashok

1998-01-01

Structures in super-sonic aircraft are subjected to conditions of high temperature and cyclic and sustained loading for extended periods of time. The durability of structures fabricated from aluminum and certain titanium alloys in such demanding conditions is of primary concern to the designers and manufacturers of futuristic transport aircraft. Accordingly, the major goal of this project was to evaluate the performance and durability of high temperature aluminum and titanium alloys for use in high speed civil transport (HSCT) structures. Additional goals were to develop time-dependent fracture mechanics methodology and test methods for characterizing and predicting elevated temperature crack growth behavior in creep-brittle materials such as ones being considered for use in HSCT structures and to explore accelerated methods of simulating microstructural degradation during service and measuring degraded properties in these materials.

Dynamic Load Predictions for Launchers Using Extra-Large Eddy Simulations X-Les

NASA Astrophysics Data System (ADS)

Maseland, J. E. J.; Soemarwoto, B. I.; Kok, J. C.

2005-02-01

Flow-induced unsteady loads can have a strong impact on performance and flight characteristics of aerospace vehicles and therefore play a crucial role in their design and operation. Complementary to costly flight tests and delicate wind-tunnel experiments, unsteady loads can be calculated using time-accurate Computational Fluid Dynamics. A capability to accurately predict the dynamic loads on aerospace structures at flight Reynolds numbers can be of great value for the design and analysis of aerospace vehicles. Advanced space launchers are subject to dynamic loads in the base region during the ascent to space. In particular the engine and nozzle experience aerodynamic pressure fluctuations resulting from massive flow separations. Understanding these phenomena is essential for performance enhancements for future launchers which operate a larger nozzle. A new hybrid RANS-LES turbulence modelling approach termed eXtra-Large Eddy Simulations (X-LES) holds the promise to capture the flow structures associated with massive separations and enables the prediction of the broad-band spectrum of dynamic loads. This type of method has become a focal point, reducing the cost of full LES, driven by the demand for their applicability in an industrial environment. The industrial feasibility of X-LES simulations is demonstrated by computing the unsteady aerodynamic loads on the main-engine nozzle of a generic space launcher configuration. The potential to calculate the dynamic loads is qualitatively assessed for transonic flow conditions in a comparison to wind-tunnel experiments. In terms of turn-around-times, X-LES computations are already feasible within the time-frames of the development process to support the structural design. Key words: massive separated flows; buffet loads; nozzle vibrations; space launchers; time-accurate CFD; composite RANS-LES formulation.

Brain Structural Integrity and Intrinsic Functional Connectivity Forecast 6 Year Longitudinal Growth in Children's Numerical Abilities.

PubMed

Evans, Tanya M; Kochalka, John; Ngoon, Tricia J; Wu, Sarah S; Qin, Shaozheng; Battista, Christian; Menon, Vinod

2015-08-19

Early numerical proficiency lays the foundation for acquiring quantitative skills essential in today's technological society. Identification of cognitive and brain markers associated with long-term growth of children's basic numerical computation abilities is therefore of utmost importance. Previous attempts to relate brain structure and function to numerical competency have focused on behavioral measures from a single time point. Thus, little is known about the brain predictors of individual differences in growth trajectories of numerical abilities. Using a longitudinal design, with multimodal imaging and machine-learning algorithms, we investigated whether brain structure and intrinsic connectivity in early childhood are predictive of 6 year outcomes in numerical abilities spanning childhood and adolescence. Gray matter volume at age 8 in distributed brain regions, including the ventrotemporal occipital cortex (VTOC), the posterior parietal cortex, and the prefrontal cortex, predicted longitudinal gains in numerical, but not reading, abilities. Remarkably, intrinsic connectivity analysis revealed that the strength of functional coupling among these regions also predicted gains in numerical abilities, providing novel evidence for a network of brain regions that works in concert to promote numerical skill acquisition. VTOC connectivity with posterior parietal, anterior temporal, and dorsolateral prefrontal cortices emerged as the most extensive network predicting individual gains in numerical abilities. Crucially, behavioral measures of mathematics, IQ, working memory, and reading did not predict children's gains in numerical abilities. Our study identifies, for the first time, functional circuits in the human brain that scaffold the development of numerical skills, and highlights potential biomarkers for identifying children at risk for learning difficulties. Children show substantial individual differences in math abilities and ease of math learning. Early numerical abilities provide the foundation for future academic and professional success in an increasingly technological society. Understanding the early identification of poor math skills has therefore taken on great significance. This work provides important new insights into brain structure and connectivity measures that can predict longitudinal growth of children's math skills over a 6 year period, and may eventually aid in the early identification of children who might benefit from targeted interventions. Copyright © 2015 the authors 0270-6474/15/3511743-08$15.00/0.

Predictable communities of soil bacteria in relation to nutrient concentration and successional stage in a laboratory culture experiment.

PubMed

Song, Woojin; Kim, Mincheol; Tripathi, Binu M; Kim, Hyoki; Adams, Jonathan M

2016-06-01

It is difficult to understand the processes that structure immensely complex bacterial communities in the soil environment, necessitating a simplifying experimental approach. Here, we set up a microcosm culturing experiment with soil bacteria, at a range of nutrient concentrations, and compared these over time to understand the relationship between soil bacterial community structure and time/nutrient concentration. DNA from each replicate was analysed using HiSeq2000 Illumina sequencing of the 16S rRNA gene. We found that each nutrient treatment, and each time point during the experiment, produces characteristic bacterial communities that occur predictably between replicates. It is clear that within the context of this experiment, many soil bacteria have distinct niches from one another, in terms of both nutrient concentration, and successional time point since a resource first became available. This fine niche differentiation may in part help to explain the coexistence of a diversity of bacteria in soils. In this experiment, we show that the unimodal relationship between nutrient concentration/time and species diversity often reported in communities of larger organisms is also evident in microbial communities. © 2015 Society for Applied Microbiology and John Wiley & Sons Ltd.

Predicting the Reliability of Ceramics Under Transient Loads and Temperatures With CARES/Life

NASA Technical Reports Server (NTRS)

Nemeth, Noel N.; Jadaan, Osama M.; Palfi, Tamas; Baker, Eric H.

2003-01-01

A methodology is shown for predicting the time-dependent reliability of ceramic components against catastrophic rupture when subjected to transient thermomechanical loads (including cyclic loads). The methodology takes into account the changes in material response that can occur with temperature or time (i.e., changing fatigue and Weibull parameters with temperature or time). This capability has been added to the NASA CARES/Life (Ceramic Analysis and Reliability Evaluation of Structures/Life) code. The code has been modified to have the ability to interface with commercially available finite element analysis (FEA) codes executed for transient load histories. Examples are provided to demonstrate the features of the methodology as implemented in the CARES/Life program.

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

Prediction of cancer cell sensitivity to natural products based on genomic and chemical properties.

PubMed

Yue, Zhenyu; Zhang, Wenna; Lu, Yongming; Yang, Qiaoyue; Ding, Qiuying; Xia, Junfeng; Chen, Yan

2015-01-01

Natural products play a significant role in cancer chemotherapy. They are likely to provide many lead structures, which can be used as templates for the construction of novel drugs with enhanced antitumor activity. Traditional research approaches studied structure-activity relationship of natural products and obtained key structural properties, such as chemical bond or group, with the purpose of ascertaining their effect on a single cell line or a single tissue type. Here, for the first time, we develop a machine learning method to comprehensively predict natural products responses against a panel of cancer cell lines based on both the gene expression and the chemical properties of natural products. The results on two datasets, training set and independent test set, show that this proposed method yields significantly better prediction accuracy. In addition, we also demonstrate the predictive power of our proposed method by modeling the cancer cell sensitivity to two natural products, Curcumin and Resveratrol, which indicate that our method can effectively predict the response of cancer cell lines to these two natural products. Taken together, the method will facilitate the identification of natural products as cancer therapies and the development of precision medicine by linking the features of patient genomes to natural product sensitivity.

Predicted Bacterial Interactions Affect in Vivo Microbial Colonization Dynamics in Nematostella

PubMed Central

Domin, Hanna; Zurita-Gutiérrez, Yazmín H.; Scotti, Marco; Buttlar, Jann; Hentschel Humeida, Ute; Fraune, Sebastian

2018-01-01

The maintenance and resilience of host-associated microbiota during development is a fundamental process influencing the fitness of many organisms. Several host properties were identified as influencing factors on bacterial colonization, including the innate immune system, mucus composition, and diet. In contrast, the importance of bacteria–bacteria interactions on host colonization is less understood. Here, we use bacterial abundance data of the marine model organism Nematostella vectensis to reconstruct potential bacteria–bacteria interactions through co-occurrence networks. The analysis indicates that bacteria–bacteria interactions are dynamic during host colonization and change according to the host’s developmental stage. To assess the predictive power of inferred interactions, we tested bacterial isolates with predicted cooperative or competitive behavior for their ability to influence bacterial recolonization dynamics. Within 3 days of recolonization, all tested bacterial isolates affected bacterial community structure, while only competitive bacteria increased bacterial diversity. Only 1 week after recolonization, almost no differences in bacterial community structure could be observed between control and treatments. These results show that predicted competitive bacteria can influence community structure for a short period of time, verifying the in silico predictions. However, within 1 week, the effects of the bacterial isolates are neutralized, indicating a high degree of resilience of the bacterial community. PMID:29740401

Use of read-across and computer-based predictive analysis for the safety assessment of PEG cocamines.

PubMed

Skare, Julie A; Blackburn, Karen; Wu, Shengde; Re, Thomas A; Duche, Daniel; Ringeissen, Stephanie; Bjerke, Donald L; Srinivasan, Viny; Eisenmann, Carol

2015-04-01

In the European Union animal testing has been eliminated for cosmetic ingredients while the US Cosmetic Ingredient Review Expert Panel may request data from animal studies. The use of read-across and predictive toxicology provides a path for filling data gaps without additional animal testing. The PEG cocamines are tertiary amines with an alkyl group derived from coconut fatty acids and two PEG chains of varying length. Toxicology data gaps for the PEG cocamines can be addressed by read-across based on structure-activity relationship using the framework described by Wu et al. (2010) for identifying suitable structural analogs. Data for structural analogs supports the conclusion that the PEG cocamines are non-genotoxic and not expected to exhibit systemic or developmental/reproductive toxicity with use in cosmetics. Due to lack of reliable dermal sensitization data for suitable analogs, this endpoint was addressed using predictive software (TIMES SS) as a first step (Laboratory of Mathematical Chemistry). The prediction for PEG cocamines was the same as that for PEGs, which have been concluded to not present a significant concern for dermal sensitization. This evaluation for PEG cocamines demonstrates the utility of read-across and predictive toxicology tools to assess the safety of cosmetic ingredients. Copyright © 2015 Elsevier Inc. All rights reserved.

Mesoscale influence on long-range transport — evidence from ETEX modelling and observations

NASA Astrophysics Data System (ADS)

Sørensen, Jens Havskov; Rasmussen, Alix; Ellermann, Thomas; Lyck, Erik

During the first European Tracer Experiment (ETEX) tracer gas was released from a site in Brittany, France, and subsequently observed over a range of 2000 km. Hourly measurements were taken at the National Environmental Research Institute (NERI) located at Risø, Denmark, using two measurement techniques. At this location, the observed concentration time series shows a double-peak structure occurring between two and three days after the release. By using the Danish Emergency Response Model of the Atmosphere (DERMA), which is developed at the Danish Meteorological Institute (DMI), simulations of the dispersion of the tracer gas have been performed. Using numerical weather-prediction data from the European Centre for Medium-Range Weather Forecast (ECMWF) by DERMA, the arrival time of the tracer is quite well predicted, so also is the duration of the passage of the plume, but the double-peak structure is not reproduced. However, using higher-resolution data from the DMI version of the HIgh Resolution Limited Area Model (DMI-HIRLAM), DERMA reproduces the observed structure very well. The double-peak structure is caused by the influence of a mesoscale anti-cyclonic eddy on the tracer gas plume about one day earlier.

HART-II Acoustic Predictions using a Coupled CFD/CSD Method

NASA Technical Reports Server (NTRS)

Boyd, D. Douglas, Jr.

2009-01-01

This paper documents results to date from the Rotorcraft Acoustic Characterization and Mitigation activity under the NASA Subsonic Rotary Wing Project. The primary goal of this activity is to develop a NASA rotorcraft impulsive noise prediction capability which uses first principles fluid dynamics and structural dynamics. During this effort, elastic blade motion and co-processing capabilities have been included in a recent version of the computational fluid dynamics code (CFD). The CFD code is loosely coupled to computational structural dynamics (CSD) code using new interface codes. The CFD/CSD coupled solution is then used to compute impulsive noise on a plane under the rotor using the Ffowcs Williams-Hawkings solver. This code system is then applied to a range of cases from the Higher Harmonic Aeroacoustic Rotor Test II (HART-II) experiment. For all cases presented, the full experimental configuration (i.e., rotor and wind tunnel sting mount) are used in the coupled CFD/CSD solutions. Results show good correlation between measured and predicted loading and loading time derivative at the only measured radial station. A contributing factor for a typically seen loading mean-value offset between measured data and predictions data is examined. Impulsive noise predictions on the measured microphone plane under the rotor compare favorably with measured mid-frequency noise for all cases. Flow visualization of the BL and MN cases shows that vortex structures generated in the prediction method are consist with measurements. Future application of the prediction method is discussed.

Predicting the Intention to Use Condoms and Actual Condom Use Behaviour: A Three-Wave Longitudinal Study in Ghana

PubMed Central

Teye-Kwadjo, Enoch; Kagee, Ashraf; Swart, Hermann

2017-01-01

Background Growing cross-sectional research shows that the theory of planned behaviour (TPB) is robust in predicting intentions to use condoms and condom use behaviour. Yet, little is known about the TPB’s utility in explaining intentions to use condoms and condom use behaviour over time. Methods This study used a longitudinal design and latent variable structural equation modelling to test the longitudinal relationships postulated by the TPB. School-going youths in Ghana provided data on attitudes, subjective norms, perceived control, intentions, and behaviour regarding condom use at three-time points, spaced approximately three-months apart. Results As predicted by the TPB, the results showed that attitudes were significantly positively associated with intentions to use condoms over time. Contrary to the TPB, subjective norms were not significantly associated with intentions to use condoms over time. Perceived control did not predict intentions to use condoms over time. Moreover, intentions to use condoms were not significantly associated with self-reported condom use over time. Conclusion These results suggest that school-going youths in Ghana may benefit from sex education programmes that focus on within-subject attitude formation and activation. The theoretical and methodological implications of these results are discussed. PMID:27925435

Predicting domain-domain interaction based on domain profiles with feature selection and support vector machines

PubMed Central

2010-01-01

Background Protein-protein interaction (PPI) plays essential roles in cellular functions. The cost, time and other limitations associated with the current experimental methods have motivated the development of computational methods for predicting PPIs. As protein interactions generally occur via domains instead of the whole molecules, predicting domain-domain interaction (DDI) is an important step toward PPI prediction. Computational methods developed so far have utilized information from various sources at different levels, from primary sequences, to molecular structures, to evolutionary profiles. Results In this paper, we propose a computational method to predict DDI using support vector machines (SVMs), based on domains represented as interaction profile hidden Markov models (ipHMM) where interacting residues in domains are explicitly modeled according to the three dimensional structural information available at the Protein Data Bank (PDB). Features about the domains are extracted first as the Fisher scores derived from the ipHMM and then selected using singular value decomposition (SVD). Domain pairs are represented by concatenating their selected feature vectors, and classified by a support vector machine trained on these feature vectors. The method is tested by leave-one-out cross validation experiments with a set of interacting protein pairs adopted from the 3DID database. The prediction accuracy has shown significant improvement as compared to InterPreTS (Interaction Prediction through Tertiary Structure), an existing method for PPI prediction that also uses the sequences and complexes of known 3D structure. Conclusions We show that domain-domain interaction prediction can be significantly enhanced by exploiting information inherent in the domain profiles via feature selection based on Fisher scores, singular value decomposition and supervised learning based on support vector machines. Datasets and source code are freely available on the web at http://liao.cis.udel.edu/pub/svdsvm. Implemented in Matlab and supported on Linux and MS Windows. PMID:21034480

Mean-field approximations of fixation time distributions of evolutionary game dynamics on graphs

NASA Astrophysics Data System (ADS)

Ying, Li-Min; Zhou, Jie; Tang, Ming; Guan, Shu-Guang; Zou, Yong

2018-02-01

The mean fixation time is often not accurate for describing the timescales of fixation probabilities of evolutionary games taking place on complex networks. We simulate the game dynamics on top of complex network topologies and approximate the fixation time distributions using a mean-field approach. We assume that there are two absorbing states. Numerically, we show that the mean fixation time is sufficient in characterizing the evolutionary timescales when network structures are close to the well-mixing condition. In contrast, the mean fixation time shows large inaccuracies when networks become sparse. The approximation accuracy is determined by the network structure, and hence by the suitability of the mean-field approach. The numerical results show good agreement with the theoretical predictions.

Spatial structure increases the waiting time for cancer

PubMed Central

Martens, Erik A.; Kostadinov, Rumen; Maley, Carlo C.; Hallatschek, Oskar

2012-01-01

Cancer results from a sequence of genetic and epigenetic changes which lead to a variety of abnormal phenotypes including increased proliferation and survival of somatic cells, and thus, to a selective advantage of pre-cancerous cells. The notion of cancer progression as an evolutionary process has been experiencing increasing interest in recent years. Many efforts have been made to better understand and predict the progression to cancer using mathematical models; these mostly consider the evolution of a well-mixed cell population, even though pre-cancerous cells often evolve in highly structured epithelial tissues. In this study, we propose a novel model of cancer progression that considers a spatially structured cell population where clones expand via adaptive waves. This model is used to assess two different paradigms of asexual evolution that have been suggested to delineate the process of cancer progression. The standard scenario of periodic selection assumes that driver mutations are accumulated strictly sequentially over time. However, when the mutation supply is sufficiently high, clones may arise simultaneously on distinct genetic backgrounds, and clonal adaptation waves interfere with each other. We find that in the presence of clonal interference, spatial structure increases the waiting time for cancer, leads to a patchwork structure of non-uniformly sized clones, decreases the survival probability of virtually neutral (passenger) mutations, and that genetic distance begins to increase over a characteristic length scale Lc. These characteristic features of clonal interference may help to predict the onset of cancers with pronounced spatial structure and to interpret spatially-sampled genetic data obtained from biopsies. Our estimates suggest that clonal interference likely occurs in the progression of colon cancer, and possibly other cancers where spatial structure matters. PMID:22707911

Fast structure similarity searches among protein models: efficient clustering of protein fragments

PubMed Central

2012-01-01

Background For many predictive applications a large number of models is generated and later clustered in subsets based on structure similarity. In most clustering algorithms an all-vs-all root mean square deviation (RMSD) comparison is performed. Most of the time is typically spent on comparison of non-similar structures. For sets with more than, say, 10,000 models this procedure is very time-consuming and alternative faster algorithms, restricting comparisons only to most similar structures would be useful. Results We exploit the inverse triangle inequality on the RMSD between two structures given the RMSDs with a third structure. The lower bound on RMSD may be used, when restricting the search of similarity to a reasonably low RMSD threshold value, to speed up similarity searches significantly. Tests are performed on large sets of decoys which are widely used as test cases for predictive methods, with a speed-up of up to 100 times with respect to all-vs-all comparison depending on the set and parameters used. Sample applications are shown. Conclusions The algorithm presented here allows fast comparison of large data sets of structures with limited memory requirements. As an example of application we present clustering of more than 100000 fragments of length 5 from the top500H dataset into few hundred representative fragments. A more realistic scenario is provided by the search of similarity within the very large decoy sets used for the tests. Other applications regard filtering nearly-indentical conformation in selected CASP9 datasets and clustering molecular dynamics snapshots. Availability A linux executable and a Perl script with examples are given in the supplementary material (Additional file 1). The source code is available upon request from the authors. PMID:22642815

Spatial structure increases the waiting time for cancer

NASA Astrophysics Data System (ADS)

Martens, Erik A.; Kostadinov, Rumen; Maley, Carlo C.; Hallatschek, Oskar

2011-11-01

Cancer results from a sequence of genetic and epigenetic changes that lead to a variety of abnormal phenotypes including increased proliferation and survival of somatic cells and thus to a selective advantage of pre-cancerous cells. The notion of cancer progression as an evolutionary process has been attracting increasing interest in recent years. A great deal of effort has been made to better understand and predict the progression to cancer using mathematical models; these mostly consider the evolution of a well-mixed cell population, even though pre-cancerous cells often evolve in highly structured epithelial tissues. In this study, we propose a novel model of cancer progression that considers a spatially structured cell population where clones expand via adaptive waves. This model is used to assess two different paradigms of asexual evolution that have been suggested to delineate the process of cancer progression. The standard scenario of periodic selection assumes that driver mutations are accumulated strictly sequentially over time. However, when the mutation supply is sufficiently high, clones may arise simultaneously on distinct genetic backgrounds, and clonal adaptation waves interfere with each other. We find that in the presence of clonal interference, spatial structure increases the waiting time for cancer, leads to a patchwork structure of non-uniformly sized clones and decreases the survival probability of virtually neutral (passenger) mutations, and that genetic distance begins to increase over a characteristic length scale Lc. These characteristic features of clonal interference may help us to predict the onset of cancers with pronounced spatial structure and to interpret spatially sampled genetic data obtained from biopsies. Our estimates suggest that clonal interference likely occurs in the progression of colon cancer and possibly other cancers where spatial structure matters.

Closed coronal structures. V - Gasdynamic models of flaring loops and comparison with SMM observations

NASA Technical Reports Server (NTRS)

Peres, G.; Serio, S.; Vaiana, G.; Acton, L.; Leibacher, J.; Rosner, R.; Pallavicini, R.

1983-01-01

A time-dependent one-dimensional code incorporating energy, momentum and mass conservation equations, and taking the entire solar atmospheric structure into account, is used to investigate the hydrodynamic response of confined magnetic structures to strong heating perturbations. Model calculation results are compared with flare observations which include the light curves of spectral lines formed over a wide range of coronal flare temperatures, as well as determinations of Doppler shifts for the high temperature plasma. It is shown that the numerical simulation predictions are in good overall agreement with the observed flare coronal plasma evolution, correctly reproducing the temporal profile of X-ray spectral lines and their relative intensities. The predicted upflow velocities support the interpretation of the blueshifts as due to evaporation of chromospheric material.

Thermodynamic stability of RNA structures formed by CNG trinucleotide repeats. Implication for prediction of RNA structure.

PubMed

Broda, Magdalena; Kierzek, Elzbieta; Gdaniec, Zofia; Kulinski, Tadeusz; Kierzek, Ryszard

2005-08-16

Trinucleotide repeat expansion diseases (TREDs) are correlated with elongation of CNG DNA and RNA repeats to pathological level. This paper shows, for the first time, complete data concerning thermodynamic stabilities of RNA with CNG trinucleotide repeats. Our studies include the stability of oligoribonucleotides composed of two to seven of CAG, CCG, CGG, and CUG repeats. The thermodynamic parameters of helix propagation correlated with the presence of multiple N-N mismatches within CNG RNA duplexes were also determined. Moreover, the total stability of CNG RNA hairpins, as well as the contribution of trinucleotide repeats placed only in the stem or loop regions, was evaluated. The improved thermodynamic parameters allow to predict much more accurately the thermodynamic stabilities and structures of CNG RNAs.

Toward superconducting critical current by design

DOE PAGES

Sadovskyy, Ivan A.; Jia, Ying; Leroux, Maxime; ...

2016-03-31

The interaction of vortex matter with defects in applied superconductors directly determines their current carrying capacity. Defects range from chemically grown nanostructures and crystalline imperfections to the layered structure of the material itself. The vortex-defect interactions are non-additive in general, leading to complex dynamic behavior that has proven difficult to capture in analytical models. With recent rapid progress in computational powers, a new paradigm has emerged that aims at simulation assisted design of defect structures with predictable ‘critical-current-by-design’: analogous to the materials genome concept of predicting stable materials structures of interest. We demonstrate the feasibility of this paradigm by combiningmore » large-scale time-dependent Ginzburg-Landau numerical simulations with experiments on commercial high temperature superconductor (HTS) containing well-controlled correlated defects.« less

Research and application of borehole structure optimization based on pre-drill risk assessment

NASA Astrophysics Data System (ADS)

Zhang, Guohui; Liu, Xinyun; Chenrong; Hugui; Yu, Wenhua; Sheng, Yanan; Guan, Zhichuan

2017-11-01

Borehole structure design based on pre-drill risk assessment and considering risks related to drilling operation is the pre-condition for safe and smooth drilling operation. Major risks of drilling operation include lost circulation, blowout, sidewall collapsing, sticking and failure of drilling tools etc. In the study, studying data from neighboring wells was used to calculate the profile of formation pressure with credibility in the target well, then the borehole structure design for the target well assessment by using the drilling risk assessment to predict engineering risks before drilling. Finally, the prediction results were used to optimize borehole structure design to prevent such drilling risks. The newly-developed technique provides a scientific basis for lowering probability and frequency of drilling engineering risks, and shortening time required to drill a well, which is of great significance for safe and high-efficient drilling.

Correlation between spin structure oscillations and domain wall velocities

PubMed Central

Bisig, André; Stärk, Martin; Mawass, Mohamad-Assaad; Moutafis, Christoforos; Rhensius, Jan; Heidler, Jakoba; Büttner, Felix; Noske, Matthias; Weigand, Markus; Eisebitt, Stefan; Tyliszczak, Tolek; Van Waeyenberge, Bartel; Stoll, Hermann; Schütz, Gisela; Kläui, Mathias

2013-01-01

Magnetic sensing and logic devices based on the motion of magnetic domain walls rely on the precise and deterministic control of the position and the velocity of individual magnetic domain walls in curved nanowires. Varying domain wall velocities have been predicted to result from intrinsic effects such as oscillating domain wall spin structure transformations and extrinsic pinning due to imperfections. Here we use direct dynamic imaging of the nanoscale spin structure that allows us for the first time to directly check these predictions. We find a new regime of oscillating domain wall motion even below the Walker breakdown correlated with periodic spin structure changes. We show that the extrinsic pinning from imperfections in the nanowire only affects slow domain walls and we identify the magnetostatic energy, which scales with the domain wall velocity, as the energy reservoir for the domain wall to overcome the local pinning potential landscape. PMID:23978905

FAST Simulation Tool Containing Methods for Predicting the Dynamic Response of Wind Turbines

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

Jonkman, Jason

2015-08-12

FAST is a simulation tool (computer software) for modeling tlie dynamic response of horizontal-axis wind turbines. FAST employs a combined modal and multibody structural-dynamics formulation in the time domain.

Prediction of solar energetic particle event histories using real-time particle and solar wind measurements

NASA Technical Reports Server (NTRS)

Roelof, E. C.; Gold, R. E.

1978-01-01

The comparatively well-ordered magnetic structure in the solar corona during the decline of Solar Cycle 20 revealed a characteristic dependence of solar energetic particle injection upon heliographic longitude. When analyzed using solar wind mapping of the large scale interplanetary magnetic field line connection from the corona to the Earth, particle fluxes display an approximately exponential dependence on heliographic longitude. Since variations in the solar wind velocity (and hence the coronal connection longitude) can severely distort the simple coronal injection profile, the use of real-time solar wind velocity measurements can be of great aid in predicting the decay of solar particle events. Although such exponential injection profiles are commonplace during 1973-1975, they have also been identified earlier in Solar Cycle 20, and hence this structure may be present during the rise and maximum of the cycle, but somewhat obscured by greater temporal variations in particle injection.

Unified constitutive material models for nonlinear finite-element structural analysis. [gas turbine engine blades and vanes

NASA Technical Reports Server (NTRS)

Kaufman, A.; Laflen, J. H.; Lindholm, U. S.

1985-01-01

Unified constitutive material models were developed for structural analyses of aircraft gas turbine engine components with particular application to isotropic materials used for high-pressure stage turbine blades and vanes. Forms or combinations of models independently proposed by Bodner and Walker were considered. These theories combine time-dependent and time-independent aspects of inelasticity into a continuous spectrum of behavior. This is in sharp contrast to previous classical approaches that partition inelastic strain into uncoupled plastic and creep components. Predicted stress-strain responses from these models were evaluated against monotonic and cyclic test results for uniaxial specimens of two cast nickel-base alloys, B1900+Hf and Rene' 80. Previously obtained tension-torsion test results for Hastelloy X alloy were used to evaluate multiaxial stress-strain cycle predictions. The unified models, as well as appropriate algorithms for integrating the constitutive equations, were implemented in finite-element computer codes.

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.

Challenges in predicting climate change impacts on pome fruit phenology

NASA Astrophysics Data System (ADS)

Darbyshire, Rebecca; Webb, Leanne; Goodwin, Ian; Barlow, E. W. R.

2014-08-01

Climate projection data were applied to two commonly used pome fruit flowering models to investigate potential differences in predicted full bloom timing. The two methods, fixed thermal time and sequential chill-growth, produced different results for seven apple and pear varieties at two Australian locations. The fixed thermal time model predicted incremental advancement of full bloom, while results were mixed from the sequential chill-growth model. To further investigate how the sequential chill-growth model reacts under climate perturbed conditions, four simulations were created to represent a wider range of species physiological requirements. These were applied to five Australian locations covering varied climates. Lengthening of the chill period and contraction of the growth period was common to most results. The relative dominance of the chill or growth component tended to predict whether full bloom advanced, remained similar or was delayed with climate warming. The simplistic structure of the fixed thermal time model and the exclusion of winter chill conditions in this method indicate it is unlikely to be suitable for projection analyses. The sequential chill-growth model includes greater complexity; however, reservations in using this model for impact analyses remain. The results demonstrate that appropriate representation of physiological processes is essential to adequately predict changes to full bloom under climate perturbed conditions with greater model development needed.

Fast H-DROP: A thirty times accelerated version of H-DROP for interactive SVM-based prediction of helical domain linkers

NASA Astrophysics Data System (ADS)

Richa, Tambi; Ide, Soichiro; Suzuki, Ryosuke; Ebina, Teppei; Kuroda, Yutaka

2017-02-01

Efficient and rapid prediction of domain regions from amino acid sequence information alone is often required for swift structural and functional characterization of large multi-domain proteins. Here we introduce Fast H-DROP, a thirty times accelerated version of our previously reported H-DROP (Helical Domain linker pRediction using OPtimal features), which is unique in specifically predicting helical domain linkers (boundaries). Fast H-DROP, analogously to H-DROP, uses optimum features selected from a set of 3000 ones by combining a random forest and a stepwise feature selection protocol. We reduced the computational time from 8.5 min per sequence in H-DROP to 14 s per sequence in Fast H-DROP on an 8 Xeon processor Linux server by using SWISS-PROT instead of Genbank non-redundant (nr) database for generating the PSSMs. The sensitivity and precision of Fast H-DROP assessed by cross-validation were 33.7 and 36.2%, which were merely 2% lower than that of H-DROP. The reduced computational time of Fast H-DROP, without affecting prediction performances, makes it more interactive and user-friendly. Fast H-DROP and H-DROP are freely available from http://domserv.lab.tuat.ac.jp/.

Comparing the Performance of Three Land Models in Global C Cycle Simulations: A Detailed Structural Analysis: Structural Analysis of Land Models

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

Rafique, Rashid; Xia, Jianyang; Hararuk, Oleksandra

Land models are valuable tools to understand the dynamics of global carbon (C) cycle. Various models have been developed and used for predictions of future C dynamics but uncertainties still exist. Diagnosing the models’ behaviors in terms of structures can help to narrow down the uncertainties in prediction of C dynamics. In this study three widely used land surface models, namely CSIRO’s Atmosphere Biosphere Land Exchange (CABLE) with 9 C pools, Community Land Model (version 3.5) combined with Carnegie-Ames-Stanford Approach (CLM-CASA) with 12 C pools and Community Land Model (version 4) (CLM4) with 26 C pools were driven by themore » observed meteorological forcing. The simulated C storage and residence time were used for analysis. The C storage and residence time were computed globally for all individual soil and plant pools, as well as net primary productivity (NPP) and its allocation to different plant components’ based on these models. Remotely sensed NPP and statistically derived HWSD, and GLC2000 datasets were used as a reference to evaluate the performance of these models. Results showed that CABLE exhibited better agreement with referenced C storage and residence time for plant and soil pools, as compared with CLM-CASA and CLM4. CABLE had longer bulk residence time for soil C pools and stored more C in roots, whereas, CLM-CASA and CLM4 stored more C in woody pools due to differential NPP allocation. Overall, these results indicate that the differences in C storage and residence times in three models are largely due to the differences in their fundamental structures (number of C pools), NPP allocation and C transfer rates. Our results have implications in model development and provide a general framework to explain the bias/uncertainties in simulation of C storage and residence times from the perspectives of model structures.« less

The impact of water loading on postglacial decay times in Hudson Bay

NASA Astrophysics Data System (ADS)

Han, Holly Kyeore; Gomez, Natalya

2018-05-01

Ongoing glacial isostatic adjustment (GIA) due to surface loading (ice and water) variations during the last glacial cycle has been contributing to sea-level changes globally throughout the Holocene, especially in regions like Canada that were heavily glaciated during the Last Glacial Maximum (LGM). The spatial and temporal distribution of GIA, as manifested in relative sea-level (RSL) change, are sensitive to the ice history and the rheological structure of the solid Earth, both of which are uncertain. It has been shown that RSL curves near the center of previously glaciated regions with no ongoing surface loading follow an exponential-like form, with the postglacial decay times associated with that form having a weak sensitivity to the details of the ice loading history. Postglacial decay time estimates thus provide a powerful datum for constraining the Earth's viscous structure and improving GIA predictions. We explore spatial patterns of postglacial decay time predictions in Hudson Bay by decomposing numerically modeled RSL changes into contributions from water and ice loading effects, and computing their relative impact on the decay times. We demonstrate that ice loading can contribute a strong geographic trend on the decay time estimates if the time window used to compute decay times includes periods that are temporally close to (i.e. contemporaneous with, or soon after) periods of active loading. This variability can be avoided by choosing a suitable starting point for the decay time window. However, more surprisingly, we show that across any adopted time window, water loading effects associated with inundation into, and postglacial flux out of, Hudson Bay and James Bay will impart significant geographic variability onto decay time estimates. We emphasize this issue by considering both maps of predicted decay times across the region and site-specific estimates, and we conclude that variability in observed decay times (whether based on existing or future data sets) may reflect this water loading signal.

Performance comparison of the Prophecy (forecasting) Algorithm in FFT form for unseen feature and time-series prediction

NASA Astrophysics Data System (ADS)

Jaenisch, Holger; Handley, James

2013-06-01

We introduce a generalized numerical prediction and forecasting algorithm. We have previously published it for malware byte sequence feature prediction and generalized distribution modeling for disparate test article analysis. We show how non-trivial non-periodic extrapolation of a numerical sequence (forecast and backcast) from the starting data is possible. Our ancestor-progeny prediction can yield new options for evolutionary programming. Our equations enable analytical integrals and derivatives to any order. Interpolation is controllable from smooth continuous to fractal structure estimation. We show how our generalized trigonometric polynomial can be derived using a Fourier transform.

A Technique for Transient Thermal Testing of Thick Structures

NASA Technical Reports Server (NTRS)

Horn, Thomas J.; Richards, W. Lance; Gong, Leslie

1997-01-01

A new open-loop heat flux control technique has been developed to conduct transient thermal testing of thick, thermally-conductive aerospace structures. This technique uses calibration of the radiant heater system power level as a function of heat flux, predicted aerodynamic heat flux, and the properties of an instrumented test article. An iterative process was used to generate open-loop heater power profiles prior to each transient thermal test. Differences between the measured and predicted surface temperatures were used to refine the heater power level command profiles through the iteration process. This iteration process has reduced the effects of environmental and test system design factors, which are normally compensated for by closed-loop temperature control, to acceptable levels. The final revised heater power profiles resulted in measured temperature time histories which deviated less than 25 F from the predicted surface temperatures.

Predicting and Tracking Short Term Disease Progression in Amnestic Mild Cognitive Impairment Patients with Prodromal Alzheimer's Disease: Structural Brain Biomarkers.

PubMed

Marizzoni, Moira; Ferrari, Clarissa; Jovicich, Jorge; Albani, Diego; Babiloni, Claudio; Cavaliere, Libera; Didic, Mira; Forloni, Gianluigi; Galluzzi, Samantha; Hoffmann, Karl-Titus; Molinuevo, José Luis; Nobili, Flavio; Parnetti, Lucilla; Payoux, Pierre; Ribaldi, Federica; Rossini, Paolo Maria; Schönknecht, Peter; Soricelli, Andrea; Hensch, Tilman; Tsolaki, Magda; Visser, Pieter Jelle; Wiltfang, Jens; Richardson, Jill C; Bordet, Régis; Blin, Olivier; Frisoni, Giovanni B

2018-06-09

Early Alzheimer's disease (AD) detection using cerebrospinal fluid (CSF) biomarkers has been recommended as enrichment strategy for trials involving mild cognitive impairment (MCI) patients. To model a prodromal AD trial for identifying MRI structural biomarkers to improve subject selection and to be used as surrogate outcomes of disease progression. APOE ɛ4 specific CSF Aβ42/P-tau cut-offs were used to identify MCI with prodromal AD (Aβ42/P-tau positive) in the WP5-PharmaCog (E-ADNI) cohort. Linear mixed models were performed 1) with baseline structural biomarker, time, and biomarker×time interaction as factors to predict longitudinal changes in ADAS-cog13, 2) with Aβ42/P-tau status, time, and Aβ42/P-tau status×time interaction as factors to explain the longitudinal changes in MRI measures, and 3) to compute sample size estimation for a trial implemented with the selected biomarkers. Only baseline lateral ventricle volume was able to identify a subgroup of prodromal AD patients who declined faster (interaction, p = 0.003). Lateral ventricle volume and medial temporal lobe measures were the biomarkers most sensitive to disease progression (interaction, p≤0.042). Enrichment through ventricular volume reduced the sample size that a clinical trial would require from 13 to 76%, depending on structural outcome variable. The biomarker needing the lowest sample size was the hippocampal subfield GC-ML-DG (granule cells of molecular layer of the dentate gyrus) (n = 82 per arm to demonstrate a 20% atrophy reduction). MRI structural biomarkers can enrich prodromal AD with fast progressors and significantly decrease group size in clinical trials of disease modifying drugs.

Brief summary of the evolution of high-temperature creep-fatigue life prediction models for crack initiation

NASA Technical Reports Server (NTRS)

Halford, Gary R.

1993-01-01

The evolution of high-temperature, creep-fatigue, life-prediction methods used for cyclic crack initiation is traced from inception in the late 1940's. The methods reviewed are material models as opposed to structural life prediction models. Material life models are used by both structural durability analysts and by material scientists. The latter use micromechanistic models as guidance to improve a material's crack initiation resistance. Nearly one hundred approaches and their variations have been proposed to date. This proliferation poses a problem in deciding which method is most appropriate for a given application. Approaches were identified as being combinations of thirteen different classifications. This review is intended to aid both developers and users of high-temperature fatigue life prediction methods by providing a background from which choices can be made. The need for high-temperature, fatigue-life prediction methods followed immediately on the heels of the development of large, costly, high-technology industrial and aerospace equipment immediately following the second world war. Major advances were made in the design and manufacture of high-temperature, high-pressure boilers and steam turbines, nuclear reactors, high-temperature forming dies, high-performance poppet valves, aeronautical gas turbine engines, reusable rocket engines, etc. These advances could no longer be accomplished simply by trial and error using the 'build-em and bust-em' approach. Development lead times were too great and costs too prohibitive to retain such an approach. Analytic assessments of anticipated performance, cost, and durability were introduced to cut costs and shorten lead times. The analytic tools were quite primitive at first and out of necessity evolved in parallel with hardware development. After forty years more descriptive, more accurate, and more efficient analytic tools are being developed. These include thermal-structural finite element and boundary element analyses, advanced constitutive stress-strain-temperature-time relations, and creep-fatigue-environmental models for crack initiation and propagation. The high-temperature durability methods that have evolved for calculating high-temperature fatigue crack initiation lives of structural engineering materials are addressed. Only a few of the methods were refined to the point of being directly useable in design. Recently, two of the methods were transcribed into computer software for use with personal computers.

Brief summary of the evolution of high-temperature creep-fatigue life prediction models for crack initiation

NASA Astrophysics Data System (ADS)

Halford, Gary R.

1993-10-01

The evolution of high-temperature, creep-fatigue, life-prediction methods used for cyclic crack initiation is traced from inception in the late 1940's. The methods reviewed are material models as opposed to structural life prediction models. Material life models are used by both structural durability analysts and by material scientists. The latter use micromechanistic models as guidance to improve a material's crack initiation resistance. Nearly one hundred approaches and their variations have been proposed to date. This proliferation poses a problem in deciding which method is most appropriate for a given application. Approaches were identified as being combinations of thirteen different classifications. This review is intended to aid both developers and users of high-temperature fatigue life prediction methods by providing a background from which choices can be made. The need for high-temperature, fatigue-life prediction methods followed immediately on the heels of the development of large, costly, high-technology industrial and aerospace equipment immediately following the second world war. Major advances were made in the design and manufacture of high-temperature, high-pressure boilers and steam turbines, nuclear reactors, high-temperature forming dies, high-performance poppet valves, aeronautical gas turbine engines, reusable rocket engines, etc. These advances could no longer be accomplished simply by trial and error using the 'build-em and bust-em' approach. Development lead times were too great and costs too prohibitive to retain such an approach. Analytic assessments of anticipated performance, cost, and durability were introduced to cut costs and shorten lead times. The analytic tools were quite primitive at first and out of necessity evolved in parallel with hardware development. After forty years more descriptive, more accurate, and more efficient analytic tools are being developed. These include thermal-structural finite element and boundary element analyses, advanced constitutive stress-strain-temperature-time relations, and creep-fatigue-environmental models for crack initiation and propagation. The high-temperature durability methods that have evolved for calculating high-temperature fatigue crack initiation lives of structural engineering materials are addressed. Only a few of the methods were refined to the point of being directly useable in design.

Molecular Basis of Ligand Dissociation from G Protein-Coupled Receptors and Predicting Residence Time.

PubMed

Guo, Dong; IJzerman, Adriaan P

2018-01-01

G protein-coupled receptors (GPCRs) are integral membrane proteins and represent the largest class of drug targets. During the past decades progress in structural biology has enabled the crystallographic elucidation of the architecture of these important macromolecules. It also provided atomic-level visualization of ligand-receptor interactions, dramatically boosting the impact of structure-based approaches in drug discovery. However, knowledge obtained through crystallography is limited to static structural information. Less information is available showing how a ligand associates with or dissociates from a given receptor, whose importance is in fact increasingly recognized by the drug research community. Owing to recent advances in computer power and algorithms, molecular dynamics stimulations have become feasible that help in analyzing the kinetics of the ligand binding process. Here, we review what is currently known about the dynamics of GPCRs in the context of ligand association and dissociation, as determined through both crystallography and computer simulations. We particularly focus on the molecular basis of ligand dissociation from GPCRs and provide case studies that predict ligand dissociation pathways and residence time.

Application of the Aqueous Porous Pathway Model to Quantify the Effect of Sodium Lauryl Sulfate on Ultrasound-Induced Skin Structural Perturbation

PubMed Central

Polat, Baris E.; Seto, Jennifer E.; Blankschtein, Daniel; Langer, Robert

2011-01-01

This study investigated the effect of sodium lauryl sulfate (SLS) on skin structural perturbation when utilized simultaneously with low-frequency sonophoresis (LFS). Pig full-thickness skin (FTS) and pig split-thickness skin (STS) treated with LFS/SLS and LFS were analyzed in the context of the aqueous porous pathway model to quantify skin perturbation through changes in skin pore radius and porosity-to-tortuosity ratio (ε/τ). In addition, skin treatment times required to attain specific levels of skin electrical resistivity were analyzed to draw conclusions about the effect of SLS on reproducibility and predictability of skin perturbation. We found that LFS/SLS-treated FTS, LFS/SLS-treated STS, and LFS-treated FTS exhibited similar skin perturbation. However, LFS-treated STS exhibited significantly higher skin perturbation, suggesting greater structural changes to the less robust STS induced by the purely physical enhancement mechanism of LFS. Evaluation of ε/τ values revealed that LFS/SLS-treated FTS and STS have similar transport pathways, while LFS-treated FTS and STS have lower ε/τ values. In addition, LFS/SLS treatment times were much shorter than LFS treatment times for both FTS and STS. Moreover, the simultaneous use of SLS and LFS not only results in synergistic enhancement, as reflected in the shorter skin treatment times, but also in more predictable and reproducible skin perturbation. PMID:20963845

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.

The evolutionary time machine: forecasting how populations can adapt to changing environments using dormant propagules

PubMed Central

Orsini, Luisa; Schwenk, Klaus; De Meester, Luc; Colbourne, John K.; Pfrender, Michael E.; Weider, Lawrence J.

2013-01-01

Evolutionary changes are determined by a complex assortment of ecological, demographic and adaptive histories. Predicting how evolution will shape the genetic structures of populations coping with current (and future) environmental challenges has principally relied on investigations through space, in lieu of time, because long-term phenotypic and molecular data are scarce. Yet, dormant propagules in sediments, soils and permafrost are convenient natural archives of population-histories from which to trace adaptive trajectories along extended time periods. DNA sequence data obtained from these natural archives, combined with pioneering methods for analyzing both ecological and population genomic time-series data, are likely to provide predictive models to forecast evolutionary responses of natural populations to environmental changes resulting from natural and anthropogenic stressors, including climate change. PMID:23395434

FPGA accelerator for protein secondary structure prediction based on the GOR algorithm

PubMed Central

2011-01-01

Background Protein is an important molecule that performs a wide range of functions in biological systems. Recently, the protein folding attracts much more attention since the function of protein can be generally derived from its molecular structure. The GOR algorithm is one of the most successful computational methods and has been widely used as an efficient analysis tool to predict secondary structure from protein sequence. However, the execution time is still intolerable with the steep growth in protein database. Recently, FPGA chips have emerged as one promising application accelerator to accelerate bioinformatics algorithms by exploiting fine-grained custom design. Results In this paper, we propose a complete fine-grained parallel hardware implementation on FPGA to accelerate the GOR-IV package for 2D protein structure prediction. To improve computing efficiency, we partition the parameter table into small segments and access them in parallel. We aggressively exploit data reuse schemes to minimize the need for loading data from external memory. The whole computation structure is carefully pipelined to overlap the sequence loading, computing and back-writing operations as much as possible. We implemented a complete GOR desktop system based on an FPGA chip XC5VLX330. Conclusions The experimental results show a speedup factor of more than 430x over the original GOR-IV version and 110x speedup over the optimized version with multi-thread SIMD implementation running on a PC platform with AMD Phenom 9650 Quad CPU for 2D protein structure prediction. However, the power consumption is only about 30% of that of current general-propose CPUs. PMID:21342582

Computing the Partition Function for Kinetically Trapped RNA Secondary Structures

PubMed Central

Lorenz, William A.; Clote, Peter

2011-01-01

An RNA secondary structure is locally optimal if there is no lower energy structure that can be obtained by the addition or removal of a single base pair, where energy is defined according to the widely accepted Turner nearest neighbor model. Locally optimal structures form kinetic traps, since any evolution away from a locally optimal structure must involve energetically unfavorable folding steps. Here, we present a novel, efficient algorithm to compute the partition function over all locally optimal secondary structures of a given RNA sequence. Our software, RNAlocopt runs in time and space. Additionally, RNAlocopt samples a user-specified number of structures from the Boltzmann subensemble of all locally optimal structures. We apply RNAlocopt to show that (1) the number of locally optimal structures is far fewer than the total number of structures – indeed, the number of locally optimal structures approximately equal to the square root of the number of all structures, (2) the structural diversity of this subensemble may be either similar to or quite different from the structural diversity of the entire Boltzmann ensemble, a situation that depends on the type of input RNA, (3) the (modified) maximum expected accuracy structure, computed by taking into account base pairing frequencies of locally optimal structures, is a more accurate prediction of the native structure than other current thermodynamics-based methods. The software RNAlocopt constitutes a technical breakthrough in our study of the folding landscape for RNA secondary structures. For the first time, locally optimal structures (kinetic traps in the Turner energy model) can be rapidly generated for long RNA sequences, previously impossible with methods that involved exhaustive enumeration. Use of locally optimal structure leads to state-of-the-art secondary structure prediction, as benchmarked against methods involving the computation of minimum free energy and of maximum expected accuracy. Web server and source code available at http://bioinformatics.bc.edu/clotelab/RNAlocopt/. PMID:21297972

Can personality traits predict increases in manic and depressive symptoms?

PubMed

Lozano, B E; Johnson, S L

2001-03-01

There has been limited research investigating personality traits as predictors of manic and depressive symptoms in bipolar individuals. The present study investigated the relation between personality traits and the course of bipolar disorder. The purpose of this study was to identify specific personality traits that predict the course of manic and depressive symptoms experienced by bipolar individuals. The sample consisted of 39 participants with bipolar I disorder assessed by the Structured Clinical Interview for DSM-IV. Personality was assessed using the NEO Five-Factor Inventory. The Modified Hamilton Rating Scale for Depression and the Bech-Rafaelsen Mania Rating Scale were used to assess symptom severity on a monthly basis. Consistent with previous research on unipolar depression, high Neuroticism predicted increases in depressive symptoms across time while controlling for baseline symptoms. Additionally, high Conscientiousness, particularly the Achievement Striving facet, predicted increases in manic symptoms across time. The current study was limited by the small number of participants, the reliance on a shortened version of a self-report personality measure, and the potential state-dependency of the personality measures. Specific personality traits may assist in predicting bipolar symptoms across time. Further studies are needed to tease apart the state-dependency of personality.

Bridging the gap between marker-assisted and genomic selection of heading time and plant height in hybrid wheat.

PubMed

Zhao, Y; Mette, M F; Gowda, M; Longin, C F H; Reif, J C

2014-06-01

Based on data from field trials with a large collection of 135 elite winter wheat inbred lines and 1604 F1 hybrids derived from them, we compared the accuracy of prediction of marker-assisted selection and current genomic selection approaches for the model traits heading time and plant height in a cross-validation approach. For heading time, the high accuracy seen with marker-assisted selection severely dropped with genomic selection approaches RR-BLUP (ridge regression best linear unbiased prediction) and BayesCπ, whereas for plant height, accuracy was low with marker-assisted selection as well as RR-BLUP and BayesCπ. Differences in the linkage disequilibrium structure of the functional and single-nucleotide polymorphism markers relevant for the two traits were identified in a simulation study as a likely explanation for the different trends in accuracies of prediction. A new genomic selection approach, weighted best linear unbiased prediction (W-BLUP), designed to treat the effects of known functional markers more appropriately, proved to increase the accuracy of prediction for both traits and thus closes the gap between marker-assisted and genomic selection.

Bridging the gap between marker-assisted and genomic selection of heading time and plant height in hybrid wheat

PubMed Central

Zhao, Y; Mette, M F; Gowda, M; Longin, C F H; Reif, J C

2014-01-01

Based on data from field trials with a large collection of 135 elite winter wheat inbred lines and 1604 F1 hybrids derived from them, we compared the accuracy of prediction of marker-assisted selection and current genomic selection approaches for the model traits heading time and plant height in a cross-validation approach. For heading time, the high accuracy seen with marker-assisted selection severely dropped with genomic selection approaches RR-BLUP (ridge regression best linear unbiased prediction) and BayesCπ, whereas for plant height, accuracy was low with marker-assisted selection as well as RR-BLUP and BayesCπ. Differences in the linkage disequilibrium structure of the functional and single-nucleotide polymorphism markers relevant for the two traits were identified in a simulation study as a likely explanation for the different trends in accuracies of prediction. A new genomic selection approach, weighted best linear unbiased prediction (W-BLUP), designed to treat the effects of known functional markers more appropriately, proved to increase the accuracy of prediction for both traits and thus closes the gap between marker-assisted and genomic selection. PMID:24518889

A test of basic psychological needs theory in young soccer players: time-lagged design at the individual and team levels.

PubMed

González, L; Tomás, I; Castillo, I; Duda, J L; Balaguer, I

2017-11-01

Within the framework of basic psychological needs theory (Deci & Ryan, 2000), multilevel structural equation modeling (MSEM) with a time-lagged design was used to test a mediation model examining the relationship between perceptions of coaches' interpersonal styles (autonomy supportive and controlling), athletes' basic psychological needs (satisfaction and thwarting), and indicators of well-being (subjective vitality) and ill-being (burnout), estimating separately between and within effects. The participants were 597 Spanish male soccer players aged between 11 and 14 years (M = 12.57, SD = 0.54) from 40 teams who completed a questionnaire package at two time points in a competitive season. Results revealed that at the individual level, athletes' perceptions of autonomy support positively predicted athletes' need satisfaction (autonomy, competence, and relatedness), whereas athletes' perceptions of controlling style positively predicted athletes' need thwarting (autonomy, competence, and relatedness). In turn, all three athletes' need satisfaction dimensions predicted athletes' subjective vitality and burnout (positively and negatively, respectively), whereas competence thwarting negatively predicted subjective vitality and competence and relatedness positively predicted burnout. At the team level, team perceptions of autonomy supportive style positively predicted team autonomy and relatedness satisfaction. Mediation effects only appeared at the individual level. © 2016 John Wiley & Sons A/S. Published by John Wiley & Sons Ltd.

Moving Aerospace Structural Design Practice to a Load and Resistance Factor Approach

NASA Technical Reports Server (NTRS)

Larsen, Curtis E.; Raju, Ivatury S.

2016-01-01

Aerospace structures are traditionally designed using the factor of safety (FOS) approach. The limit load on the structure is determined and the structure is then designed for FOS times the limit load - the ultimate load. Probabilistic approaches utilize distributions for loads and strengths. Failures are predicted to occur in the region of intersection of the two distributions. The load and resistance factor design (LRFD) approach judiciously combines these two approaches by intensive calibration studies on loads and strength to result in structures that are efficient and reliable. This paper discusses these three approaches.

Cooperation, competition and goal interdependence in work teams: a multilevel approach.

PubMed

Aritzeta, Aitor; Balluerka, Nekane

2006-11-01

The aim of this research was to predict cooperative and competitive conflict management styles in 26 new start-up work teams (time 1), and after one year of functioning (time 2) in an automotive company. Vertical-horizontal, individualism-collectivism cultural patterns were used as predictive variables. It was predicted that goal interdependence would moderate the relationship between cultural patterns and conflict management styles. Because of the hierarchically nested data structure, a Multilevel Analysis approach was used. Horizontal and vertical collectivism increased cooperation, and horizontal and vertical individualism increased competition. Only when work teams had been functioning for a year, goal interdependence increased cooperation and interaction effects between goal interdependence and vertical types of individualism and collectivism were observed. Implications for team-building as organizational transformational strategies are discussed.

RNA-SSPT: RNA Secondary Structure Prediction Tools.

PubMed

Ahmad, Freed; Mahboob, Shahid; Gulzar, Tahsin; Din, Salah U; Hanif, Tanzeela; Ahmad, Hifza; Afzal, Muhammad

2013-01-01

The prediction of RNA structure is useful for understanding evolution for both in silico and in vitro studies. Physical methods like NMR studies to predict RNA secondary structure are expensive and difficult. Computational RNA secondary structure prediction is easier. Comparative sequence analysis provides the best solution. But secondary structure prediction of a single RNA sequence is challenging. RNA-SSPT is a tool that computationally predicts secondary structure of a single RNA sequence. Most of the RNA secondary structure prediction tools do not allow pseudoknots in the structure or are unable to locate them. Nussinov dynamic programming algorithm has been implemented in RNA-SSPT. The current studies shows only energetically most favorable secondary structure is required and the algorithm modification is also available that produces base pairs to lower the total free energy of the secondary structure. For visualization of RNA secondary structure, NAVIEW in C language is used and modified in C# for tool requirement. RNA-SSPT is built in C# using Dot Net 2.0 in Microsoft Visual Studio 2005 Professional edition. The accuracy of RNA-SSPT is tested in terms of Sensitivity and Positive Predicted Value. It is a tool which serves both secondary structure prediction and secondary structure visualization purposes.

RNA-SSPT: RNA Secondary Structure Prediction Tools

PubMed Central

Ahmad, Freed; Mahboob, Shahid; Gulzar, Tahsin; din, Salah U; Hanif, Tanzeela; Ahmad, Hifza; Afzal, Muhammad

2013-01-01

The prediction of RNA structure is useful for understanding evolution for both in silico and in vitro studies. Physical methods like NMR studies to predict RNA secondary structure are expensive and difficult. Computational RNA secondary structure prediction is easier. Comparative sequence analysis provides the best solution. But secondary structure prediction of a single RNA sequence is challenging. RNA-SSPT is a tool that computationally predicts secondary structure of a single RNA sequence. Most of the RNA secondary structure prediction tools do not allow pseudoknots in the structure or are unable to locate them. Nussinov dynamic programming algorithm has been implemented in RNA-SSPT. The current studies shows only energetically most favorable secondary structure is required and the algorithm modification is also available that produces base pairs to lower the total free energy of the secondary structure. For visualization of RNA secondary structure, NAVIEW in C language is used and modified in C# for tool requirement. RNA-SSPT is built in C# using Dot Net 2.0 in Microsoft Visual Studio 2005 Professional edition. The accuracy of RNA-SSPT is tested in terms of Sensitivity and Positive Predicted Value. It is a tool which serves both secondary structure prediction and secondary structure visualization purposes. PMID:24250115

Conceptual modelling to predict unobserved system states - the case of groundwater flooding in the UK Chalk

NASA Astrophysics Data System (ADS)

Hartmann, A. J.; Ireson, A. M.

2017-12-01

Chalk aquifers represent an important source of drinking water in the UK. Due to its fractured-porous structure, Chalk aquifers are characterized by highly dynamic groundwater fluctuations that enhance the risk of groundwater flooding. The risk of groundwater flooding can be assessed by physically-based groundwater models. But for reliable results, a-priori information about the distribution of hydraulic conductivities and porosities is necessary, which is often not available. For that reason, conceptual simulation models are often used to predict groundwater behaviour. They commonly require calibration by historic groundwater observations. Consequently, their prediction performance may reduce significantly, when it comes to system states that did not occur within the calibration time series. In this study, we calibrate a conceptual model to the observed groundwater level observations at several locations within a Chalk system in Southern England. During the calibration period, no groundwater flooding occurred. We then apply our model to predict the groundwater dynamics of the system at a time that includes a groundwater flooding event. We show that the calibrated model provides reasonable predictions before and after the flooding event but it over-estimates groundwater levels during the event. After modifying the model structure to include topographic information, the model is capable of prediction the groundwater flooding event even though groundwater flooding never occurred in the calibration period. Although straight forward, our approach shows how conceptual process-based models can be applied to predict system states and dynamics that did not occur in the calibration period. We believe such an approach can be transferred to similar cases, especially to regions where rainfall intensities are expected to trigger processes and system states that may have not yet been observed.

Identification of driving network of cellular differentiation from single sample time course gene expression data

NASA Astrophysics Data System (ADS)

Chen, Ye; Wolanyk, Nathaniel; Ilker, Tunc; Gao, Shouguo; Wang, Xujing

Methods developed based on bifurcation theory have demonstrated their potential in driving network identification for complex human diseases, including the work by Chen, et al. Recently bifurcation theory has been successfully applied to model cellular differentiation. However, there one often faces a technical challenge in driving network prediction: time course cellular differentiation study often only contains one sample at each time point, while driving network prediction typically require multiple samples at each time point to infer the variation and interaction structures of candidate genes for the driving network. In this study, we investigate several methods to identify both the critical time point and the driving network through examination of how each time point affects the autocorrelation and phase locking. We apply these methods to a high-throughput sequencing (RNA-Seq) dataset of 42 subsets of thymocytes and mature peripheral T cells at multiple time points during their differentiation (GSE48138 from GEO). We compare the predicted driving genes with known transcription regulators of cellular differentiation. We will discuss the advantages and limitations of our proposed methods, as well as potential further improvements of our methods.

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.

Thermo-mechanical simulations of early-age concrete cracking with durability predictions

NASA Astrophysics Data System (ADS)

Havlásek, Petr; Šmilauer, Vít; Hájková, Karolina; Baquerizo, Luis

2017-09-01

Concrete performance is strongly affected by mix design, thermal boundary conditions, its evolving mechanical properties, and internal/external restraints with consequences to possible cracking with impaired durability. Thermo-mechanical simulations are able to capture those relevant phenomena and boundary conditions for predicting temperature, strains, stresses or cracking in reinforced concrete structures. In this paper, we propose a weakly coupled thermo-mechanical model for early age concrete with an affinity-based hydration model for thermal part, taking into account concrete mix design, cement type and thermal boundary conditions. The mechanical part uses B3/B4 model for concrete creep and shrinkage with isotropic damage model for cracking, able to predict a crack width. All models have been implemented in an open-source OOFEM software package. Validations of thermo-mechanical simulations will be presented on several massive concrete structures, showing excellent temperature predictions. Likewise, strain validation demonstrates good predictions on a restrained reinforced concrete wall and concrete beam. Durability predictions stem from induction time of reinforcement corrosion, caused by carbonation and/or chloride ingress influenced by crack width. Reinforcement corrosion in concrete struts of a bridge will serve for validation.

Optimization of processing parameters of UAV integral structural components based on yield response

NASA Astrophysics Data System (ADS)

Chen, Yunsheng

2018-05-01

In order to improve the overall strength of unmanned aerial vehicle (UAV), it is necessary to optimize the processing parameters of UAV structural components, which is affected by initial residual stress in the process of UAV structural components processing. Because machining errors are easy to occur, an optimization model for machining parameters of UAV integral structural components based on yield response is proposed. The finite element method is used to simulate the machining parameters of UAV integral structural components. The prediction model of workpiece surface machining error is established, and the influence of the path of walking knife on residual stress of UAV integral structure is studied, according to the stress of UAV integral component. The yield response of the time-varying stiffness is analyzed, and the yield response and the stress evolution mechanism of the UAV integral structure are analyzed. The simulation results show that this method is used to optimize the machining parameters of UAV integral structural components and improve the precision of UAV milling processing. The machining error is reduced, and the deformation prediction and error compensation of UAV integral structural parts are realized, thus improving the quality of machining.

Proton Radiography Peers into Metal Solidification

DOE PAGES

Clarke, Amy J.; Imhoff, Seth D.; Gibbs, Paul J.; ...

2013-06-19

Historically, metals are cut up and polished to see the structure and to infer how processing influences the evolution. We can now peer into a metal during processing without destroying it using proton radiography. Understanding the link between processing and structure is important because structure profoundly affects the properties of engineering materials. Synchrotron x-ray radiography has enabled real-time glimpses into metal solidification. However, x-ray energies favor the examination of small volumes and low density metals. In this study, we use high energy proton radiography for the first time to image a large metal volume (>10,000 mm 3) during melting andmore » solidification. We also show complementary x-ray results from a small volume (<1mm 3), bridging four orders of magnitude. In conclusion, real-time imaging will enable efficient process development and the control of the structure evolution to make materials with intended properties; it will also permit the development of experimentally informed, predictive structure and process models.« less

Estimation of Dynamic Systems for Gene Regulatory Networks from Dependent Time-Course Data.

PubMed

Kim, Yoonji; Kim, Jaejik

2018-06-15

Dynamic system consisting of ordinary differential equations (ODEs) is a well-known tool for describing dynamic nature of gene regulatory networks (GRNs), and the dynamic features of GRNs are usually captured through time-course gene expression data. Owing to high-throughput technologies, time-course gene expression data have complex structures such as heteroscedasticity, correlations between genes, and time dependence. Since gene experiments typically yield highly noisy data with small sample size, for a more accurate prediction of the dynamics, the complex structures should be taken into account in ODE models. Hence, this study proposes an ODE model considering such data structures and a fast and stable estimation method for the ODE parameters based on the generalized profiling approach with data smoothing techniques. The proposed method also provides statistical inference for the ODE estimator and it is applied to a zebrafish retina cell network.

Pair-Wise and Many-Body Dispersive Interactions Coupled to an Optimally Tuned Range-Separated Hybrid Functional.

PubMed

Agrawal, Piyush; Tkatchenko, Alexandre; Kronik, Leeor

2013-08-13

We propose a nonempirical, pair-wise or many-body dispersion-corrected, optimally tuned range-separated hybrid functional. This functional retains the advantages of the optimal-tuning approach in the prediction of the electronic structure. At the same time, it gains accuracy in the prediction of binding energies for dispersively bound systems, as demonstrated on the S22 and S66 benchmark sets of weakly bound dimers.

Implicit theories about intelligence and growth (personal best) goals: Exploring reciprocal relationships.

PubMed

Martin, Andrew J

2015-06-01

There has been increasing interest in growth approaches to students' academic development, including value-added models, modelling of academic trajectories, growth motivation orientations, growth mindsets, and growth goals. This study sought to investigate the relationships between implicit theories about intelligence (incremental and entity theories) and growth (personal best, PB) goals - with particular interest in the ordering of factors across time. The study focused on longitudinal data of 969 Australian high school students. The classic cross-lagged panel design (using structural equation modelling) was employed to shed light on the ordering of Time 1 growth goals, incremental theories, and entity theories relative to Time 2 (1 year later) growth goals, incremental theories, and entity theories. Findings showed that Time 1 growth goals predicted Time 2 incremental theories (positively) and entity theories (negatively); Time 1 entity and incremental theories negatively predicted Time 2 incremental and entity theories respectively; but, Time 1 incremental theories and entity theories did not predict growth goals at Time 2. This suggests that entity and incremental theories are negatively reciprocally related across time, but growth goals seem to be directionally salient over incremental and entity theories. Implications for promoting growth goals and growth mindsets are discussed. © 2014 The British Psychological Society.

Research and development program for non-linear structural modeling with advanced time-temperature dependent constitutive relationships

NASA Technical Reports Server (NTRS)

Walker, K. P.

1981-01-01

Results of a 20-month research and development program for nonlinear structural modeling with advanced time-temperature constitutive relationships are reported. The program included: (1) the evaluation of a number of viscoplastic constitutive models in the published literature; (2) incorporation of three of the most appropriate constitutive models into the MARC nonlinear finite element program; (3) calibration of the three constitutive models against experimental data using Hastelloy-X material; and (4) application of the most appropriate constitutive model to a three dimensional finite element analysis of a cylindrical combustor liner louver test specimen to establish the capability of the viscoplastic model to predict component structural response.

Improving the long-lead predictability of El Niño using a novel forecasting scheme based on a dynamic components model

NASA Astrophysics Data System (ADS)

Petrova, Desislava; Koopman, Siem Jan; Ballester, Joan; Rodó, Xavier

2017-02-01

El Niño (EN) is a dominant feature of climate variability on inter-annual time scales driving changes in the climate throughout the globe, and having wide-spread natural and socio-economic consequences. In this sense, its forecast is an important task, and predictions are issued on a regular basis by a wide array of prediction schemes and climate centres around the world. This study explores a novel method for EN forecasting. In the state-of-the-art the advantageous statistical technique of unobserved components time series modeling, also known as structural time series modeling, has not been applied. Therefore, we have developed such a model where the statistical analysis, including parameter estimation and forecasting, is based on state space methods, and includes the celebrated Kalman filter. The distinguishing feature of this dynamic model is the decomposition of a time series into a range of stochastically time-varying components such as level (or trend), seasonal, cycles of different frequencies, irregular, and regression effects incorporated as explanatory covariates. These components are modeled separately and ultimately combined in a single forecasting scheme. Customary statistical models for EN prediction essentially use SST and wind stress in the equatorial Pacific. In addition to these, we introduce a new domain of regression variables accounting for the state of the subsurface ocean temperature in the western and central equatorial Pacific, motivated by our analysis, as well as by recent and classical research, showing that subsurface processes and heat accumulation there are fundamental for the genesis of EN. An important feature of the scheme is that different regression predictors are used at different lead months, thus capturing the dynamical evolution of the system and rendering more efficient forecasts. The new model has been tested with the prediction of all warm events that occurred in the period 1996-2015. Retrospective forecasts of these events were made for long lead times of at least two and a half years. Hence, the present study demonstrates that the theoretical limit of ENSO prediction should be sought much longer than the commonly accepted "Spring Barrier". The high correspondence between the forecasts and observations indicates that the proposed model outperforms all current operational statistical models, and behaves comparably to the best dynamical models used for EN prediction. Thus, the novel way in which the modeling scheme has been structured could also be used for improving other statistical and dynamical modeling systems.

Modeling Temporal Behavior in Large Networks: A Dynamic Mixed-Membership Model

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

Rossi, R; Gallagher, B; Neville, J

Given a large time-evolving network, how can we model and characterize the temporal behaviors of individual nodes (and network states)? How can we model the behavioral transition patterns of nodes? We propose a temporal behavior model that captures the 'roles' of nodes in the graph and how they evolve over time. The proposed dynamic behavioral mixed-membership model (DBMM) is scalable, fully automatic (no user-defined parameters), non-parametric/data-driven (no specific functional form or parameterization), interpretable (identifies explainable patterns), and flexible (applicable to dynamic and streaming networks). Moreover, the interpretable behavioral roles are generalizable, computationally efficient, and natively supports attributes. We applied ourmore » model for (a) identifying patterns and trends of nodes and network states based on the temporal behavior, (b) predicting future structural changes, and (c) detecting unusual temporal behavior transitions. We use eight large real-world datasets from different time-evolving settings (dynamic and streaming). In particular, we model the evolving mixed-memberships and the corresponding behavioral transitions of Twitter, Facebook, IP-Traces, Email (University), Internet AS, Enron, Reality, and IMDB. The experiments demonstrate the scalability, flexibility, and effectiveness of our model for identifying interesting patterns, detecting unusual structural transitions, and predicting the future structural changes of the network and individual nodes.« less

UV-vis degradation of α-tocopherol in a model system and in a cosmetic emulsion-Structural elucidation of photoproducts and toxicological consequences.

PubMed

De Vaugelade, Ségolène; Nicol, Edith; Vujovic, Svetlana; Bourcier, Sophie; Pirnay, Stéphane; Bouchonnet, Stéphane

2017-09-29

The UV-vis photodegradation of α-tocopherol was investigated in a model system and in a cosmetic emulsion. Both gas chromatography coupled with tandem mass spectrometry (GC-MS/MS) and high performance liquid chromatography coupled with ultrahigh resolution Fourier transform ion cyclotron resonance mass spectrometry (LC-UHR-MS) were used for photoproducts structural identification. Nine photoproduct families were detected and identified based on their mass spectra and additional experiments with α-tocopherol-d 9 ; phototransformation mechanisms were postulated to rationalize their formation under irradiation. In silico QSAR (Quantitative Structure Activity Relationship) toxicity predictions were conducted with the Toxicity Estimation Software Tool (T.E.S.T.). Low oral rat LD50 values of 466.78mgkg -1 and 467.9mgkg -1 were predicted for some photoproducts, indicating a potential toxicity more than 10 times greater that of α-tocopherol (5742.54mgkg -1 ). In vitro assays on Vibrio fischeri bacteria showed that the global ecotoxicity of the α-tocopherol solution significantly increases with irradiation time. One identified product should contribute to this ecotoxicity enhancement since in silico estimations for D. magna provide a LC50 value 4 times lower than that of the parent molecule. Copyright © 2017. Published by Elsevier B.V.

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.

Predictability of weather and climate in a coupled ocean-atmosphere model: A dynamical systems approach. Ph.D. Thesis

NASA Technical Reports Server (NTRS)

Nese, Jon M.

1989-01-01

A dynamical systems approach is used to quantify the instantaneous and time-averaged predictability of a low-order moist general circulation model. Specifically, the effects on predictability of incorporating an active ocean circulation, implementing annual solar forcing, and asynchronously coupling the ocean and atmosphere are evaluated. The predictability and structure of the model attractors is compared using the Lyapunov exponents, the local divergence rates, and the correlation, fractal, and Lyapunov dimensions. The Lyapunov exponents measure the average rate of growth of small perturbations on an attractor, while the local divergence rates quantify phase-spatial variations of predictability. These local rates are exploited to efficiently identify and distinguish subtle differences in predictability among attractors. In addition, the predictability of monthly averaged and yearly averaged states is investigated by using attractor reconstruction techniques.

Subwavelength diffractive acoustics and wavefront manipulation with a reflective acoustic metasurface

NASA Astrophysics Data System (ADS)

Wang, Wenqi; Xie, Yangbo; Popa, Bogdan-Ioan; Cummer, Steven A.

2016-11-01

Acoustic metasurfaces provide useful wavefront shaping capabilities, such as beam steering, acoustic focusing, and asymmetric transmission, in a compact structure. Most acoustic metasurfaces described in the literature are transmissive devices and focus their performance on steering sound beam of the fundamental diffractive order. In addition, the range of incident angles studied is usually below the critical incidence predicted by generalized Snell's law of reflection. In this work, we comprehensively analyze the wave interaction with a generic periodic phase-modulating structure in order to predict the behavior of all diffractive orders, especially for cases beyond critical incidence. Under the guidance of the presented analysis, a broadband reflective metasurface is designed based on an expanded library of labyrinthine acoustic metamaterials. Various local and nonlocal wavefront shaping properties are experimentally demonstrated, and enhanced absorption of higher order diffractive waves is experimentally shown for the first time. The proposed methodology provides an accurate approach for predicting practical diffracted wave behaviors and opens a new perspective for the study of acoustic periodic structures. The designed metasurface extends the functionalities of acoustic metasurfaces and paves the way for the design of thin planar reflective structures for broadband acoustic wave manipulation and extraordinary absorption.

Energy use in repairs by cover concrete replacement or silane treatment for extending service life of chloride-exposed concrete structures

NASA Astrophysics Data System (ADS)

Petcherdchoo, A.

2018-05-01

In this study, the service life of repaired concrete structures under chloride environment is predicted. This prediction is performed by considering the mechanism of chloride ion diffusion using the partial differential equation (PDE) of the Fick’s second law. The one-dimensional PDE cannot simply be solved, when concrete structures are cyclically repaired with cover concrete replacement or silane treatment. The difficulty is encountered in solving position-dependent chloride profile and diffusion coefficient after repairs. In order to remedy the difficulty, the finite difference method is used. By virtue of numerical computation, the position-dependent chloride profile can be treated position by position. And, based on the Crank-Nicolson scheme, a proper formulation embedded with position-dependent diffusion coefficient can be derived. By using the aforementioned idea, position- and time-dependent chloride ion concentration profiles for concrete structures with repairs can be calculated and shown, and their service life can be predicted. Moreover, the use of energy in different repair actions is also considered for comparison. From the study, it is found that repairs can control rebar corrosion and/or concrete cracking depending on repair actions.

Multifractality and autoregressive processes of dry spell lengths in Europe: an approach to their complexity and predictability

NASA Astrophysics Data System (ADS)

Lana, X.; Burgueño, A.; Serra, C.; Martínez, M. D.

2017-01-01

Dry spell lengths, DSL, defined as the number of consecutive days with daily rain amounts below a given threshold, may provide relevant information about drought regimes. Taking advantage of a daily pluviometric database covering a great extension of Europe, a detailed analysis of the multifractality of the dry spell regimes is achieved. At the same time, an autoregressive process is applied with the aim of predicting DSL. A set of parameters, namely Hurst exponent, H, estimated from multifractal spectrum, f( α), critical Hölder exponent, α 0, for which f( α) reaches its maximum value, spectral width, W, and spectral asymmetry, B, permits a first clustering of European rain gauges in terms of the complexity of their DSL series. This set of parameters also allows distinguishing between time series describing fine- or smooth-structure of the DSL regime by using the complexity index, CI. Results of previous monofractal analyses also permits establishing comparisons between smooth-structures, relatively low correlation dimensions, notable predictive instability and anti-persistence of DSL for European areas, sometimes submitted to long droughts. Relationships are also found between the CI and the mean absolute deviation, MAD, and the optimum autoregressive order, OAO, of an ARIMA( p, d,0) autoregressive process applied to the DSL series. The detailed analysis of the discrepancies between empiric and predicted DSL underlines the uncertainty over predictability of long DSL, particularly for the Mediterranean region.

Aromatase inhibitory activity of 1,4-naphthoquinone derivatives and QSAR study

PubMed Central

Prachayasittikul, Veda; Pingaew, Ratchanok; Worachartcheewan, Apilak; Sitthimonchai, Somkid; Nantasenamat, Chanin; Prachayasittikul, Supaluk; Ruchirawat, Somsak; Prachayasittikul, Virapong

2017-01-01

A series of 2-amino(chloro)-3-chloro-1,4-naphthoquinone derivatives (1-11) were investigated for their aromatase inhibitory activities. 1,4-Naphthoquinones 1 and 4 were found to be the most potent compounds affording IC50 values 5.2 times lower than the reference drug, ketoconazole. A quantitative structure-activity relationship (QSAR) model provided good predictive performance (R2CV = 0.9783 and RMSECV = 0.0748) and indicated mass (Mor04m and H8m), electronegativity (Mor08e), van der Waals volume (G1v) and structural information content index (SIC2) descriptors as key descriptors governing the activity. To investigate the effects of structural modifications on aromatase inhibitory activity, the model was employed to predict the activities of an additional set of 39 structurally modified compounds constructed in silico. The prediction suggested that the 2,3-disubstitution of 1,4-naphthoquinone ring with halogen atoms (i.e., Br, I and F) is the most effective modification for potent activity (1a, 1b and 1c). Importantly, compound 1b was predicted to be more potent than its parent compound 1 (11.90-fold) and the reference drug, letrozole (1.03-fold). The study suggests the 1,4-naphthoquinone derivatives as promising compounds to be further developed as a novel class of aromatase inhibitors. PMID:28827987

Sensitivity analysis of tall buildings in Semarang, Indonesia due to fault earthquakes with maximum 7 Mw

NASA Astrophysics Data System (ADS)

Partono, Windu; Pardoyo, Bambang; Atmanto, Indrastono Dwi; Azizah, Lisa; Chintami, Rouli Dian

2017-11-01

Fault is one of the dangerous earthquake sources that can cause building failure. A lot of buildings were collapsed caused by Yogyakarta (2006) and Pidie (2016) fault source earthquakes with maximum magnitude 6.4 Mw. Following the research conducted by Team for Revision of Seismic Hazard Maps of Indonesia 2010 and 2016, Lasem, Demak and Semarang faults are three closest earthquake sources surrounding Semarang. The ground motion from those three earthquake sources should be taken into account for structural design and evaluation. Most of tall buildings, with minimum 40 meter high, in Semarang were designed and constructed following the 2002 and 2012 Indonesian Seismic Code. This paper presents the result of sensitivity analysis research with emphasis on the prediction of deformation and inter-story drift of existing tall building within the city against fault earthquakes. The analysis was performed by conducting dynamic structural analysis of 8 (eight) tall buildings using modified acceleration time histories. The modified acceleration time histories were calculated for three fault earthquakes with magnitude from 6 Mw to 7 Mw. The modified acceleration time histories were implemented due to inadequate time histories data caused by those three fault earthquakes. Sensitivity analysis of building against earthquake can be predicted by evaluating surface response spectra calculated using seismic code and surface response spectra calculated from acceleration time histories from a specific earthquake event. If surface response spectra calculated using seismic code is greater than surface response spectra calculated from acceleration time histories the structure will stable enough to resist the earthquake force.

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.

Processing bottlenecks in dual-task performance: structural limitation or strategic postponement?

NASA Technical Reports Server (NTRS)

Ruthruff, E.; Pashler, H. E.; Klaassen, A.

2001-01-01

Recent evidence indicates that a central bottleneck causes much of the slowing that occurs when two tasks are performed at the same time. This bottleneck might reflect a structural limitation inherent in the cognitive architecture. Alternatively, the bottleneck might reflect strategic (i.e., voluntary) postponement, induced by instructions to emphasize one task over the other. To distinguish structural limitations from strategic postponement, we examine a new paradigm in which subjects are told to place equal emphasis on both tasks and to emit both responses at about the same time. An experiment using this paradigm demonstrated patterns of interference that cannot easily be attributed to strategic postponement, preparation effects, or conflicts in response production. The data conform closely to the predictions of structural central bottleneck models.

Temporal transferability of LiDAR-based imputation of forest structure attributes

Treesearch

Patrick A. Fekety; Michael J. Falkowski; Andrew T. Hudak

2015-01-01

Forest inventory and planning decisions are frequently informed by LiDAR data. Repeated LiDAR acquisitions offer an opportunity to update forest inventories and potentially improve forest inventory estimates through time. We leveraged repeated LiDAR and ground measures for a study area in northern Idaho, U.S.A., to predict (via imputation) - across both space and time-...

Aging and inhibitory control of action: cortico-subthalamic connection strength predicts stopping performance.

PubMed

Coxon, James P; Van Impe, Annouchka; Wenderoth, Nicole; Swinnen, Stephan P

2012-06-13

Diffusion weighted imaging (DWI) studies in humans have shown that seniors exhibit reduced white matter integrity compared with young adults, with the most pronounced change occurring in frontal white matter. It is generally assumed that this structural deterioration underlies inhibitory control deficits in old age, but specific evidence from a structural neuroscience perspective is lacking. Cognitive action control is thought to rely on an interconnected network consisting of right inferior frontal cortex (r-IFC), pre-supplementary motor area (preSMA), and the subthalamic nucleus (STN). Here we performed probabilistic DWI tractography to delineate this cognitive control network and had the same individuals (20 young, 20 older adults) perform a task probing both response inhibition and action reprogramming. We hypothesized that structural integrity (fractional anisotropy) and connection strength within this network would be predictive of individual and age-related differences in task performance. We show that the integrity of r-IFC white matter is an age-independent predictor of stop-signal reaction time (SSRT). We further provide evidence that the integrity of white matter projecting to STN predicts both outright stopping (SSRT) and transient braking of response initiation to buy time for action reprogramming (stopping interference effects). These associations remain even after controlling for Go task performance, demonstrating specificity to the Stop component of this task. Finally, a multiple regression analysis reveals bilateral preSMA-STN tract strength as a significant predictor of SSRT in older adults. Our data link age-related decline in inhibitory control with structural decline of STN projections.

Modelling soil-water dynamics in the rootzone of structured and water-repellent soils

NASA Astrophysics Data System (ADS)

Brown, Hamish; Carrick, Sam; Müller, Karin; Thomas, Steve; Sharp, Joanna; Cichota, Rogerio; Holzworth, Dean; Clothier, Brent

2018-04-01

In modelling the hydrology of Earth's critical zone, there are two major challenges. The first is to understand and model the processes of infiltration, runoff, redistribution and root-water uptake in structured soils that exhibit preferential flows through macropore networks. The other challenge is to parametrise and model the impact of ephemeral hydrophobicity of water-repellent soils. Here we have developed a soil-water model, which is based on physical principles, yet possesses simple functionality to enable easier parameterisation, so as to predict soil-water dynamics in structured soils displaying time-varying degrees of hydrophobicity. Our model, WEIRDO (Water Evapotranspiration Infiltration Redistribution Drainage runOff), has been developed in the APSIM Next Generation platform (Agricultural Production Systems sIMulation). The model operates on an hourly time-step. The repository for this open-source code is https://github.com/APSIMInitiative/ApsimX. We have carried out sensitivity tests to show how WEIRDO predicts infiltration, drainage, redistribution, transpiration and soil-water evaporation for three distinctly different soil textures displaying differing hydraulic properties. These three soils were drawn from the UNSODA (Unsaturated SOil hydraulic Database) soils database of the United States Department of Agriculture (USDA). We show how preferential flow process and hydrophobicity determine the spatio-temporal pattern of soil-water dynamics. Finally, we have validated WEIRDO by comparing its predictions against three years of soil-water content measurements made under an irrigated alfalfa (Medicago sativa L.) trial. The results provide validation of the model's ability to simulate soil-water dynamics in structured soils.

Highly Dynamic Anion-Quadrupole Networks in Proteins.

PubMed

Kapoor, Karan; Duff, Michael R; Upadhyay, Amit; Bucci, Joel C; Saxton, Arnold M; Hinde, Robert J; Howell, Elizabeth E; Baudry, Jerome

2016-11-01

The dynamics of anion-quadrupole (or anion-π) interactions formed between negatively charged (Asp/Glu) and aromatic (Phe) side chains are for the first time computationally characterized in RmlC (Protein Data Bank entry 1EP0 ), a homodimeric epimerase. Empirical force field-based molecular dynamics simulations predict anion-quadrupole pairs and triplets (anion-anion-π and anion-π-π) are formed by the protein during the simulated trajectory, which suggests that the anion-quadrupole interactions may provide a significant contribution to the overall stability of the protein, with an average of -1.6 kcal/mol per pair. Some anion-π interactions are predicted to form during the trajectory, extending the number of anion-quadrupole interactions beyond those predicted from crystal structure analysis. At the same time, some anion-π pairs observed in the crystal structure exhibit marginal stability. Overall, most anion-π interactions alternate between an "on" state, with significantly stabilizing energies, and an "off" state, with marginal or null stabilizing energies. The way proteins possibly compensate for transient loss of anion-quadrupole interactions is characterized in the RmlC aspartate 84-phenylalanine 112 anion-quadrupole pair observed in the crystal structure. A double-mutant cycle analysis of the thermal stability suggests a possible loss of anion-π interactions compensated by variations of hydration of the residues and formation of compensating electrostatic interactions. These results suggest that near-planar anion-quadrupole pairs can exist, sometimes transiently, which may play a role in maintaining the structural stability and function of the protein, in an otherwise very dynamic interplay of a nonbonded interaction network as well as solvent effects.

Deterministic and Probabilistic Creep and Creep Rupture Enhancement to CARES/Creep: Multiaxial Creep Life Prediction of Ceramic Structures Using Continuum Damage Mechanics and the Finite Element Method

NASA Technical Reports Server (NTRS)

Jadaan, Osama M.; Powers, Lynn M.; Gyekenyesi, John P.

1998-01-01

High temperature and long duration applications of monolithic ceramics can place their failure mode in the creep rupture regime. A previous model advanced by the authors described a methodology by which the creep rupture life of a loaded component can be predicted. That model was based on the life fraction damage accumulation rule in association with the modified Monkman-Grant creep ripture criterion However, that model did not take into account the deteriorating state of the material due to creep damage (e.g., cavitation) as time elapsed. In addition, the material creep parameters used in that life prediction methodology, were based on uniaxial creep curves displaying primary and secondary creep behavior, with no tertiary regime. The objective of this paper is to present a creep life prediction methodology based on a modified form of the Kachanov-Rabotnov continuum damage mechanics (CDM) theory. In this theory, the uniaxial creep rate is described in terms of stress, temperature, time, and the current state of material damage. This scalar damage state parameter is basically an abstract measure of the current state of material damage due to creep deformation. The damage rate is assumed to vary with stress, temperature, time, and the current state of damage itself. Multiaxial creep and creep rupture formulations of the CDM approach are presented in this paper. Parameter estimation methodologies based on nonlinear regression analysis are also described for both, isothermal constant stress states and anisothermal variable stress conditions This creep life prediction methodology was preliminarily added to the integrated design code CARES/Creep (Ceramics Analysis and Reliability Evaluation of Structures/Creep), which is a postprocessor program to commercially available finite element analysis (FEA) packages. Two examples, showing comparisons between experimental and predicted creep lives of ceramic specimens, are used to demonstrate the viability of this methodology and the CARES/Creep program.

Creep Life Prediction of Ceramic Components Using the Finite Element Based Integrated Design Program (CARES/Creep)

NASA Technical Reports Server (NTRS)

Jadaan, Osama M.; Powers, Lynn M.; Gyekenyesi, John P.

1997-01-01

The desirable properties of ceramics at high temperatures have generated interest in their use for structural applications such as in advanced turbine systems. Design lives for such systems can exceed 10,000 hours. Such long life requirements necessitate subjecting the components to relatively low stresses. The combination of high temperatures and low stresses typically places failure for monolithic ceramics in the creep regime. The objective of this work is to present a design methodology for predicting the lifetimes of structural components subjected to multiaxial creep loading. This methodology utilizes commercially available finite element packages and takes into account the time varying creep stress distributions (stress relaxation). In this methodology, the creep life of a component is divided into short time steps, during which, the stress and strain distributions are assumed constant. The damage, D, is calculated for each time step based on a modified Monkman-Grant creep rupture criterion. For components subjected to predominantly tensile loading, failure is assumed to occur when the normalized accumulated damage at any point in the component is greater than or equal to unity.

Validity of the growth model of the 'computerized visual perception assessment tool for Chinese characters structures'.

PubMed

Wu, Huey-Min; Li, Cheng-Hsaun; Kuo, Bor-Chen; Yang, Yu-Mao; Lin, Chin-Kai; Wan, Wei-Hsiang

2017-08-01

Morphological awareness is the foundation for the important developmental skills involved with vocabulary, as well as understanding the meaning of words, orthographic knowledge, reading, and writing. Visual perception of space and radicals in two-dimensional positions of Chinese characters' morphology is very important in identifying Chinese characters. The important predictive variables of special and visual perception in Chinese characters identification were investigated in the growth model in this research. The assessment tool is the "Computerized Visual Perception Assessment Tool for Chinese Characters Structures" developed by this study. There are two constructs, basic stroke and character structure. In the basic stroke, there are three subtests of one, two, and more than three strokes. In the character structure, there are three subtests of single-component character, horizontal-compound character, and vertical-compound character. This study used purposive sampling. In the first year, 551 children 4-6 years old participated in the study and were monitored for one year. In the second year, 388 children remained in the study and the successful follow-up rate was 70.4%. This study used a two-wave cross-lagged panel design to validate the growth model of the basic stroke and the character structure. There was significant correlation of the basic stroke and the character structure at different time points. The abilities in the basic stroke and in the character structure steadily developed over time for preschool children. Children's knowledge of the basic stroke effectively predicted their knowledge of the basic stroke and the character structure. Copyright © 2017 Elsevier Ltd. All rights reserved.

Determinants of the spatiotemporal dynamics of the 2009 H1N1 pandemic in Europe: implications for real-time modelling.

PubMed

Merler, Stefano; Ajelli, Marco; Pugliese, Andrea; Ferguson, Neil M

2011-09-01

Influenza pandemics in the last century were characterized by successive waves and differences in impact and timing between different regions, for reasons not clearly understood. The 2009 H1N1 pandemic showed rapid global spread, but with substantial heterogeneity in timing within each hemisphere. Even within Europe substantial variation was observed, with the UK being unique in experiencing a major first wave of transmission in early summer and all other countries having a single major epidemic in the autumn/winter, with a West to East pattern of spread. Here we show that a microsimulation model, parameterised using data about H1N1pdm collected by the beginning of June 2009, explains the occurrence of two waves in UK and a single wave in the rest of Europe as a consequence of timing of H1N1pdm spread, fluxes of travels from US and Mexico, and timing of school vacations. The model provides a description of pandemic spread through Europe, depending on intra-European mobility patterns and socio-demographic structure of the European populations, which is in broad agreement with observed timing of the pandemic in different countries. Attack rates are predicted to depend on the socio-demographic structure, with age dependent attack rates broadly agreeing with available serological data. Results suggest that the observed heterogeneity can be partly explained by the between country differences in Europe: marked differences in school calendars, mobility patterns and sociodemographic structures. Moreover, higher susceptibility of children to infection played a key role in determining the epidemiology of the 2009 pandemic. Our work shows that it would have been possible to obtain a broad-brush prediction of timing of the European pandemic well before the autumn of 2009, much more difficult to achieve with simpler models or pre-pandemic parameterisation. This supports the use of models accounting for the structure of complex modern societies for giving insight to policy makers.

On the monitoring and implications of growing damages caused by manufacturing defects in composite structures

NASA Astrophysics Data System (ADS)

Schagerl, M.; Viechtbauer, C.; Hörrmann, S.

2015-07-01

Damage tolerance is a classical safety concept for the design of aircraft structures. Basically, this approach considers possible damages in the structure, predicts the damage growth under applied loading conditions and predicts the following decrease of the structural strength. As a fundamental result the damage tolerance approach yields the maximum inspection interval, which is the time a damage grows from a detectable to a critical level. The above formulation of the damage tolerance safety concept targets on metallic structures where the damage is typically a simple fatigue crack. Fiber-reinforced polymers show a much more complex damage behavior, such as delaminationsin laminated composites. Moreover, progressive damage in composites is often initiated by manufacturing defects. The complex manufacturing processes for composite structures almost certainly yield parts with defects, e.g. pores in the matrix or undulations of fibers. From such defects growing damages may start after a certain time of operation. The demand to simplify or even avoid the inspection of composite structures has therefore led to a comeback of the traditional safe-life safety concept. The aim of the so-called safe-life flaw tolerance concept is a structure that is capable of carrying the static loads during operation, despite significant damages and after a representative fatigue load spectrum. A structure with this property does not need to be inspected, respectively monitored at all during its service life. However, its load carrying capability is thereby not fully utilized. This article presents the possible refinement of the state-of-the-art safe-life flaw tolerance concept for composite structures towards a damage tolerance approach considering also the influence of manufacturing defects on damage initiation and growth. Based on fundamental physical relations and experimental observations the challenges when developing damage growth and residual strength curves are discussed.

Folding molecular dynamics simulations accurately predict the effect of mutations on the stability and structure of a vammin-derived peptide.

PubMed

Koukos, Panagiotis I; Glykos, Nicholas M

2014-08-28

Folding molecular dynamics simulations amounting to a grand total of 4 μs of simulation time were performed on two peptides (with native and mutated sequences) derived from loop 3 of the vammin protein and the results compared with the experimentally known peptide stabilities and structures. The simulations faithfully and accurately reproduce the major experimental findings and show that (a) the native peptide is mostly disordered in solution, (b) the mutant peptide has a well-defined and stable structure, and (c) the structure of the mutant is an irregular β-hairpin with a non-glycine β-bulge, in excellent agreement with the peptide's known NMR structure. Additionally, the simulations also predict the presence of a very small β-hairpin-like population for the native peptide but surprisingly indicate that this population is structurally more similar to the structure of the native peptide as observed in the vammin protein than to the NMR structure of the isolated mutant peptide. We conclude that, at least for the given system, force field, and simulation protocol, folding molecular dynamics simulations appear to be successful in reproducing the experimentally accessible physical reality to a satisfactory level of detail and accuracy.

Improved cryoEM-Guided Iterative Molecular Dynamics–Rosetta Protein Structure Refinement Protocol for High Precision Protein Structure Prediction

PubMed Central

2016-01-01

Many excellent methods exist that incorporate cryo-electron microscopy (cryoEM) data to constrain computational protein structure prediction and refinement. Previously, it was shown that iteration of two such orthogonal sampling and scoring methods – Rosetta and molecular dynamics (MD) simulations – facilitated exploration of conformational space in principle. Here, we go beyond a proof-of-concept study and address significant remaining limitations of the iterative MD–Rosetta protein structure refinement protocol. Specifically, all parts of the iterative refinement protocol are now guided by medium-resolution cryoEM density maps, and previous knowledge about the native structure of the protein is no longer necessary. Models are identified solely based on score or simulation time. All four benchmark proteins showed substantial improvement through three rounds of the iterative refinement protocol. The best-scoring final models of two proteins had sub-Ångstrom RMSD to the native structure over residues in secondary structure elements. Molecular dynamics was most efficient in refining secondary structure elements and was thus highly complementary to the Rosetta refinement which is most powerful in refining side chains and loop regions. PMID:25883538

Semiempirical prediction of protein folds

NASA Astrophysics Data System (ADS)

Fernández, Ariel; Colubri, Andrés; Appignanesi, Gustavo

2001-08-01

We introduce a semiempirical approach to predict ab initio expeditious pathways and native backbone geometries of proteins that fold under in vitro renaturation conditions. The algorithm is engineered to incorporate a discrete codification of local steric hindrances that constrain the movements of the peptide backbone throughout the folding process. Thus, the torsional state of the chain is assumed to be conditioned by the fact that hopping from one basin of attraction to another in the Ramachandran map (local potential energy surface) of each residue is energetically more costly than the search for a specific (Φ, Ψ) torsional state within a single basin. A combinatorial procedure is introduced to evaluate coarsely defined torsional states of the chain defined ``modulo basins'' and translate them into meaningful patterns of long range interactions. Thus, an algorithm for structure prediction is designed based on the fact that local contributions to the potential energy may be subsumed into time-evolving conformational constraints defining sets of restricted backbone geometries whereupon the patterns of nonbonded interactions are constructed. The predictive power of the algorithm is assessed by (a) computing ab initio folding pathways for mammalian ubiquitin that ultimately yield a stable structural pattern reproducing all of its native features, (b) determining the nucleating event that triggers the hydrophobic collapse of the chain, and (c) comparing coarse predictions of the stable folds of moderately large proteins (N~100) with structural information extracted from the protein data bank.

Prediction of the characteristics of two types of pressure waves in the cochlea: Theoretical considerations

NASA Astrophysics Data System (ADS)

Andoh, Masayoshi; Wada, Hiroshi

2004-07-01

The aim of this study was to predict the characteristics of two types of cochlear pressure waves, so-called fast and slow waves. A two-dimensional finite-element model of the organ of Corti (OC), including fluid-structure interaction with the surrounding lymph fluid, was constructed. The geometry of the OC at the basal turn was determined from morphological measurements of others in the gerbil hemicochlea. As far as mechanical properties of the materials within the OC are concerned, previously determined mechanical properties of portions within the OC were adopted, and unknown mechanical features were determined from the published measurements of static stiffness. Time advance of the fluid-structure scheme was achieved by a staggered approach. Using the model, the magnitude and phase of the fast and slow waves were predicted so as to fit the numerically obtained pressure distribution in the scala tympani with what is known about intracochlear pressure measurement. When the predicted pressure waves were applied to the model, the numerical result of the velocity of the basilar membrane showed good agreement with the experimentally obtained velocity of the basilar membrane documented by others. Thus, the predicted pressure waves appeared to be reliable. Moreover, it was found that the fluid-structure interaction considerably influences the dynamic behavior of the OC at frequencies near the characteristic frequency.

A Numerical Model of Unsteady, Subsonic Aeroelastic Behavior. Ph.D Thesis

NASA Technical Reports Server (NTRS)

Strganac, Thomas W.

1987-01-01

A method for predicting unsteady, subsonic aeroelastic responses was developed. The technique accounts for aerodynamic nonlinearities associated with angles of attack, vortex-dominated flow, static deformations, and unsteady behavior. The fluid and the wing together are treated as a single dynamical system, and the equations of motion for the structure and flow field are integrated simultaneously and interactively in the time domain. The method employs an iterative scheme based on a predictor-corrector technique. The aerodynamic loads are computed by the general unsteady vortex-lattice method and are determined simultaneously with the motion of the wing. Because the unsteady vortex-lattice method predicts the wake as part of the solution, the history of the motion is taken into account; hysteresis is predicted. Two models are used to demonstrate the technique: a rigid wing on an elastic support experiencing plunge and pitch about the elastic axis, and an elastic wing rigidly supported at the root chord experiencing spanwise bending and twisting. The method can be readily extended to account for structural nonlinearities and/or substitute aerodynamic load models. The time domain solution coupled with the unsteady vortex-lattice method provides the capability of graphically depicting wing and wake motion.

Stressful Life Events Predict Eating Disorder Relapse Following Remission: Six-Year Prospective Outcomes

PubMed Central

Grilo, Carlos M.; Pagano, Maria E.; Stout, Robert L.; Markowitz, John C.; Ansell, Emily B.; Pinto, Anthony; Zanarini, Mary C.; Yen, Shirley; Skodol, Andrew E.

2012-01-01

Objective To examine prospectively the natural course of bulimia nervosa (BN) and eating disorder not-otherwise-specified (EDNOS) and test for the effects of stressful life events (SLE) on relapse after remission from these eating disorders. Method 117 female patients with BN (N = 35) or EDNOS (N = 82) were prospectively followed for 72 months using structured interviews performed at baseline, 6- and 12-months, and then yearly thereafter. ED were assessed with the structured clinical interview for DSM-IV, and monitored over time with the longitudinal interval follow-up evaluation. Personality disorders were assessed with the diagnostic interview for DSM-IV-personality-disorders, and monitored over time with the follow-along-version. The occurrence and specific timing of SLE were assessed with the life events assessment interview. Cox proportional-hazard-regression-analyses tested associations between time-varying levels of SLE and ED relapse, controlling for comorbid psychiatric disorders, ED duration, and time-varying personality-disorder status. Results ED relapse probability was 43%; BN and EDNOS did not differ in time to relapse. Negative SLE significantly predicted ED relapse; elevated work and social stressors were significant predictors. Psychiatric comorbidity, ED duration, and time-varying personality-disorder status were not significant predictors. Discussion Higher work and social stress represent significant warning signs for triggering relapse for women with remitted BN and EDNOS. PMID:21448971

Tests of Predictions of the Algebraic Cluster Model: the Triangular D 3h Symmetry of 12C

NASA Astrophysics Data System (ADS)

Gai, Moshe

2016-07-01

A new theoretical approach to clustering in the frame of the Algebraic Cluster Model (ACM) has been developed. It predicts rotation-vibration structure with rotational band of an oblate equilateral triangular symmetric spinning top with a D 3h symmetry characterized by the sequence of states: 0+, 2+, 3-, 4±, 5- with a degenerate 4+ and 4- (parity doublet) states. Our measured new 2+ 2 in 12C allows the first study of rotation-vibration structure in 12C. The newly measured 5- state and 4- states fit very well the predicted ground state rotational band structure with the predicted sequence of states: 0+, 2+, 3-, 4±, 5- with almost degenerate 4+ and 4- (parity doublet) states. Such a D 3h symmetry is characteristic of triatomic molecules, but it is observed in the ground state rotational band of 12C for the first time in a nucleus. We discuss predictions of the ACM of other rotation-vibration bands in 12 C such as the (0+) Hoyle band and the (1-) bending mode with prediction of (“missing 3- and 4-”) states that may shed new light on clustering in 12C and light nuclei. In particular, the observation (or non observation) of the predicted (“missing”) states in the Hoyle band will allow us to conclude the geometrical arrangement of the three alpha particles composing the Hoyle state at 7.6542 MeV in 12C. We discuss proposed research programs at the Darmstadt S-DALINAC and at the newly constructed ELI-NP facility near Bucharest to test the predictions of the ACM in isotopes of carbon.

Computational methodology to predict satellite system-level effects from impacts of untrackable space debris

NASA Astrophysics Data System (ADS)

Welty, N.; Rudolph, M.; Schäfer, F.; Apeldoorn, J.; Janovsky, R.

2013-07-01

This paper presents a computational methodology to predict the satellite system-level effects resulting from impacts of untrackable space debris particles. This approach seeks to improve on traditional risk assessment practices by looking beyond the structural penetration of the satellite and predicting the physical damage to internal components and the associated functional impairment caused by untrackable debris impacts. The proposed method combines a debris flux model with the Schäfer-Ryan-Lambert ballistic limit equation (BLE), which accounts for the inherent shielding of components positioned behind the spacecraft structure wall. Individual debris particle impact trajectories and component shadowing effects are considered and the failure probabilities of individual satellite components as a function of mission time are calculated. These results are correlated to expected functional impairment using a Boolean logic model of the system functional architecture considering the functional dependencies and redundancies within the system.

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.

Surrogate Modeling of High-Fidelity Fracture Simulations for Real-Time Residual Strength Predictions

NASA Technical Reports Server (NTRS)

Spear, Ashley D.; Priest, Amanda R.; Veilleux, Michael G.; Ingraffea, Anthony R.; Hochhalter, Jacob D.

2011-01-01

A surrogate model methodology is described for predicting, during flight, the residual strength of aircraft structures that sustain discrete-source damage. Starting with design of experiment, an artificial neural network is developed that takes as input discrete-source damage parameters and outputs a prediction of the structural residual strength. Target residual strength values used to train the artificial neural network are derived from 3D finite element-based fracture simulations. Two ductile fracture simulations are presented to show that crack growth and residual strength are determined more accurately in discrete-source damage cases by using an elastic-plastic fracture framework rather than a linear-elastic fracture mechanics-based method. Improving accuracy of the residual strength training data does, in turn, improve accuracy of the surrogate model. When combined, the surrogate model methodology and high fidelity fracture simulation framework provide useful tools for adaptive flight technology.

Comparison of CME/Shock Propagation Models with Heliospheric Imaging and In Situ Observations

NASA Astrophysics Data System (ADS)

Zhao, Xinhua; Liu, Ying D.; Inhester, Bernd; Feng, Xueshang; Wiegelmann, Thomas; Lu, Lei

2016-10-01

The prediction of the arrival time for fast coronal mass ejections (CMEs) and their associated shocks is highly desirable in space weather studies. In this paper, we use two shock propagation models, I.e., Data Guided Shock Time Of Arrival (DGSTOA) and Data Guided Shock Propagation Model (DGSPM), to predict the kinematical evolution of interplanetary shocks associated with fast CMEs. DGSTOA is based on the similarity theory of shock waves in the solar wind reference frame, and DGSPM is based on the non-similarity theory in the stationary reference frame. The inputs are the kinematics of the CME front at the maximum speed moment obtained from the geometric triangulation method applied to STEREO imaging observations together with the Harmonic Mean approximation. The outputs provide the subsequent propagation of the associated shock. We apply these models to the CMEs on 2012 January 19, January 23, and March 7. We find that the shock models predict reasonably well the shock’s propagation after the impulsive acceleration. The shock’s arrival time and local propagation speed at Earth predicted by these models are consistent with in situ measurements of WIND. We also employ the Drag-Based Model (DBM) as a comparison, and find that it predicts a steeper deceleration than the shock models after the rapid deceleration phase. The predictions of DBM at 1 au agree with the following ICME or sheath structure, not the preceding shock. These results demonstrate the applicability of the shock models used here for future arrival time prediction of interplanetary shocks associated with fast CMEs.

SCPRED: accurate prediction of protein structural class for sequences of twilight-zone similarity with predicting sequences.

PubMed

Kurgan, Lukasz; Cios, Krzysztof; Chen, Ke

2008-05-01

Protein structure prediction methods provide accurate results when a homologous protein is predicted, while poorer predictions are obtained in the absence of homologous templates. However, some protein chains that share twilight-zone pairwise identity can form similar folds and thus determining structural similarity without the sequence similarity would be desirable for the structure prediction. The folding type of a protein or its domain is defined as the structural class. Current structural class prediction methods that predict the four structural classes defined in SCOP provide up to 63% accuracy for the datasets in which sequence identity of any pair of sequences belongs to the twilight-zone. We propose SCPRED method that improves prediction accuracy for sequences that share twilight-zone pairwise similarity with sequences used for the prediction. SCPRED uses a support vector machine classifier that takes several custom-designed features as its input to predict the structural classes. Based on extensive design that considers over 2300 index-, composition- and physicochemical properties-based features along with features based on the predicted secondary structure and content, the classifier's input includes 8 features based on information extracted from the secondary structure predicted with PSI-PRED and one feature computed from the sequence. Tests performed with datasets of 1673 protein chains, in which any pair of sequences shares twilight-zone similarity, show that SCPRED obtains 80.3% accuracy when predicting the four SCOP-defined structural classes, which is superior when compared with over a dozen recent competing methods that are based on support vector machine, logistic regression, and ensemble of classifiers predictors. The SCPRED can accurately find similar structures for sequences that share low identity with sequence used for the prediction. The high predictive accuracy achieved by SCPRED is attributed to the design of the features, which are capable of separating the structural classes in spite of their low dimensionality. We also demonstrate that the SCPRED's predictions can be successfully used as a post-processing filter to improve performance of modern fold classification methods.

SCPRED: Accurate prediction of protein structural class for sequences of twilight-zone similarity with predicting sequences

PubMed Central

Kurgan, Lukasz; Cios, Krzysztof; Chen, Ke

2008-01-01

Background Protein structure prediction methods provide accurate results when a homologous protein is predicted, while poorer predictions are obtained in the absence of homologous templates. However, some protein chains that share twilight-zone pairwise identity can form similar folds and thus determining structural similarity without the sequence similarity would be desirable for the structure prediction. The folding type of a protein or its domain is defined as the structural class. Current structural class prediction methods that predict the four structural classes defined in SCOP provide up to 63% accuracy for the datasets in which sequence identity of any pair of sequences belongs to the twilight-zone. We propose SCPRED method that improves prediction accuracy for sequences that share twilight-zone pairwise similarity with sequences used for the prediction. Results SCPRED uses a support vector machine classifier that takes several custom-designed features as its input to predict the structural classes. Based on extensive design that considers over 2300 index-, composition- and physicochemical properties-based features along with features based on the predicted secondary structure and content, the classifier's input includes 8 features based on information extracted from the secondary structure predicted with PSI-PRED and one feature computed from the sequence. Tests performed with datasets of 1673 protein chains, in which any pair of sequences shares twilight-zone similarity, show that SCPRED obtains 80.3% accuracy when predicting the four SCOP-defined structural classes, which is superior when compared with over a dozen recent competing methods that are based on support vector machine, logistic regression, and ensemble of classifiers predictors. Conclusion The SCPRED can accurately find similar structures for sequences that share low identity with sequence used for the prediction. The high predictive accuracy achieved by SCPRED is attributed to the design of the features, which are capable of separating the structural classes in spite of their low dimensionality. We also demonstrate that the SCPRED's predictions can be successfully used as a post-processing filter to improve performance of modern fold classification methods. PMID:18452616

A structural model for the flexural mechanics of nonwoven tissue engineering scaffolds.

PubMed

Engelmayr, George C; Sacks, Michael S

2006-08-01

The development of methods to predict the strength and stiffness of biomaterials used in tissue engineering is critical for load-bearing applications in which the essential functional requirements are primarily mechanical. We previously quantified changes in the effective stiffness (E) of needled nonwoven polyglycolic acid (PGA) and poly-L-lactic acid (PLLA) scaffolds due to tissue formation and scaffold degradation under three-point bending. Toward predicting these changes, we present a structural model for E of a needled nonwoven scaffold in flexure. The model accounted for the number and orientation of fibers within a representative volume element of the scaffold demarcated by the needling process. The spring-like effective stiffness of the curved fibers was calculated using the sinusoidal fiber shapes. Structural and mechanical properties of PGA and PLLA fibers and PGA, PLLA, and 50:50 PGA/PLLA scaffolds were measured and compared with model predictions. To verify the general predictive capability, the predicted dependence of E on fiber diameter was compared with experimental measurements. Needled nonwoven scaffolds were found to exhibit distinct preferred (PD) and cross-preferred (XD) fiber directions, with an E ratio (PD/XD) of approximately 3:1. The good agreement between the predicted and experimental dependence of E on fiber diameter (R2 = 0.987) suggests that the structural model can be used to design scaffolds with E values more similar to native soft tissues. A comparison with previous results for cell-seeded scaffolds (Engelmayr, G. C., Jr., et al., 2005, Biomaterials, 26(2), pp. 175-187) suggests, for the first time, that the primary mechanical effect of collagen deposition is an increase in the number of fiber-fiber bond points yielding effectively stiffer scaffold fibers. This finding indicated that the effects of tissue deposition on needled nonwoven scaffold mechanics do not follow a rule-of-mixtures behavior. These important results underscore the need for structural approaches in modeling the effects of engineered tissue formation on nonwoven scaffolds, and their potential utility in scaffold design.

A Grammatical Approach to RNA-RNA Interaction Prediction

NASA Astrophysics Data System (ADS)

Kato, Yuki; Akutsu, Tatsuya; Seki, Hiroyuki

2007-11-01

Much attention has been paid to two interacting RNA molecules involved in post-transcriptional control of gene expression. Although there have been a few studies on RNA-RNA interaction prediction based on dynamic programming algorithm, no grammar-based approach has been proposed. The purpose of this paper is to provide a new modeling for RNA-RNA interaction based on multiple context-free grammar (MCFG). We present a polynomial time parsing algorithm for finding the most likely derivation tree for the stochastic version of MCFG, which is applicable to RNA joint secondary structure prediction including kissing hairpin loops. Also, elementary tests on RNA-RNA interaction prediction have shown that the proposed method is comparable to Alkan et al.'s method.

Cheminformatics-aided pharmacovigilance: application to Stevens-Johnson Syndrome

PubMed Central

Low, Yen S; Caster, Ola; Bergvall, Tomas; Fourches, Denis; Zang, Xiaoling; Norén, G Niklas; Rusyn, Ivan; Edwards, Ralph

2016-01-01

Objective Quantitative Structure-Activity Relationship (QSAR) models can predict adverse drug reactions (ADRs), and thus provide early warnings of potential hazards. Timely identification of potential safety concerns could protect patients and aid early diagnosis of ADRs among the exposed. Our objective was to determine whether global spontaneous reporting patterns might allow chemical substructures associated with Stevens-Johnson Syndrome (SJS) to be identified and utilized for ADR prediction by QSAR models. Materials and Methods Using a reference set of 364 drugs having positive or negative reporting correlations with SJS in the VigiBase global repository of individual case safety reports (Uppsala Monitoring Center, Uppsala, Sweden), chemical descriptors were computed from drug molecular structures. Random Forest and Support Vector Machines methods were used to develop QSAR models, which were validated by external 5-fold cross validation. Models were employed for virtual screening of DrugBank to predict SJS actives and inactives, which were corroborated using knowledge bases like VigiBase, ChemoText, and MicroMedex (Truven Health Analytics Inc, Ann Arbor, Michigan). Results We developed QSAR models that could accurately predict if drugs were associated with SJS (area under the curve of 75%–81%). Our 10 most active and inactive predictions were substantiated by SJS reports (or lack thereof) in the literature. Discussion Interpretation of QSAR models in terms of significant chemical descriptors suggested novel SJS structural alerts. Conclusions We have demonstrated that QSAR models can accurately identify SJS active and inactive drugs. Requiring chemical structures only, QSAR models provide effective computational means to flag potentially harmful drugs for subsequent targeted surveillance and pharmacoepidemiologic investigations. PMID:26499102

Teachers' goal orientations: Effects on classroom goal structures and emotions.

PubMed

Wang, Hui; Hall, Nathan C; Goetz, Thomas; Frenzel, Anne C

2017-03-01

Prior research has shown teachers' goal orientations to influence classroom goal structures (Retelsdorf et al., 2010, Learning and Instruction, 20, 30) and to also impact their emotions (Schutz et al., 2007, Emotion in education, Academic Press, Amsterdam, the Netherlands). However, empirical research evaluating possible causal ordering and mediation effects involving these variables in teachers is presently lacking. The present 6-month longitudinal study investigated the relations between varied motivational, behavioural, and emotional variables in practising teachers. More specifically, this study examined the reciprocal, longitudinal relations between teachers' achievement goals, classroom goal structures, and teaching-related emotions, as well as cumulative mediational models in which observed causal relations were evaluated. Participants were 495 practising teachers from Canada (86% female, M = 42 years). Teachers completed a web-based questionnaire at two time points assessing their instructional goals, perceived classroom goal structures, achievement emotions, and demographic items. Results from cross-lagged analyses and structural equation modelling showed teachers' achievement goals to predict their perceived classroom goal structures that, in turn, predicted their teaching-related emotions. The present results inform both Butler's (2012, Journal of Educational Psychology, 104, 726) theory on teachers' achievement goals and Frenzel's (2014, International handbook of emotions in education, Routledge, New York, NY) model of teachers' emotions in showing teachers' instructional goals to both directly predict their teaching-related emotions, as well as indirectly through the mediating effects of classroom goal structures. © 2016 The British Psychological Society.

Probability-based constrained MPC for structured uncertain systems with state and random input delays

NASA Astrophysics Data System (ADS)

Lu, Jianbo; Li, Dewei; Xi, Yugeng

2013-07-01

This article is concerned with probability-based constrained model predictive control (MPC) for systems with both structured uncertainties and time delays, where a random input delay and multiple fixed state delays are included. The process of input delay is governed by a discrete-time finite-state Markov chain. By invoking an appropriate augmented state, the system is transformed into a standard structured uncertain time-delay Markov jump linear system (MJLS). For the resulting system, a multi-step feedback control law is utilised to minimise an upper bound on the expected value of performance objective. The proposed design has been proved to stabilise the closed-loop system in the mean square sense and to guarantee constraints on control inputs and system states. Finally, a numerical example is given to illustrate the proposed results.

Chemorheology of in-mold coating for compression molded SMC applications

NASA Astrophysics Data System (ADS)

Ko, Seunghyun; Straus, Elliott J.; Castro, Jose M.

2015-05-01

In-mold coating (IMC) is applied to compression molded sheet molding compound (SMC) exterior automotive or truck body panels as an environmentally friendly alternative to make the surface conductive for subsequent electrostatic painting operations. The coating is a thermosetting liquid that when injected onto the surface of the part cures and bonds to provide a smooth conductive surface. In order to optimize the IMC process, it is essential to predict the time available for flow, that is the time before the thermosetting reaction starts (inhibition time) as well as the time when the coating has enough structural integrity so that the mold can be opened without damaging the part surface (cure time). To predict both the inhibition time and the cure time, it is critical to study the chemorheology of IMC. In this paper, we study the chemorheology for a typical commercial IMC system, and show its relevance to both the flow and cure time for the IMC stage during SMC compression molding.

A 2.5-dimensional method for the prediction of structure-borne low-frequency noise from concrete rail transit bridges.

PubMed

Li, Qi; Song, Xiaodong; Wu, Dingjun

2014-05-01

Predicting structure-borne noise from bridges subjected to moving trains using the three-dimensional (3D) boundary element method (BEM) is a time consuming process. This paper presents a two-and-a-half dimensional (2.5D) BEM-based procedure for simulating bridge-borne low-frequency noise with higher efficiency, yet no loss of accuracy. The two-dimensional (2D) BEM of a bridge with a constant cross section along the track direction is adopted to calculate the spatial modal acoustic transfer vectors (MATVs) of the bridge using the space-wave number transforms of its 3D modal shapes. The MATVs calculated using the 2.5D method are then validated by those computed using the 3D BEM. The bridge-borne noise is finally obtained through the MATVs and modal coordinate responses of the bridge, considering time-varying vehicle-track-bridge dynamic interaction. The presented procedure is applied to predict the sound pressure radiating from a U-shaped concrete bridge, and the computed results are compared with those obtained from field tests on Shanghai rail transit line 8. The numerical results match well with the measured results in both time and frequency domains at near-field points. Nevertheless, the computed results are smaller than the measured ones for far-field points, mainly due to the sound radiation from adjacent spans neglected in the current model.

The HADDOCK2.2 Web Server: User-Friendly Integrative Modeling of Biomolecular Complexes.

PubMed

van Zundert, G C P; Rodrigues, J P G L M; Trellet, M; Schmitz, C; Kastritis, P L; Karaca, E; Melquiond, A S J; van Dijk, M; de Vries, S J; Bonvin, A M J J

2016-02-22

The prediction of the quaternary structure of biomolecular macromolecules is of paramount importance for fundamental understanding of cellular processes and drug design. In the era of integrative structural biology, one way of increasing the accuracy of modeling methods used to predict the structure of biomolecular complexes is to include as much experimental or predictive information as possible in the process. This has been at the core of our information-driven docking approach HADDOCK. We present here the updated version 2.2 of the HADDOCK portal, which offers new features such as support for mixed molecule types, additional experimental restraints and improved protocols, all of this in a user-friendly interface. With well over 6000 registered users and 108,000 jobs served, an increasing fraction of which on grid resources, we hope that this timely upgrade will help the community to solve important biological questions and further advance the field. The HADDOCK2.2 Web server is freely accessible to non-profit users at http://haddock.science.uu.nl/services/HADDOCK2.2. Copyright © 2015 The Authors. Published by Elsevier Ltd.. All rights reserved.

Prediction of the low-velocity distribution from the pore structure in simple porous media

NASA Astrophysics Data System (ADS)

de Anna, Pietro; Quaife, Bryan; Biros, George; Juanes, Ruben

2017-12-01

The macroscopic properties of fluid flow and transport through porous media are a direct consequence of the underlying pore structure. However, precise relations that characterize flow and transport from the statistics of pore-scale disorder have remained elusive. Here we investigate the relationship between pore structure and the resulting fluid flow and asymptotic transport behavior in two-dimensional geometries of nonoverlapping circular posts. We derive an analytical relationship between the pore throat size distribution fλ˜λ-β and the distribution of the low fluid velocities fu˜u-β /2 , based on a conceptual model of porelets (the flow established within each pore throat, here a Hagen-Poiseuille flow). Our model allows us to make predictions, within a continuous-time random-walk framework, for the asymptotic statistics of the spreading of fluid particles along their own trajectories. These predictions are confirmed by high-fidelity simulations of Stokes flow and advective transport. The proposed framework can be extended to other configurations which can be represented as a collection of known flow distributions.

Behavioral correlates of changes in hippocampal gray matter structure during acquisition of foreign vocabulary.

PubMed

Bellander, Martin; Berggren, Rasmus; Mårtensson, Johan; Brehmer, Yvonne; Wenger, Elisabeth; Li, Tie-Qiang; Bodammer, Nils C; Shing, Yee-Lee; Werkle-Bergner, Markus; Lövdén, Martin

2016-05-01

Experience can affect human gray matter volume. The behavioral correlates of individual differences in such brain changes are not well understood. In a group of Swedish individuals studying Italian as a foreign language, we investigated associations among time spent studying, acquired vocabulary, baseline performance on memory tasks, and gray matter changes. As a way of studying episodic memory training, the language learning focused on acquiring foreign vocabulary and lasted for 10weeks. T1-weighted structural magnetic resonance imaging and cognitive testing were performed before and after the studies. Learning behavior was monitored via participants' use of a smartphone application dedicated to the study of vocabulary. A whole-brain analysis showed larger changes in gray matter structure of the right hippocampus in the experimental group (N=33) compared to an active control group (N=23). A first path analyses revealed that time spent studying rather than acquired knowledge significantly predicted change in gray matter structure. However, this association was not significant when adding performance on baseline memory measures into the model, instead only the participants' performance on a short-term memory task with highly similar distractors predicted the change. This measure may tap similar individual difference factors as those involved in gray matter plasticity of the hippocampus. Copyright © 2015 Elsevier Inc. All rights reserved.

Synthesis, Spectra, and Theoretical Investigations of 1,3,5-Triazines Compounds as Ultraviolet Rays Absorber Based on Time-Dependent Density Functional Calculations and three-Dimensional Quantitative Structure-Property Relationship.

PubMed

Wang, Xueding; Xu, Yilian; Yang, Lu; Lu, Xiang; Zou, Hao; Yang, Weiqing; Zhang, Yuanyuan; Li, Zicheng; Ma, Menglin

2018-03-01

A series of 1,3,5-triazines were synthesized and their UV absorption properties were tested. The computational chemistry methods were used to construct quantitative structure-property relationship (QSPR), which was used to computer aided design of new 1,3,5-triazines ultraviolet rays absorber compounds. The experimental UV absorption data are in good agreement with those predicted data using the Time-dependent density functional theory (TD-DFT) [B3LYP/6-311 + G(d,p)]. A suitable forecasting model (R > 0.8, P < 0.0001) was revealed. Predictive three-dimensional quantitative structure-property relationship (3D-QSPR) model was established using multifit molecular alignment rule of Sybyl program, which conclusion is consistent with the TD-DFT calculation. The exceptional photostability mechanism of such ultraviolet rays absorber compounds was studied and confirmed as principally banked upon their ability to undergo excited-state deactivation via an ultrafast excited-state proton transfer (ESIPT). The intramolecular hydrogen bond (IMHB) of 1,3,5-triazines compounds is the basis for the excited state proton transfer, which was explored by IR spectroscopy, UV spectra, structural and energetic aspects of different conformers and frontier molecular orbitals analysis.

Assemblages of animals around urban structures: testing hypotheses of patterns in sediments under boat-mooring pontoons.

PubMed

Lindegarth, M

2001-05-01

Assemblages of animals in soft-sediments were studied in relation to pontoons for mooring private boats in two estuaries near Sydney, Australia. Based on previously observed patterns around other types of artificial structures, it was predicted that assemblages of animals under pontoons would be different from those in similar areas away from pontoons. Hypotheses about overall differences in average abundance and composition between sites with and without pontoons were tested, as were hypotheses about variable differences among and within estuaries. Analyses revealed that there were fewer crustaceans under pontoons in one estuary. The most conspicuous patterns related to pontoons were, however, differences in variability among sites with pontoons compared to sites without pontoons. Differences in spatial variability were found for the overall multivariate structure using Bray-Curtis dissimilarities and for abundances of most major taxa. Total abundance was approximately 60 times more variable among sites without pontoons and number of taxa were seven times more variable among sites with pontoons. Such patterns indicate that impacts of pontoons occur at some sites but not at others. This may be explained by intrinsic differences among sites or by differences in practices for maintenance. Predictions from these two contrasting models need to be tested in order to achieve efficient management of this type of structure.

Critical Features of Fragment Libraries for Protein Structure Prediction

PubMed Central

dos Santos, Karina Baptista

2017-01-01

The use of fragment libraries is a popular approach among protein structure prediction methods and has proven to substantially improve the quality of predicted structures. However, some vital aspects of a fragment library that influence the accuracy of modeling a native structure remain to be determined. This study investigates some of these features. Particularly, we analyze the effect of using secondary structure prediction guiding fragments selection, different fragments sizes and the effect of structural clustering of fragments within libraries. To have a clearer view of how these factors affect protein structure prediction, we isolated the process of model building by fragment assembly from some common limitations associated with prediction methods, e.g., imprecise energy functions and optimization algorithms, by employing an exact structure-based objective function under a greedy algorithm. Our results indicate that shorter fragments reproduce the native structure more accurately than the longer. Libraries composed of multiple fragment lengths generate even better structures, where longer fragments show to be more useful at the beginning of the simulations. The use of many different fragment sizes shows little improvement when compared to predictions carried out with libraries that comprise only three different fragment sizes. Models obtained from libraries built using only sequence similarity are, on average, better than those built with a secondary structure prediction bias. However, we found that the use of secondary structure prediction allows greater reduction of the search space, which is invaluable for prediction methods. The results of this study can be critical guidelines for the use of fragment libraries in protein structure prediction. PMID:28085928

Critical Features of Fragment Libraries for Protein Structure Prediction.

PubMed

Trevizani, Raphael; Custódio, Fábio Lima; Dos Santos, Karina Baptista; Dardenne, Laurent Emmanuel

2017-01-01

The use of fragment libraries is a popular approach among protein structure prediction methods and has proven to substantially improve the quality of predicted structures. However, some vital aspects of a fragment library that influence the accuracy of modeling a native structure remain to be determined. This study investigates some of these features. Particularly, we analyze the effect of using secondary structure prediction guiding fragments selection, different fragments sizes and the effect of structural clustering of fragments within libraries. To have a clearer view of how these factors affect protein structure prediction, we isolated the process of model building by fragment assembly from some common limitations associated with prediction methods, e.g., imprecise energy functions and optimization algorithms, by employing an exact structure-based objective function under a greedy algorithm. Our results indicate that shorter fragments reproduce the native structure more accurately than the longer. Libraries composed of multiple fragment lengths generate even better structures, where longer fragments show to be more useful at the beginning of the simulations. The use of many different fragment sizes shows little improvement when compared to predictions carried out with libraries that comprise only three different fragment sizes. Models obtained from libraries built using only sequence similarity are, on average, better than those built with a secondary structure prediction bias. However, we found that the use of secondary structure prediction allows greater reduction of the search space, which is invaluable for prediction methods. The results of this study can be critical guidelines for the use of fragment libraries in protein structure prediction.

Predicting Tropical Cyclogenesis with a Global Mesoscale Model: Preliminary Results with Very Severe Cyclonic Storm Nargis (2008)

NASA Astrophysics Data System (ADS)

Shen, B.; Tao, W.; Atlas, R.

2008-12-01

Very Severe Cyclonic Storm Nargis, the deadliest named tropical cyclone (TC) in the North Indian Ocean Basin, devastated Burma (Myanmar) in May 2008, causing tremendous damage and numerous fatalities. An increased lead time in the prediction of TC Nargis would have increased the warning time and may therefore have saved lives and reduced economic damage. Recent advances in high-resolution global models and supercomputers have shown the potential for improving TC track and intensity forecasts, presumably by improving multi-scale simulations. The key but challenging questions to be answered include: (1) if and how realistic, in terms of timing, location and TC general structure, the global mesoscale model (GMM) can simulate TC genesis and (2) under what conditions can the model extend the lead time of TC genesis forecasts. In this study, we focus on genesis prediction for TCs in the Indian Ocean with the GMM. Preliminary real-data simulations show that the initial formation and intensity variations of TC Nargis can be realistically predicted at a lead time of up to 5 days. These simulations also suggest that the accurate representations of a westerly wind burst (WWB) and an equatorial trough, associated with monsoon circulations and/or a Madden-Julian Oscillation (MJO), are important for predicting the formation of this kind of TC. In addition to the WWB and equatorial trough, other favorable environmental conditions will be examined, which include enhanced monsoonal circulation, upper-level outflow, low- and middle-level moistening, and surface fluxes.

Macroscopic aspects of interfacial reactions

NASA Technical Reports Server (NTRS)

Heckel, R. W.

1976-01-01

The extent of interdiffusion and formation of new phases is determined by the constitution diagram of the alloy system, the interdiffusion coefficients of the phases present, and the thermal conditions (temperature and time) associated with the bonding process and/or subsequent use of the bonded structure. In many instance, the kinetics of interdiffusion and phase formation can be predicted from known parameters using numerical methods and computer techniques. Predictions are compared with experimentally determined parameters for a variety of metallurgical alloy systems.

The Numerical Simulation of Time Dependent Flow Structures Over a Natural Gravel Surface.

NASA Astrophysics Data System (ADS)

Hardy, R. J.; Lane, S. N.; Ferguson, R. I.; Parsons, D. R.

2004-05-01

Research undertaken over the last few years has demonstrated the importance of the structure of gravel river beds for understanding the interaction between fluid flow and sediment transport processes. This includes the observation of periodic high-speed fluid wedges interconnected by low-speed flow regions. Our understanding of these flows has been enhanced significantly through a series of laboratory experiments and supported by field observations. However, the potential of high resolution three dimensional Computational Fluid Dynamics (CFD) modeling has yet to be fully developed. This is largely the result of the problems of designing numerically stable meshes for use with complex bed topographies and that Reynolds averaged turbulence schemes are applied. This paper develops two novel techniques for dealing with these issues. The first is the development and validation of a method for representing the complex surface topography of gravel-bed rivers in high resolution three-dimensional computational fluid dynamic models. This is based upon a porosity treatment with a regular structured grid and the application of a porosity modification to the mass conservation equation in which: fully blocked cells are assigned a porosity of zero; fully unblocked cells are assigned a porosity of one; and partly blocked cells are assigned a porosity of between 0 and 1, according to the percentage of the cell volume that is blocked. The second is the application of Large Eddy Simulation (LES) which enables time dependent flow structures to be numerically predicted over the complex bed topographies. The regular structured grid with the embedded porosity algorithm maintains a constant grid cell size throughout the domain implying a constant filter scale for the LES simulation. This enables the prediction of coherent structures, repetitive quasi-cyclic large-scale turbulent motions, over the gravel surface which are of a similar magnitude and frequency to those previously observed in both flume and field studies. These structures are formed by topographic forcing within the domain and are scaled with the flow depth. Finally, this provides the numerical framework for the prediction of sediment transport within a time dependent framework. The turbulent motions make a significant contribution to the turbulent shear stress and the pressure fluctuations which significantly affect the forces acting on the bed and potentially control sediment motion.

RNAstructure: software for RNA secondary structure prediction and analysis.

PubMed

Reuter, Jessica S; Mathews, David H

2010-03-15

To understand an RNA sequence's mechanism of action, the structure must be known. Furthermore, target RNA structure is an important consideration in the design of small interfering RNAs and antisense DNA oligonucleotides. RNA secondary structure prediction, using thermodynamics, can be used to develop hypotheses about the structure of an RNA sequence. RNAstructure is a software package for RNA secondary structure prediction and analysis. It uses thermodynamics and utilizes the most recent set of nearest neighbor parameters from the Turner group. It includes methods for secondary structure prediction (using several algorithms), prediction of base pair probabilities, bimolecular structure prediction, and prediction of a structure common to two sequences. This contribution describes new extensions to the package, including a library of C++ classes for incorporation into other programs, a user-friendly graphical user interface written in JAVA, and new Unix-style text interfaces. The original graphical user interface for Microsoft Windows is still maintained. The extensions to RNAstructure serve to make RNA secondary structure prediction user-friendly. The package is available for download from the Mathews lab homepage at http://rna.urmc.rochester.edu/RNAstructure.html.

RNA-Puzzles: A CASP-like evaluation of RNA three-dimensional structure prediction

PubMed Central

Cruz, José Almeida; Blanchet, Marc-Frédérick; Boniecki, Michal; Bujnicki, Janusz M.; Chen, Shi-Jie; Cao, Song; Das, Rhiju; Ding, Feng; Dokholyan, Nikolay V.; Flores, Samuel Coulbourn; Huang, Lili; Lavender, Christopher A.; Lisi, Véronique; Major, François; Mikolajczak, Katarzyna; Patel, Dinshaw J.; Philips, Anna; Puton, Tomasz; Santalucia, John; Sijenyi, Fredrick; Hermann, Thomas; Rother, Kristian; Rother, Magdalena; Serganov, Alexander; Skorupski, Marcin; Soltysinski, Tomasz; Sripakdeevong, Parin; Tuszynska, Irina; Weeks, Kevin M.; Waldsich, Christina; Wildauer, Michael; Leontis, Neocles B.; Westhof, Eric

2012-01-01

We report the results of a first, collective, blind experiment in RNA three-dimensional (3D) structure prediction, encompassing three prediction puzzles. The goals are to assess the leading edge of RNA structure prediction techniques; compare existing methods and tools; and evaluate their relative strengths, weaknesses, and limitations in terms of sequence length and structural complexity. The results should give potential users insight into the suitability of available methods for different applications and facilitate efforts in the RNA structure prediction community in ongoing efforts to improve prediction tools. We also report the creation of an automated evaluation pipeline to facilitate the analysis of future RNA structure prediction exercises. PMID:22361291

Satisfaction with Health Care among Latinas

PubMed Central

Abraído-Lanza, Ana F.; Céspedes, Amarilis; Daya, Shaira; Flórez, Karen R.; White, Kellee

2013-01-01

Despite growing interest in disparities in access to health care, relatively little is known about different facets of care among Latinas, their satisfaction with the care they receive, and the predictors of satisfaction. This study examined whether various health care access and context factors, the quality of the patient-physician interaction, and medical mistrust predict satisfaction with health care among Latina immigrants in New York City. Structured interviews were conducted with 220 Latinas predominantly from the Dominican Republic and aged 40 years or over. Of the access to health care variables examined, greater waiting time predicted dissatisfaction with health care. Greater quality of the patient-physician interaction predicted less dissatisfaction. The effect of the patient-physician interaction on dissatisfaction was mediated, in part, by waiting time. The results illustrate the important role of specific health care factors in satisfaction with care. PMID:21551929

Fabrication of Graded Porous and Skin-Core Structure RDX-Based Propellants via Supercritical CO2 Concentration Profile

NASA Astrophysics Data System (ADS)

Yang, Weitao; Li, Yuxiang; Ying, Sanjiu

2015-04-01

A fabrication process to produce graded porous and skin-core structure propellants via supercritical CO2 concentration profile is reported in this article. It utilizes a partial gas saturation technique to obtain nonequilibrium gas concentration profiles in propellants. Once foamed, the propellant obtains a graded porous or skin-pore structure. This fabrication method was studied with RDX(Hexogen)-based propellant under an SC-CO2 saturation condition. The principle was analyzed and the one-dimensional diffusion model was employed to estimate the gas diffusion coefficient and to predict the gas concentration profiles inside the propellant. Scanning electron microscopy images were used to analyze the effects of partial saturation on the inner structure. The results also suggested that the sorption time and desorption time played an important role in gas profile generation and controlled the inner structure of propellants.

Hierarchical learning induces two simultaneous, but separable, prediction errors in human basal ganglia.

PubMed

Diuk, Carlos; Tsai, Karin; Wallis, Jonathan; Botvinick, Matthew; Niv, Yael

2013-03-27

Studies suggest that dopaminergic neurons report a unitary, global reward prediction error signal. However, learning in complex real-life tasks, in particular tasks that show hierarchical structure, requires multiple prediction errors that may coincide in time. We used functional neuroimaging to measure prediction error signals in humans performing such a hierarchical task involving simultaneous, uncorrelated prediction errors. Analysis of signals in a priori anatomical regions of interest in the ventral striatum and the ventral tegmental area indeed evidenced two simultaneous, but separable, prediction error signals corresponding to the two levels of hierarchy in the task. This result suggests that suitably designed tasks may reveal a more intricate pattern of firing in dopaminergic neurons. Moreover, the need for downstream separation of these signals implies possible limitations on the number of different task levels that we can learn about simultaneously.

Perfect coupling of light to a periodic dielectric/metal/dielectric structure

NASA Astrophysics Data System (ADS)

Wang, Zhengling; Li, Shiqiang; Chang, R. P. H.; Ketterson, John B.

2014-07-01

Using the finite difference time domain method, it is demonstrated that perfect coupling can be achieved between normally incident light and a periodic dielectric/metal/dielectric structure. The structure serves as a diffraction grating that excites modes related to the long range surface plasmon and short range surface plasmon modes that propagate on continuous metallic films. By optimizing the structural dimensions, perfect coupling is achieved between the incident light and these modes. A high Q of 697 and an accompanying ultrasharp linewidth of 0.8 nm are predicted for a 10 nm silver film for optimal conditions.

Modelling the spatio-temporal cell dynamics reveals novel insights on cell differentiation and proliferation in the small intestinal crypt.

PubMed

Pin, Carmen; Watson, Alastair J M; Carding, Simon R

2012-01-01

We developed a slow structural relaxation model to describe cellular dynamics in the crypt of the mouse small intestine. Cells are arranged in a three dimensional spiral the size of which dynamically changes according to cell production demands of adjacent villi. Cell differentiation and proliferation is regulated through Wnt and Notch signals, the strength of which depends on the local cell composition. The highest level of Wnt activity is associated with maintaining equipotent stem cells (SC), Paneth cells and common goblet-Paneth cell progenitors (CGPCPs) intermingling at the crypt bottom. Low levels of Wnt signalling area are associated with stem cells giving rise to secretory cells (CGPCPs, enteroendocrine or Tuft cells) and proliferative absorptive progenitors. Deciding between these two fates, secretory and stem/absorptive cells, depends on Notch signalling. Our model predicts that Notch signalling inhibits secretory fate if more than 50% of cells they are in contact with belong to the secretory lineage. CGPCPs under high Wnt signalling will differentiate into Paneth cells while those migrating out from the crypt bottom differentiate into goblet cells. We have assumed that mature Paneth cells migrating upwards undergo anoikis. Structural relaxation explains the localisation of Paneth cells to the crypt bottom in the absence of active forces. The predicted crypt generation time from one SC is 4-5 days with 10-12 days needed to reach a structural steady state. Our predictions are consistent with experimental observations made under altered Wnt and Notch signalling. Mutations affecting stem cells located at the crypt floor have a 50% chance of being propagated throughout the crypt while mutations in cells above are rarely propagated. The predicted recovery time of an injured crypt losing half of its cells is approximately 2 days.

Racial Discrimination during Adolescence Predicts Mental Health Deterioration in Adulthood: Gender Differences among Blacks.

PubMed

Assari, Shervin; Moazen-Zadeh, Ehsan; Caldwell, Cleopatra Howard; Zimmerman, Marc A

2017-01-01

Despite the existing knowledge regarding the negative mental health consequences of perceived racial discrimination, very few researchers have used a longitudinal design with long-term follow-up periods to explore gender differences in this association over time. The current longitudinal study aimed to investigate gender differences in predictive role of an increase in perceived racial discrimination during adolescence for mental health deterioration a decade later when they are transitioning to young adulthood. Current study followed 681 Black youths for 18 years from 1994 (mean age 15) to 2012 (mean age 32). All participants spent their adolescence and transition to young adulthood in an economically disadvantaged urban area in the Midwest of the United States. Independent variable was perceived racial discrimination measured in 1999 and 2002. Outcomes were psychological symptoms (anxiety and depression) measured in 1999 and at end of follow-up (2012). Covariates included sociodemographics (age, family structure, and parental employment) measured in 1994. Gender was used to define groups in a multigroup structural equation model to test moderating effects. Multigroup structural equation modeling showed that among male Black youth, an increase in perceived racial discrimination from age 20 to 23 was predictive for an increase in symptoms of anxiety and depression from age 20 to 32. Among female Black youth, change in perceived racial discrimination did not predict future change in depressive or anxiety symptoms. While racial discrimination is associated with negative mental health consequences for both genders, male and female Black youth differ in regard to long-term effects of an increase in perceived discrimination on deterioration of psychological symptoms. Black males seem to be more susceptible than Black females to the psychological effects of an increase in racial discrimination over time.

Quantitative structure-retention relationships applied to development of liquid chromatography gradient-elution method for the separation of sartans.

PubMed

Golubović, Jelena; Protić, Ana; Otašević, Biljana; Zečević, Mira

2016-04-01

QSRR are mathematically derived relationships between the chromatographic parameters determined for a representative series of analytes in given separation systems and the molecular descriptors accounting for the structural differences among the investigated analytes. Artificial neural network is a technique of data analysis, which sets out to emulate the human brain's way of working. The aim of the present work was to optimize separation of six angiotensin receptor antagonists, so-called sartans: losartan, valsartan, irbesartan, telmisartan, candesartan cilexetil and eprosartan in a gradient-elution HPLC method. For this purpose, ANN as a mathematical tool was used for establishing a QSRR model based on molecular descriptors of sartans and varied instrumental conditions. The optimized model can be further used for prediction of an external congener of sartans and analysis of the influence of the analyte structure, represented through molecular descriptors, on retention behaviour. Molecular descriptors included in modelling were electrostatic, geometrical and quantum-chemical descriptors: connolly solvent excluded volume non-1,4 van der Waals energy, octanol/water distribution coefficient, polarizability, number of proton-donor sites and number of proton-acceptor sites. Varied instrumental conditions were gradient time, buffer pH and buffer molarity. High prediction ability of the optimized network enabled complete separation of the analytes within the run time of 15.5 min under following conditions: gradient time of 12.5 min, buffer pH of 3.95 and buffer molarity of 25 mM. Applied methodology showed the potential to predict retention behaviour of an external analyte with the properties within the training space. Connolly solvent excluded volume, polarizability and number of proton-acceptor sites appeared to be most influential paramateres on retention behaviour of the sartans. Copyright © 2015 Elsevier B.V. All rights reserved.

Combining Thermal And Structural Analyses

NASA Technical Reports Server (NTRS)

Winegar, Steven R.

1990-01-01

Computer code makes programs compatible so stresses and deformations calculated. Paper describes computer code combining thermal analysis with structural analysis. Called SNIP (for SINDA-NASTRAN Interfacing Program), code provides interface between finite-difference thermal model of system and finite-element structural model when no node-to-element correlation between models. Eliminates much manual work in converting temperature results of SINDA (Systems Improved Numerical Differencing Analyzer) program into thermal loads for NASTRAN (NASA Structural Analysis) program. Used to analyze concentrating reflectors for solar generation of electric power. Large thermal and structural models needed to predict distortion of surface shapes, and SNIP saves considerable time and effort in combining models.

RNA-Puzzles Round III: 3D RNA structure prediction of five riboswitches and one ribozyme

PubMed Central

Biesiada, Marcin; Boniecki, Michał J.; Chou, Fang-Chieh; Ferré-D'Amaré, Adrian R.; Das, Rhiju; Dunin-Horkawicz, Stanisław; Geniesse, Caleb; Kappel, Kalli; Kladwang, Wipapat; Krokhotin, Andrey; Łach, Grzegorz E.; Major, François; Mann, Thomas H.; Pachulska-Wieczorek, Katarzyna; Patel, Dinshaw J.; Piccirilli, Joseph A.; Popenda, Mariusz; Purzycka, Katarzyna J.; Ren, Aiming; Rice, Greggory M.; Santalucia, John; Tandon, Arpit; Trausch, Jeremiah J.; Wang, Jian; Weeks, Kevin M.; Williams, Benfeard; Xiao, Yi; Zhang, Dong; Zok, Tomasz

2017-01-01

RNA-Puzzles is a collective experiment in blind 3D RNA structure prediction. We report here a third round of RNA-Puzzles. Five puzzles, 4, 8, 12, 13, 14, all structures of riboswitch aptamers and puzzle 7, a ribozyme structure, are included in this round of the experiment. The riboswitch structures include biological binding sites for small molecules (S-adenosyl methionine, cyclic diadenosine monophosphate, 5-amino 4-imidazole carboxamide riboside 5′-triphosphate, glutamine) and proteins (YbxF), and one set describes large conformational changes between ligand-free and ligand-bound states. The Varkud satellite ribozyme is the most recently solved structure of a known large ribozyme. All puzzles have established biological functions and require structural understanding to appreciate their molecular mechanisms. Through the use of fast-track experimental data, including multidimensional chemical mapping, and accurate prediction of RNA secondary structure, a large portion of the contacts in 3D have been predicted correctly leading to similar topologies for the top ranking predictions. Template-based and homology-derived predictions could predict structures to particularly high accuracies. However, achieving biological insights from de novo prediction of RNA 3D structures still depends on the size and complexity of the RNA. Blind computational predictions of RNA structures already appear to provide useful structural information in many cases. Similar to the previous RNA-Puzzles Round II experiment, the prediction of non-Watson–Crick interactions and the observed high atomic clash scores reveal a notable need for an algorithm of improvement. All prediction models and assessment results are available at http://ahsoka.u-strasbg.fr/rnapuzzles/. PMID:28138060

Year 2 Report: Protein Function Prediction Platform

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

Zhou, C E

2012-04-27

Upon completion of our second year of development in a 3-year development cycle, we have completed a prototype protein structure-function annotation and function prediction system: Protein Function Prediction (PFP) platform (v.0.5). We have met our milestones for Years 1 and 2 and are positioned to continue development in completion of our original statement of work, or a reasonable modification thereof, in service to DTRA Programs involved in diagnostics and medical countermeasures research and development. The PFP platform is a multi-scale computational modeling system for protein structure-function annotation and function prediction. As of this writing, PFP is the only existing fullymore » automated, high-throughput, multi-scale modeling, whole-proteome annotation platform, and represents a significant advance in the field of genome annotation (Fig. 1). PFP modules perform protein functional annotations at the sequence, systems biology, protein structure, and atomistic levels of biological complexity (Fig. 2). Because these approaches provide orthogonal means of characterizing proteins and suggesting protein function, PFP processing maximizes the protein functional information that can currently be gained by computational means. Comprehensive annotation of pathogen genomes is essential for bio-defense applications in pathogen characterization, threat assessment, and medical countermeasure design and development in that it can short-cut the time and effort required to select and characterize protein biomarkers.« less

Structural and Predictive Properties of the Emotional Quotient Inventory Youth Version-Short Form (EQ-i:YV[S]).

PubMed

Davis, Sarah K; Wigelsworth, Michael

2018-01-01

Emotional intelligence (EI) is a popular construct with concentrated areas of application in education and health contexts. There is a need for reliable and valid measurement of EI in young people, with brief yet sensitive measures of the construct preferable for use in time-limited settings. However, the proliferation of EI measures has often outpaced rigorous psychometric evaluation (Gignac, 2009 ). Using data from 849 adolescents (407 females, 422 males) aged 11 to 16 years (M age 13.4, SD = 1.2 years), this article systematically examines the structural and predictive properties of a frequently employed measure of adolescent trait EI-the Emotional Quotient Inventory Youth Version-Short Form (EQ-i:YV[S]); Bar-On & Parker, 2000 ). Although the intended multidimensional factor structure was recovered through confirmatory factor analysis, the statistical and conceptual coherency of the underlying model was inadequate. Using a multitrait-multimethod approach, the EQ-i:YV(S) was found to converge with other measures of EI; however, evidence for divergent validity (Big Five personality dimensions) was less robust. Predictive utility for adolescent mental health outcomes (depression, disruptive behavior) was also limited. Findings suggest that use of the EQ-i:YV(S) for predictive or evaluative purposes should be avoided until refinements to the scale are made.

Work overload, burnout, and psychological ill-health symptoms: a three-wave mediation model of the employee health impairment process.

PubMed

de Beer, Leon T; Pienaar, Jaco; Rothmann, Sebastiaan

2016-07-01

The study reported here investigated the causal relationships in the health impairment process of employee well-being, and the mediating role of burnout in the relationship between work overload and psychological ill-health symptoms, over time. The research is deemed important due to the need for longitudinal evidence of the health impairment process of employee well-being over three waves of data. A quantitative survey design was followed. Participants constituted a longitudinal sample of 370 participants, at three time points, after attrition. Descriptive statistics and structural equation modeling methods were implemented. Work overload at time one predicted burnout at time two, and burnout at time two predicted psychological ill-health symptoms at time three. Indirect effects were found between work overload time one and psychological ill-health symptoms time three via burnout time two, and also between burnout time one and psychological ill-health symptoms time three, via burnout time two. The results provided supportive evidence for an "indirect-only" mediation effect, for burnout's causal mediation mechanism in the health impairment process between work overload and psychological ill-health symptoms.

Modeling Nonstationarity in Space and Time

PubMed Central

2017-01-01

Summary We propose to model a spatio-temporal random field that has nonstationary covariance structure in both space and time domains by applying the concept of the dimension expansion method in Bornn et al. (2012). Simulations are conducted for both separable and nonseparable space-time covariance models, and the model is also illustrated with a streamflow dataset. Both simulation and data analyses show that modeling nonstationarity in both space and time can improve the predictive performance over stationary covariance models or models that are nonstationary in space but stationary in time. PMID:28134977

Modeling nonstationarity in space and time.

PubMed

Shand, Lyndsay; Li, Bo

2017-09-01

We propose to model a spatio-temporal random field that has nonstationary covariance structure in both space and time domains by applying the concept of the dimension expansion method in Bornn et al. (2012). Simulations are conducted for both separable and nonseparable space-time covariance models, and the model is also illustrated with a streamflow dataset. Both simulation and data analyses show that modeling nonstationarity in both space and time can improve the predictive performance over stationary covariance models or models that are nonstationary in space but stationary in time. © 2017, The International Biometric Society.

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

DOE PAGES

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

2015-08-01

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. Our 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 themore » reactive transport of hexavalent uranium (U(VI)) based on observations at a former uranium mill site near Naturita, CO. Moreover, 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. Finally, limitations of applying MLBMA to the synthetic study and future real-world modeling are discussed.« less

Shame and guilt as shared vulnerability factors: Shame, but not guilt, prospectively predicts both social anxiety and bulimic symptoms.

PubMed

Levinson, Cheri A; Byrne, Meghan; Rodebaugh, Thomas L

2016-08-01

Social anxiety disorder (SAD) and bulimia nervosa (BN) are highly comorbid. However, little is known about the shared vulnerability factors that prospectively predict both SA and BN symptoms. Two potential factors that have not yet been tested are shame and guilt. In the current study we tested if shame and guilt were shared vulnerability factors for SA and BN symptoms. Women (N=300) completed measures of SA symptoms, BN symptoms, state shame and guilt, and trait negative affect at two time points, two months apart. Utilizing structural equation modeling we tested a cross-sectional and prospective model of SA and BN vulnerability. We found that shame prospectively predicted both SA and BN symptoms. We did not find that guilt prospectively predicted SA or BN symptoms. However, higher levels of both BN and SA symptoms predicted increased guilt over time. We found support for shame as a shared prospective vulnerability factor between BN and SA symptoms. Interventions that focus on decreasing shame could potentially alleviate symptoms of BN and SA in one protocol. Copyright © 2016 Elsevier Ltd. All rights reserved.

Multi-step prediction for influenza outbreak by an adjusted long short-term memory.

PubMed

Zhang, J; Nawata, K

2018-05-01

Influenza results in approximately 3-5 million annual cases of severe illness and 250 000-500 000 deaths. We urgently need an accurate multi-step-ahead time-series forecasting model to help hospitals to perform dynamical assignments of beds to influenza patients for the annually varied influenza season, and aid pharmaceutical companies to formulate a flexible plan of manufacturing vaccine for the yearly different influenza vaccine. In this study, we utilised four different multi-step prediction algorithms in the long short-term memory (LSTM). The result showed that implementing multiple single-output prediction in a six-layer LSTM structure achieved the best accuracy. The mean absolute percentage errors from two- to 13-step-ahead prediction for the US influenza-like illness rates were all <15%, averagely 12.930%. To the best of our knowledge, it is the first time that LSTM has been applied and refined to perform multi-step-ahead prediction for influenza outbreaks. Hopefully, this modelling methodology can be applied in other countries and therefore help prevent and control influenza worldwide.

Comparison of RF spectrum prediction methods for dynamic spectrum access

NASA Astrophysics Data System (ADS)

Kovarskiy, Jacob A.; Martone, Anthony F.; Gallagher, Kyle A.; Sherbondy, Kelly D.; Narayanan, Ram M.

2017-05-01

Dynamic spectrum access (DSA) refers to the adaptive utilization of today's busy electromagnetic spectrum. Cognitive radio/radar technologies require DSA to intelligently transmit and receive information in changing environments. Predicting radio frequency (RF) activity reduces sensing time and energy consumption for identifying usable spectrum. Typical spectrum prediction methods involve modeling spectral statistics with Hidden Markov Models (HMM) or various neural network structures. HMMs describe the time-varying state probabilities of Markov processes as a dynamic Bayesian network. Neural Networks model biological brain neuron connections to perform a wide range of complex and often non-linear computations. This work compares HMM, Multilayer Perceptron (MLP), and Recurrent Neural Network (RNN) algorithms and their ability to perform RF channel state prediction. Monte Carlo simulations on both measured and simulated spectrum data evaluate the performance of these algorithms. Generalizing spectrum occupancy as an alternating renewal process allows Poisson random variables to generate simulated data while energy detection determines the occupancy state of measured RF spectrum data for testing. The results suggest that neural networks achieve better prediction accuracy and prove more adaptable to changing spectral statistics than HMMs given sufficient training data.

Use of Linear Prediction Uncertainty Analysis to Guide Conditioning of Models Simulating Surface-Water/Groundwater Interactions

NASA Astrophysics Data System (ADS)

Hughes, J. D.; White, J.; Doherty, J.

2011-12-01

Linear prediction uncertainty analysis in a Bayesian framework was applied to guide the conditioning of an integrated surface water/groundwater model that will be used to predict the effects of groundwater withdrawals on surface-water and groundwater flows. Linear prediction uncertainty analysis is an effective approach for identifying (1) raw and processed data most effective for model conditioning prior to inversion, (2) specific observations and periods of time critically sensitive to specific predictions, and (3) additional observation data that would reduce model uncertainty relative to specific predictions. We present results for a two-dimensional groundwater model of a 2,186 km2 area of the Biscayne aquifer in south Florida implicitly coupled to a surface-water routing model of the actively managed canal system. The model domain includes 5 municipal well fields withdrawing more than 1 Mm3/day and 17 operable surface-water control structures that control freshwater releases from the Everglades and freshwater discharges to Biscayne Bay. More than 10 years of daily observation data from 35 groundwater wells and 24 surface water gages are available to condition model parameters. A dense parameterization was used to fully characterize the contribution of the inversion null space to predictive uncertainty and included bias-correction parameters. This approach allows better resolution of the boundary between the inversion null space and solution space. Bias-correction parameters (e.g., rainfall, potential evapotranspiration, and structure flow multipliers) absorb information that is present in structural noise that may otherwise contaminate the estimation of more physically-based model parameters. This allows greater precision in predictions that are entirely solution-space dependent, and reduces the propensity for bias in predictions that are not. Results show that application of this analysis is an effective means of identifying those surface-water and groundwater data, both raw and processed, that minimize predictive uncertainty, while simultaneously identifying the maximum solution-space dimensionality of the inverse problem supported by the data.

Application of the aqueous porous pathway model to quantify the effect of sodium lauryl sulfate on ultrasound-induced skin structural perturbation.

PubMed

Polat, Baris E; Seto, Jennifer E; Blankschtein, Daniel; Langer, Robert

2011-04-01

This study investigated the effect of sodium lauryl sulfate (SLS) on skin structural perturbation when utilized simultaneously with low-frequency sonophoresis (LFS). Pig full-thickness skin (FTS) and pig split-thickness skin (STS) treated with LFS/SLS and LFS were analyzed in the context of the aqueous porous pathway model to quantify skin perturbation through changes in skin pore radius and porosity-to-tortuosity ratio (ε/τ). In addition, skin treatment times required to attain specific levels of skin electrical resistivity were analyzed to draw conclusions about the effect of SLS on reproducibility and predictability of skin perturbation. We found that LFS/SLS-treated FTS, LFS/SLS-treated STS, and LFS-treated FTS exhibited similar skin perturbation. However, LFS-treated STS exhibited significantly higher skin perturbation, suggesting greater structural changes to the less robust STS induced by the purely physical enhancement mechanism of LFS. Evaluation of ε/τ values revealed that LFS/SLS-treated FTS and STS have similar transport pathways, whereas LFS-treated FTS and STS have lower ε/τ values. In addition, LFS/SLS treatment times were much shorter than LFS treatment times for both FTS and STS. Moreover, the simultaneous use of SLS and LFS not only results in synergistic enhancement, as reflected in the shorter skin treatment times, but also in more predictable and reproducible skin perturbation. Copyright © 2010 Wiley-Liss, Inc.

Reconstructing mantle heterogeneity with data assimilation based on the back-and-forth nudging method: Implications for mantle-dynamic fitting of past plate motions

NASA Astrophysics Data System (ADS)

Glišović, Petar; Forte, Alessandro

2016-04-01

The paleo-distribution of density variations throughout the mantle is unknown. To address this question, we reconstruct 3-D mantle structure over the Cenozoic era using a data assimilation method that implements a new back-and-forth nudging algorithm. For this purpose, we employ convection models for a compressible and self-gravitating mantle that employ 3-D mantle structure derived from joint seismic-geodynamic tomography as a starting condition. These convection models are then integrated backwards in time and are required to match geologic estimates of past plate motions derived from marine magnetic data. Our implementation of the nudging algorithm limits the difference between a reconstruction (backward-in-time solution) and a prediction (forward-in-time solution) on over a sequence of 5-million-year time windows that span the Cenozoic. We find that forward integration of reconstructed mantle heterogeneity that is constrained to match past plate motions delivers relatively poor fits to the seismic-tomographic inference of present-day mantle heterogeneity in the upper mantle. We suggest that uncertainties in the past plate motions, related for example to plate reorganization episodes, could partly contribute to the poor match between predicted and observed present-day heterogeneity. We propose that convection models that allow tectonic plates to evolve freely in accord with the buoyancy forces and rheological structure in the mantle could provide additional constraints on geologic estimates of paleo-configurations of the major tectonic plates.

Detecting Lung and Colorectal Cancer Recurrence Using Structured Clinical/Administrative Data to Enable Outcomes Research and Population Health Management.

PubMed

Hassett, Michael J; Uno, Hajime; Cronin, Angel M; Carroll, Nikki M; Hornbrook, Mark C; Ritzwoller, Debra

2017-12-01

Recurrent cancer is common, costly, and lethal, yet we know little about it in community-based populations. Electronic health records and tumor registries contain vast amounts of data regarding community-based patients, but usually lack recurrence status. Existing algorithms that use structured data to detect recurrence have limitations. We developed algorithms to detect the presence and timing of recurrence after definitive therapy for stages I-III lung and colorectal cancer using 2 data sources that contain a widely available type of structured data (claims or electronic health record encounters) linked to gold-standard recurrence status: Medicare claims linked to the Cancer Care Outcomes Research and Surveillance study, and the Cancer Research Network Virtual Data Warehouse linked to registry data. Twelve potential indicators of recurrence were used to develop separate models for each cancer in each data source. Detection models maximized area under the ROC curve (AUC); timing models minimized average absolute error. Algorithms were compared by cancer type/data source, and contrasted with an existing binary detection rule. Detection model AUCs (>0.92) exceeded existing prediction rules. Timing models yielded absolute prediction errors that were small relative to follow-up time (<15%). Similar covariates were included in all detection and timing algorithms, though differences by cancer type and dataset challenged efforts to create 1 common algorithm for all scenarios. Valid and reliable detection of recurrence using big data is feasible. These tools will enable extensive, novel research on quality, effectiveness, and outcomes for lung and colorectal cancer patients and those who develop recurrence.

Quantifying predictability variations in a low-order ocean-atmosphere model - A dynamical systems approach

NASA Technical Reports Server (NTRS)

Nese, Jon M.; Dutton, John A.

1993-01-01

The predictability of the weather and climatic states of a low-order moist general circulation model is quantified using a dynamic systems approach, and the effect of incorporating a simple oceanic circulation on predictability is evaluated. The predictability and the structure of the model attractors are compared using Liapunov exponents, local divergence rates, and the correlation and Liapunov dimensions. It was found that the activation of oceanic circulation increases the average error doubling time of the atmosphere and the coupled ocean-atmosphere system by 10 percent and decreases the variance of the largest local divergence rate by 20 percent. When an oceanic circulation develops, the average predictability of annually averaged states is improved by 25 percent and the variance of the largest local divergence rate decreases by 25 percent.

Polarity, cell division, and out-of-equilibrium dynamics control the growth of epithelial structures

PubMed Central

Cerruti, Benedetta; Puliafito, Alberto; Shewan, Annette M.; Yu, Wei; Combes, Alexander N.; Little, Melissa H.; Chianale, Federica; Primo, Luca; Serini, Guido; Mostov, Keith E.; Celani, Antonio

2013-01-01

The growth of a well-formed epithelial structure is governed by mechanical constraints, cellular apico-basal polarity, and spatially controlled cell division. Here we compared the predictions of a mathematical model of epithelial growth with the morphological analysis of 3D epithelial structures. In both in vitro cyst models and in developing epithelial structures in vivo, epithelial growth could take place close to or far from mechanical equilibrium, and was determined by the hierarchy of time-scales of cell division, cell–cell rearrangements, and lumen dynamics. Equilibrium properties could be inferred by the analysis of cell–cell contact topologies, and the nonequilibrium phenotype was altered by inhibiting ROCK activity. The occurrence of an aberrant multilumen phenotype was linked to fast nonequilibrium growth, even when geometric control of cell division was correctly enforced. We predicted and verified experimentally that slowing down cell division partially rescued a multilumen phenotype induced by altered polarity. These results improve our understanding of the development of epithelial organs and, ultimately, of carcinogenesis. PMID:24145168

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

Change in "resolved plans" and "suicidal ideation" factors of suicidality after participation in an intensive outpatient treatment program.

PubMed

Minnix, Jennifer A; Romero, Catherine; Joiner, Thomas E; Weinberg, Elizabeth F

2007-11-01

This study aims to investigate factors related to suicide in a unique clinical population with more chronic psychopathology than many outpatient samples. One hundred and five adult outpatients were included in the current study. We predicted that higher scores on the resolved plans and preparation (RPP) factor of the Beck Suicide Scale [Beck, A.T., Kovacs, M., Weissman, M., (1979). Assessment of suicidal intention: The scale for suicidal ideation. Journal of Consulting and Clinical Psychology 47, 343-352] would predict multiple attempter status even after accounting for co-morbid diagnoses and suicidal ideation (SI) factor scores. Additionally, we predicted that the scores on the RPP factor would decrease less over time than scores on the SI factor. Results were consistent with both hypotheses, suggesting that RPP factor scores were uniquely predictive of status as a multiple attempter and were more stable over time. Mental health diagnoses were rendered without the use of a structured interview and therefore no reliability data were collected.

Predictable internal brain dynamics in EEG and its relation to conscious states

PubMed Central

Yoo, Jaewook; Kwon, Jaerock; Choe, Yoonsuck

2014-01-01

Consciousness is a complex and multi-faceted phenomenon defying scientific explanation. Part of the reason why this is the case is due to its subjective nature. In our previous computational experiments, to avoid such a subjective trap, we took a strategy to investigate objective necessary conditions of consciousness. Our basic hypothesis was that predictive internal dynamics serves as such a condition. This is in line with theories of consciousness that treat retention (memory), protention (anticipation), and primary impression as the tripartite temporal structure of consciousness. To test our hypothesis, we analyzed publicly available sleep and awake electroencephalogram (EEG) data. Our results show that EEG signals from awake or rapid eye movement (REM) sleep states have more predictable dynamics compared to those from slow-wave sleep (SWS). Since awakeness and REM sleep are associated with conscious states and SWS with unconscious or less consciousness states, these results support our hypothesis. The results suggest an intricate relationship among prediction, consciousness, and time, with potential applications to time perception and neurorobotics. PMID:24917813

RAG-3D: A search tool for RNA 3D substructures

DOE PAGES

Zahran, Mai; Sevim Bayrak, Cigdem; Elmetwaly, Shereef; ...

2015-08-24

In this study, to address many challenges in RNA structure/function prediction, the characterization of RNA's modular architectural units is required. Using the RNA-As-Graphs (RAG) database, we have previously explored the existence of secondary structure (2D) submotifs within larger RNA structures. Here we present RAG-3D—a dataset of RNA tertiary (3D) structures and substructures plus a web-based search tool—designed to exploit graph representations of RNAs for the goal of searching for similar 3D structural fragments. The objects in RAG-3D consist of 3D structures translated into 3D graphs, cataloged based on the connectivity between their secondary structure elements. Each graph is additionally describedmore » in terms of its subgraph building blocks. The RAG-3D search tool then compares a query RNA 3D structure to those in the database to obtain structurally similar structures and substructures. This comparison reveals conserved 3D RNA features and thus may suggest functional connections. Though RNA search programs based on similarity in sequence, 2D, and/or 3D structural elements are available, our graph-based search tool may be advantageous for illuminating similarities that are not obvious; using motifs rather than sequence space also reduces search times considerably. Ultimately, such substructuring could be useful for RNA 3D structure prediction, structure/function inference and inverse folding.« less

RAG-3D: a search tool for RNA 3D substructures

PubMed Central

Zahran, Mai; Sevim Bayrak, Cigdem; Elmetwaly, Shereef; Schlick, Tamar

2015-01-01

To address many challenges in RNA structure/function prediction, the characterization of RNA's modular architectural units is required. Using the RNA-As-Graphs (RAG) database, we have previously explored the existence of secondary structure (2D) submotifs within larger RNA structures. Here we present RAG-3D—a dataset of RNA tertiary (3D) structures and substructures plus a web-based search tool—designed to exploit graph representations of RNAs for the goal of searching for similar 3D structural fragments. The objects in RAG-3D consist of 3D structures translated into 3D graphs, cataloged based on the connectivity between their secondary structure elements. Each graph is additionally described in terms of its subgraph building blocks. The RAG-3D search tool then compares a query RNA 3D structure to those in the database to obtain structurally similar structures and substructures. This comparison reveals conserved 3D RNA features and thus may suggest functional connections. Though RNA search programs based on similarity in sequence, 2D, and/or 3D structural elements are available, our graph-based search tool may be advantageous for illuminating similarities that are not obvious; using motifs rather than sequence space also reduces search times considerably. Ultimately, such substructuring could be useful for RNA 3D structure prediction, structure/function inference and inverse folding. PMID:26304547

RAG-3D: A search tool for RNA 3D substructures

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

Zahran, Mai; Sevim Bayrak, Cigdem; Elmetwaly, Shereef

In this study, to address many challenges in RNA structure/function prediction, the characterization of RNA's modular architectural units is required. Using the RNA-As-Graphs (RAG) database, we have previously explored the existence of secondary structure (2D) submotifs within larger RNA structures. Here we present RAG-3D—a dataset of RNA tertiary (3D) structures and substructures plus a web-based search tool—designed to exploit graph representations of RNAs for the goal of searching for similar 3D structural fragments. The objects in RAG-3D consist of 3D structures translated into 3D graphs, cataloged based on the connectivity between their secondary structure elements. Each graph is additionally describedmore » in terms of its subgraph building blocks. The RAG-3D search tool then compares a query RNA 3D structure to those in the database to obtain structurally similar structures and substructures. This comparison reveals conserved 3D RNA features and thus may suggest functional connections. Though RNA search programs based on similarity in sequence, 2D, and/or 3D structural elements are available, our graph-based search tool may be advantageous for illuminating similarities that are not obvious; using motifs rather than sequence space also reduces search times considerably. Ultimately, such substructuring could be useful for RNA 3D structure prediction, structure/function inference and inverse folding.« less

Evaluation of the heat transfer module (FAHT) of Failure Analysis Nonlinear Thermal And Structural Integrated Code (FANTASTIC)

NASA Technical Reports Server (NTRS)

Keyhani, Majid

1989-01-01

The heat transfer module of FANTASTIC Code (FAHT) is studied and evaluated to the extend possible during the ten weeks duration of this project. A brief background of the previous studies is given and the governing equations as modeled in FAHT are discussed. FAHT's capabilities and limitations based on these equations and its coding methodology are explained in detail. It is established that with improper choice of element size and time step FAHT's temperature field prediction at some nodes will be below the initial condition. The source of this unrealistic temperature prediction is identified and a procedure is proposed for avoiding this phenomenon. It is further shown that the proposed procedure will converge to an accurate prediction upon mesh refinement. Unfortunately due to lack of time FAHT's ability to accurately account for pyrolysis and surface ablation has not been verified. Therefore, at the present time it can be stated with confidence that FAHT can accurately predict the temperature field for a transient multi-dimensional, orthotropic material with directional dependence, variable property, with nonlinear boundary condition. Such a prediction will provide an upper limit for the temperature field in an ablating decomposing nozzle liner. The pore pressure field, however, will not be known.

Can personality traits predict increases in manic and depressive symptoms?

PubMed Central

Lozano, Brian E.; Johnson, Sheri L.

2010-01-01

Background There has been limited research investigating personality traits as predictors of manic and depressive symptoms in bipolar individuals. The present study investigated the relation between personality traits and the course of bipolar disorder. The purpose of this study was to identify specific personality traits that predict the course of manic and depressive symptoms experienced by bipolar individuals. Methods The sample consisted of 39 participants with bipolar I disorder assessed by the Structured Clinical Interview for DSM-IV. Personality was assessed using the NEO Five-Factor Inventory. The Modified Hamilton Rating Scale for Depression and the Bech–Rafaelsen Mania Rating Scale were used to assess symptom severity on a monthly basis. Results Consistent with previous research on unipolar depression, high Neuroticism predicted increases in depressive symptoms across time while controlling for baseline symptoms. Additionally, high Conscientiousness, particularly the Achievement Striving facet, predicted increases in manic symptoms across time. Limitations The current study was limited by the small number of participants, the reliance on a shortened version of a self-report personality measure, and the potential state-dependency of the personality measures. Conclusions Specific personality traits may assist in predicting bipolar symptoms across time. Further studies are needed to tease apart the state-dependency of personality. PMID:11246086

Creep fatigue life prediction for engine hot section materials (isotropic)

NASA Technical Reports Server (NTRS)

Moreno, Vito; Nissley, David; Lin, Li-Sen Jim

1985-01-01

The first two years of a two-phase program aimed at improving the high temperature crack initiation life prediction technology for gas turbine hot section components are discussed. In Phase 1 (baseline) effort, low cycle fatigue (LCF) models, using a data base generated for a cast nickel base gas turbine hot section alloy (B1900+Hf), were evaluated for their ability to predict the crack initiation life for relevant creep-fatigue loading conditions and to define data required for determination of model constants. The variables included strain range and rate, mean strain, strain hold times and temperature. None of the models predicted all of the life trends within reasonable data requirements. A Cycle Damage Accumulation (CDA) was therefore developed which follows an exhaustion of material ductility approach. Material ductility is estimated based on observed similarities of deformation structure between fatigue, tensile and creep tests. The cycle damage function is based on total strain range, maximum stress and stress amplitude and includes both time independent and time dependent components. The CDA model accurately predicts all of the trends in creep-fatigue life with loading conditions. In addition, all of the CDA model constants are determinable from rapid cycle, fully reversed fatigue tests and monotonic tensile and/or creep data.

Adaptive Neuro-Fuzzy Inference system analysis on adsorption studies of Reactive Red 198 from aqueous solution by SBA-15/CTAB composite

NASA Astrophysics Data System (ADS)

Aghajani, Khadijeh; Tayebi, Habib-Allah

2017-01-01

In this study, the Mesoporous material SBA-15 were synthesized and then, the surface was modified by the surfactant Cetyltrimethylammoniumbromide (CTAB). Finally, the obtained adsorbent was used in order to remove Reactive Red 198 (RR 198) from aqueous solution. Transmission electron microscope (TEM), Fourier transform infra-red spectroscopy (FTIR), Thermogravimetric analysis (TGA), X-ray diffraction (XRD), and BET were utilized for the purpose of examining the structural characteristics of obtained adsorbent. Parameters affecting the removal of RR 198 such as pH, the amount of adsorbent, and contact time were investigated at various temperatures and were also optimized. The obtained optimized condition is as follows: pH = 2, time = 60 min and adsorbent dose = 1 g/l. Moreover, a predictive model based on ANFIS for predicting the adsorption amount according to the input variables is presented. The presented model can be used for predicting the adsorption rate based on the input variables include temperature, pH, time, dosage, concentration. The error between actual and approximated output confirm the high accuracy of the proposed model in the prediction process. This fact results in cost reduction because prediction can be done without resorting to costly experimental efforts. SBA-15, CTAB, Reactive Red 198, adsorption study, Adaptive Neuro-Fuzzy Inference systems (ANFIS).

Frequency domain and full waveform time domain inversion of ground based magnetometer, electrometer and incoherent scattering radar arrays to image strongly heterogenous 3-D Earth structure, ionospheric structure, and to predict the intensity of GICs in the power grid

NASA Astrophysics Data System (ADS)

Schultz, A.; Imamura, N.; Bonner, L. R., IV; Cosgrove, R. B.

2016-12-01

Ground-based magnetometer and electrometer arrays provide the means to probe the structure of the Earth's interior, the interactions of space weather with the ionosphere, and to anticipate the intensity of geomagnetically induced currents (GICs) in power grids. We present a local-to-continental scale view of a heterogeneous 3-D crust and mantle as determined from magnetotelluric (MT) observations across arrays of ground-based electric and magnetic field sensors. MT impedance tensors describe the relationship between electric and magnetic fields at a given site, thus implicitly they contain all known information on the 3-D electrical resistivity structure beneath and surrounding that site. By using multivariate transfer functions to project real-time magnetic observatory network data to areas surrounding electric power grids, and by projecting those magnetic fields through MT impedance tensors, the projected magnetic field can be transformed into predictions of electric fields along the path of the transmission lines, an essential element of predicting the intensity of GICs in the grid. Finally, we explore GICs, i.e. Earth-ionosphere coupling directly in the time-domain. We consider the fully coupled EM system, where we allow for a non-stationary ionospheric source field of arbitrary complexity above a 3-D Earth. We solve the simultaneous inverse problem for 3-D Earth conductivity and source field structure directly in the time domain. In the present work, we apply this method to magnetotelluric data obtained from a synchronously operating array of 25 MT stations that collected continuous MT waveform data in the interior of Alaska during the autumn and winter of 2015 under the footprint of the Poker Flat (Alaska) Incoherent Scattering Radar (PFISR). PFISR data yield functionals of the ionospheric electric field and ionospheric conductivity that constrain the MT source field. We show that in this region conventional robust MT processing methods struggle to produce reliable MT response functions at periods much greater than about 2,000 s, a consequence, we believe, of the complexity of the ionospheric source fields in this high latitude setting. This provides impetus for direct waveform inversion methods that dispense with typical parametric assumptions made about the MT source fields.

Possibility of Earthquake-prediction by analyzing VLF signals

NASA Astrophysics Data System (ADS)

Ray, Suman; Chakrabarti, Sandip Kumar; Sasmal, Sudipta

2016-07-01

Prediction of seismic events is one of the most challenging jobs for the scientific community. Conventional ways for prediction of earthquakes are to monitor crustal structure movements, though this method has not yet yield satisfactory results. Furthermore, this method fails to give any short-term prediction. Recently, it is noticed that prior to any seismic event a huge amount of energy is released which may create disturbances in the lower part of D-layer/E-layer of the ionosphere. This ionospheric disturbance may be used as a precursor of earthquakes. Since VLF radio waves propagate inside the wave-guide formed by lower ionosphere and Earth's surface, this signal may be used to identify ionospheric disturbances due to seismic activity. We have analyzed VLF signals to find out the correlations, if any, between the VLF signal anomalies and seismic activities. We have done both the case by case study and also the statistical analysis using a whole year data. In both the methods we found that the night time amplitude of VLF signals fluctuated anomalously three days before the seismic events. Also we found that the terminator time of the VLF signals shifted anomalously towards night time before few days of any major seismic events. We calculate the D-layer preparation time and D-layer disappearance time from the VLF signals. We have observed that this D-layer preparation time and D-layer disappearance time become anomalously high 1-2 days before seismic events. Also we found some strong evidences which indicate that it may possible to predict the location of epicenters of earthquakes in future by analyzing VLF signals for multiple propagation paths.

Statistical mechanical approach to secondary processes and structural relaxation in glasses and glass formers: a leading model to describe the onset of Johari-Goldstein processes and their relationship with fully cooperative processes.

PubMed

Crisanti, A; Leuzzi, L; Paoluzzi, M

2011-09-01

The interrelation of dynamic processes active on separated time-scales in glasses and viscous liquids is investigated using a model displaying two time-scale bifurcations both between fast and secondary relaxation and between secondary and structural relaxation. The study of the dynamics allows for predictions on the system relaxation above the temperature of dynamic arrest in the mean-field approximation, that are compared with the outcomes of the equations of motion directly derived within the Mode Coupling Theory (MCT) for under-cooled viscous liquids. By varying the external thermodynamic parameters, a wide range of phenomenology can be represented, from a very clear separation of structural and secondary peak in the susceptibility loss to excess wing structures.

A learning curve for solar thermal power

NASA Astrophysics Data System (ADS)

Platzer, Werner J.; Dinter, Frank

2016-05-01

Photovoltaics started its success story by predicting the cost degression depending on cumulated installed capacity. This so-called learning curve was published and used for predictions for PV modules first, then predictions of system cost decrease also were developed. This approach is less sensitive to political decisions and changing market situations than predictions on the time axis. Cost degression due to innovation, use of scaling effects, improved project management, standardised procedures including the search for better sites and optimization of project size are learning effects which can only be utilised when projects are developed. Therefore a presentation of CAPEX versus cumulated installed capacity is proposed in order to show the possible future advancement of the technology to politics and market. However from a wide range of publications on cost for CSP it is difficult to derive a learning curve. A logical cost structure for direct and indirect capital expenditure is needed as the basis for further analysis. Using derived reference cost for typical power plant configurations predictions of future cost have been derived. Only on the basis of that cost structure and the learning curve levelised cost of electricity for solar thermal power plants should be calculated for individual projects with different capacity factors in various locations.

The Smoking Consequences Questionnaire: Factor structure and predictive validity among Spanish-speaking Latino smokers in the United States.

PubMed

Vidrine, Jennifer Irvin; Vidrine, Damon J; Costello, Tracy J; Mazas, Carlos; Cofta-Woerpel, Ludmila; Mejia, Luz Maria; Wetter, David W

2009-11-01

Much of the existing research on smoking outcome expectancies has been guided by the Smoking Consequences Questionnaire (SCQ ). Although the original version of the SCQ has been modified over time for use in different populations, none of the existing versions have been evaluated for use among Spanish-speaking Latino smokers in the United States. The present study evaluated the factor structure and predictive validity of the 3 previously validated versions of the SCQ--the original, the SCQ-Adult, and the SCQ-Spanish, which was developed with Spanish-speaking smokers in Spain--among Spanish-speaking Latino smokers in Texas. The SCQ-Spanish represented the least complex solution. Each of the SCQ-Spanish scales had good internal consistency, and the predictive validity of the SCQ-Spanish was partially supported. Nearly all the SCQ-Spanish scales predicted withdrawal severity even after controlling for demographics and dependence. Boredom Reduction predicted smoking relapse across the 5- and 12-week follow-up assessments in a multivariate model that also controlled for demographics and dependence. Our results support use of the SCQ-Spanish with Spanish-speaking Latino smokers in the United States.

A simplified method for prediction of long-term prestress loss in post-tensioned concrete bridges.

DOT National Transportation Integrated Search

2006-07-01

Creep and shrinkage of concrete and relaxation of prestressing steel cause time-dependent changes in : the stresses and strains of concrete structures. These changes result in continuous reduction in the : concrete compression stresses and in the ten...

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

Michaelides, Angelos, E-mail: angelos.michaelides@ucl.ac.uk; Martinez, Todd J.; Alavi, Ali

This Special Topic section on Advanced Electronic Structure Methods for Solids and Surfaces contains a collection of research papers that showcase recent advances in the high accuracy prediction of materials and surface properties. It provides a timely snapshot of a growing field that is of broad importance to chemistry, physics, and materials science.

Ameba-like diffusion in two-dimensional polymer melts: how critical exponents determine the structural relaxation

NASA Astrophysics Data System (ADS)

Kreer, Torsten; Meyer, Hendrik; Baschnagel, Joerg

2008-03-01

By means of numerical investigations we demonstrate that the structural relaxation of linear polymers in two dimensional (space-filling) melts is characterized by ameba-like diffusion, where the chains relax via frictional dissipation at their interfacial contact lines. The perimeter length of the contact line determines a new length scale, which does not exist in three dimensions. We show how this length scale follows from the critical exponents, which hence characterize not only the static but also the dynamic properties of the melt. Our data is in agreement with recent theoretical predictions, concerning the time-dependence of single-monomer mean-square displacements and the scaling of concomitant relaxation times with the degree of polymerization. For the latter we demonstrate a density crossover-scaling as an additional test for ameba-like relaxation. We compare our results to the conceptually different Rouse model, which predicts numerically close exponents. Our data can clearly rule out the classical picture as the relevant relaxation mechanism in two-dimensional polymer melts.

Evaluation of acoustic testing techniques for spacecraft systems

NASA Technical Reports Server (NTRS)

Cockburn, J. A.

1971-01-01

External acoustic environments, structural responses, noise reductions, and the internal acoustic environments have been predicted for a typical shroud/spacecraft system during lift-off and various critical stages of flight. Spacecraft responses caused by energy transmission from the shroud via mechanical and acoustic paths have been compared and the importance of the mechanical path has been evaluated. Theoretical predictions have been compared extensively with available laboratory and in-flight measurements. Equivalent laboratory acoustic fields for simulation of shroud response during the various phases of flight have been derived and compared in detail. Techniques for varying the time-space correlations of laboratory acoustic fields have been examined, together with methods for varying the time and spatial distribution of acoustic amplitudes. Possible acoustic testing configurations for shroud/spacecraft systems have been suggested and trade-off considerations have been reviewed. The problem of simulating the acoustic environments versus simulating the structural responses has been considered and techniques for testing without the shroud installed have been discussed.

Transient Spectra in TDDFT: Corrections and Correlations

NASA Astrophysics Data System (ADS)

Parkhill, John; Nguyen, Triet

We introduce an atomistic, all-electron, black-box electronic structure code to simulate transient absorption (TA) spectra and apply it to simulate pyrazole and a GFP chromophore derivative. The method is an application of OSCF2, our dissipative extension of time-dependent density functional theory. We compare our simulated spectra directly with recent ultra-fast spectroscopic experiments, showing that they are usefully predicted. We also relate bleaches in the TA signal to Fermi-blocking which would be missed in a simplified model. An important ingredient in the method is the stationary-TDDFT correction scheme recently put forwards by Fischer, Govind, and Cramer which allows us to overcome a limitation of adiabatic TDDFT. We demonstrate that OSCF2 is able to predict both the energies of bleaches and induced absorptions, as well as the decay of the transient spectrum, with only the molecular structure as input. With remaining time we will discuss corrections which resolve the non-resonant behavior of driven TDDFT, and correlated corrections to mean-field dynamics.

Sub-keV ring current ions as the tracer of substorm injection

NASA Astrophysics Data System (ADS)

Yamauchi, M.; Lundin, R.

2006-03-01

The dynamics of the energy-latitude dispersed sub-keV trapped ions inside the ring current region, the so-called wedge-like dispersions structure, were statistically studied using Viking satellite data. Probabilities with/without these signatures at various local times in the dayside are obtained in terms of different time-lags from the substorm activity monitored by the AE index. The structure appears in the early morning sector within a few hours after the substorm, and it slowly propagates eastward while decaying with a time scale of several hours. The result qualitatively confirmed the previous model that the wedge-like dispersions are originated from past substorm-related plasma injections into the nightside ring current region, and that the dispersion is formed when these injected plasma slowly moves eastward to the dayside by the drift motion (E×B (eastward), grad-<|B| (westward), and curvature (westward) drifts). However, the appearance of the structure is twice or three times faster than the model prediction, and some structure reaches even to the evening sector. The results indicate that the start location of the drift is not as far as midnight and that the drift speed is slightly faster than the model prediction. The former means that the substorm-related increase of hot plasma in the ring current region shifts or extends to the early morning sector for large substorms, and the latter means that the substantial electric field driving the sub-keV ion drift is slightly different from the model field. We also detected the evacuating effect starting right after the substorm (or storm) onset. The electric field imposed in the dayside magnetosphere seems to remove the remainder of trapped ions.

Improving transmembrane protein consensus topology prediction using inter-helical interaction.

PubMed

Wang, Han; Zhang, Chao; Shi, Xiaohu; Zhang, Li; Zhou, You

2012-11-01

Alpha helix transmembrane proteins (αTMPs) represent roughly 30% of all open reading frames (ORFs) in a typical genome and are involved in many critical biological processes. Due to the special physicochemical properties, it is hard to crystallize and obtain high resolution structures experimentally, thus, sequence-based topology prediction is highly desirable for the study of transmembrane proteins (TMPs), both in structure prediction and function prediction. Various model-based topology prediction methods have been developed, but the accuracy of those individual predictors remain poor due to the limitation of the methods or the features they used. Thus, the consensus topology prediction method becomes practical for high accuracy applications by combining the advances of the individual predictors. Here, based on the observation that inter-helical interactions are commonly found within the transmembrane helixes (TMHs) and strongly indicate the existence of them, we present a novel consensus topology prediction method for αTMPs, CNTOP, which incorporates four top leading individual topology predictors, and further improves the prediction accuracy by using the predicted inter-helical interactions. The method achieved 87% prediction accuracy based on a benchmark dataset and 78% accuracy based on a non-redundant dataset which is composed of polytopic αTMPs. Our method derives the highest topology accuracy than any other individual predictors and consensus predictors, at the same time, the TMHs are more accurately predicted in their length and locations, where both the false positives (FPs) and the false negatives (FNs) decreased dramatically. The CNTOP is available at: http://ccst.jlu.edu.cn/JCSB/cntop/CNTOP.html. Copyright © 2012 Elsevier B.V. All rights reserved.

Less Structured Movement Patterns Predict Severity of Positive Syndrome, Excitement, and Disorganization

PubMed Central

Walther, Sebastian

2014-01-01

Disorganized behavior is a key symptom of schizophrenia. The objective assessment of disorganized behavior is particularly challenging. Actigraphy has enabled the objective assessment of motor behavior in various settings. Reduced motor activity was associated with negative syndrome scores, but simple motor activity analyses were not informative on other symptom dimensions. The analysis of movement patterns, however, could be more informative for assessing schizophrenia symptom dimensions. Here, we use time series analyses on actigraphic data of 100 schizophrenia spectrum disorder patients. Actigraphy recording intervals were set at 2 s. Data from 2 defined 60-min periods were analyzed, and partial autocorrelations of the actigraphy time series indicated predictability of movements in each individual. Increased positive syndrome scores were associated with reduced predictability of movements but not with the overall amount of movement. Negative syndrome scores were associated with low activity levels but unrelated with predictability of movement. The factors disorganization and excitement were related to movement predictability but emotional distress was not. Thus, the predictability of objectively assessed motor behavior may be a marker of positive symptoms and disorganized behavior. This behavior could become relevant for translational research. PMID:23502433

Evaluation and comparison of the ability of online available prediction programs to predict true linear B-cell epitopes.

PubMed

Costa, Juan G; Faccendini, Pablo L; Sferco, Silvano J; Lagier, Claudia M; Marcipar, Iván S

2013-06-01

This work deals with the use of predictors to identify useful B-cell linear epitopes to develop immunoassays. Experimental techniques to meet this goal are quite expensive and time consuming. Therefore, we tested 5 free, online prediction methods (AAPPred, ABCpred, BcePred, BepiPred and Antigenic) widely used for predicting linear epitopes, using the primary structure of the protein as the only input. We chose a set of 65 experimentally well documented epitopes obtained by the most reliable experimental techniques as our true positive set. To compare the quality of the predictor methods we used their positive predictive value (PPV), i.e. the proportion of the predicted epitopes that are true, experimentally confirmed epitopes, in relation to all the epitopes predicted. We conclude that AAPPred and ABCpred yield the best results as compared with the other programs and with a random prediction procedure. Our results also indicate that considering the consensual epitopes predicted by several programs does not improve the PPV.

ProTSAV: A protein tertiary structure analysis and validation server.

PubMed

Singh, Ankita; Kaushik, Rahul; Mishra, Avinash; Shanker, Asheesh; Jayaram, B

2016-01-01

Quality assessment of predicted model structures of proteins is as important as the protein tertiary structure prediction. A highly efficient quality assessment of predicted model structures directs further research on function. Here we present a new server ProTSAV, capable of evaluating predicted model structures based on some popular online servers and standalone tools. ProTSAV furnishes the user with a single quality score in case of individual protein structure along with a graphical representation and ranking in case of multiple protein structure assessment. The server is validated on ~64,446 protein structures including experimental structures from RCSB and predicted model structures for CASP targets and from public decoy sets. ProTSAV succeeds in predicting quality of protein structures with a specificity of 100% and a sensitivity of 98% on experimentally solved structures and achieves a specificity of 88%and a sensitivity of 91% on predicted protein structures of CASP11 targets under 2Å.The server overcomes the limitations of any single server/method and is seen to be robust in helping in quality assessment. ProTSAV is freely available at http://www.scfbio-iitd.res.in/software/proteomics/protsav.jsp. Copyright © 2015 Elsevier B.V. All rights reserved.

Enhanced CARES Software Enables Improved Ceramic Life Prediction

NASA Technical Reports Server (NTRS)

Janosik, Lesley A.

1997-01-01

The NASA Lewis Research Center has developed award-winning software that enables American industry to establish the reliability and life of brittle material (e.g., ceramic, intermetallic, graphite) structures in a wide variety of 21st century applications. The CARES (Ceramics Analysis and Reliability Evaluation of Structures) series of software is successfully used by numerous engineers in industrial, academic, and government organizations as an essential element of the structural design and material selection processes. The latest version of this software, CARES/Life, provides a general- purpose design tool that predicts the probability of failure of a ceramic component as a function of its time in service. CARES/Life was recently enhanced by adding new modules designed to improve functionality and user-friendliness. In addition, a beta version of the newly-developed CARES/Creep program (for determining the creep life of monolithic ceramic components) has just been released to selected organizations.

A shock absorber model for structure-borne noise analyses

NASA Astrophysics Data System (ADS)

Benaziz, Marouane; Nacivet, Samuel; Thouverez, Fabrice

2015-08-01

Shock absorbers are often responsible for undesirable structure-borne noise in cars. The early numerical prediction of this noise in the automobile development process can save time and money and yet remains a challenge for industry. In this paper, a new approach to predicting shock absorber structure-borne noise is proposed; it consists in modelling the shock absorber and including the main nonlinear phenomena responsible for discontinuities in the response. The model set forth herein features: compressible fluid behaviour, nonlinear flow rate-pressure relations, valve mechanical equations and rubber mounts. The piston, base valve and complete shock absorber model are compared with experimental results. Sensitivity of the shock absorber response is evaluated and the most important parameters are classified. The response envelope is also computed. This shock absorber model is able to accurately reproduce local nonlinear phenomena and improves our state of knowledge on potential noise sources within the shock absorber.

Nurse practitioners: leadership behaviors and organizational climate.

PubMed

Jones, L C; Guberski, T D; Soeken, K L

1990-01-01

The purpose of this article is to examine the relationships of individual nurse practitioners' perceptions of the leadership climate in their organizations and self-reported formal and informal leadership behaviors. The nine climate dimensions (Structure, Responsibility, Reward, Perceived Support of Risk Taking, Warmth, Support, Standard Setting, Conflict, and Identity) identified by Litwin and Stringer in 1968 were used to predict five leadership dimensions (Meeting Organizational Needs, Managing Resources, Leadership Competence, Task Accomplishment, and Communications). Demographic variables of age, educational level, and percent of time spent performing administrative functions were forced as a first step in each multiple regression analysis and used to explain a significant amount of variance in all but one analysis. All leadership dimensions were predicted by at least one organizational climate dimension: (1) Meeting Organizational Needs by Risk and Reward; (2) Managing Resources by Risk and Structure; (3) Leadership Competence by Risk and Standards; (4) Task Accomplishment by Structure, Risk, and Standards; and (5) Communication by Rewards.

Atomic scale modelling of hexagonal structured metallic fission product alloys

PubMed Central

Middleburgh, S. C.; King, D. M.; Lumpkin, G. R.

2015-01-01

Noble metal particles in the Mo-Pd-Rh-Ru-Tc system have been simulated on the atomic scale using density functional theory techniques for the first time. The composition and behaviour of the epsilon phases are consistent with high-entropy alloys (or multi-principal component alloys)—making the epsilon phase the only hexagonally close packed high-entropy alloy currently described. Configurational entropy effects were considered to predict the stability of the alloys with increasing temperatures. The variation of Mo content was modelled to understand the change in alloy structure and behaviour with fuel burnup (Mo molar content decreases in these alloys as burnup increases). The predicted structures compare extremely well with experimentally ascertained values. Vacancy formation energies and the behaviour of extrinsic defects (including iodine and xenon) in the epsilon phase were also investigated to further understand the impact that the metallic precipitates have on fuel performance. PMID:26064629

Modeling the fusion of cylindrical bioink particles in post bioprinting structure formation

NASA Astrophysics Data System (ADS)

McCune, Matt; Shafiee, Ashkan; Forgacs, Gabor; Kosztin, Ioan

2015-03-01

Cellular Particle Dynamics (CPD) is an effective computational method to describe the shape evolution and biomechanical relaxation processes in multicellular systems. Thus, CPD is a useful tool to predict the outcome of post-printing structure formation in bioprinting. The predictive power of CPD has been demonstrated for multicellular systems composed of spherical bioink units. Experiments and computer simulations were related through an independently developed theoretical formalism based on continuum mechanics. Here we generalize the CPD formalism to (i) include cylindrical bioink particles often used in specific bioprinting applications, (ii) describe the more realistic experimental situation in which both the length and the volume of the cylindrical bioink units decrease during post-printing structure formation, and (iii) directly connect CPD simulations to the corresponding experiments without the need of the intermediate continuum theory inherently based on simplifying assumptions. Work supported by NSF [PHY-0957914]. Computer time provided by the University of Missouri Bioinformatics Consortium.

CARES/LIFE Ceramics Analysis and Reliability Evaluation of Structures Life Prediction Program

NASA Technical Reports Server (NTRS)

Nemeth, Noel N.; Powers, Lynn M.; Janosik, Lesley A.; Gyekenyesi, John P.

2003-01-01

This manual describes the Ceramics Analysis and Reliability Evaluation of Structures Life Prediction (CARES/LIFE) computer program. The program calculates the time-dependent reliability of monolithic ceramic components subjected to thermomechanical and/or proof test loading. CARES/LIFE is an extension of the CARES (Ceramic Analysis and Reliability Evaluation of Structures) computer program. The program uses results from MSC/NASTRAN, ABAQUS, and ANSYS finite element analysis programs to evaluate component reliability due to inherent surface and/or volume type flaws. CARES/LIFE accounts for the phenomenon of subcritical crack growth (SCG) by utilizing the power law, Paris law, or Walker law. The two-parameter Weibull cumulative distribution function is used to characterize the variation in component strength. The effects of multiaxial stresses are modeled by using either the principle of independent action (PIA), the Weibull normal stress averaging method (NSA), or the Batdorf theory. Inert strength and fatigue parameters are estimated from rupture strength data of naturally flawed specimens loaded in static, dynamic, or cyclic fatigue. The probabilistic time-dependent theories used in CARES/LIFE, along with the input and output for CARES/LIFE, are described. Example problems to demonstrate various features of the program are also included.

An Efficient Interval Type-2 Fuzzy CMAC for Chaos Time-Series Prediction and Synchronization.

PubMed

Lee, Ching-Hung; Chang, Feng-Yu; Lin, Chih-Min

2014-03-01

This paper aims to propose a more efficient control algorithm for chaos time-series prediction and synchronization. A novel type-2 fuzzy cerebellar model articulation controller (T2FCMAC) is proposed. In some special cases, this T2FCMAC can be reduced to an interval type-2 fuzzy neural network, a fuzzy neural network, and a fuzzy cerebellar model articulation controller (CMAC). So, this T2FCMAC is a more generalized network with better learning ability, thus, it is used for the chaos time-series prediction and synchronization. Moreover, this T2FCMAC realizes the un-normalized interval type-2 fuzzy logic system based on the structure of the CMAC. It can provide better capabilities for handling uncertainty and more design degree of freedom than traditional type-1 fuzzy CMAC. Unlike most of the interval type-2 fuzzy system, the type-reduction of T2FCMAC is bypassed due to the property of un-normalized interval type-2 fuzzy logic system. This causes T2FCMAC to have lower computational complexity and is more practical. For chaos time-series prediction and synchronization applications, the training architectures with corresponding convergence analyses and optimal learning rates based on Lyapunov stability approach are introduced. Finally, two illustrated examples are presented to demonstrate the performance of the proposed T2FCMAC.

Numerical and Qualitative Contrasts of Two Statistical Models ...

EPA Pesticide Factsheets

Two statistical approaches, weighted regression on time, discharge, and season and generalized additive models, have recently been used to evaluate water quality trends in estuaries. Both models have been used in similar contexts despite differences in statistical foundations and products. This study provided an empirical and qualitative comparison of both models using 29 years of data for two discrete time series of chlorophyll-a (chl-a) in the Patuxent River estuary. Empirical descriptions of each model were based on predictive performance against the observed data, ability to reproduce flow-normalized trends with simulated data, and comparisons of performance with validation datasets. Between-model differences were apparent but minor and both models had comparable abilities to remove flow effects from simulated time series. Both models similarly predicted observations for missing data with different characteristics. Trends from each model revealed distinct mainstem influences of the Chesapeake Bay with both models predicting a roughly 65% increase in chl-a over time in the lower estuary, whereas flow-normalized predictions for the upper estuary showed a more dynamic pattern, with a nearly 100% increase in chl-a in the last 10 years. Qualitative comparisons highlighted important differences in the statistical structure, available products, and characteristics of the data and desired analysis. This manuscript describes a quantitative comparison of two recently-

Exploring space-time structure of human mobility in urban space

NASA Astrophysics Data System (ADS)

Sun, J. B.; Yuan, J.; Wang, Y.; Si, H. B.; Shan, X. M.

2011-03-01

Understanding of human mobility in urban space benefits the planning and provision of municipal facilities and services. Due to the high penetration of cell phones, mobile cellular networks provide information for urban dynamics with a large spatial extent and continuous temporal coverage in comparison with traditional approaches. The original data investigated in this paper were collected by cellular networks in a southern city of China, recording the population distribution by dividing the city into thousands of pixels. The space-time structure of urban dynamics is explored by applying Principal Component Analysis (PCA) to the original data, from temporal and spatial perspectives between which there is a dual relation. Based on the results of the analysis, we have discovered four underlying rules of urban dynamics: low intrinsic dimensionality, three categories of common patterns, dominance of periodic trends, and temporal stability. It implies that the space-time structure can be captured well by remarkably few temporal or spatial predictable periodic patterns, and the structure unearthed by PCA evolves stably over time. All these features play a critical role in the applications of forecasting and anomaly detection.

Chaotic examination

NASA Astrophysics Data System (ADS)

Bildirici, Melike; Sonustun, Fulya Ozaksoy; Sonustun, Bahri

2018-01-01

In the regards of chaos theory, new concepts such as complexity, determinism, quantum mechanics, relativity, multiple equilibrium, complexity, (continuously) instability, nonlinearity, heterogeneous agents, irregularity were widely questioned in economics. It is noticed that linear models are insufficient for analyzing unpredictable, irregular and noncyclical oscillations of economies, and for predicting bubbles, financial crisis, business cycles in financial markets. Therefore, economists gave great consequence to use appropriate tools for modelling non-linear dynamical structures and chaotic behaviors of the economies especially in macro and the financial economy. In this paper, we aim to model the chaotic structure of exchange rates (USD-TL and EUR-TL). To determine non-linear patterns of the selected time series, daily returns of the exchange rates were tested by BDS during the period from January 01, 2002 to May 11, 2017 which covers after the era of the 2001 financial crisis. After specifying the non-linear structure of the selected time series, it was aimed to examine the chaotic characteristic for the selected time period by Lyapunov Exponents. The findings verify the existence of the chaotic structure of the exchange rate returns in the analyzed time period.

Functional brain connectivity is predictable from anatomic network's Laplacian eigen-structure.

PubMed

Abdelnour, Farras; Dayan, Michael; Devinsky, Orrin; Thesen, Thomas; Raj, Ashish

2018-05-15

How structural connectivity (SC) gives rise to functional connectivity (FC) is not fully understood. Here we mathematically derive a simple relationship between SC measured from diffusion tensor imaging, and FC from resting state fMRI. We establish that SC and FC are related via (structural) Laplacian spectra, whereby FC and SC share eigenvectors and their eigenvalues are exponentially related. This gives, for the first time, a simple and analytical relationship between the graph spectra of structural and functional networks. Laplacian eigenvectors are shown to be good predictors of functional eigenvectors and networks based on independent component analysis of functional time series. A small number of Laplacian eigenmodes are shown to be sufficient to reconstruct FC matrices, serving as basis functions. This approach is fast, and requires no time-consuming simulations. It was tested on two empirical SC/FC datasets, and was found to significantly outperform generative model simulations of coupled neural masses. Copyright © 2018. Published by Elsevier Inc.

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

Aeroservoelasticity

NASA Technical Reports Server (NTRS)

Noll, Thomas E.

1990-01-01

The paper describes recent accomplishments and current research projects along four main thrusts in aeroservoelasticity at NASA Langley. One activity focuses on enhancing the modeling and analysis procedures to accurately predict aeroservoelastic interactions. Improvements to the minimum-state method of approximating unsteady aerodynamics are shown to provide precise low-order models for design and simulation tasks. Recent extensions in aerodynamic correction-factor methodology are also described. With respect to analysis procedures, the paper reviews novel enhancements to matched filter theory and random process theory for predicting the critical gust profile and the associated time-correlated gust loads for structural design considerations. Two research projects leading towards improved design capability are also summarized: (1) an integrated structure/control design capability and (2) procedures for obtaining low-order robust digital control laws for aeroelastic applications.

Analysis of shell-type structures subjected to time-dependent mechanical and thermal loading

NASA Technical Reports Server (NTRS)

Simitses, George J.

1990-01-01

The development of a general mathematical model and solution methodologies for analyzing structural response of thin, metallic shell-like structures under dynamic and/or static thermomechanical loads is examined. In the mathematical model, geometric as well as material-type of nonlinearities are considered. Traditional as well as novel approaches are reported and detailed applications are presented in the appendices. The emphasis for the mathematical model, the related solution schemes, and the applications, is on thermal viscoelastic and viscoplastic phenomena, which can predict creep and ratchetting.